The demo videos are compelling, and the pitch is always some version of the same thing: why spend time and money talking to real users when AI can simulate them for you?

We decided to find out if any of that holds up. Over the course of February and March, The Good’s team of UX researchers and strategists ran hands-on evaluations of six AI user research tools.

We tested them against real client projects, comparing outputs side-by-side with findings from our established methods, and sitting through demos with enough pointed questions to make the sales reps uncomfortable.

Our answer isn’t a simple thumbs up or thumbs down. Some of these tools are genuinely useful for the right team, in the right situation. Others are impressive on the surface, with not much underneath. And nearly all of them, once you get past the marketing language, will quietly acknowledge they can’t replace real user testing.

Here’s what we actually found.

First: “AI user research” is not one category

Before getting into the tools, it’s worth noting something that may not be intuitive to all. “AI user research” is a catch-all term that covers fundamentally different capabilities. Just as we have a variety of research tools and methods as an expert UX agency, the AI tool market includes a variety of tools with fundamentally different capabilities.

As we went deeper, we found most tools fall into one or more of these buckets:

• AI-assisted study setup: Helps you design a study or write a test plan.
• AI-moderated interviews: Replaces a human moderator with AI-guided conversation.
• Synthetic users: Generates AI personas that simulate user responses.
• AI follow-up questions: Dynamic probing within surveys or tests based on participant responses.
• AI analysis and synthesis: Themes survey responses, generates summaries, builds highlight reels.
• AI-driven roadmap and recommendation tools: Scans a site and generates prioritized UX recommendations.
• AI-powered heatmaps: Predicts visual attention without requiring real user data.

Knowing which category a tool belongs to matters because it changes what you should expect from it and what you shouldn’t.

The tools we evaluated

1. Synthetic Users

Category: Synthetic users/AI-assisted study setup

What it does:

Generates AI user profiles and simulates how those users would respond to a screenshot or Figma prototype, producing a full usability report with findings, quotes, and prioritized recommendations.

What we did:

One of our strategists ran the same test through Synthetic Users and through PlaybookUX with real recruited participants. It was the same landing page, with the same research questions. This is as close to a controlled comparison as we could get.

Where the findings matched:

• Both groups flagged information overload and cluttered design as major issues.
• Both raised privacy concerns about submitting a phone number.
• Both produced skepticism about the page’s bold marketing claims.
• Severity rankings were similar across both sets, and some of the language in the synthetic “user quotes” was remarkably close to what real participants said.

Where they diverged:

Synthetic Users can only test screenshots or Figma prototypes, not live URLs. No live interactions, form fills, or navigation behavior. In that way, real users provided behavioral data (where they actually clicked, how long they hesitated, what they scrolled past) that synthetic users can’t replicate.

The report itself output eight sections, including executive summary, task-by-task analysis, error patterns, user flows, learnability, satisfaction ratings, recommendations, and sub-sections. Each section largely repeated the same three or four findings in different formats. An experienced researcher would synthesize this into a focused, actionable deck while the tool generates volume.

The bottom line:

Synthetic Users got the big, surface-level findings right. If your question is “are there obvious issues with this page?”, it can answer that quickly. If your question is “how do real users actually interact with this experience?” it falls short. Think of it as a fast, automated heuristic review. It’s useful as a starting point, not a replacement for behavioral data.

2. Uxia

Category: Synthetic users/prototype testing

What it does:

Creates custom AI-generated users based on the audience information and test plan you provide (a step you can review and refine manually before the test runs). Those users then move through your prototype, and the tool automatically produces a synthesized report with ranked themes, findings, and a shareable output. No manual analysis required.

What we did:

One of our researchers gave Uxia’s team a Figma prototype of a site element that we had already tested and synthesized using Lyssna. This gave us a direct basis for comparison between their AI-generated output and our real user findings.

What worked:

The output is genuinely robust. Themes are already pulled and ranked, the report generates automatically, and it’s ready to share without anyone watching hours of recordings first. For an in-house team without a dedicated researcher, that’s a real time savings.

The AI users also flagged the same top finding that we identified in our test with real users. That kind of alignment on a specific, nuanced finding was notable.

Uxia positions itself honestly as a supplement to real user testing, not a replacement for it. They expect their users to be running studies with real participants alongside the tool, and they’re upfront about that. Researchers using their tool actually conduct more research because of the fast turnaround, not less.

Where they diverged:

AI users interpreted placeholder imagery as real content and confused a navigation menu for a standalone page.

Our team’s assessment: it doesn’t have the emotional intelligence a human user would.

What Uxia catches are surface-level friction points, including broken flows, confusing layouts, and missing content hierarchy. What it misses are the nuanced reactions that drive the most valuable optimization decisions.

The deeper limitation is scope. Many of the test types we run with human participants simply cannot be conducted with synthetic users. If 20 out of 30 real users say something similar, that’s a trustworthy signal built from independent behavior. If AI generates 30 synthetic responses that say the same thing, that’s one opinion multiplied.

Price is custom per team.

The bottom line:

Uxia works best as a pre-step. Running a prototype through it before live user testing to catch dead ends early, or to inform A/B test concepts, could be helpful. It’s not a replacement for behavioral research. The tool’s honest positioning about this was one of the more refreshing things we encountered in this evaluation.

3. Maze

Category: AI-moderated interviews / unmoderated testing

What it does:

Unmoderated usability testing platform that’s bolting on AI features, including AI-moderated interviews. Functionally similar to Lyssna, with AI moderation as the main differentiator.

What we did:

Our team ran a full walkthrough and tested its core capabilities.

What we found:

Maze predates AI. It’s a standard unmoderated testing tool that’s adding AI capabilities, not a purpose-built AI research solution. The AI moderation feature is built for teams that run a high volume of moderated studies and want to scale without adding headcount.

The AI follow-up question feature, which probes participants based on their responses, felt shallow in practice. It pulls a word from what someone typed and asks them to elaborate. One of our team members called it “advanced survey piping.” It’s an improvement over a static questionnaire, but it’s not a substitute for a skilled moderator who follows a line of inquiry.

The bottom line:

This is a capable unmoderated testing tool. The AI moderation pitch is most relevant to agencies or in-house teams running dozens of moderated sessions monthly. If you’re already using Lyssna and happy with it, there’s no compelling reason to switch.

4. Strella

Category: AI-moderated interviews/analysis and synthesis

What it does:

Replaces human moderators with AI-guided voice interviews, then auto-generates highlight reels, segmentation analysis, and synthesized findings reports.

What we did:

One of our researchers completed a demo and detailed review of capabilities and pricing.

What we found:

Strella’s synthesis features are genuinely interesting. Auto highlight reels, AI-generated segmentation, and an analysis interface that lets you ask questions of your data are all capabilities that could save significant time for teams running large volumes of qualitative research.

The problem is the price at $5,000 or more per project, not including participant recruitment or incentives. That math only works for organizations doing frequent, large-scale interview research.

We also acknowledge a gap in our evaluation here: we weren’t able to run a direct comparison of a real moderated interview against an AI-moderated one, because we don’t often conduct live moderated sessions for clients. Before making a definitive claim about quality differences, we’d want to test that directly. What we can say is that the tool solves problems a specific type of research operation has, not most in-house optimization teams.

The bottom line:

Potentially compelling for agencies or enterprise teams doing 20+ moderated studies a year. At current pricing, it’s a hard sell for most others. The synthesis capabilities are the most interesting part of the product, and we will be watching for those features to appear in more accessible tools.

5. Baymard UX-Ray

Category: AI-driven roadmap and recommendation tool

What it does:

Scans a website and generates a prioritized UX recommendation report, pulling from Baymard’s extensive research library to categorize and rank issues by page type and severity.

What we did:

We evaluated UX-Ray’s output against a real site, reviewed the tool’s methodology, and attended a Baymard-led NNG webinar where the founders discussed AI accuracy in UX recommendations.

What we found:

UX-Ray generated 342 UX insights for one site, a number that sounds impressive until you’re in the report and realize that quantity isn’t the same as usefulness. Many of the insights are gated behind paid tiers, and without the ability to prioritize by business impact, revenue potential, or implementation effort, a list of 342 findings is as overwhelming as it is informative.

The tool’s presentation is polished: dynamic, clickable, and organized by page type with thumbnail previews. And Baymard’s content library is a trusted source in UX research, whose credibility carries into the tool.

But the more fundamental limitation isn’t accuracy, it’s context. UX-Ray scans your site against a library of best practices and known UX patterns. It has no visibility into who your actual users are, how your specific audience behaves, or where your real conversion friction lives.

Entering a URL without that context assumes a lot. A recommendation that’s technically correct by best-practice standards may be irrelevant, or even counterproductive, for your particular visitors and traffic mix. Best practices are a starting point, not a strategy. That’s as true here as it is anywhere else in optimization.

Mid-tier pricing is $399 per month.

The bottom line:

Useful for teams that want a structured starting point for a UX audit and have the expertise to evaluate and filter the output. It’s not a replacement for a research-informed optimization strategy. The accuracy caveat matters; a list of 342 recommendations that’s 70–95% correct still requires an expert to separate the signal from the noise.

6. Brainsight

Category: AI-powered heatmaps

What it does:

Generates predictive attention heatmaps without requiring real user data, using AI trained on eye-tracking studies to model where users will look on a given page.

What we did: Unlike the other tools in this evaluation, we already use Brainsight in select client work. We’ve used it extensively enough to have a genuine, experience-based opinion.

What we found:

Of all the tools in this evaluation, Brainsight is the one we recommend most readily. But we present it with caveats, because that’s the honest way to use it.

The predictive heatmaps are reliable as a starting point. The tool reads contrast, copy, imagery, and dark areas on screen and makes assumptions about where human attention will land. That works often enough to be useful.

They also compare favorably to DIY AI heatmap alternatives (which our team found consistently unreliable), and the tool is priced accessibly enough to function as a genuine entry point for teams that haven’t yet invested in full heatmap research.

But it’s modeling visual salience, not actual user behavior. A true heatmap might show no heat on a long block of text that the AI flagged as a high-attention area, because real users navigated away without reading it. The AI doesn’t know that. It sees contrast; it doesn’t see intent.

So, this is a good starting point, not a definitive picture. If you want heatmap data you can trust completely, that comes from real users in a full engagement.

Here’s how we’d describe Brainsight to any client considering it: it gets you to 70% of the answer faster and cheaper than doing nothing. You’ll see where attention concentrates, where it drops off, and what’s fighting for visual priority.

The remaining 30%, understanding why users look where they look, what they do next, and what it means for your conversion strategy, is where a full optimization strategy makes the difference.’

Brainsight is also adding AI-generated recommendations following the heatmap output, a feature we haven’t fully evaluated yet. We’ll be watching it closely.

The bottom line:

This is a tool we use and would actively recommend as a starting point. Best positioned as an affordable entry into attention data, with the honest caveat that real engagement data tells you more.

What we learned across all of it

After evaluating all six tools, a few themes cut across the whole category.

They find the obvious. They miss the subtle.

In every comparison, AI-generated findings matched the surface-level issues an experienced researcher would spot in the first ten minutes of reviewing a page, for example, information overload, privacy friction, and confusing hierarchy.

The gap shows up in depth: navigation hesitation, emotional reactions, and the unexpected workaround a user invents that tells you your information architecture is broken. For high-stakes optimization decisions, the subtle findings are where the value lives.

More output is not better output.

Volume was the consistent way these tools tried to signal quality. 342 UX insights. Eight report sections for a single landing page. 12-page persona profiles in under a minute. Quantity without prioritization and context is noise. A skilled researcher delivers fewer, better, more actionable insights and knows which ones actually matter.

They’re genuinely useful for teams starting from zero.

This is worth saying clearly. An in-house optimization team that has never run a user test would benefit from these tools. Getting 70% of the answer is better than getting none.

These tools lower the barrier to research-informed decision-making. The risk isn’t using them, it’s treating their output as final rather than as a starting point that needs validation with real users.

The best use cases aren’t what the tools advertise.

The most promising applications we found weren’t the primary pitch of any tool we evaluated. Running a prototype through a synthetic user tool before live user testing to catch dead ends. Using Brainsight as a fast stakeholder-conversation starter. Using AI synthesis tools to surface patterns in data that a team has already collected but hasn’t had time to analyze. None of these tools market themselves this way, which our team found consistently surprising.

The vendors themselves will tell you.

This was the most telling finding of the entire evaluation. Every single tool vendor, once you moved past the landing page and into a real conversation, acknowledged that their tool won’t replace real user testing. When the sellers aren’t making the replacement claim, pay attention.

When to use AI research tools (and when not to).

AI tools earn their place when you’re tracking patterns over time, when the problem is well-defined and the stakes are low, when you need directional input quickly and the alternative is doing nothing, or when you’re QA-checking a prototype before investing in live user testing.

Keep humans in the lead when the decision is high-stakes, when you need behavioral data (not just stated responses), when you’re entering an unfamiliar market, or when you need findings you can defend with evidence.

For most teams, the answer isn’t either/or. These tools slot into a research process as a first step, a pre-launch check, or an accelerator for analysis you’re already doing. Their ceiling is lower than the marketing suggests, and their floor is higher than the skeptics give them credit for. Use them where they fit.

Frequently asked questions on AI user research

Can AI replace user research?

As of right now, no. And the vendors building these tools will tell you the same thing.

AI research tools can surface obvious usability issues, generate directional insights quickly, and lower the barrier to research-informed decision-making for teams that have never run a study before.

What they can’t do is replicate real user behavior: the navigation hesitation, the emotional response, the unexpected workaround that tells you something important about your experience.

For low-stakes, directional questions, AI tools are a reasonable starting point. For decisions that matter, real users are non-negotiable.

What is the difference between Synthetic Users and Uxia?

Both tools generate AI-simulated users to evaluate a design, but they serve slightly different purposes. Synthetic Users runs AI personas through screenshots or Figma prototypes and produces a full usability report with findings, quotes, and severity ratings, functioning most like an automated heuristic review.

Uxia takes a similar approach but focuses more specifically on prototype testing and positions itself explicitly as a first step alongside, not instead of, real user research.

In our side-by-side comparisons, both tools got the big surface-level findings right and missed the behavioral nuance. Uxia’s honest framing about its own limitations stood out as a green flag.

Is Brainsight accurate?

In our experience, yes. More so than DIY AI heatmap alternatives, which our team found consistently unreliable.

Brainsight’s predictive heatmaps are trained on real eye-tracking data and produce results we’ve found dependable enough to use in client sprint work. That said, predictive heatmaps model where users are likely to look based on visual patterns, they don’t capture actual user behavior, intent, or what users do after their attention lands somewhere.

We use Brainsight as a fast, accessible starting point. Real engagement data from actual sessions tells a more complete story.

How accurate are AI-generated UX recommendations?

According to Baymard’s own founders, AI-generated UX recommendations are generally around 70% accurate across the industry.

Baymard claims their UX-Ray tool performs at approximately 95%, but even at that rate, a meaningful portion of recommendations in any given report shouldn’t be implemented without validation.

The more important point: Baymard itself says all AI-generated recommendations require testing before you act on them. A tool that generates hundreds of insights you still need to verify manually isn’t saving as much time as the pitch suggests.

When should a team use AI user research tools?

AI research tools make the most sense when the alternative is doing no research at all, when you need quick directional input on a well-defined and lower-stakes question, when you’re doing pre-launch QA on a prototype before investing in live testing, or when you have existing data that needs faster synthesis.

They make the least sense when you’re making high-stakes optimization decisions, entering an unfamiliar market, or need findings you can defend with behavioral evidence. For those situations, real users and experienced researchers aren’t optional; they’re the whole point.

Do AI user research tools save time?

For teams with established research processes, the promised time savings largely didn’t materialize in our evaluation. Research teams can build test plans in their sleep and likely use AI to assist with analysis.

The tools that promised speed often delivered volume, lengthy reports that repeated the same findings across multiple sections, requiring a researcher to synthesize the synthesis.

For teams earlier in their research maturity, the time savings are more real: automating analysis and report generation genuinely helps when the alternative is doing it manually from scratch. But they will likely be bogged down in these unnecessarily long reports.

The verdict

We say the same thing about AI user research tools that we say about best practices: they’re a starting point, not a strategy. They get teams that have never done research to 70% of the answer. For a team with established processes and real users to test with, they don’t solve problems we have.

The hype runs well ahead of the utility, and the most dangerous outcome isn’t a team using these tools and getting incomplete results; it’s a team using them and thinking they have the full picture.

The last 30% of research quality, the part that connects real human behavior to your most important optimization decisions, still requires real users, real data, and experienced researchers who know what to do with both.

Not sure where AI tools fit in your research process…or if they should? Our team has done the testing. Book a call and let’s talk through it.

The post We Tested 6 AI Research Tools Against Real Users. Here’s What We Found. appeared first on The Good.

They don’t need research for research’s sake. They need a clear path from “we have a problem” to “here’s what to do about it.”

Research should systematically move you from understanding problems to implementing solutions. No guesswork. No dead ends. Just a repeatable process that gets you actionable answers.

That’s what a double diamond UX research framework does.

The double diamond UX research framework: design the right thing, then design the thing right

We wanted to share more about the double diamond UX research framework after seeing too many teams jump straight from problem to solution, without doing the work of understanding whether they’re solving the right problem in the first place.

The framework works because it forces teams to slow down in the right places and speed up in others. The framework is named for its diamond shape that emphasizes diverging to explore, then converging to decide.

You do this twice. First to ensure you’re solving the right problem, and then again to ensure you’re solving it the right way.

The first diamond is to design the right thing:

​​Expand your understanding of the problem space, then narrow it to the most important opportunities.

1. Define problems (research)
2. Deduce opportunities (synthesis)

The second diamond is to design the thing right:

Explore multiple solution approaches, then validate which one actually works.

1. Create choices (ideation)
2. Evaluate options (implementation)

Each phase has a specific purpose, uses different research methods, and answers different questions.

When to use this framework

This UX research framework works best when:

• You’re stuck. Something’s not working, and you’re not sure why. The framework helps you diagnose root causes instead of treating symptoms.
• You have options. You’ve got a few directions you could pursue, but need validation before committing resources.
• Stakes are high. You’re making changes to core experiences, entering new markets, or redesigning critical flows where getting it wrong is expensive.
• You want speed without guesswork. You need answers quickly but can’t afford to be wrong.
• You need team alignment. Different stakeholders have different opinions, and you need objective data to move forward.

How it works phase by phase

But of course, this framework is only useful if you know how to apply it! Here’s a breakdown of each phase: when you need it, which research methods to use, and real examples of how we’ve used it to solve specific problems.

Phases 1-2: Design the right thing (generative research)

The first diamond uses the same research toolkit for both defining problems and deducing opportunities. What changes is how you apply these methods:

Phase 1 (Define Problems): You’re gathering data, uncovering what’s broken, and understanding the full scope of user pain points.

Phase 2 (Deduce Opportunities): You’re synthesizing that data, identifying patterns, and prioritizing which problems matter most.

Research toolkit:

• User interviews: Understand motivations, mental models, and pain points
• User testing: Watch real users interact with your product to identify friction
• Heatmap analysis: See where users actually click, scroll, and spend time
• Data analysis: Identify patterns in behavior and correlations with outcomes
• Journey analysis: Map complete user experiences to find friction points
• Surveys: Gather quantitative data on behaviors and preferences
• Ethnographic analysis: Observe users in their natural context to understand real-world behavior

Phase 1: Define problems through generative research

Before you can solve anything, you need to understand what’s actually broken. This is where generative research comes in. This research is designed to uncover opportunities and pain points you might not even know exist.

When you need this phase:

• You know something’s not working, but can’t pinpoint what
• You’re entering a new market or launching a new product
• Conversion rates have plateaued, and you’re not sure why
• You’re getting conflicting feedback from different user segments

Case study:

The problem: A SaaS productivity platform noticed that users who adopted their collaboration feature had 40% better retention, but only 12% of new users ever tried it. The product team had already attempted several fixes, including in-app tooltips, email campaigns, and adding the feature to the main navigation, but none moved the adoption needle. They knew they had a problem, but couldn’t pinpoint the root cause.

What we did: We ran user testing with 20 recent sign-ups, watching them complete typical workflows while thinking aloud. We analyzed heatmap data to see where users looked during key moments. We conducted interviews with 12 users (6 who adopted the feature, 6 who didn’t) to understand their mental models and pain points. Finally, we analyzed behavioral data across 50,000 user sessions to identify patterns in how people discovered features.

What we found: Most users saw the feature prompts, so the issue wasn’t awareness. The problem was timing and relevance. Users ignored prompts for the collaboration feature when they appeared during solo work. But when users received documents with stakeholder comments, they actively looked for ways to coordinate feedback. The feature solved a real problem, but only at specific moments. Showing it at the wrong time made it feel like noise.

Phase 2: Deduce opportunities through synthesis

Raw research data doesn’t solve problems, but synthesis does. This phase is about taking everything you learned and distilling it into clear, prioritized opportunities.

When you need this:

• You have research findings, but need to prioritize
• Stakeholders are divided on which direction to pursue
• You need to build a business case for investment
• You’re trying to align multiple teams around a shared strategy

Case study:

What we did: We took the same data from Phase 1 but shifted our analysis to look for patterns and opportunities. We mapped user journeys to identify the specific trigger moments when collaboration pain became acute. We segmented users by workflow type to understand who encountered collaboration needs most frequently. We analyzed the correlation between feature discovery timing and adoption rates.

The opportunity: Instead of promoting the feature broadly or adding more persistent navigation, we identified a single high-value trigger: show the collaboration feature contextually when users open documents containing comments or tracked changes. This represented the precise moment when users experienced the pain this feature solved.

The impact potential: Based on session data, 43% of new users encountered stakeholder-reviewed documents within their first week. If we could convert just 50% of those moments into feature trials, we’d nearly triple overall adoption.

What happened next: We had a clear, data-backed opportunity. Now we needed to figure out how to present this feature at that contextual moment. That’s where Phase 3 came in.

Phase 3: Create choices through competitive analysis

Once you know what problems to solve, you need to explore how to solve them. This phase is about generating multiple possible solutions and understanding how others have approached similar challenges.

Research toolkit:

• Competitive analysis: Study how industry leaders solve similar problems
• Landscape analysis: Identify patterns across different approaches

When you need this:

• You need to narrow down design directions
• Stakeholders want to see options before committing
• You’re redesigning an existing feature and need alternatives
• You’re unsure which approach will resonate with users

Case study:

The problem: We knew when to show the collaboration feature, but we didn’t know how. Should it be a modal that interrupts the workflow? A subtle banner? A tutorial overlay? Each approach had trade-offs, and stakeholders had different opinions about what would work.

What we did: We conducted a competitive analysis of 12 productivity and collaboration tools, studying how they introduced features at contextual moments. We analyzed landscape patterns across project management, document editing, and communication platforms to understand what users had learned to expect.

What we found: The highest-performing patterns shared three characteristics:

1. They acknowledged the user’s current task (“We see you’re reviewing comments”)
2. They offered immediate value related to that task (“Collect all feedback in one place”)
3. They provided a low-friction way to try (“Add your first comment now”)

The choices we created: Based on these patterns, we designed three variations:

1. Inline prompt: A compact banner within the document that appeared next to existing comments
2. Modal introduction: A full-screen overlay explaining the feature with a demo
3. Progressive disclosure: A subtle sidebar element that expanded when users hovered near comments

Where this led: We had three viable approaches grounded in what worked for others. In Phase 4, we tested which resonated most with actual users.

Phase 4: Evaluate options

Before you ship, you need to validate. This final phase uses evaluative research methods to test solutions quickly and make confident decisions.

Research toolkit:

• User testing: Validate whether users can actually complete tasks
• Prototype testing: Test concepts before full development
• Rapid testing: Get fast feedback on specific design decisions (preference tests, first-click tests, tree tests, design surveys, card sorts)
• Sentiment testing: Measure emotional response to different approaches
• A/B testing: Compare variations with real users in production

When you need this:

• You have a few strong directions and need to pick one
• You want to validate before investing in full development
• You need fast feedback to maintain momentum
• You’re making changes to high-traffic, high-value experiences

Case study:

The problem: All three approaches looked good on paper, but we needed to know which one users would actually respond to. We didn’t have time to build and A/B test all three variations in production. We needed a faster way to validate the direction before investing in full development.

What we did: We created rapid prototypes of all three approaches and ran user testing with 24 people who matched our target audience. We showed each user a realistic scenario: opening a document with stakeholder comments. Then we measured task completion (could they figure out how to use the collaboration feature?), time to first action (how quickly did they engage?), and sentiment (how did they feel about the interruption?).

What we found: The inline prompt won on every metric, and users described the inline prompt as “helpful” and “right when I needed it,” while the modal felt “intrusive” and progressive disclosure was “too easy to miss.”

The decision: The team built and shipped the inline prompt, and feature adoption increased.

Why this worked: By following all four phases of the double diamond ux research framework, we didn’t just guess at a solution. We systematically moved from “we have a problem” (low adoption) to “here’s the root cause” (timing and relevance) to “here’s how others solve this” (contextual introduction patterns) to “here’s proof this will work” (validated direction through testing). Each phase built on the last, reducing risk at every step.

Why this UX research framework works

The double diamond framework prevents the most common research mistakes:

It prevents solution jumping

Teams often skip straight from “we have a problem” to “here’s what we’ll build.” Our framework forces you to first understand the problem deeply (Phase 1), then synthesize opportunities (Phase 2) before exploring solutions (Phase 3) and validating them (Phase 4).

It matches research methods to questions

Generative research (Phases 1-2) answers “what problems exist?” Evaluative research (Phases 3-4) answers “does this solution work?” Using the wrong method for your question wastes time and money.

It creates natural checkpoints

Each phase ends with a clear deliverable: defined problems, prioritized opportunities, multiple solutions, or validated direction. Stakeholders can weigh in at the right moments without derailing progress.

It scales to any timeline

Need an answer this week? Run a rapid test. Have a month? Go deeper with interviews and competitive analysis. Have a quarter? Complete the full cycle. The framework adapts to your constraints while maintaining rigor.

How you can adapt the framework to increase revenue from your digital experience

While it would be ideal to run the full framework on all product decisions, we don’t always have the luxury of time. Here’s how we typically adapt the framework depending on project needs.

Fast decision (1-2 weeks): Skip straight to Phase 4 with rapid testing when you have clear options and need directional data quickly.

Optimization project (2-4 weeks): Focus on Phases 1-2 (define problems, deduce opportunities) when you need to diagnose what’s broken and prioritize fixes.

Strategic initiative (4-8 weeks): Full cycle through all four phases when you’re entering new territory or making high-stakes changes.

Ongoing partnership: Rotate through phases as needs evolve. We may use competitive analysis this month, user testing next month, and rapid testing the month after.

The key is matching research depth to risk and constraints.

You’re not trying to run the most comprehensive study possible. Instead, you’re trying to give yourself the confidence to make the right decision with the time and budget you actually have.

Ready to solve your research challenge?

The double diamond framework gives you a proven way to move from problem to solution. Whether you need a quick rapid test to validate a design decision or a comprehensive research engagement to inform strategy, we adapt this framework to your specific needs.

The teams we work with don’t need more data; they need clarity. They need to know what’s broken, why it matters, and what to do about it. That’s exactly what this UX research framework delivers.

And when you work with The Good, here’s what research deliverables look like:

• Problem definition: Clear documentation of user pain points, friction in the experience, and opportunities ranked by impact and effort.
• Competitive analysis: Side-by-side comparison of how others solve similar problems, with specific recommendations for what to adopt, adapt, or avoid.
• User testing results: Video clips of real users, annotated with insights, organized by finding severity, and accompanied by specific design recommendations.
• Rapid test reports: Statistical analysis of user preferences with clear winners, qualitative feedback explaining why users chose what they did, and next-step recommendations.
• Strategic recommendations: Prioritized roadmap of what to build, test, or optimize based on research findings and business goals.

Everything is actionable. We don’t just tell you what we found, we tell you what to do about it.

Want to see how this framework would apply to your specific challenge? Let’s talk about your research needs.

The post How to Use the Double Diamond UX Research Framework to Solve Real Problems appeared first on The Good.

If you’re asking this question, you’re already ahead of most product leaders. You recognize the problem. But here’s what many miss: conversion is a symptom, not the root cause of the problem.

SaaS churn often happens before users ever consider paying.

It’s common for users to hit friction points you didn’t know existed. They encounter gates that make no sense in context. They drop off at moments when just a bit more clarity could have kept them engaged.

The good news? You can fix this. But not by guessing. Not by copying what Dropbox or Notion does. And usually not by adding more features.

What you need is a systematic audit of your free or anonymous user experience. One that reveals exactly where users hit walls, why they bounce, and what you can do to keep them engaged long enough to see value.

This article walks through a five-step framework that SaaS product and growth leaders can use to audit their free experience and reduce churn. It’s the same approach we use with clients, adapted so you can run it internally. Fair warning: this takes work. But if you’re serious about improving SaaS user retention, it’s worth every hour.

Why your free experience impacts your retention rate

Before we get into the framework, let’s be clear about what we mean by “free experience.”

This includes any interaction where users engage with your product without paying. That could be a free trial, a freemium tier, anonymous tool usage, or limited feature access. It’s the first impression, the test drive, the “try before you buy” phase.

And it matters more than you think.

Most SaaS companies obsess over free-to-paid conversion rates. But conversion is a lagging indicator. By the time a user decides not to convert, the damage is already done. They disengaged days or weeks ago. They just didn’t tell you.

The real opportunity sits upstream. If you can identify and remove friction in the free experience, you don’t just improve conversion rates. You improve activation rates, engagement, time-to-value, and long-term retention. You build a user base that actually wants to pay because they’ve already seen the value.

Here’s how to find those friction points.

Step 1: Review your data for drop-off points

Start with what’s already happening in your product. Before you talk to anyone or look at competitors, you need to know exactly where users are getting stuck.

Dig into your product analytics. You’re looking for three things:

Activation drop-offs: Where do users abandon the onboarding flow? Which steps have the highest exit rates? If 60% of users drop off when asked to invite teammates, that’s a signal.

Feature engagement patterns: Which features do free users actually use? Which ones do they try once and never touch again? Are there features you’ve gated that users don’t even attempt to access?

Time-to-value analysis: How long does it take users to complete their first valuable action? And what percentage of users never get there? If your median time-to-value is three days, but 70% of users churn within 48 hours, you have a problem.

Set up a dashboard that tracks these metrics by cohort. New signups this week versus last month. Users from different acquisition channels. Free trial versus freemium. The patterns that emerge will guide your optimization priorities.

Layer on session recordings and heatmaps to see exactly what’s happening at key drop-off points. Numbers tell you where the problem is. Qualitative data tells you why.

Watch 20-30 sessions of users who churned in their first week. What did they try to do? Where did they get stuck? What confusion or frustration is evident in their behavior?

This isn’t just a data review. It’s detective work. You’re building a picture of where your free experience breaks down.

Step 2: Talk to users (both active and churned)

Now that you’ve identified drop-off points in your analytics, it’s time to understand the human story behind those numbers. Conduct 10-15 interviews, split between two groups:

Active free users (people still using your product but haven’t upgraded): Why are they still here? What value are they getting? What would make them pay? What’s holding them back?

Churned users (people who tried your product and left): What were they trying to accomplish? Where did they get stuck? What made them give up? What would have kept them engaged?

Keep these conversations short (15-20 minutes) and focused. You’re not selling. You’re learning.

Sample questions for active free users:

• What problem were you trying to solve when you first signed up?
• Walk me through how you use [product] today.
• What features do you wish you had access to?
• What would need to change for you to consider upgrading?
• If we removed [specific free feature], would you still use the product?

Sample questions for churned users:

• What were you hoping to accomplish with [product]?
• Where did you get stuck?
• Was there a specific moment when you decided it wasn’t for you?
• Did you consider other tools? What made you choose them instead?

Record these conversations (with permission) and transcribe them. The exact language users employ to describe their experience reveals friction points you’d never spot in analytics alone.

Pay special attention when users mention alternatives they considered or are currently using. This context becomes critical in the next step.

Step 3: Map what your users are being offered in the market

You now understand what’s happening in your product and why users make the decisions they do. The next question is: what are they comparing you against?

Your users don’t evaluate your free experience in a vacuum. They’re weighing it against every other tool they’ve tried, every competitor they’re considering, and every product they wish yours worked more like.

This step isn’t about copying competitors. It’s about understanding the full landscape of options your users are navigating.

Create a comprehensive inventory of how other products in your space (and adjacent to it) handle their free experiences. Document what your users are seeing elsewhere.

Here’s what to capture in a Figma or Notion file.

Set up a page with one row per product. For each one, document:

• What features are available without registration
• What requires an email address but remains free
• Where the hard paywalls sit
• How they communicate limits (countdown timers, credit displays, etc.)
• Placement and messaging of upgrade prompts
• Onboarding flows and activation sequences

Don’t limit yourself to direct competitors. Look at the tools your users mentioned in interviews. If they’re comparing your productivity tool to Notion, your design tool to Figma, or your automation platform to Zapier, study how those products handle free users.

Pro tip: Screenshot everything. Your database should include visual documentation of every monetization touchpoint, limit notification, and upgrade CTA. These screenshots become invaluable references when you’re making decisions about your own experience.

This exercise typically takes 8-12 hours for a thorough analysis of five to seven products. You’ll surface approaches you hadn’t considered and identify industry patterns that users have come to expect.

The goal here is context. When a user hits a limit in your product, they’re mentally comparing that experience to how Dropbox handles storage limits, how Canva displays upgrade options, or how Grammarly shows premium features. Understanding those reference points helps you design a free experience that meets or exceeds market expectations.

Step 4: Run a verb scoring exercise

With data, user insights, and market context in hand, it’s time to systematically evaluate your own product’s free experience. This is where verb scoring comes in.

Verb scoring evaluates the discrete actions users can take in your product and assigns each one a “score” based on the level of friction required. The six verb scores are:

• Anonymous – Users can take this action without providing any information
• Limited Anonymous Use – Users can take this action without registration, but only a limited number of times
• Free with Registration – Users must register (email + basic info), but can take this action unlimited times for free
• Limited Registered Use – Registered users can take this action, but with caps or restrictions
• Trial with Payment – Users must provide payment information to access this action (even if they’re not charged immediately)
• Gated – Only paying customers can take this action

List every meaningful action users can take in your product. Not features, but actions. “Create a document” is a verb. “Edit collaboratively” is a verb. “Export to PDF” is a verb. “Share via link” is a verb.

Then score each one. Where does it fall on the spectrum from Anonymous to Gated?

This exercise reveals your actual monetization strategy, not the one you think you have. You’ll often find that verbs are gated inconsistently, or that you’re giving away too much (or too little) at critical moments.

For a detailed walkthrough of verb scoring, including decision trees and examples, see our guide on verb scoring for product strategy.

Create a verb scoring matrix that maps all your verbs against these six scores. This becomes your baseline. It shows exactly where friction exists in your free experience, allowing you to compare it directly to what you documented in Step 3.

Step 5: Connect the dots between data, users, market context, and verb scoring

This is where the audit comes together. You now have four layers of insight:

1. Quantitative and qualitative data: Where users drop off and what they’re doing (or not doing)
2. User feedback: Why they drop off and what they’re thinking
3. Market context: What alternatives they’re comparing you against
4. Verb scoring matrix: Where friction exists in your own product

Lay them side by side. Look for patterns.

Here’s what you’re hunting for:

Friction without reason

Look out for verb scores that create unnecessary barriers relative to market norms. For example, if your data shows 40% of users bounce before registering, user interviews reveal confusion about what your product does, and your market analysis shows that competitors allow anonymous exploration, you’re likely losing users before they experience value. Your verb scoring can reveal that you’re gating too early.

Value leaks

Check for free features that users love but don’t move them toward conversion. If your most-used free features have no connection to paid capabilities, and users in interviews can’t articulate why they’d upgrade, you’re building a user base that will never pay. Your verb scoring might show you’re giving away too many “Free with Registration” verbs without strategic “Limited Registered Use” prompts.

Invisible gates

Paywalls that users hit without understanding why. Your data shows sudden drop-offs at specific upgrade prompts. User interviews reveal confusion about value or poor timing. Market analysis shows competitors explain premium benefits more clearly. Your verb scoring identifies which verbs are gated, but not whether those gates make sense to users.

Poorly timed friction

Limits or gates that appear before users have experienced enough value. Data shows high bounce rates at the first upgrade prompt. User interviews reveal frustration: “I hadn’t even figured out the basics yet.” Market analysis shows that similar tools delay friction until after activation. Your verb scoring might reveal that you’re using “Limited Anonymous Use” or “Trial with Payment” too early in the journey.

Market misalignment

Patterns where your verb scoring differs significantly from market norms, and your churn data supports that this matters. For instance, if every competitor allows free PDF exports but you gate this behind payment, your churned user interviews will likely mention this as a dealbreaker.

Create a prioritized list of friction points based on:

• Impact (how many users are affected, based on your data?)
• Confidence (do your user interviews confirm this is a problem?)
• Effort (how hard is this to fix?)
• Market expectation (is this friction standard, or are you an outlier?)

This becomes your retention optimization roadmap.

Why this framework works

This five-step audit framework delivers three specific outcomes that improve SaaS user retention:

Get a clear path to higher retention rates: No more guessing. You’ll have a prioritized list of friction points ranked by impact and effort. Fix the top three and you’ll see measurable improvement in activation, engagement, and conversion.

Make data-driven decisions: Create a culture of user-centered decisions rather than those based on the highest-paid person’s opinion, historical choices, or a gut feeling. When you combine quantitative data, qualitative research, market context, and systematic verb scoring, arguments become easy to settle.

Prevent feature flop: Validate changes before implementation. You’ll know which gates to remove, which features to add to your free tier, and which upgrade prompts to reposition, all before you waste valuable development resources.

Teams that run this audit consistently report two things: first, they’re surprised by what they find. Assumptions they’d held for months or years turn out to be wrong. Second, the fixes are often simpler than expected. Sometimes all it takes is moving an upgrade prompt, clarifying messaging, or ungating a single feature.

Running this audit takes time (and that’s the point)

Let’s be honest: this framework requires a meaningful investment. Between data analysis, user interviews, market research, and verb scoring, you’re looking at 40-60 hours of work.

That’s assuming you have the right tools, know how to set up proper analytics, can recruit and interview users effectively, and have experience interpreting qualitative data.

For many SaaS teams, that’s exactly the problem. You know you need to audit your free experience. You know churn is killing growth. But your product team is building features, your growth team is running acquisition campaigns, and nobody has the bandwidth or expertise to run a proper retention audit.

That’s where The Good’s Digital Experience Optimization Program comes in.

We’ve run this exact process dozens of times for SaaS companies between product-market fit and scale. Companies like yours with $1M-$30M ARR and pressure to accelerate growth while battling churn.

Our team conducts the full audit, including data review, user research, market analysis, and verb scoring, and delivers a prioritized roadmap of friction points with specific recommendations. Then we help you implement, test, and optimize the changes.

The result? Clients typically see measurable improvements in activation and retention within 60-90 days. More importantly, they build an optimization discipline that compounds over time.

Want to see where your free experience is bleeding users? Schedule an introductory call to discuss how we can help you reduce churn and improve SaaS user retention.

FREE RESOURCE

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How Top AI Tools Turn Free Users Into Paying Customers

The post Why Are Free Users Churning? A Growth Leader’s 5-Step Guide To Auditing The Free User Experience appeared first on The Good.

Healthcare software companies can’t freely experiment with patient data. Financial technology firms face strict compliance requirements that limit onsite testing capabilities. Government contractors operate under security clearances that restrict user research. Insurance platforms must navigate complex regulatory frameworks. HR and ATS software handle sensitive employee data that requires careful privacy protection.

Experimentation seems nearly impossible under these circumstances, and the product-led growth strategies these teams see working for companies riding exponential growth waves like Linktree or Lovable can’t work for them.

These regulated SaaS companies still need to grow. They have the same fundamental challenges as any SaaS business: converting leads, reducing churn, and improving user experience. But the traditional growth toolkit doesn’t fit their reality, so let’s explore what can work.

The problem with product-led growth in regulated industries

Product-led growth has become the gold standard for SaaS success.

Companies like Canva, Grammarly, and Spotify have proven that letting users experience your product before purchasing leads to higher conversion rates, lower customer acquisition costs, and sustainable growth.

The strategy is to remove obstacles to product adoption, offer free trials or freemium versions, and let the product sell itself. These companies often move quickly and test new features relentlessly as a way to “hack” growth.

The product-led growth playbook includes:

• Free trials and freemium models that give users immediate product access
• Continuous A/B testing on live user experiences
• Extensive user tracking and behavioral analytics to optimize conversion funnels
• Rapid iteration based on user feedback and behavior data
• Self-service onboarding that guides users to their “aha moment“
• Viral growth loop, where users invite others or share content

And it works…for many. But regulated SaaS companies see these success stories and struggle to replicate them.

How do you offer a free trial for an HR tool that has to be rolled out across an entire organization to be useful? How do you minimize sign-up friction for a fintech software that requires bank information to function?

Experimenting with new features is too risky when system failure or emergency calling disruptions in telecommunications could result in massive fines.

Sometimes the stakes are too high for the product-led growth best practices that we see working in less-restrictive industries.

Regulated SaaS challenges are unique, and their growth solutions should be too

The challenges for this subset of SaaS companies are real and varied.

Compliance and privacy restrictions: Healthcare companies can’t freely test with patient data. Financial services face strict data handling requirements. Government contractors operate under security clearances.

Low traffic volume: Many regulated SaaS companies serve niche markets with limited user bases, making traditional A/B testing statistically impossible.

Long testing cycles: By the time regulated companies collect enough data from different regions and customer segments, it can take years to reach statistical significance. Different customers use different features across various geographical locations, making it difficult to design meaningful experiments that won’t disrupt service.

Risk-averse customers: Enterprise clients in regulated industries don’t want to be testing subjects for new features or experiences.

Resource constraints: Many regulated SaaS companies are highly technical but lack dedicated growth or UX teams.

Unique challenges require unique solutions, and that is what The Good can provide.

The alternative: off-site experiment-led growth

The solution isn’t to abandon growth optimization. It’s to use different methods that work within regulatory constraints.

This is where off-site experiment-led growth becomes the game-changer.

Experiment-led growth is a strategic approach that relies on continuous research, experimentation, and data-driven decision-making to drive business improvements. It allows teams to rapidly iterate on ideas that improve UX, marketing, and more.

Regulated SaaS can add an extra layer to experiment-led growth by taking things off-site or out of the product experience. Moving the growth tactics and experimentation away from the regulated environment and live user base gives teams the chance to make changes freely and quickly, gauge user reaction to those changes, and either launch with confidence or kill the ideas.

While product-led growth relies on in-product experimentation with real users, off-site experiment-led growth validates hypotheses and optimizes experiences before they ever touch your production environment. Instead of letting users test drive your product to discover value, you test drive your assumptions about users to deliver value immediately.

This approach flips the model to accommodate some of the restraints that regulated SaaS companies face. It’s no longer required to iterate on live systems with real customer data. There is an option to conduct experiments in controlled environments that don’t compromise compliance or risk customer relationships. You gather similar insights that drive product-led growth success, but through methods that work within constraints.

The result is a growth strategy that’s both data-driven and compliant, giving regulated SaaS companies access to the same optimization advantages that unrestricted companies enjoy, just through different means.

Off-site experiment-led growth tactics

Here are a few of the methods we use to deliver optimization outcomes for companies with the challenges and constraints outlined earlier in the article.

User testing

Because of the difficulty in getting customer data, there can be a disconnect between product teams and users.

Lookalike user testing solves this by bringing external participants who match the ideal customer profiles through your live experience. They complete tasks while thinking out loud, revealing friction points and confusion without exposing any sensitive data or requiring system changes.

This helps understand user behavior patterns, identify conversion barriers, and validate solutions, all without touching your production environment or compromising compliance.

AI-powered heatmaps and analytics

AI-generated heatmaps can predict user behavior with 92% accuracy without requiring any actual user data. These tools can analyze your interface and predict where users will look, what they’ll miss, and how long they’ll engage with different elements.

This is particularly valuable for regulated companies because you can understand user attention patterns and optimize layouts before the system is used.

Rapid testing

Experimentation is a proven way to get essential feedback on new features or website changes. And with A/B testing off the table in many regulated industries, rapid testing can fill in the gaps.

Unlike traditional A/B testing, rapid testing doesn’t require code changes, live traffic, or long research cycles. Instead, it uses a combination of techniques to validate hypotheses and inform decisions before anything goes live.

Rapid experimentation is not a one-size-fits-all process. Different scenarios call for different types of tests. Here are some common methods:

• First-click tests: First-click tests evaluate whether users can intuitively find the primary action or information on a page.
• Tree tests: Tree testing is a usability technique that helps you understand how users navigate through your website or app’s structure.
• 5-second tests: 5-second tests assess a user’s immediate impression of a design or message.
• Design surveys: Design surveys collect qualitative feedback on wireframes or mockups.
• Preference tests: This test involves showing users two or more design variations and asking which they prefer and why. It’s perfect for narrowing down visual or messaging options before launching a formal test.
• Card sorting: Card sorting is a research technique used to understand how users organize and categorize information.

These are just six of the many types of rapid experimentation.

While none deliver a 1:1 result when compared to A/B or multivariate testing, rapid experimentation offers a way for regulated SaaS companies to focus their development resources on work that has already shown positive signals from users.

For a tangible example, imagine a company struggling with positioning (a common challenge in technical, regulated industries). Five-second testing provides immediate feedback on messaging effectiveness. Users see your page for five seconds, then recall what they remember.

Competitive intelligence and market research

Structured competitive analysis and market research don’t require access to your own user base.

Understanding how competitors position themselves, what messaging resonates in your industry, and what user expectations exist can inform optimization decisions.

Also, gathering growth strategies from businesses in a similar industry with compliance or other restraints will offer a starting point to come up with new ideas that you can rapid test later on.

Getting started with optimization

Optimization can be intimidating and complex for regulated SaaS companies. Based on experiences working with teams like yours, here’s how to get started implementing growth optimization within your constraints.

1. Start with an audit or assessment of your current situation

Before making any changes, conduct a comprehensive audit of your current digital experience. This includes:

• Technical tracking setup to understand what data you can legally collect
• User journey mapping to identify critical conversion points
• Competitive analysis to understand industry standards and opportunities
• Stakeholder interviews to align on growth priorities and compliance requirements

2. Implement the methodologies we covered

Focus on techniques that provide insights without requiring on-site or in-product experimentation:

• User testing with 5-7 participants per user type (you’ll get 80% of insights from this small sample)
• Message testing to validate positioning and value propositions
• Prototype testing for new features or flows before development
• Heat mapping to understand attention patterns and interaction likelihood

3. Prioritize based on impact and compliance

Create a roadmap that balances growth potential with regulatory requirements. Focus on:

• High-impact, low-risk optimizations that don’t require system changes
• Messaging and positioning improvements that can be implemented quickly
• User experience enhancements that reduce friction without compromising security
• Qualification improvements to ensure you’re attracting the right prospects

4. Build your internal capabilities and outsource what you can’t

Many regulated SaaS companies lack dedicated growth resources. Consider:

• Training technical teams on user experience principles
• Establishing research processes that work within compliance frameworks
• Creating feedback loops between customer-facing teams and product development
• Implementing regular optimization cycles that don’t disrupt core operations
• Outsourcing what you just can’t manage internally

Growth within constraints isn’t impossible

Regulated SaaS companies don’t need to accept mediocre growth because of their constraints. They need different approaches that work within their reality.

The key is recognizing that optimization isn’t restricted to product-led strategies or A/B testing. Understanding your users, validating your assumptions, and making data-driven decisions can deliver outcomes that are just as impactful.

Whether you’re in healthcare, financial services, government, or any other regulated industry, growth optimization is possible. It just requires the right toolkit and a willingness to think beyond traditional approaches.

Making off-site experiment-led growth work within your regulatory constraints starts with a conversation. Learn what’s actually possible when you have the right methodology and expertise guiding your optimization efforts by getting in touch with our team.

The post Regulated SaaS Companies Need a Different Approach to Growth. What Actually Works? appeared first on The Good.

While 89% of product teams are conducting user interviews according to recent industry data, there’s a critical gap between gathering user input and uncovering the insights that actually drive business results.

We see this all the time in our client work. Teams building features that competitors have without competitor data, or developing features based on the loudest customers without checking the significance of those friction points.

So what’s the solution?

The companies consistently shipping features that move the needle know the difference between asking users what they want and understanding what they actually need. It starts with discovery research.

What is discovery research in UX?

Discovery research in UX is the foundational phase of user research that focuses on understanding user problems, needs, and contexts before any solutions are designed.

Unlike evaluative research methods that test existing designs or prototypes, discovery research explores the unknown territory of user behavior to uncover opportunities and define problems worth solving.

Discovery research helps you understand use cases and user needs. It can ground you in what problems to solve and what is going on in the market.

This grounding is essential for product teams who want to build features that users actually need and will drive growth.

Discovery research typically involves methods like user interviews, field studies, diary studies, and market analysis. These approaches help teams understand the broader context of user goals and challenges before jumping into design solutions. The insights gathered during this phase become the strategic foundation for all subsequent product decisions.

Discovery research versus UX discovery

While these terms are often used interchangeably, there’s an important distinction that affects how product teams approach their research strategy.

Discovery research specifically refers to the research methods and activities used to understand user needs and identify problems. It’s the “how” of gathering insights through interviews, observations, and analysis. This includes techniques like ethnographic studies, user interviews, and competitive analysis.

UX discovery, on the other hand, is the broader strategic phase that encompasses discovery research, but also includes other activities such as technical feasibility assessments, business viability analysis, and stakeholder alignment. UX discovery is the “what and why” that frames the entire early-stage product exploration.

Think of discovery research as the tactical execution within the strategic framework of UX discovery. A comprehensive UX discovery process will include multiple types of discovery research methods. It also considers business constraints, technical limitations, and market opportunities.

For SaaS product teams, this distinction matters because it clarifies roles and expectations. UX researchers lead discovery research activities, while product managers typically orchestrate the broader UX discovery process that incorporates research findings into strategic decisions.

Understanding this difference helps teams avoid the common mistake of treating research as a checkbox activity rather than a strategic input that informs product direction.

Benefits of discovery research

Discovery research delivers tangible benefits that extend far beyond the research team, directly impacting product success and business outcomes.

Reduces development risk and waste

The most immediate benefit of discovery research is risk reduction. By understanding user needs and the specific problems before development begins, teams avoid building features that miss the mark. This is particularly critical for SaaS teams where failed features mean ongoing maintenance costs and technical debt that compound over time.

Enables data-driven product decisions

Discovery research transforms product decisions from opinion-based to evidence-based. Instead of stakeholder preferences driving priorities, user insights guide development resources toward the highest-potential impact opportunities.

Uncover hidden opportunities

Discovery research often reveals unmet user needs that aren’t obvious from analytics or existing feedback channels. These insights can become the foundation for innovative features that differentiate your product in the market.

Improves cross-team alignment

When discovery research findings are shared across product, design, and development teams, everyone gains a shared understanding of user priorities. This alignment reduces conflicting opinions and streamlines the development process.

Accelerates time-to-market for successful features

While discovery research requires upfront time investment, it actually accelerates the development of successful features by ensuring teams build the right things from the start.

Enhances user satisfaction and retention

Products built on solid discovery research foundations better meet user expectations, leading to higher satisfaction scores and improved retention rates. Users feel heard and understood when products solve their actual problems rather than perceived problems.

This is essential for SaaS businesses where discovery research can identify the difference between features that drive daily engagement versus one-time usage, directly impacting churn rates.

When to use discovery research

Discovery research is best leveraged as part of a continuous research strategy.

Teresa Torres, expert and author of Continuous Discovery Habits, recommends weekly conversations with customers. “Continuous discovery means weekly touchpoints with customers by the team building the product, where they conduct small research activities in pursuit of a desired outcome.”

The goal is to take research from something you pause to do, into something you always do.

Many leaders will have experimentation rituals that allow quick and consistent feedback on ideas/products, but it’s rarer to see teams prioritize discovery on a frequent cadence.

When you manage discovery in batches or isolated sprints, it can mean you miss out on opportunities or delay solving urgent problems for customers.

Common discovery activities in UX

Effective discovery research employs multiple methods to build an understanding of the problem landscape and market conditions. Not all are required, but a combination will give a better picture to work off.

Diary studies

For understanding user behavior over time, diary studies ask participants to record their experiences, thoughts, and interactions over days or weeks. This method is particularly valuable for SaaS products where user needs evolve or vary based on different use cases and timeframes.

User interviews

One-on-one conversations with users can be a great pillar of discovery research. The key to successful interviews in discovery is asking open-ended questions that help explore user motivations, frustrations, and workflows. A good foundation is to conduct 6-8 interviews per user segment to get a picture of current challenges and behaviors.

Field studies and contextual inquiry

Observing users in their natural environment provides insights that interviews alone can’t capture. Field studies reveal the environmental, social, and technical factors that influence user behavior, uncovering needs that users might not articulate in interviews.

Competitive analysis and market research

Understanding the competitive landscape helps identify opportunities for differentiation. It also uncovers whether user problems are being adequately solved by existing solutions. This desk research complements user-facing research methods.

Jobs-to-be-done (JTBD) research framework

JTBD research helps frame what job users are “hiring” your product to do. It can help you think beyond features to understand the fundamental progress users are trying to make in their lives or work.

Card sorting

This method helps teams understand how users categorize information and conceptualize problem spaces. Card sorting is particularly useful for discovering how users naturally group features or content areas.

Survey research

While qualitative methods provide depth, surveys can help uncover findings across larger user populations. Use surveys to quantify the prevalence of problems discovered through qualitative research.

Leveraging discovery research for better outcomes

In an era where 83% of designers, product managers, and researchers agree that research should be conducted at every stage of product development, it’s critical to understand discovery research in UX.

Discovery research is a tool that helps you dig into current user needs and prioritize the problems worth solving. It provides the user insights needed to build theme-based roadmaps, prioritize high-impact features, and avoid costly development mistakes. Most importantly, it ensures that every dollar spent on product development addresses real user needs rather than perceived problems.

Ready to make discovery research work for your product team? The Good specializes in helping SaaS companies uncover the user insights that drive product success. Our team combines deep research expertise with practical product strategy to ensure your research translates into features that drive growth.

Get in touch with The Good to discuss how discovery research can accelerate your product development and improve user satisfaction. Let’s turn your user insights into your competitive advantage.

The post What Is Discovery Research in UX? appeared first on The Good.

This thinking is not only wrong, but also expensive and potentially harmful to your product strategy.

Today’s SaaS products exist across multiple “surfaces,” not just desktop and mobile apps, but also mobile web, browser extensions, widgets, and even smart TVs. Each surface represents a different way users can interact with your product, and each can serve a distinct purpose.

After working with dozens of scaling SaaS companies and analyzing surface strategies across hundreds of products, we’ve discovered that the most successful companies don’t aim for feature parity. Instead, they make deliberate, strategic decisions about which surfaces serve which purposes in their ecosystem.

Here’s the framework that’s helping product leaders at companies like Adobe, Slack, and emerging SaaS startups rethink their entire multi-surface strategy.

Organic growth spurs feature parity

The pressure to achieve feature parity stems from a fundamental misunderstanding of how users actually interact with different surfaces. Product teams often default to replicating their experience across surfaces without considering the strategic implications.

“Most products start with just one surface,” explains Natalie Thomas, Director of Strategy & UX at The Good. “Adobe started with a desktop app, and YouTube started on the web. Then they often bleed into other surfaces. The family of surfaces is likely to grow over time, and they are of different strategic importance.”

This organic growth pattern creates a dangerous assumption that every surface should eventually do everything the original surface does. But here’s what we’ve learned from analyzing successful SaaS ecosystems: the most strategic approach isn’t about matching features. It’s about defining distinct purposes.

The four strategic surface types every product leader should know

Rather than thinking in terms of feature parity, successful SaaS companies categorize their surfaces based on strategic purpose. This categorization is based on our analysis of high-performing SaaS ecosystems.

1. Replica surfaces

These are true feature-parity experiences where users expect identical functionality across platforms.

Example: Workplace productivity tools where users frequently switch between devices. Slack exemplifies this perfectly. You can upload documents, chat, huddle, and access virtually every feature across web, desktop, and mobile.

For collaboration tools, inconsistent experiences create friction in team workflows. Users expect to pick up exactly where they left off, regardless of device.

2. Utility surfaces

These platforms fundamentally can’t work without each other. One surface serves as a critical utility that supports the primary platform.

Example: TLDV’s Chrome extension functions as a utility for their web-based recording platform. “In this situation, we’re not really looking for feature parity in the Chrome extension because it really does serve as a utility that adds a lot of functionality and depth to what we are able to get out of the web experience,” notes Natalie.

Don’t waste product development resources building standalone functionality in utility surfaces. Their entire value comes from integration with the core platform.

3. Accessory/companion surfaces

These add value to the main platform but can’t function independently.

Example: Figma’s mobile app serves as a companion to their desktop design tool. Users can’t create designs on mobile, but they can preview prototypes and test user flows on actual devices.

You can’t do anything without the main surface, but the accessory/companion adds value. The mobile app enhances the design process without attempting to replicate the full desktop experience.

4. Growth lever surfaces

These exist primarily to acquire new users, not to provide comprehensive functionality.

Example: Adobe’s free web tools, like online PDF converters, serve as growth levers. Users get limited functionality for free, experience the brand value, then convert to paid desktop or mobile experiences.

“A surface, especially one with very, very limited capabilities, can exist solely as a strategic growth lever. It doesn’t have to exist just to get feature parity or to add value to an existing platform. It can exist just to try to get new customers in the door,” explains Natalie.

What it looks like to intentionally limit feature parity

One of the most instructive examples of strategic surface limitation comes from Instagram’s deliberate choice to restrict posting capabilities on desktop. While it can frustrate users, it actually reveals Instagram’s strategic genius. By limiting posting to mobile, they:

• Maintain their mobile-first brand identity
• Prevent the platform from becoming a business publishing tool
• Keep content creation spontaneous and authentic
• Reduce operational complexity

Mobile-first continues to dominate 2025 SaaS trends, with companies prioritizing mobile user experiences over desktop feature replication.

The lesson? Sometimes the features you don’t build are more strategically important than the ones you do.

How to start building a surface strategy that avoids the feature parity trap

So, with all of this in mind, how do you build a great surface strategy? Instead of blindly building features across all surfaces, successful SaaS companies have a few strategies in common to make smarter surface decisions.

1. Let platform economics shape your strategy

Understanding how users discover and purchase your product should directly influence your surface strategy. The path differs dramatically between mobile apps and web/desktop experiences.

Mobile considerations:

• App store optimization becomes critical
• Apple retains approximately 30% of subscription revenue
• Updates require user opt-in and are often batched
• Attribution becomes increasingly difficult

Web/desktop considerations:

• Direct-to-payment journeys possible
• Immediate updates without user intervention
• Better attribution tracking
• More flexible pricing models

These fundamental differences should influence not just your pricing strategy, but also which surfaces you prioritize for different user segments.

2. Build where your users engage

How users engage with surfaces could shape your strategy. For example, mobile users are significantly more likely to opt into push notifications than desktop users.

While working on surface strategy for a leading SaaS company, our client shared, “Opt-in rates for push notifications on desktop are so low that the only avenue to do outreach to those existing dormant customers is through emails.”

In this case, the ideal was to build any push notification functionality into mobile because on desktop it was practically useless. The learning can be applied across the board. Build your retention features on surfaces where users actually engage, not where you think they should engage.

3. Design for authentication, not attribution

Cross-device attribution is getting harder thanks to privacy changes and cookie deprecation. Instead of fighting this trend with complex tracking, design surface experiences that get users logged in quickly.

“Once someone is logged in, all bets are off; we’ve got good information about them. But until then, they are anonymous and we’re generally not able to attribute data,” says Natalie.

This means prioritizing authentication flows over extensive anonymous functionality. In this case, depending on your growth initiatives, your surface strategy may prioritize guiding users toward logged-in states rather than providing comprehensive experiences for guest users.

4. Match your tools to your strategy

Most SaaS companies default to familiar tools like Google Analytics and Hotjar because they’ve historically focused on web experiences. But scaling to multiple surfaces requires different technology approaches.

Web-Focused Tools:

• Google Analytics
• Hotjar
• Traditional A/B testing platforms

App-Optimized Tools:

• Amplitude: Combines analytics and testing specifically for app experiences; allows product managers direct data access
• Pendo: Integrates surveys, heat maps, and onboarding flows for mobile apps
• Adobe Journey Optimizer: Enables in-product testing across surfaces

Choose tools that support your surface strategy rather than forcing your strategy to fit your existing tool stack. Surface strategy is a business decision that should be driven by user needs, revenue models, and competitive positioning, not technical capability.

5. Define success differently for each surface

A growth lever surface shouldn’t be measured the same way as a full-featured replica surface. Define success metrics that align with each surface’s strategic purpose:

• Growth surfaces: Conversion rate to core platform; cost per qualified lead
• Utility surfaces: Integration success rate; core platform usage lift
• Companion surfaces: Feature adoption in main platform; user satisfaction
• Replica surfaces: Cross-device workflow completion; feature usage parity

Stop measuring everything the same way. Different surfaces serve different purposes and should be evaluated accordingly.

6. Start with purpose, not capability

The wrong question: “Can we build this feature on mobile?” The right question: “Should this feature exist on mobile given our strategic purpose for this surface?”

Before building anything new, clearly define what strategic purpose each surface serves:

• Growth lever: Limited functionality to drive awareness and conversion
• Utility: Essential support that makes the core platform more valuable
• Companion: Unique value that leverages platform-specific capabilities
• Replica: Full feature parity for seamless cross-device workflows

Once you’re clear on purpose, feature decisions become much easier to make.

Building everything, everywhere, isn’t the answer. Many product teams default to feature parity because it feels “fair” to users. In reality, this often creates mediocre user experiences across all surfaces instead of excellent user experiences where they matter most.

Getting started with a surface strategy that doesn’t over-emphasize feature parity

The companies winning in the multi-surface SaaS landscape aren’t the ones with the most features across the most platforms. They’re the ones making the smartest strategic decisions about where to focus their development resources.

If you’re struggling with where to start, here are a few ideas:

• Start with one surface audit. Pick your least strategic surface and honestly evaluate whether you’re over-building functionality that doesn’t serve your business goals or user needs in the name of feature parity.
• Question your assumptions about user expectations. Users might actually prefer a focused, excellent user experience over a comprehensive one that is mediocre.
• Align your team around surface strategy. Make sure product, engineering, and growth teams understand the strategic purpose of each surface, not just the feature requirements.

The goal isn’t to build less, it’s to build more strategically.

Ready to optimize your SaaS surface strategy? At The Good, we help scaling SaaS companies make smarter product decisions through data-driven audits and optimization. Our team has guided companies, from Adobe to emerging startups, in creating multi-surface user experiences that actually drive growth, rather than just checking feature boxes.

Schedule a strategic consultation to discover which surfaces are driving growth and which are consuming resources without strategic return. Let’s turn your multi-surface challenge into your competitive advantage.

The post Why Feature Parity Isn’t Always the Goal: A Guide to Cross-device SaaS Strategy appeared first on The Good.

Truthfully, most people couldn’t care less if you offer one feature or 50. They just want the product to solve their problems and make their life easier.

So, if you want to convert free trial users, your task is to show them how your product meets their needs. If you can’t help them achieve a desired outcome, they simply won’t buy.

In this article, we’re going to discuss how to convert free trial users to paying customers. We’ll talk about your free-to-paid conversion rate and offer some strategies to boost conversions.

What is a Free-to-Paid Conversion Rate?

Free-to-Paid Conversion Rate measures the percentage of users who transition from using a free version of a product or service to a paid version.

If you want users to upgrade to paid tiers of your product, this is an important metric to track.

It’s also important for freemium models, where users can access some features for free and are encouraged to pay for premium features.

Here’s the formula to calculate a Free-to-Paid Conversion Rate:

Free-to-Paid Conversion Rate = (number of users who convert to paid / total number of free users) X 100%

For example, if a SaaS company has 10,000 free users and 500 of them upgrade to the paid version, the Free-to-Paid Conversion Rate would be:

(500/10,000) X 100% = 5%

As you would imagine, you’ll want to push this number as high as possible, as more conversions to paid accounts mean more revenue.

What We Mean by “Free Trial”

Before we discuss converting free trial users, let’s clarify the different free trial strategies.

Freemium Model

Freemium is a two-tiered model with a free tier and a premium plan. The free tier usually grants perpetual access to a restricted version of the product, either by limiting the accessible features (e.g., four of six features available) or placing caps on features (e.g., a limit of 20 downloads per month).

Freemium product users can upgrade to a paid version to access the full features. In some cases, freemium users are charged a la carte for product usage.

Reverse Trial

In a reverse trial, a time-based approach coined by Elena Verna, Head of Growth at Dropbox, users start with full access to all features for a limited time during a trial phase. Then they get moved to a freemium plan with limited product features.

With this system, they get the product’s maximum value from the beginning of the trial experience. If they want to regain access to full features, they need to purchase the paid plan.

Trial With Payment

In a trial with payment, users are required to provide payment information upfront (a credit card) to gain full access to the product for a limited period of time. The trial is free, but upon a specific date, they will be charged to use the full suite of product features.

Which Model is Right for You?

Naturally, it depends on your product and potential customers. There is a ton of nuance in understanding and building your product strategy.

Two helpful tools to leverage when exploring the right fit for you and your users are the ROPES framework and verb scoring.

Once you know what your customers expect and need, you can choose the trial offering that matches their journey.

How Do I Convert Free Users to Paid Users?

Converting free trial users to paid users is about demonstrating your product’s value. You can do this by strategically placing messaging throughout your site and/or app.

Keep in mind that your free trial signups already know the product is good. That’s why they signed up in the first place. Your job is to convince them that the value they’ll get from the product is worth the price.

Basically, you need them to conduct a cost-benefit analysis of your product and decide that your product comes out on top. Highlighting benefits, offering social proof, giving product tours, and boosting user engagement are just some of the techniques brands use to increase activation rates.

You can’t invite this kind of thinking unless you know your customers well. Exceedingly well. Only once you know what triggers them to buy can you build a user experience that entices them to convert.

9 Free Trial Conversion Strategies

Let’s walk through some powerful free-to-paid conversion strategies. Use some or all of these to turn free trial users into paying customers. As always, experiment and test to find the techniques that produce the best results.

1. Remind Users to Upgrade Early and Often

Many SaaS organizations make the mistake of waiting until the end of the free trial to prompt users to upgrade, often by direct outreach from the sales team. However, this approach fails to make use of numerous opportunities that could lead to a potential conversion.

Start prompting users to upgrade from the beginning of their experience and remind them that they aren’t getting the full feature suite. Do this often using CTAs, in-app notifications, tooltips, popup overlays, onboarding videos, support messages, and marketing emails.

Dropbox isn’t shy about prompting non-paying users to upgrade on each page of the dashboard.

2. Drive Users to Value Quickly

As we’ve mentioned a few times, people use your product to benefit themselves. If you want them to think it’s worth the cost, help them achieve that value quickly.

First, you have to understand what that “moment of value” is for your customers. This requires robust knowledge of your customers’ problems and needs. The moment they see value in your product may not be as intuitive as you think. For example, you might think the most valuable feature is Download when really it is Share or Cloud Storage.

Next, drive your users toward that valuable moment by nudging user behavior. Checklists and progress bars are great tools here. Give your users a concise set of steps to follow that culminates in the moment of value.

When a user begins a checklist, psychological motivators like the sunk cost dilemma and endowed progress encourage them to complete it.

Throughout the checklist, you walk free trialists through any tasks necessary to get to the moment of value, like adding contacts, filling out their profile, integrating other apps, etc. At the end, the user should achieve something that makes them think, “Oh, this product actually solves my problem.”

3. Present Gated Features Near Free Features

By strategically placing prompts or highlights for premium features adjacent to free ones, you create an opportunity for trial users to envision how the paid features can enhance their experience.

For example, PDF Converter lets you convert PDF files into other formats for free. However, the premium feature (a higher print quality) is positioned nearby.

This ensures that users are consistently reminded of what they are missing, sparks curiosity, and demonstrates the tangible benefits of upgrading.

Using visual cues, such as icons, badges, or color contrasts, can further draw attention to these gated features. For example, a lock icon next to an advanced tool can indicate that it is part of the paid tier, prompting users to consider the upgrade.

4. Make Your Calls-to-Action Clear and Consistent

A call-to-action (CTA) is a quintessential marketing tool. Clear and consistent CTAs placed throughout your user journey are great ways to guide users toward the next step. In this case, the next step is a paid tier of your product.

Your CTAs should be direct and easy to understand. There’s no room for ambiguity here. Users should immediately understand what will happen when they click that button.

Avoid vague or overly complex language. Use straightforward phrases like “Upgrade Now,” “Unlock Premium Fonts,” or “Start Your Subscription.”

Place your CTAs in locations where users are most likely to engage with them, such as:

• Onboarding screens
• Dashboards and home screens
• Email campaigns
• Global header
• In-product

Maintain a consistent design for your CTAs so they are recognizable across your platform. Use uniform colors, fonts, and button styles.

It’s also helpful to accompany each CTA with some brief text that highlights the benefit. For instance, “Upgrade Now to Access Advanced Analytics” or “Unlock Unlimited Storage.” This helps remind users that the upgrade is worth their investment.

Canva uses a great call-to-action. It describes the benefits and what users get and reminds the user that they can cancel at any time. The app uses upgrade buttons of similar design elsewhere in the app.

5. Be Thoughtful About Which Features are Gated

Generally, you want to give away enough value with the free version of your product to build a solid user base. This will help users make the connection that the paid version offers even more value.

Offer free features that make users reliant on the product. You want them to build it into their personal and professional workflow.

For instance, if your product involves storing users’ files, give away some storage space for free to bring them into the product, then charge for additional storage. They will be more likely to purchase your storage because their files are already there rather than switch to a new provider.

Canva is a notable example of this. Creating documents is free, but exporting them into certain formats is gated behind a paywall. Which formats are gated? The ones associated with experts or business users.

6. Make Free Users Aware of Their Trial Time

Keeping your users aware of their remaining time can create a sense of urgency and encourage them to consider transitioning to a paid tier before the trial expires.

Clear and Frequent Reminders

Send regular emails or in-app notifications to inform users about their trial status. These reminders should start as soon as they begin the trial and become more frequent as the trial period nears its end.

Countdown Timers

Incorporate countdown timers within your app or on your website. These visual cues serve as constant reminders of the trial period’s ticking clock, subtly urging users to make a decision.

Highlight Benefits

Each reminder should not only inform users about the time left but also emphasize the value and benefits of the paid version. Use these touchpoints to showcase features they haven’t explored yet or to highlight how the paid tier can solve specific pain points they’ve experienced.

Offer Limited-Time Discounts

As the trial period comes to a close, consider offering a limited-time discount for upgrading. This tactic leverages the sense of urgency created by the trial countdown and adds an additional incentive to convert.

That said, we don’t always recommend discounting your product. It can be useful to get someone in the door, but it can also devalue your product and brand. Be very careful with discounts.

7. Offer a Great Onboarding Experience

The onboarding process is the first impression users have of your product or service. A positive experience can significantly influence their decision to upgrade.

Provide a clear and concise step-by-step guide to help users navigate your product. Use tooltips, interactive tutorials, or walkthroughs to highlight key features and demonstrate how to use them effectively.

Emphasize the unique features and benefits of the paid version. Show users how these features can solve their problems or enhance their experience.

It’s also smart to help users achieve quick success to boost their confidence and satisfaction with your product. These early wins can be as simple as completing a task, setting up their profile, or customizing their dashboard.

8. Use Paywalls to Demonstrate Paid Features

Paywalls demonstrate the value and benefits of premium features, which entices users to upgrade to unlock full access. When designed thoughtfully, they can drive conversions without causing frustration.

Place your paywalls strategically at points where users are likely to see the value of upgrading. These can include:

• Feature usage: When a user attempts to access a feature that is only available in the paid tier, present a paywall that explains the benefits of that feature. For example, if your product is a photo editing tool, a paywall might appear when a user tries to use advanced filters or high-resolution exports.
• Content access: Content-based platforms, such as news websites or educational sites, use paywalls to restrict access to premium articles, videos, or courses. Clearly communicate the added value of the premium content to encourage users to upgrade.
• Usage limits: Implement usage-based paywalls where users can access basic features for free but encounter limits on their usage. For example, a project management tool might allow a certain number of projects or tasks in the free version, with a paywall prompting an upgrade to manage more.

Each paywall should clearly articulate the benefits of upgrading to the paid version. Use persuasive messaging to highlight key value propositions, such as enhanced features, better performance, exclusive content, etc.

9. Clearly Label Your Paid Features

Transparent and distinct labeling helps users understand what they are missing out on and how the paid version can enhance their experience. This fosters a sense of curiosity and desire.

Ensure that the paid features are visibly differentiated from free ones. Use consistent visual cues such as icons, badges, or color schemes to indicate premium features.

For example, you might use a lock icon or a different color for buttons and menus that lead to paid features. This visual differentiation helps users easily identify what they can unlock by upgrading.

Use in-context prompts to highlight paid features during the user’s interaction with your product. For example, if a user is using a basic editing tool, a prompt might suggest, “Upgrade to access advanced editing options,” along with a brief description of the additional tools they would receive.

Improve Your Free-to-Paid Conversion Strategy with The Right Disciplines

While you may handle some strategies internally, improving your free-to-paid conversion rate requires a specific skill set and multiple disciplines. The Good’s Digital Experience Optimization Program offers a comprehensive solution tailored for SaaS, ecommerce, and product marketing teams.

Clients like Adobe and The Telegraph have praised The Good for our ability to validate hypotheses, drive engagement, and achieve substantial growth.

How it works: We conduct a full funnel analysis of your digital product using heatmap analysis, session recordings, and usability testing. Then, based on those insights, we build a custom program that includes road mapping, experimentation, and customer journey mapping.

Ready to see how your strategy can be optimized? Schedule an introductory call and unlock your brand’s full potential.

The post How to Convert Free Trial Users to Paying Customers appeared first on The Good.

Let’s talk about how the right level of design detail in user tests can save you time, money, and deliver a better user experience.

What is prototyping?

Prototyping is an essential part of the UX design process and can unlock your team’s ability to validate ideas before you send them to development.

In literal terms, a prototype is a first or early model of a proposed design passed to the development team before being coded onto the website. For ecommerce and product marketing teams, prototypes are early samples of a product intentionally designed for testing.

They can range from simple pen and paper sketches to highly interactive mockups in tools such as Figma. With prototypes, you can get user feedback on pages or app elements, which can be used to iterate your way to a better digital experience for your users.

To illustrate the idea, you may use a prototype when redesigning your website’s landing page. You may sketch ideas out in a wireframe and get either internal or external feedback before layering on your brand design and sending it to development for implementation.

What is fidelity?

That brings me to the next point–prototypes can range in their level of detail, identified by their fidelity. You’ve probably heard of low fidelity (simple, typically sketched designs) and high fidelity (more complex, close to the actual design of your digital experience). But there is magic in the often skipped-over mid-fidelity prototypes.

Mid-fidelity mockups or prototypes can improve efficiency, increase testing velocity, and focus your users on what matters.

There is, of course, a time and a place for all three fidelity types, which we will cover. But, considering rapid testing as an undervalued way to improve your website I’ll focus on the benefits you might be missing if you’re overlooking mid-fidelity designs. And even more specifically their use case for rapid testing.

When should I use low versus mid versus high fidelity?

• Low Fidelity: This level involves basic, hand-drawn sketches or paper prototypes. Colors are grayscale and placeholder images and text are often used. It’s ideal for brainstorming, generating ideas, and exploring concepts internally.
• Mid Fidelity: Also known as medium fidelity, this level is the Goldilocks between low and high fidelity. It may or may not include clickable elements relevant to the test’s goals without distracting testers with superfluous content. Mid fidelity is the best choice for rapid testing. This is the best method for focusing on the problem–not border widths or hex codes.
• High Fidelity: The most detailed level, high-fidelity mockups closely resemble the final product, with intricate interactions, pixel-perfect designs, brand colors, fonts, and every element clickable. It is used when testing an entire website or app and passing designs to the development team for implementation.

Let’s take a look at the details and pros and cons of each prototype fidelity.

Pros and cons of low fidelity

Low fidelity is reserved for brainstorming, idea generation, and internal exploration. It is not suitable for rapid testing due to its lack of detail.

Pros:

• Cost Effective: Low fidelity is a cost-effective option, making it suitable for early ideation and concept generation.
• Rapid Ideation: Hand-drawn sketches and basic prototypes allow for quick idea generation and exploration.
• Internal Collaboration: Ideal for internal use, low fidelity facilitates collaboration and idea sharing among team members.

Cons:

• Lack of Detail: Low fidelity lacks the detail for accurate user testing. It may not provide a realistic representation of the final product.
• Limited External Use: Not suitable for external presentations or client interactions due to its basic and rough nature.

Pros and cons of mid fidelity

As I mentioned, mid fidelity is the often-overlooked prototyping model. Particularly suitable for rapid testing, it’s the happy medium between designing a mockup for external use without over-resourcing before validation.

Pros:

• Time Efficiency: Mid-fidelity designs save time, making them ideal for rapid testing scenarios with tight timelines.
• Focused Testing: By prioritizing core functionalities, mid fidelity ensures that users focus on what’s important, leading to more meaningful insights and qualitative data.
• Balanced Detailing: Mid fidelity strikes a balance between low and high fidelity, providing enough detail for testing without unnecessary intricacies.

Cons:

• Not Pixel Perfect: Unlike high-fidelity designs, mid fidelity lacks pixel-perfect detailing. This may be a drawback when detailed, final designs are necessary.
• Limited Use Cases: Mid fidelity is most effective in scenarios like rapid testing. There may be better choices for situations requiring highly detailed or finalized designs, such as A/B testing.

Pros and cons of high fidelity

High-fidelity prototypes are used when passing designs to the development team for implementation, especially for complex scenarios with multiple states. High fidelity prototypes can distract users from their tasks and requires extensive time and budget that you shouldn’t waste before validation.

Pros:

• Realistic Representation: High fidelity provides a detailed and realistic representation of the final product, aiding in client presentations and developer handovers.
• Accurate User Testing: Ideal for complex scenarios with multiple states, high fidelity ensures proper user testing with intricate interactions.
• Developer-Friendly: The closer the design is to the final product, the easier it is for developers to implement the final product, reducing potential misinterpretations.

Cons:

• Time Consuming: Creating high-fidelity prototypes is time-consuming, which may not align with the rapid pace of specific testing scenarios.
• Resource Intensive: Requires more design expertise, time, and resources, potentially delaying the testing process.
• Highly Detailed: The added detail and functionality of high-fidelity prototypes can create unnecessary distractions for user testers, causing possible derailment from the goal of the test.

Why less is more when prototyping for rapid testing

The fidelity level of your mockups can make or break your rapid test results–bleeding time and financial resources while also hindering valuable user insights. Imagine you want to test if changing the category page name improves user understanding and boosts conversions. Crafting a high-fidelity, fully interactive prototype might seem impressive, but it can backfire. The intricate details distract users, drawing them outside the test’s scope and obscuring relevant feedback. This can put users into cognitive overload.

That’s where the mid-fidelity mockup steps in.

It shows just enough detail and the relevant design elements (like the navigation bar and category name) with enough clarity to incite meaningful feedback.

Mid-fidelity also focuses feedback. With no functional interactions, users stay within the test boundary, providing insights directly related to your research question.

Here’s an analogy: You wouldn’t build a full kitchen to test a new icing recipe. You’d bake a simple cake base to focus on the icing’s impact on taste and texture. Similarly, a mid-fidelity mockup acts as your cake base, allowing you to hone in on the specific design element you’re testing.

In our 15+ years of experience in digital experience optimization, mid fidelity emerges as a strategic choice for rapid testing. Offering a happy medium between speed, detail, and focus, mid-fidelity mockups give users the right amount of information to provide insightful feedback without distracting or over-resourcing.

The post What Is Prototyping And Why Is Mid Fidelity Its Unsung Hero In Rapid Testing? appeared first on The Good.

About This Episode:

AI is a pretty buzzy term, and it can mean different things to different people. In this episode, Jon and Ryan engage in a lively discussion about the use of AI, with a specific focus on its application in UX and optimization. 

They start by differentiating actual AI tools from AI enabled applications. Then, they delve into the practical applications and potential benefits of ChatGPT, touching on its affordability, effectiveness in idea generation, and visual design capabilities.  

Listen to the full episode if you want to learn:

1. What you can accomplish with a free version of ChatGPT
2. How to write specific prompts for brainstorming and ideation
3. How to use ChatGPT to write codes and create prototypes for your website 
4. Why AI is important for rewriting and condensing data, information and website copy
5. How to apply AI in visual design and user research

Check out Tldraw in action!

If you have questions, ideas, or feedback to share, hit us up on Twitter. We’re @jonmacdonald and @ryangarrow.

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Episode Transcript:

Announcer:
You’re listening to Drive and Convert, a podcast about helping online brands to build a better e-commerce growth engine with Jon MacDonald and Ryan Garrow.

Ryan:
Jon, we have some exciting stuff today. Not only are we in the middle of holiday, which is chaotic regardless of where you fall in the e-comm ecosystem, but it’s fun and the data’s exciting. But you’ve put together some notes and things around AI in CRO and optimization of your conversion rates that I’m excited to get into, because I know probably more than most about what you do, and I probably wouldn’t have come up with an idea of how to bring in my ChatGPT system into CRO, but I bet people want to and you probably have to answer this question all the time.
I’m excited because I want to be able to use AI where I can, I guess, and where it actually makes me more efficient, rather than distracts me and sends me down rabbit holes, which maybe, I need to go down sometimes. But AI is a pretty buzzy term and it probably means a lot of different things to different people. So, when you hear AI, do you just cringe when people are talking to you about CRO? Or is it like, “Oh, this is going to be great and we can probably do a lot of good things”?

Jon:
I normally cringe because let’s be honest, when most people say AI, they’re probably talking about ChatGPT. That’s what the public knows about. And in fact, most of what we’re going to talk about today can be done with ChatGPT. So, I’m going to put that out there. Just know that’s probably the only tool you really need at this stage. And then, there’s a ton of SaaS apps out there, things like that, that say they’re an AI tool, when really, maybe they’re using the ChatGPT API, or they’re pulling from some other AI service, but they’re not natively AI, they’re not really AI. They maybe are AI enabled in some way. They’re thinking about user experience optimization, digital journey optimization. We do this with User Input, which is our user testing tool that we have.
You take your videos and we can provide sentiment analysis on those. We’re doing that through AI. That transcribes it, looks for sentiment, tells you, “There was a positive reaction at this time. There was a negative at this time.” But that doesn’t make it an AI tool. It is just using AI, it’s AI enabled to some degree, to make a function that would be much harder to do on our own. So, when I’m talking about AI, I really want people to understand how they could actually use AI to help them. It’s never going to replace them. Let me step that back. Maybe one day, but we’re not close. We’re nowhere close to replacing a human in understanding other human interactions and being able to optimize a digital purchase journey. It’s just unlikely to happen at this stage.

Ryan:
I feel like as a service-based business, if you don’t claim some level of AI, you’re automatically at a disadvantage because people aren’t going to trust that you’re advanced enough or efficient enough because you’re not using any type of AI.

Jon:
Yeah, that’s a good point. I’ve intentionally not brought up AI on Drive and Convert because it just has felt like a buzzword. It’s felt like would be just going after listeners and to try to get some buzz, instead of actually delivering value. And I kind of still feel that way with The Good. We use AI internally, but we don’t really talk about it. It does make us more efficient in a lot of ways, but we’re not out there promoting saying, “We’re an AI optimization firm.” Are we? No. There’s humans behind everything we do. To say anything else that I feel would be disingenuous.
So, yes, there’s a lot out there. I feel like it’s confusing for the general population. I think that we’re at the cusp where it’s a tool and you need to use the tool appropriately. And if you’re not learning about the tools right now, then yeah, you’re going to be behind in pretty short order. So, it might be something you’re going to want to at least study up on. So, really, today I’m scratching the surface with this, right? I’m not getting too deep into it, but I’m thinking about ways that if you’re optimizing your site, what are ways you could use, let’s just say ChatGPT for the most part, you could use AI, ChatGPT to help you perform better, think about maybe different things that you wouldn’t have come up with yourself or take the hive mind, if you will, right? That entire knowledge base, that ChatGPT has behind it and offer up some suggestions?

Ryan:
Perfect. Okay, so step one is going to be getting a ChatGPT account, I assume. For most of what you’re talking about, is the free version going to be applicable or do you need to jump in and start paying for one [inaudible 00:04:57]?

Jon:
Yeah, you could use the free version, but the benefits of paying right now are that you can open an instance of ChatGPT and start training it on particular things, so you won’t have to repeat yourself every single time. So, you open a new ChatGPT session and you say, “I want to talk about this ideal customer profile. And I want to talk about this type of product line. And I’d love for you to write a product description that’s geared towards that ICP.” Then, you wouldn’t have to, every time, come back. You’d just say, “Oh, here’s another product. Write the description targeted towards that same ICP.” If you have the free version, every time you leave the chat window, you’re going to have to come back and start over, and that’s where it gets a little more time-consuming and just annoying, but it’s a great place to start. Could you do this? Yeah, most of this you could do in the free version, I would think.

Ryan:
Got it. But for 20 bucks a month, if you are optimizing your own site, 250 bucks a year is not something that should break the bank if you’re really leaning in to leverage these tools, because it also can do other things for you. It doesn’t have to just be for your CRO.

Jon:
Well, I mean, one of the things it can do now if you pay is it can generate images. So, that is an area I’ll talk about today, and that you do need a paid account. But again, $20 to play around with it. If you find it’s not useful for you yet, then just stop paying for it, right? 20 bucks is-

Ryan:
Yeah, you don’t have to prepay for a year, so easy.

Jon:
Yeah, exactly. If 20 bucks is going to hurt you, then you probably should just stop listening now and go back to some of our earlier episodes about more foundational things you can do.

Ryan:
We’re assuming you’re advanced at episode 95 by now, and not just jumping in. Okay, so you’ve got your ChatGPT ready, you’ve paid your 20 bucks because you’ve gotten to that point where that’s okay in the business. And it’s on the business card where you’re getting rewards, so that’s good.

Jon:
There you go.

Ryan:
Where are you going to start with this? What’s step one when you’ve got your prompt open?

Jon:
I would say idea generation, and I started here because this is the highest level.

Ryan:
It’s also my favorite. You got a brainstorm buddy. If you’re working at home that’s the part I hate about working at home, by the way. It’s not as easy to be like, “I need to come up with some ideas that aren’t mine to juice my brain up.”

Jon:
I would argue it’s more efficient too, because knowing you, I bet you miss the, let’s call it water cooler chat. Like, “Hey, how was your weekend?” That’s great, but I would say this is more efficient than in-person brainstorming even because you’re able to say, “These are the type of things I’m thinking about,” and you start circling it and then it starts helping you come up with those ideas. So, in this case for idea generation, I would really just prompt ChatGPT with constraints or themes to focus on. So, give it some barriers, and then ask it to find alternative suggestions or blind spots, those themes or constraints.

Ryan:
So, I’ve got my Shopify site and I’m like, “Am I going to prompt it with this URL? I want to improve conversion rates or I want to improve conversion rates on PDP pages for e-comm.”

Jon:
Right. So, the more specific you get, the better information you’re going to get. And you really can’t say, “Hey, I want to improve conversion.” You can, but what you’re going to get back is probably not amazing. But what you could say is, “Here’s my product detail page for this product and I want to tailor it more for this particular profile that we’re targeting in an ad campaign we’re running. And here’s the visuals for the ad. So, here’s the ad. Now, can you help me rewrite the copy on this page to be more tailored towards that audience?” And so, you give it some constraints. “I want you to write tailored copy. I want you to do it in a way that is in alignment with the entire digital journey.” So, starting with that ad that they saw and they click through, so you’re giving it constraints.
So, just start prompting it with more constraints and you’re going to get better stuff back. So, really, here what we’re looking for though is to find alternative suggestions. So, we’re looking for it to help you brainstorm and not necessarily do the work for you. So, in that sense, what you could say is, “Here’s my PDP. Here’s the ad campaign. How could I improve this copy?” It’s really good at copy, that’s why I keep going back to that. That’s probably it’s best is text. But we’ll talk later, there are ways to even use it for visuals quite a bit. So, one other thing you could do here is have it share examples. These are concepts maybe you’re interested in learning more about. So, you could say, “Hey, here’s my PDP, I’m tailoring. I want your help tailoring it for this audience. What are the competitors I should be thinking about?” Or, “What are other PDPs targeting this same audience?” So, then, you start kind of digging in and start asking it some more questions. You can get more examples that you can learn from.

Ryan:
So, using it in that sense, almost like a Google like, “Hey, who else seems to be targeting this product with the same persona?”

Jon:
Well, that right there, why do you think Microsoft owns a big portion of OpenAI? That’s exactly what they have it for, is to, eventually, try to overtake Google in search engine because they want to use ChatGPT or AI, essentially, to improve their search engine.

Ryan:
Got it. Okay. So, I’ve got some ideas of the PDP page and some of the copy. By the way, how many times do you think people have to rewrite that copy within ChatGPT to get something usable? Most often, I found the first one is like, sounds good, but that’s definitely not what-

Jon:
Again, the more constraints you give it, the better it’s going to do.

Ryan:
So, training your own, which is another reason for the $20 ChatGPT.

Jon:
Right.

Ryan:
It gets better and better knowing what you’re looking for and what your tone of voice-

Jon:
Yeah, exactly. Because then you could say, “Here’s all of my products.” Upload a CSV file of all your products and all the product descriptions, and it will learn how you write. Now, is it going to be perfect? Unlikely. Again, it’s still early. ChatGPT’s been out a year, I think. If you asked your toddler to write, probably not going to be great, even if you gave it a bunch of examples. You’re going to need some help. So, that’s really where it’s at, but it’s getting way, way better.

Ryan:
Okay. So, it’s got ideas. And then, you put a note in. The next one is what you refer to as prototyping. I mean, I understand what a prototype is, but what does that mean as you process through AI to get to the final product?

Jon:
Well, what I’m talking about here is let’s just say you have a new PDP and you’re trying to put all the content together that goes into it. So, you maybe want to run user testing on that before you launch it or build it into an actual template. So, the idea behind the prototyping stage here is what can you do before you have actual code written? Which ChatGPT can actually help you write some code. But the reality here is I’m saying, what’s the least amount of effort you could put in to get a high-impact feedback? And a prototype is really going to help you do that. So, you could have it write realistic copy, and so you feed in a prototype, you take a picture of your prototype or upload an image that has lorem ipsum, and let it write the copy there instead. It can replace it and do that.
I’ve mentioned visuals a few times. You could have it boost the realism of all your mock-up visuals. So, ChatGPT, if you pay for it, can now generate images, including ones that will match your brand tone and style. So, again, you got to provide enough constraints, maybe give it some examples, but it can certainly do that. What’s awesome is I saw this recently, you can actually upload a sketch of a wireframe. So, take a piece of paper, pen, draw it out, and it will turn it into a higher-fidelity wireframe or mock-up that you can then utilize for testing. And it can even do some visual design. So, you say, “Here’s my brand style guide and here’s a sketch of this page. Can you turn that into a wireframe or can you turn it into an actual page that I can use for user testing, a visual?” And it will do that.
And you can also take that even a step further and create actual functional prototypes. There’s a tool out there called tldraw, T-L-D-R-A-W. It’s a AI tool that allows you to turn your sketches into functional applications without any programming experience.

Ryan:
Holy smokes.

Jon:
So, that’s where I would go is you create a sketch of your application, it analyzes it, and then it uses AI to generate functional code for that. And you don’t have to know anything. So, we’ll put in the show notes. There’s a whole Instagram video that the company did, and we’ll include that in the show notes. But is it perfect? No. But is it way better? Say, I don’t know how to code at all and I want to start doing some user testing on a mock-up. This is amazing. This is a great way to do a clickable wireframe prototype that could be the entire customer journey of a website or a SaaS application I want do, or a mobile app. And now, I want to take it to market and do UX testing on it. And it would certify for that.

Ryan:
Dang. And I also like that their name starts with TLDR, which is appropriate. Like too long, didn’t read. That’s pretty much what I do a lot of. But coding would generally indicate you may not be on a Shopify platform. In a template, you’re somewhat limited.

Jon:
Well, it can write to Shopify template specs without a problem. I have clearly seen people who fed it a template file and said, “I need to make these changes. Can you make them?”

Ryan:
Perfect.

Jon:
It’s done that and it does it pretty well. Is it perfect? Not always, but it’s functional. It’ll pass. Maybe you have a developer look at it if needed, but you’re spending 15 minutes of their time looking over that template file versus two hours of them writing it. That’s a heck of a productivity boost.

Ryan:
Oh, yeah. Yeah. So, I would challenge a lot of Shopify template listeners out there that you can modify Shopify templates because they were modified from Shopify code to make it look like that. So, there is code in there. You got to be careful when you slow it down though. So, that’s where a developer is going to come into play. If you get this really cool, beautiful visual PDP, but it loads in 15 seconds, it doesn’t matter how good it could convert because it’s just not going to.

Jon:
Yeah, and look, I mean, is ChatGPT going to have your style of visual design that you want at this high level? Absolutely not. It’s not. But would it do a prototype that then you could hand to a developer and say, “Here’s what I need. Make sure the code’s cleaned up and ready to go”? Yeah, you could totally do that.

Ryan:
Huge cost savings there. You paid 20 bucks and some of your time to get it [inaudible 00:15:31].

Jon:
20 bucks a month. So, imagine how many times you could get a page out of it over a course of a month, right?

Announcer:
You’re listening to Drive and Convert, a podcast focused on e-commerce growth. Your hosts are Jon MacDonald, founder of The Good, a conversion rate optimization agency that works with e-commerce brands to help convert more of their visitors into buyers. And Ryan Garrow of Logical Position, the digital marketing agency offering pay-per-click management, search engine optimization, and website design services to brands of all sizes. If you find this podcast helpful, please help us out by leaving a review on Apple Podcasts and sharing it with a friend or colleague. Thank you.

Ryan:
Okay. And then, copywriting is what it’s known for. And so, outside of prototype or revamping a PDP page and giving it some ideas on there, are there pieces of the site that you find that ChatGPT is going to be better at versus… Truth be told, I know brand owners that have done it for their mission statements, and it’s not always for that, but-

Jon:
Just what you want a robot writing.

Ryan:
… where do you guide… Keep them first.

Jon:
Yeah. Well, I think in the simplest form, I like to talk about how brands often have these walls of text. They’re just paragraphs of text they put up on the site and nobody’s reading it. You do all the heat maps, eye tracking studies.

Ryan:
[inaudible 00:16:56].

Jon:
People read the first couple words and they’re like, “Oh yeah, I’m not reading all this,” and they just skip right through it. So, what we’ve actually done is use it to shorten the text while keeping the main points. So, you say, “Here’s a paragraph of text,” might be 100 words, “shorten this to 20 words, keep the main points.” And it usually does a pretty good job of that. Or, “Give me bullet points out of this.” That works really well too. So, you have a couple of good options there. You can also use it to, again, make copy persona specific. Mentioned this a few times. So, you describe those personas, ask it to rewrite the copy to be directed at that persona. That works really, really well.
In a slightly different approach, if you’re trying to gain support internally, this is a really cool trick. You can use it to rewrite your reports for clarity. So, you’re saying, “Hey, I have something that’s really technical. It has a lot of data about why we did something. I need you to rewrite this to target a VP level who doesn’t understand data science.” And it will do it. It will help you rewrite that copy. And I think this is a cheat code for service providers or consultants, but I think that it’s really great for gaining internal support. So, if you’re an e-comm manager, VP of e-comm, and you’re trying to present to a C-level who does not truly understand what’s being said to them and you really want them to get the results out of it, then I think this works extremely well for that.

Ryan:
Got it. So, it’s almost like condensing, here’s why we’re doing CRO. Here’s some of the results. It’s going to tell your story better than probably you would on the first pass through.

Jon:
Yeah. We’ve had clients who take our reports and run them through and say, “Give me the key points of this slide for somebody who does not understand optimization.” And I don’t know the exact prompt they used, but they told me they did this to present it to their executive team and that it worked extremely well. Because they were like, “I understand this information, but how do I present this to the executive team? And I don’t want to rewrite all these slides all the time.” So, they decided, “Oh, we’ll just paste it in and see what happens.” And it worked really well for them.

Ryan:
Dang. Yeah, telling that story ends up becoming so important from the marketing team to the exec team to justify and explain things. Like, yes, you do need CRO and you need it all the time, not just two months here, two months there. Anyway, I won’t do your sales for you.

Jon:
No, by all means, please continue.

Ryan:
Your next point here is my favorite part of, honestly, on AI, what people use AI tools for. But it’s visually, what can it do? The stuff it can do with images-

Jon:
It’s getting so much better.

Ryan:
… boggles my mind.

Jon:
Have you seen the latest trend where somebody took… He said, “Give me a picture of a happy kitten.” And then they said, “Make it happier.” And then, “Make it more happy.”

Ryan:
Oh, yeah. People do this all over LinkedIn. “If you like this, I’ll make it work harder,” or, “I’ll make the beaver-“

Jon:
Yeah. And so, there’s this whole meme of people now taking things and making them more. The first one that somebody did it was, “Make this kitten happier.” And they were just playing around with it, and it turned out that ChatGPT was coming back with these descriptions and these images that were hilarious. And the kitten kept getting happier to the point where the last one, it looks like something out of space. And it’s like this is the ultimate embodiment of happiness. There’s nothing in the world that is possibly happier than this. It is beyond the realm of physical, and it starts getting into this really theoretical stuff that was hilarious in the end, but it all started from a picture of a kitten that they were like, “Can you just make the kitten happier?” And then it’s like, “Sure, here’s a happier kitten.” And then, they’re like, “I wonder, could you do happier?” And it just keeps going.

Ryan:
That’s crazy.

Jon:
So, basically, the point here is there’s a lot of ways to use this for visual design. I think the first example I really like is persona illustrations. It really makes something that’s theoretical, concrete. What I mean by that is you prompt it with a description of your persona and then ask it to share a visual representation of it. Again, if you’re selling through to executives, this is great. You’re giving them a visual image of the persona you’re trying to reach and talk to, so that could be helpful.
And journey map. So, I really like the idea of asking it to feed it something, a site, et cetera, and then say, “Describe all the steps in a journey from research, to buying, to conversion.” And it will tell you what the steps are along the way. And you could even feed it that and say, “Create me a journey map, a visual representation of all these steps,” and it will do that for you.
So, other UX ways to use this, we’ve talked about drafting designs of pages. I think that’s a really valuable one. It works surprisingly well. Comes with risk as, again, not being as specific to your brand or your persona, but again, that’s all in the constraints you give it. It depends how much time you want to spend feeding the information and training it, and I’ll put that in air quotes. But you can ask it to provide a visual design for specific pages of your site and feed it information to occlude on that. “I need a product category page. Here’s all the images and descriptions and titles of the products. Go.” And it will create one. Does pretty good. It’ll get you part of the way there to at least something to start with.
And the last is kind of what we were talking about with the kitten, right? It’s AI-driven images and brand assets. So, I’ve seen a lot of smaller brands do this. I don’t see as many large brands do this because it’s not a resource constraint for them, but if you’re a brand that’s starting out on e-comm and you need some visual assets and don’t really have a design budget, maybe you want to use them in ad campaigns, et cetera. If you can get good at describing what you want, they work really, really well in a pinch. So, you can easily get some graphics together that way.

Ryan:
Have you ever used it to increase how large or the pixelation of an image? I see a lot of that where you get a really small image to start with. You’re like, “I like this, but it can’t be used in a vector.”

Jon:
Right. Yeah, I-

Ryan:
I assume it can do that.

Jon:
I haven’t tried it, but I imagine it could. I don’t see why it couldn’t. It can generate new images, so you could just say, “Hey, take this image and increase the resolution of it.” It would probably be very good at that. I don’t see why it wouldn’t. Now, it’s going to guess a little bit. Is it going to be the actual data that was there when it was a larger image? No. But I would imagine it’d get close enough. Somebody who hadn’t seen the image before wouldn’t know.

Ryan:
[inaudible 00:23:33] your vector and it’s too small. Use ChatGPT, “Give me a big image.”

Jon:
Yeah, there you go. Yeah, user research is really the last area that I would use this for in UX. I’ve mentioned it a few times already today, but I love to use it to draft interview questions. So, again, this is dependent on the prompt used. All of this is. I mean, that’s a huge preface for all of these. It’s like crap in, crap out. So, you really got to work with it a little bit. But you have to make it detailed enough to have it come up with great interview questions, but you can ask it to draft those for user testing. It’s not awesome, but it will do it, and I think it will only get better if you spend more time training it.
You can have a draft test tasks for user testing. So, not just the questions, but also, what you want people to do along the way. So. You’re saying, “Hey, I want you to add something to the cart. Take the next step along the user journey.” So, see how these are kind of building upon each other? You have it map out that digital journey. Then you say, “Hey, can you have some tasks that a user would do in user testing along that user journey?” Then, “What questions would you ask as they’re completing those tasks?” So, now you’re starting to get a whole user testing program together. You have that journey you want someone to take. You’re giving them tasks to get each of that step together, and then you’re having questions that you can ask them at each step as well to get good user feedback.
We talked a little bit about sentiment analysis. Then, you take all of the response videos and you feed them in and say, “Tell me what the overall sentiment is of this,” or, “What’s the high point and low point of the sentiment?” And then, you could go in and instead of having to watch 50 videos front to back, you can now just go to those time codes that it gives you and says, “Yeah, at minute 1:05, there was a positive sentiment, but at minute 3:05, it was negative.” Wow, you probably want to watch that two minutes. What the heck happened in that two minutes that somebody went from happy to being upset?
You could tell it to extract themes. So, user testing scripts and videos. You could say, “What’s the theme coming out of these?” And it will tell you, “Oh, well, a lot of people talked about the product detail page,” or, “A lot of people talked about the add to cart button.” Then, you kind of have some idea of where to go optimize first. And this is kind of along those lines, but categorizing issues from the interviews. So, use the transcripts and have it pool all the themes on ideas of what should be optimized. It’s really good at this type of work. Really good.
Give it lists of things and tell it to pull themes out and comb through that data. ChatGPT can do that in seconds and it saves you so much time. So, that’s a really good way to do it. My biggest concern with all of this is that folks will use it and aren’t really learning about user testing and user experience. Instead, they’re relying on ChatGPT to come up with all the answers. I think that’s the unfortunate part of doing this is you get really good at ChatGPT and not very good at user testing, because you just rely on it to give you the answers.

Ryan:
It’s user testing for a reason.

Jon:
Right, it’s not ChatGPT testing.

Ryan:
It’s actual users. Yeah, I mean it’s limited by the publicly-available information, and I’m guessing all of your user tests over the past 15 years are not publicly available to ChatGPT necessarily.

Jon:
No, but one thing that I’m not ready to fully announce yet, but I will say that we are coming up with… One of the things that OpenAI allows you to do now, they just announced, was create your own ChatGPT. And so, we are going to be doing one that feeds in our 15 years of content that we’ve put together. So, I’ve been on so many podcasts, we’ve written so many things, so many webinars, case studies. Every week we write at least 2,000 words. I have two books plus that I’ve been part of and written. All of that content, all the videos I’ve recorded, et cetera, can go into this. And then, some people can ask it questions, and it’s trained on all of that data already, so they don’t have to train it. They get the 15 years of The Good’s brain as a basis point.

Ryan:
And you’re just going to give it to everybody for free, right? It’s just going to be wonderful.

Jon:
We’ll see about that. I want to try it out first, but it’s in development.

Ryan:
You’ll be able access Jon’s brain through a ChatGPT. That’s pretty crazy.

Jon:
Yeah. And so, that’s really the idea. That was part of the basis for coming up with this. If I did that tool, how would people use it? Here are some ideas of how people might use that type of tool. And all the stuff I talked about today would be things they would use the JonGPT for or TheGoodGPT, whatever you want to call it.

Ryan:
Well, you can get some bracelets like WWJD, what would Jon do?

Jon:
There you go. Well, unfortunately that one’s probably taken.

Ryan:
That one might be taken WWJonD. What would Jon do? So, with all these things, I mean, you’ve told us how essentially to use ChatGPT. So, if I pay 20 bucks, I don’t need to work with any consultants, right? I mean, it’s done for 20 bucks.

Jon:
Yeah, I wish you luck with that. Here’s the thing, it has come a long way, but let’s keep in mind it is a year old. It is moving very quickly. Again, is it going to replace a human? Look, if I had to invest in some type of really long-term mutual fund around that idea, I would say yes. But I think probably not before I retire. I think that it’s very likely it will be able to do a lot of things, but I also think that there’s nuance in a lot of optimization that will be really hard for it to just completely take a human’s job. Now, segments of that for sure. Data analysis, all the stuff we talked about today, yeah, for sure. If that’s all you do, then you probably need to start broadening your skillset to be more valuable. That is how I would look at it.

Ryan:
The e-commerce industry as a whole has been using AI for a long time. It’s not new. I’ll say it’s like new direct-to-consumer is what AI is. It’s been B2B for a while. Google’s been using it inside their algorithms for a very long time. You’ve been using it on the backend within your systems for a long time. We use it at LP. It’s not new inside of our businesses.

Jon:
Right. What’s new is you’re giving the consumers of the work product a way to engage with that AI, so you’re moving it forward a little more, right? That’s what’s really happened. You’re right. Google Analytics, a lot of that was AI based. What do I mean by that? Well, in the sense that if you weren’t paying for Analytics 360, you weren’t getting 100% accurate data. You were getting data that was partly accurate, and then partly extrapolated from that by an algorithm, right? An algorithm can be artificial intelligence in that degree. So, now I can ask ChatGPT to pull in my analytics data that I feed it through a export and I want it to answer questions for me. That’s moving it forward another step towards the consumer, and that’s when it’s becoming more and more valuable.

Ryan:
Yeah, I think some of the minutia of extracting of data and analyzing that is what’s really becoming the biggest use case of AI that I’ve seen. Being able to look at large sets of data, and instead of doing that manually, it’s like, “Okay, I want you to condense this and focus where I need to spend my analysis. You see all these conversion rates year over year on all these landing pages. Where might my problems be?” Okay, well, that’s very simple for ChatGPT to look at all of it and be like, “I’d focus here.” Great. Now, we get the strategists and human brainpower to go look at those areas.

Jon:
Wonderful. Well-

Ryan:
I like it.

Jon:
… there’s hopefully enough in here for people to get started. Again, most of it, I tried to keep the ChatGPT because I think that’s the most accessible option. I’m sure there’s way more we could do here, but this is scratching the surface and hopefully we’ll start people down the right path.

Ryan:
Yeah. Well, congratulations on getting the busiest term in our podcast headline yet.

Jon:
Win.

Ryan:
So, hey, we may be off on a hockey stick growth. We’ll see.

Jon:
Thank you, Ry.

Ryan:
Thanks, Jon.

Announcer:
Thanks for listening to Drive and Convert with Jon MacDonald and Ryan Garrow. To keep up to date with new episodes, you can subscribe at driveandconvert.com.

The post Drive and Convert (Ep. 095): How to Leverage AI in UX Optimization appeared first on The Good.

During this era of augmented realities and machine automation, businesses are concerned with connectivity, data, analytics, intelligence, and so much more. This new digital transformation, ushered in by the fourth industrial revolution, sees companies gearing up to be digital-first.

In fact, according to a report by Foundry, 89% of companies have already implemented a digital-first strategy or are planning to do so. Gartner also shared that digitalization is a top priority for 87% of senior business leaders. But even companies that are keeping up with the trends of the digital age can find themselves failing.

Why?

Because most companies don’t understand that with this digital revolution, there was also a paradigm shift from the traditional product-centric approach to one that focuses on the customer.

Now, more than ever, brands need to be able to cater to customer needs and preferences. Taking this customer-centric approach creates a satisfying customer experience that becomes the key to crafting winning digital experiences that will, in turn, create loyal customers.

For years, brands have utilized personalization as a means of offering a customized experience for customers. While personalization is still relevant, the shift in the digital age calls for more tailored offerings.

Brands can harness data and leverage technology to create these tailored offerings, improve communications, and increase engagements, but only if they remember to focus on the needs and preferences of their customers.

In this article, we discuss the shift from product-centricity to customer-centricity. We’ll also explore the role of customer orchestration in customer-centric digital experiences, practical ways to make your brand more customer-centric, and winning examples that can inspire you.

The Digital Era: The Need to Shift from Product-Centricity to Customer-Centricity

Gone are the days when brands could differentiate themselves from their competitors solely through their products.

According to a Forbes article, approximately 1.1 million new small businesses open in a year. With saturated markets across different industries and the addition of new players, it’s becoming increasingly difficult to stand out simply through the superiority of your products or services – especially if other brands are also claiming to do it better, faster, or cheaper than you. However, there are still companies who prefer this product-centric approach regardless of the demand and the customers.

While product-centric companies focus on selling the best new products, customer-centric companies make the effort to analyze customer needs through data, tools, and feedback to create the best solutions for them.

Companies also measure success very differently depending on which approach they use. When the focus is on the product, success is mostly measured through sales and how well the product sells. On the other hand, the customer-centric approach views success through customer satisfaction and customer relationships. When the focus is on the customer, success is based on customer loyalty, retention, average order value, and similar indicators.

The two strategies are very different because they are defined by different goals. The product-centric approach helps companies achieve goals relating to product quality. Meanwhile, the customer-centric approach allows brands to create the best customer experience by satisfying customer expectations.

It’s crazy to think that customer-centricity has been talked about since 1954.

The concept was born when Peter Drucker said, “It is the customer who determines what a business is, what it produces, and whether it will prosper.”

Still, the approach only became earnestly mentioned in marketing literature around the late 1990’s. Even then, production efficiencies held the highest priorities for companies. When the digital transformation took place, companies saw an opportunity to leverage information technology to increase interactions with customers by creating various touchpoints that would allow them to create personalized treatments for loyal customers. Now, companies are still struggling to apply the customer-centric approach despite claiming that they are, in fact, focused on their customers.

However, with the recent emergence of customer orchestration, there is a bigger opportunity to implement seamless touchpoints throughout the customer journey that elevate the experience and support personalization.

The Role of Customer Orchestration in Creating a Customer-Centric Digital Experience

When brands successfully find a balance between their use of technology and their understanding of their customer’s behavior, there is no stopping them. This all begins by looking at the customer journey from the perspective of the customer.

Let’s start with the basics.

A customer journey map is a visualization of the steps or actions that a company believes a customer will take when they engage with the brand. A customer journey map is neither linear nor simple. The flow can branch out in different ways and in different directions depending on the options and possible outcomes that are available to the customer.

Now, where does customer orchestration fit into this?

The equation is simple: you can’t implement a customer-centric approach without a customer journey map, and you can’t create a customer journey map without customer orchestration.

Customer orchestration takes the data you have available and applies that data to map out personalized experiences that will engage your customers. It’s like the interactive film that Netflix released, where viewers decide what the main character will do, and the narrative changes based on the decision.

All of this hinges on a company’s ability to connect with customers at certain touchpoints and gather data accordingly. The problem is not all companies may be knowledgeable, aware, or even motivated to do research or utilize tools and validation techniques to know what to do with their data. As we discussed in a previous article, there is a distinction between being data-backed and data-driven.

However, brands that are willing to invest in optimizing their digital experience and working on customer journey orchestration will be able to see the benefits. These benefits include:

• High Customer Engagement.

  Through customer orchestration, you’ll be able to segment your audience better, which then leads to better personalization and customization. Customers who come across relevant content are more likely to stay engaged with your brand and move further along the journey map.

• Increased Average Order Value

  With the data from customer journey orchestration, you will be able to create a personalized journey for your customers. Knowing what’s relevant to your customers gives you more opportunities to cross-sell and upsell products. In the long run, this can improve the average order value for your business.

• Enhanced Customer Service Delivery

  The customer research involved in journey orchestration doesn’t just tell you what your customers like but also what they don’t like. You’ll be able to identify customer service complaints and familiarize yourself with the struggles that customers encounter throughout their buying journey. This allows you to train your customer service representative accordingly and solve the root pains that your customers experience.

Now that we’ve established the role of customer orchestration in implementing a customer-centric approach, let’s dive into what that actually looks like.

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Five Practical Ways to Make Your Brand More Customer-Centric

1. Research Your Audience and Audit Your Website

The first step in creating a customer-centric online experience is through extensive user research. Collecting insights from every platform your customers use to interact with your brand will help identify friction points and uncover latent needs.

You won’t be able to meet the specific needs of your customers until you know what those needs are.

User research has a variety of different inputs to consider. Typical user research will include customer surveys, focus groups, and user testing. To get a fuller picture of how your customers feel about your brand, we recommend you analyze every outlet your customers use to talk about you.

Start broad by conducting surveys with your users, then narrow your focus with in-depth user testing sessions. The more data you gather from your customers, the better experience you’ll be able to provide for them.

If you want a more holistic approach, you can also do a website audit. This way, you can gather data about your users and find out what to improve on your website. A website audit will give you data and a roadmap that makes use of the data.

2. Utilize a multichannel marketing strategy

The more places your customers can reach you and engage with your brand, the better. Multichannel marketing focuses on engaging with the user through every outlet they use, whether that be through social media, print, email, etc. This approach puts the decision of how to connect with your brand in the consumer’s hands.

• Reach your customers on social media

  Social media has become one of the most effective methods for reaching your customers and is a relatively versatile tool that allows you to respond to customer service issues, conduct customer research, and promote your brand.With over 1 billion monthly active users, Instagram has quickly grown to be one of the best sources of user-generated content for brands. It ranks as the most effective and impactful social media platform to develop your brand or promote a product. Many brands have started to utilize customer-created Instagram content for their product detail pages and even on homepages.

• Continue utilizing email

  Despite more dynamic and personal methods of marketing emerging in recent years, email is yet to be beaten in terms of visibility and efficiency. In fact, post-purchase emails are more powerful than your typical promotional emails, yet very few brands are taking advantage of them. You have a unique opportunity to convert a new customer into a recurring customer, and once that opportunity passes, you have to work doubly hard to get it again.

3. Provide a personalized experience

Personalization is one of the most effective and simple ways to improve your business’s retention rate and become more customer-centric. If your customers can’t develop a connection with your brand, it’s very unlikely that they’ll be motivated to continue purchasing from you. Here are a few tactics we recommend you try on your site to improve customer personalization:

• Use cookies to remember website visitors

  Being able to cookie visitors on your site opens up various opportunities for personalization. If you can store basic information about a user (product preferences, time on site, page views), it’ll help you cater to each individual user’s needs.

• Create Product Recommendation Quizzes for Your Customers

  Product recommendation quizzes work towards building a better personalized and 1:1 customer experience. Think of it as a guidebook for customers on what to buy from your site.Product recommendation quizzes show customers how your product will solve their specific problems and make the purchasing journey an efficient and pleasant experience.

4. Tell a Story through Your Brand

Despite “storytelling” becoming a marketing buzzword over the last several years, there’s an undeniable value in telling a compelling story to your customers to build a personal connection. Many successful ecommerce brands that emerged in the last few years have had a compelling story associated with them that consumers have latched onto.

You can leverage the StoryBrand framework in developing your marketing message. StoryBrand has an incredibly simple and wildly effective seven-step framework for developing your marketing plan or marketing message. Humans are innately drawn to stories over a list of accolades, testimonials, and facts.

5. Define your customer experience strategy

Improving your business’s customer experience can seem like a daunting task if you don’t have a clear strategy outlining your approach. Developing a customer experience (CX) strategy will help you take the insight you gained from user research and turn it into an actionable plan for improving your site experience. Your CX strategy should be constructed with a variety of factors in mind, including:

• Market research
• User research
• Mission and vision statement
• User testing

There’s no “right way” to develop a CX strategy, but not creating one for your business would be a serious mistake. Defining a clear strategy early on will help align your team around a singular vision and goal and will inform how you continue to develop and improve your website.

Five Brands that Leverage Customer-Centric Strategies to Create Satisfying Digital Experiences

Shifting to a customer-centric approach is not easy, but it’s possible. We’ve reviewed the top 100 customer-centric brands from Forbes and picked out five brands with winning digital experiences.

1. Depop

Depop has quickly climbed in popularity, and it’s no surprise why. The social shopping platform, which targets Gen Z and Millennials, recently received $62 million dollars in funding and is on top of the Forbes list for its masterful use of social networking and omnichannel commerce.

There has been an evolution in how people consume fashion. Talented individuals create beautiful pieces that they can sell from anywhere. You don’t have to be a well-known fashion brand or have a store at the mall. Depop simply gave them a platform because they understood that, “social apps are not a choice, but simply the basis and source of all their online engagement.” 

Part of its popularity is that it allows for instant feedback when shopping and has improved interactions between sellers and buyers. 

2. Starbucks

Starbucks is on the list for many reasons.

Firstly, they have an unparalleled understanding of customer perspective, which allowed them to create one of the best customer journey experiences. They were willing to reinvent themselves by modifying their inter-departmental workflows.

Starbucks also has a superb customer loyalty program that keeps people coming back. They’ve built a brand outside of the coffee that they serve. People come to the store for the ambiance and the experience more than just the drinks.

But what really changed things for them was when they pivoted away from in-store purchases by creating a great digital experience with their delivery and pick-up options. Customers can skip long lines and wait times by ordering online. They still have full customization options, and they don’t even have to leave the house for their favorite cup of coffee.

3. Amazon

Amazon is another favorite that makes its way onto many of the lists online, and for good reason.

It’s no secret that Jeff Bezos aims to make Amazon the “most customer-centric company on Earth,” and they’ve certainly taken huge strides to accomplish this. The company is constantly innovating and finding ways to improve the customer experience and provide better service. They’re a huge fan of using personalized product recommendations and made one-click ordering a possibility.

4. Ikea

Ikea offers a winning customer experience both on and offline.

Customers are free to shop, relax, and eat at their brick-and-mortar stores. However, it’s their online experience that they’ve leveled up. Imagine being able to use your camera to place detailed, life-sized 3D Holograms of furniture in your house. That’s what Ikea offers.

Because of its wide range of product offerings, Ikea also knows that the right website navigation is essential for customers shopping online. Their use of different categories in the navigation is something we’ve seen in winning tests for our clients countless times.

5. Ulta Beauty

Similar to Ikea, Ulta Beauty prioritized the needs of their customers. When the pandemic hit and people couldn’t try out products by going to the store, Ulta launched their GLAMlab experience. It gave customers a way to try on products virtually, and Ulta kept improving the tool. They later added a skin analysis tool and updated the products that customers could try on. It’s like a product recommendation quiz, but better!

Put Customers at the Center of Your Digital Experience

These winning examples are proof that the best way to optimize your digital experience is to put your customers at the center of everything. From doing research and mapping the customer journey, to designing your website and creating marketing strategies, you have to keep your customers in mind.

You will always hear me saying, “You can’t read the label from inside the jar.” You have to change your perspective and put yourself in your customers’ shoes. With this new wave of digital revolution, you can do so much more to elevate and personalize the experience of your customers.

Things might be constantly changing in the digital landscape, but one thing remains constant: it’s always crucial to listen to your customers.

Start creating a customer-centric digital experience with The Good. Contact us.

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