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.

“Anytime we’re evaluating a product or service, we think about it subconsciously as a consumer,” Long says. “As a marketer, we need to think about the human aspect of marketing.”

Dan recently wrapped up six years at The Atlanta Journal-Constitution, one of the American South's most storied news organizations, where he served as Senior Manager of Conversions and Optimizations. In that time, AJC grew its digital subscriber base from 24,000 to over 101,000, a transformation driven by a mix of smart product strategy, rigorous testing, and a philosophy that puts the reader at the center of every decision.

We sat down with Dan for a conversation about how he thinks about the subscription funnel, what makes digital media conversion uniquely hard, and why the best optimization work always starts with the human on the other side of the screen.

A career at the intersection of journalism, technology, and revenue

Dan didn't start in conversion optimization. He started in marketing research, helping news organizations, regional papers, the Washington Post, and NPR understand what their readers actually wanted to read and how well they were delivering on it. That grounding in the reader experience never left him.

After moving to the publisher side, first at a regional paper in Dallas-Fort Worth, then at the larger competitor across town, he kept chasing the same question: how do you get people to pay for journalism? Not just tolerate a paywall, but actually recognize the value and subscribe.

"At one point, we thought the biggest challenge was time," he says. "People would say, 'I don't have time to read.' Then it became 'there are different news sources.' Now it's all of that plus: I can get my news from social media. I can use AI. There are a number of different reasons why people could say no."

That evolving challenge shaped how Dan thinks about conversion. It's not just a funnel problem; it's a relevance problem. And the solution isn't as simple as a better CTA button. It's understanding why someone might genuinely care about the product or content, in this moment, and meeting them there.

When he arrived at The Atlanta Journal-Constitution in 2019, the organization had 24,000 paid digital subscribers. The only digital value proposition offered was an ePaper, essentially a digital replica of the print product. The paywall was still in its early days, and there was significant runway ahead.

Designing for the human, not the metric

The "What's in it for me?" question isn't just a gut check; Dan applies it as a lens that has helped shape his success. It reflects how he thinks about the entire reader relationship, from the first piece of content someone clicks on, to the paywall interaction that asks them to commit, to the checkout experience that either seals the deal or loses them.

In practice, designing for the human audience starts with the brief. Before any creative work happens, Dan works with his team to understand who the target audience is, what they already know about the brand, what they perceive about its value, and what would need to be true for them to hand over their credit card.

"What would be valuable to this person?" is the question that drives everything, and it doesn't stop being relevant once the work ships.

"As we're auditing the final conversion product, whether it’s a paywall or a subscription landing page, we think about it again with that perspective of: if I were an outsider looking at this page, if I were interacting with this site, does that answer the questions appropriately? What's in it for me, for all the consumers that we have?"

That last phrase matters. "All the consumers that we have." Dan is deliberate about not treating readers as a monolith.

Different people arrive at a subscription decision from completely different places. A loyal visitor who's been hitting the article limit for months needs a different conversation than someone who landed on an AJC story through a Google search and has never heard of the newspaper or news site. Segmenting those experiences and designing each one to answer that specific person's version of the WIIFM question is where the real gains live.

There's also a persistent tension Dan navigates that most conversion teams face but rarely talk about openly: the pull between designing for the human and designing for the machine. SEO scoring tools, algorithmic recommendations, and platform optimization all have their own logic, and it doesn't always line up with what actually feels right to a reader.

"There's always tension between designing for the human and designing for the machine that's giving you a score. By default, we think about the human aspect. Does this page have personality? Does it signal to the consumer: 'Hey, we get you. We understand you'? Does it support the brand in a way that really resonates?"

When the answer isn't obvious, his team treats it as a signal to test. Which is, in a way, the most human-centered move of all; instead of overriding the uncertainty with an opinion, you ask the reader.

Why "paywall" is the wrong word for most of what you're building

With a clear philosophy about who you're designing for, the next question becomes: what are you asking them to do? Here's where Dan says most teams are working with the wrong mental model from the start when discussing paywalls

People may not expect this, but “paywalls” don't always mean you have to pay for a recurring subscription.

"We use the term fairly generically," he explains. "People tend to throw it around, and you always assume it's a hard stop that causes you to subscribe to a recurring subscription. That's often the case. But a paywall could be a number of things."

In his framework, there are really three distinct conversion mechanisms, and treating paywalls universally like a single concept is one of the most common mistakes in digital media:

• Registration walls (regwalls) collect a reader's email address in exchange for access to a piece of content. It's a simple, low-friction value exchange. The reader gets the article. You get a first-party data point and the beginning of a relationship.
• Digital passes let a reader pay once for short-term access. For example, without the commitment of a recurring subscription, a digital pass may grant access for a day or two or during the course of a local event attracting out-of-town visitors seeking information. Dan sees these as underutilized tools, particularly useful for attracting readers who want access without a long-term relationship.
• Subscription paywalls are what most people picture when they hear the word. These convert high-intent readers into subscribers who generate recurring revenue and therefore unlock more consumer benefits. They require the most convincing and the most attention to friction.

"Using all three together helps you serve more types of readers, build relationships, and capture more value," Dan says. The goal isn't to push everyone to a subscription immediately. It's to match the mechanism to where the reader is in their journey and to answer that WIIFM question in a way that actually makes sense for that moment.

The three conversion surfaces require three different strategies

The paywall/regwall/digital pass distinction is one layer of complexity. There's another layer that Dan thinks about just as carefully: not just what kind of access point you're presenting, but where the reader is coming from when they are engaging with your subscription offer.

In his experience, there are three meaningfully different conversion surfaces, each with its own psychology and requirements:

• Paywall interactions happen in the middle of a reading experience. The reader is already engaged with specific content, which is a signal of relevance. But patience is low. They want access now. "Value must be obvious and friction minimal," as Dan puts it.
• Organic landing pages attract readers who clicked through from on-site CTAs (through a banner, a button in the navigation, or a prompt at the end of a free article). These visitors are in evaluation mode. They're asking that fundamental question: "What's in it for me?" And they need room to answer it. The page has to be more informative, more thorough, more value-forward than a paywall presentation.
• Paid campaign landing pages serve a visitor who saw a social post or ad, felt some connection, and clicked. That emotional connection is the asset, and the page needs to protect it. "We want to keep that emotional tie," Dan says. "We want to make it simple. We do want to make it informative, but that emotional connection is the thread you can't break."

At the AJC, this distinction had real tactical implications. Testing showed that different promotional offers resonated differently depending on where the reader encountered them. A 99¢-for-three-months introductory rate performed well on-site and on the paywall, while a $1-a-week rate performed better for paid social campaigns. Different audiences, different presentations, different context - each optimized for conversion efficiency.

Why cross-functional alignment is the real unlock

Successful subscriber growth initiatives at media companies require cross-functional alignment on shared goals. Conversion optimization at a media company isn't a product team problem or a marketing problem. It's an everything problem.

"Cross-functional work is absolutely important in our type of business," Dan says. "We have so many different small teams. We have to communicate well and work together to be effective."

While the News side and the business side operate independently by design, at the AJC, Dan would occasionally sit in on the newsroom's daily budget meetings, which were internal editorial planning sessions where reporters pitched stories and editors set priorities. He wasn't there to influence coverage. His presence meant he could spot content with subscription conversion potential early, so his team could prepare.

That might mean flagging a story about Georgia's swing-state status as something that could attract out-of-market readers, worth treating as a top-of-funnel engagement play rather than a conversion trigger. Or recognizing that a high-profile local investigation was exactly the kind of unique, irreplaceable content that could push a longtime reader over the edge into subscribing.

"Is it worth an organic social push, or is it something we can invest in with paid social to really amplify the message?" he explains. "And then we establish some business rules: if we're promoting it socially, should we allow people to access this without any barriers, or is this content so valuable that it's worth promoting and then attempting to get a registration or subscription out of it?"

Getting to that kind of coordination requires shared goals and shared metrics across teams. Without them, teams optimize for their own objectives, and the path-to-conversion or engagement journeys may end up being ineffective.

The case for bringing in an outside perspective

Even experienced teams get comfortable with their defaults. You know your product well, your mental models are well-worn, and sometimes that familiarity is the problem.

That's what drew Dan to bringing in The Good for a Digital Experience Optimization (DXO) Audit of AJC's subscription landing pages. The Good's CEO, Jon MacDonald, has a phrase that describes well the situation Dan was in: "You can't read the label from inside the jar."

"We all need an additional outside perspective from time to time," Dan says. "The value is that The Good works with a number of industries, not just media. So, having all of that experience and all of those additional perspectives, they may think of an enhancement we haven't thought of yet or use that expertise to reinforce the value of an approach we’re evaluating. It's like sharing some lessons and keeping us grounded, so that we are doing what we should be doing and we’re continuously improving upon things that we think we already know well."

For a DXO collaboration to work, Dan emphasizes the importance of building trust early, investing real time in scoping, and then getting out of the way. "Really sitting down and investing a lot of time and energy into setting the parameters, understanding the capabilities, understanding what's needed, and building that trust between the two so that both feel comfortable allowing the other to do what they do best."

The DXO Audit identified a pattern that showed up across user testing and analytics: the subscription landing page was creating cognitive overload rather than confidence. Too many competing offers. Pricing structures that required mental math. A mobile experience that buried the primary CTA below the fold.

When the work was done, he was pleased: "The team was super easy to work with — professional, organized, methodical, yet also very friendly and engaging. It was a worthwhile project with an enjoyable team." He was eager to implement recommendations and find out if they resonated with consumers and improved their subscription landing page conversion rates in a live environment.

AJC implemented the recommendations thoughtfully, adapting guidance to their technical constraints and layering in complementary changes, including a new annual subscription tier and channel-specific offer routing for paid campaigns. Collectively, Dan believed that these optimizations would better address “what’s in it for me” with a more intuitive design, a new offer with special savings for highly engaged readers, and a mobile-friendly design. In the end, the results demonstrated value with the DXO Audit.

Results: 56% overall lift, 157% mobile improvement

What 25 years at the intersection of journalism, data, and people actually teaches you

Ask Dan what makes conversion leadership in media different from conversion leadership anywhere else, and he doesn't hesitate.

"If I were to think of a company like Nike or Adidas, they're selling shoes — and they have so many different models to sell to different audiences. People think of the news as one thing. But the content changes every day. It's a new product every single day. And that product should have something to appeal to everyone. How do you communicate that to someone visiting the site?"

It's a genuinely hard problem. Unlike a shoe brand that can segment by style, price point, or sport, a news organization is selling one thing, access to journalism, to readers who want wildly different things from it. Sports fans. Business readers. Political junkies. Parents who want to know what's happening in their school district. The product and the access to journalism are the same. The job of the conversion experience is to make each of those people feel like it was made for them. Sports readers reacted favorably to “season pass” offers and the tone used with Varsity content; likewise, Atlanta foodies felt a connection with subscription presentations related to the AJC’s “Atlanta’s 50 Best Restaurants” content.

Compound that with the cultural challenge Dan has encountered in marketing research going back decades: the widespread belief that news and access to journalism should be free. It's a friction point baked into the category itself, and no amount of landing page optimization can eliminate it. What good conversion work does is reduce every other source of friction enough that the value becomes undeniable even for the skeptic.

The data rigor is one-half of how Dan gets there. The other half is something harder to systematize: staying genuinely curious about the humans on the other side.

"Maintain that balance between the human side and the rigor of analytics. Data is super important; we need it to understand things, to make decisions. But it's equally important to always understand the human side. Be observant. Watch people. Listen to people. See how they act, see how they react. Always stay curious."

That curiosity shows up in how he presents findings to leadership, too. When the data says something inconvenient, for example, a test fails, a beloved feature is hurting conversion, a long-held assumption is wrong, Dan's default isn't a slide deck of numbers. It's a story.

"Use the approach to really communicate what we learned, how we learned it, and what does it actually means. Do it in a way that really connects with a person. Make it personal. Make it feel accessible and like it's bringing value, new insights, or additional point of view into how you might want to proceed in future initiatives."

That instinct to translate data into something a person can feel is the same instinct that underlies the whole WIIFM framework. Numbers aren’t the only thing that persuade people. Relevance does. Whether you're trying to convert a first-time reader into a subscriber, or convince a skeptical executive that an inconvenient test result is worth acting on, the approach is the same: find the version of this information that makes it matter to the specific human in front of you.

It's also, Dan would say, what makes the work worth doing.

"Marketing should be fun. We think of it as really data-intensive, and it is, but it should always be fun. Think about the person on the other side. Think about how we can make a difference and connect with somebody. That comes through in your work. That comes through in how you communicate with your audience, and it makes it more relevant for them."

Twenty-five years in, after watching the industry transform from print delivery to ePaper to live paywalls to dynamic segmentation, that's the throughline. Not the technology. Not the platform. It’s the person on the other side and the relentless curiosity about what they actually need.

The post Dan Long Built a Subscription Machine at the AJC by Designing for the Human First appeared first on The Good.

It’s tempting. The allure of real-time, live data showing exactly which price point converts better feels like the holy grail of product optimization. Fire up your testing platform, split traffic between $29 and $39, and let the numbers tell you what to charge.

But price testing is an ethical and legal minefield that can damage customer trust and put your brand at risk.

After 16 years of optimizing digital experiences, we’ve seen this scenario play out dozens of times. A client comes to us excited about testing prices, we dig into what that actually entails, and we end up recommending something entirely different: pricing research.

The difference matters. A lot.

Why we don’t recommend traditional price testing

While A/B price testing isn’t explicitly illegal in most jurisdictions, it occupies a murky grey area that should make any brand leader pause.

The legal landscape

In the United States, price testing is generally legal. The Robinson-Patman Act prohibits certain forms of price discrimination, but its scope is narrow, primarily applying to business-to-business sales of commodities where different pricing harms competition. For most consumer-facing businesses, the Act rarely applies, and violations are difficult to prove.

In the European Union, however, the situation is different. According to EU law, charging customers differently based solely on their nationality is illegal. Even in random A/B tests where nationality isn’t the determining factor, if a French customer pays more than a Belgian customer for the same product, you could face fines if complaints are filed with the European Consumer Centre.

Recent research published in the Journal of Revenue and Pricing Management highlights how pricing executives must now navigate the triangulation of legal constraints, ethical considerations, and algorithmic decision-making when setting prices.

The consumer perception problem

Beyond legality, there’s the court of public opinion.

A 2022 study from Phiture found that different generational groups react very differently to personalized pricing. While Gen X consumers sometimes try to “game the system” (clearing cookies or using incognito mode to search for better deals), many Millennials and most Boomers react negatively when they discover they’re being charged different prices than other customers.

The Instacart case proves this isn’t theoretical. A Consumer Reports survey conducted in September 2025 found that 72% of Instacart users did not want the company to charge different prices to different users for any reason.

When the investigation revealed the extent of the price testing, customers described feeling “manipulated,” “deceived,” and said they were “not as trusting of a company that practices that.” One volunteer specifically said: “All prices should be the same for everybody, whether you’re rich or poor… some people are going to have to fight back against that system.”

Within weeks of the investigation’s publication, Instacart discontinued the practice entirely, a clear signal that the reputational risk outweighed any revenue optimization gains.

Most consumers view price discrimination as fundamentally unfair, even when it’s legal. When customers discover they paid more than someone else for the exact same product at the exact same time, trust erodes quickly. And once lost, that trust is expensive to rebuild.

The technical limitations

While platforms like Shopify support native price testing functionality, testing tools typically don’t have the infrastructure to modify your actual pricing across different customer segments reliably. Even if you’re comfortable with the ethical considerations, there are practical barriers.

The margin problem

As expert pricing research from Paddle notes, even legal pricing strategies become unethical when they ignore fundamental business health. Simply optimizing for conversion without understanding contribution margin can lead you to “win” tests that actually hurt your bottom line.

Sure, we might see that Product A sold more units than Product B at a given price point, but which product has better margins? That difference fundamentally impacts whether a price change is actually driving profitability or just revenue.

The smarter alternative: pricing research

After explaining these challenges, clients often ask: “So what should we do instead?”

Structured pricing research. Rather than testing prices live on your site where you’re charging real customers different amounts, conduct research that reveals willingness to pay, price sensitivity, and optimal price points before you go to market.

Pricing research gives you the insights of price testing without the ethical baggage, legal risk, or customer trust issues. Here are the primary methodologies we recommend:

Van Westendorp Price Sensitivity Meter

Developed by Dutch economist Peter Van Westendorp in 1976, the Price Sensitivity Meter (PSM) remains one of the most effective ways to identify acceptable price ranges for products.

The methodology is elegant in its simplicity. You survey your target customers with four key questions:

1. At what price would you consider this product too expensive to purchase?
2. At what price would you consider this product expensive, but still worth considering?
3. At what price would you consider this product a bargain?
4. At what price would you consider this product so inexpensive that you’d question its quality?

By plotting cumulative responses to these questions, you can identify several critical price points:

• Point of Marginal Cheapness (PMC): The intersection of “too cheap” and “expensive” lines, which indicates your lower bound
• Point of Marginal Expensiveness (PME): The intersection of “too expensive” and “cheap” lines which indicates your upper bound
• Optimal Price Point (OPP): Where an equal number of respondents describe the price as exceeding either their upper or lower limits
• Indifference Price Point: Where the same number of people think the price is “too expensive” as those who think it’s a “bargain”

According to research from SurveyKing, Van Westendorp is particularly valuable for identifying pricing thresholds and overall market perceptions without putting actual customers in a position where they’re being charged inconsistently.

When to use it: Van Westendorp excels for new-to-world products where you’re establishing an initial price point, or when repositioning an established product in a new market segment. It’s also fast to implement because you can run a Van Westendorp study in days, not weeks.

Limitations to know: The method focuses solely on price perception without considering product features or competitive context. It also can’t predict actual purchase behavior, only price expectations. As noted in research from Conjointly, if your product has multiple configurations or you need to understand feature-specific value, other methods may be more appropriate.

Conjoint analysis

If Van Westendorp is the quick mission, conjoint analysis is the full strategic assessment.

Conjoint analysis reveals how customers value different product attributes, including price, by forcing them to make trade-offs between product profiles. Rather than asking “What would you pay for this?”, conjoint presents respondents with complete product profiles that vary across multiple dimensions (features, brand, price, etc.) and asks them to choose which they’d buy.

For example, a project management software might test profiles varying:

• Number of team members included (5, 15, or 50)
• Storage capacity (10GB, 50GB, or 250GB)
• Integration options (3, 10, or unlimited)
• Price ($19/month, $49/month, or $99/month)

Respondents see sets of 3-4 profiles at a time and select their preference. The pattern of choices reveals the relative value of each attribute, including price sensitivity.

Choice-based conjoint (CBC) is particularly powerful for pricing research because it simulates realistic purchase scenarios. Respondents don’t know you’re primarily interested in pricing; they’re just choosing products they’d actually buy. This approach delivers more honest insights than directly asking about willingness to pay.

Why it works: Conjoint lets you measure price elasticity by brand, understand optimal feature-price combinations, and run market simulations to predict revenue and share. Research from GLG shows that with conjoint data, you can model hypothetical scenarios: “If we add this feature and increase the price by $10, how many customers will we gain or lose?”

When to use it: Conjoint shines when you need to understand how price interacts with product features, or when you’re pricing complex offerings with multiple tiers or bundles. It’s the gold standard for SaaS pricing strategy because it captures the reality that customers evaluate price in context, not isolation.

What to expect: Conjoint requires more upfront investment than Van Westendorp, both in study design and sample size. You’ll need larger respondent pools (typically 300+ for reliable results), and the analysis is more sophisticated. But the insights are proportionally richer.

Segmentation and historical data analysis

Sometimes the best pricing insights are hiding in your own data.

Before running any new research, we always recommend examining your existing customer base through a segmentation lens. Different customer segments often have dramatically different price sensitivity.

Research from TRC Insights shows that price elasticity, the measure of how demand changes with price, varies significantly across customer segments. Enterprise buyers might be relatively price-insensitive (inelastic demand) for mission-critical tools, while small businesses might be highly price-sensitive (elastic demand) for the same product.

By analyzing your historical data, you can identify:

• Which segments have the highest lifetime value at different price points
• How acquisition cost varies by price tier across segments
• Retention patterns that indicate whether pricing is aligned with value delivery
• Upgrade and downgrade patterns that reveal price ceiling and floor effects

One telecommunications company we know of analyzed years of customer data to understand price elasticity by segment. They discovered that their “small business” segment was actually three distinct sub-segments with wildly different price sensitivities:

• one that behaved like enterprise (low elasticity)
• one that behaved like consumers (high elasticity)
• and one in between

This insight led them to redesign their entire pricing strategy with separate offers for each sub-segment, ultimately increasing revenue by 10%+.

When to use it: Always. Historical data analysis should be your starting point for any pricing decision. It’s low-cost (you already have the data), fast, and often reveals surprising patterns.

Gabor-Granger method

For a more direct approach to estimating demand curves, the Gabor-Granger method offers a middle ground between Van Westendorp and conjoint analysis.

The process is straightforward: show respondents a product at a specific price and ask if they’d buy it. If yes, show a higher price. If no, show a lower price. Continue until you map out their individual purchase threshold.

Aggregate these responses across your sample, and you can build demand curves that predict:

• The percentage of your market that will buy at each price point
• The revenue-maximizing price
• The volume-maximizing price
• Price elasticity at different levels

This can be particularly useful when you need quick market assessments and want to focus specifically on price sensitivity without evaluating multiple product attributes simultaneously.

When to use it: Gabor-Granger works well for single products or when product attributes are already determined, and you need to optimize pricing specifically. It’s faster than full conjoint but more direct about pricing than Van Westendorp.

Understanding price elasticity for better decisions

All of these methodologies ultimately help you understand price elasticity, how changes in price affect demand for your product.

Price elasticity is typically expressed as: % change in quantity demanded ÷ % change in price

Products with elastic demand (elasticity > 1) see large changes in demand with small price changes. Think luxury goods, or products with many substitutes. Products with inelastic demand (elasticity < 1) see relatively stable demand despite price changes. Think of necessities or products without good alternatives.

Understanding your product’s elasticity is crucial because it determines your pricing strategy’s impact. For elastic products, lowering prices can increase total revenue. For inelastic products, you might be leaving money on the table by not charging more.

Here’s what makes elasticity even more interesting: it’s not fixed. The same product can exhibit different elasticity depending on:

• Customer segment: Enterprise buyers vs. SMBs vs. individual consumers
• Time period: Demand becomes more elastic over time as customers adjust their behavior
• Market conditions: Economic downturns increase price sensitivity even for traditionally inelastic goods
• Price range: Products can be inelastic at low prices but highly elastic at high prices

Understanding these nuances helps you make smarter pricing decisions across your entire customer base, not just at a single price point.

A real example: how we approach pricing strategy

Here’s how these methodologies come together in practice.

A B2B SaaS company approached us, concerned that their pricing wasn’t optimized. They had three tiers ($49/month, $149/month, and $499/month) that had been set somewhat arbitrarily three years ago based on “what felt right” and competitive benchmarking.

Rather than jumping into A/B testing prices, here’s the path we recommended:

Phase 1: Data analysis

We started by analyzing their existing customer data:

• Segmented customers by industry, company size, and usage patterns
• Calculated lifetime value and retention by segment and tier
• Mapped upgrade/downgrade patterns to understand price ceiling effects
• Identified which features correlated with willingness to pay premium prices

This revealed that their “mid-market” segment was actually two distinct groups with different needs and willingness to pay.

Phase 2: Van Westendorp study

We ran a Van Westendorp survey with 400 prospects and recent customers across identified segments. This quickly established:

• Their $49 tier was perceived as “too cheap” by 30% of respondents, potentially signaling quality concerns
• There was an acceptable price range between $79-199 for their middle tier
• Their top tier had room to increase to $599-699 based on value perception

Phase 3: Conjoint analysis

With price ranges identified, we ran a choice-based conjoint to understand:

• Which features justified premium pricing
• How different customer segments valued different feature bundles
• Optimal price points for proposed new tiers

The conjoint revealed that their original three-tier structure was actually constraining revenue. There was demand for a fourth tier at $799/month for enterprise features, and their middle tier could be split into two offerings at $99 and $199.

Results

The company implements a new four-tier pricing structure ($69, $119, $239, $799) based on the research. Average revenue per customer would increase 23%. Customer acquisition would actually improve (lower entry price brings in more customers who later upgrade). Retention holds steady despite price increases because the value alignment was better

This approach would take 8 weeks, and the cost would be well worth it compared to the potential brand damage of customers discovering they’d been charged different prices in an A/B test, or the opportunity cost of not optimizing pricing at all.

Making the case for pricing research internally

If you’re reading this thinking, “this makes sense, but my team really wants to just A/B test prices,” here’s how to make the case:

Frame it as risk management

Price testing puts your brand reputation at risk. Pricing research gives you comparable insights without exposing you to customer backlash, legal concerns, or PR problems. Maintaining customer trust through transparent, ethical pricing practices is crucial for long-term profitability.

Emphasize the quality of insights

A/B tests tell you which price performed better in one specific context at one specific time. Pricing research tells you why that price works, how different segments perceive value, and how pricing interacts with features and positioning. Those insights compound over time.

Talk about margin, not just conversion

This one resonates with CFOs. Pure price tests optimize for conversion or revenue, but they don’t account for margin variation across products or customer acquisition costs across segments. Pricing research can be designed to optimize for profit, not just revenue.

Point to the technical limitations.

Most A/B testing platforms can’t reliably execute pure price tests anyway. You’d need to implement complex technical workarounds that introduce their own risks. Pricing research is straightforward to implement with existing survey tools.

Pricing strategy

The urge to price test is understandable. You want data-driven pricing decisions. You want to optimize this critical lever for growth.

But the best data doesn’t come from exposing real customers to different prices in an A/B test. It comes from structured research that reveals customer psychology, value perception, and willingness to pay without the ethical complications.

What you can do is test discount codes or promotional messaging. For example, we ran a test for a client where some visitors saw “$50 free shipping minimum,” while others saw “$75 free shipping minimum” or “$100 free shipping minimum.” In reality, everyone had a $50 minimum on the backend, but the messaging encouraged different customer segments to add more to their carts. This isn’t pure price testing; it’s messaging optimization that influences average order value.

We’ve spent 16 years helping ecommerce and SaaS companies optimize their digital experiences. When it comes to pricing, though, the most successful companies skip the shortcuts and invest in research that protects customer trust while delivering the insights they need.

The next time someone proposes A/B testing prices, ask them if they’ve considered the alternatives. The answer might surprise them and save your brand from an expensive mistake.

Let’s talk about your pricing strategy.

The post How to Test Your Pricing Strategy Without the Ethical Minefield appeared first on The Good.

The company had assembled a list of proof points they could showcase on their homepage: years in business, number of integrations, customer counts, implementation guarantees, security certifications, industry awards, analyst recognition, and more. But they only had space to highlight four of these benefits prominently below their hero section. They faced the classic messaging dilemma: which trust signals would actually move the needle with prospects evaluating B2B software?

This is where MaxDiff analysis becomes valuable. Instead of relying on stakeholder opinions or generic best practices, we could let their target buyers vote with data on what mattered most.

What makes MaxDiff analysis different from other survey methods

MaxDiff analysis (short for Maximum Difference Scaling) is a research methodology that forces trade-offs. Rather than asking people to rate items individually on a scale, MaxDiff presents sets of options and asks participants to identify the most and least important items in each set. This forced-choice format reveals true preferences because people can’t rate everything as “very important.”

Here’s why this matters: traditional rating scales often produce compressed results where everything scores high. When you ask customers, “How important is X on a scale of 1-10?” most people will hover around 7 or 8 for anything remotely relevant. You end up with a spreadsheet full of similar numbers and no clear direction.

MaxDiff cuts through that noise. By repeatedly asking “which of these five options matters most to you, and which matters least?” across different combinations, you build a statistical picture of relative importance. The math behind MaxDiff generates a best-worst score for each item, showing not just which options are preferred, but by how much.

For digital experience optimization, this methodology is particularly useful when you need to prioritize limited real estate on a website, determine which features to build first, or figure out which messaging will actually differentiate your brand.

How we structured the MaxDiff study for maximum insight

In the project for our client, we started by defining the target audience precisely. The company was a B2B SaaS platform serving mid-market operations teams, so we recruited 60 participants who matched their customer profile: director-level or above at companies with 50-500 employees, working in operations or supply chain roles, currently using at least two SaaS tools in their workflow, and actively evaluating solutions within the past six months.

From the initial audit and stakeholder interviews, we identified 11 potential trust signals the company could emphasize on its homepage. These included things like:

• Concrete numbers (customer counts, uptime percentages, integrations available)
• Credentials (security certifications, enterprise clients)
• Promises (implementation timelines, support response times, money-back guarantees)
• And more

Each represented something the company could truthfully claim, but we needed to know which ones would build the most trust with prospects evaluating the platform.

The survey design was straightforward. Each participant saw these 11 benefits randomized into multiple sets of five items. For each set, they selected the most important factor and the least important factor when considering whether to adopt this type of software. Participants completed several rounds of these comparisons, seeing different combinations each time.

This approach gave us enough data points to calculate a robust best-worst score for each benefit: the number of times it was selected as “most important” minus the number of times it was selected as “least important.” Positive scores indicate a strong preference, negative scores indicate a low importance, and the magnitude of the scores shows the strength of feeling.

The results revealed a clear hierarchy of trust signals

When we analyzed the MaxDiff results, the pattern was striking. The top-scoring benefits shared a common theme: they provided concrete evidence of proven reliability and satisfied users. The bottom-scoring benefits? They emphasized company scale and marketing visibility.

The four highest-scoring trust signals were clear winners. G2 or Capterra ratings scored 38 points (the highest possible), indicating this was nearly universal in its importance. The number of active customers scored 30 points. An implementation guarantee (“live in 30 days or your money back”) scored 25 points. And SOC 2 Type II certification scored 16 points.

These weren’t arbitrary marketing metrics. They were the specific signals that would make someone think, “this platform delivers real value and other companies trust them.”

The middle tier included operational details that registered as minor positives but weren’t decisive: the number of successful implementations (7 points), availability of 24/7 support (6 points). These signals suggested competence but didn’t particularly move the needle on trust.

Then came the surprises. Years in business scored -5 points, indicating it was slightly more often selected as “least important” than “most important.” The number of integrations available scored -11 points. AI-powered features claimed scored -15 points. Employee headcount scored -36 points. And recognition as a Gartner Cool Vendor scored -55 points, the lowest possible score.

Think about what prospects were telling us: “I don’t care that you have 200 employees or that Gartner mentioned you. Show me that real companies like mine trust you and that you’ll actually deliver on your promises.”

Why customers rejected company-focused metrics

The findings revealed an insight into trust-building that extends beyond this single company. B2B buyers weigh social proof and reliability guarantees far more heavily than they weigh indicators of company scale or industry recognition.

When a business talks about its employee headcount or analyst mentions, prospects interpret this as the company talking about itself. These metrics answer the question “How big is your business?” but not “Will this solve my problem?” From the buyer’s perspective, a larger team or Gartner mention doesn’t necessarily correlate with better software or smoother implementation.

By contrast, user reviews and customer counts answer the implicit question every prospect has: “Did this work for companies like mine?” A guarantee directly addresses risk: “What happens if implementation fails?” Security certifications address legitimacy: “Is this platform secure enough for our data?”

The AI-powered features claim scored poorly, likely because it felt trendy rather than practical. Prospects for this specific business weren’t primarily concerned about cutting-edge technology; they wanted a platform that would reliably solve their workflow problems. Leading with an AI angle, while possibly true, didn’t address the core decision-making criteria.

Years in business scored negatively for similar reasons. While longevity can signal stability, in this context, it didn’t address the prospect’s immediate concerns about implementation speed and user adoption. A company could be around for years while providing clunky software with poor support.

From insight to implementation: turning research into revenue

The MaxDiff analysis gave the company a clear action plan. We recommended implementing a four-part trust signal section directly below their homepage hero, featuring the top four scoring benefits in order of importance.

This meant reworking their existing homepage structure. Previously, they had emphasized their implementation guarantee in the hero area while burying customer counts and ratings further down the page. The research showed this approach had it backward. Prospects needed to see evidence of customer satisfaction first, then the implementation guarantee as additional reassurance.

We also recommended removing or de-emphasizing several elements they had been proud of. The employee headcount mention, the Gartner recognition, and several other low-scoring items were either removed entirely or moved to less prominent positions on the site. The goal was to prevent low-value signals from crowding out high-value ones.

The broader lesson here extends beyond this single homepage optimization. The MaxDiff results provided a messaging hierarchy that the company could apply across its entire go-to-market strategy. Email campaigns, landing pages, sales conversations, demo decks, and even their LinkedIn company page could now emphasize the trust signals that actually mattered to prospects.

When MaxDiff analysis makes sense for your business

MaxDiff is particularly valuable when you’re facing a prioritization problem with limited data. It works best in these scenarios:

• You have more options than you can implement. Whether that’s features to build, benefits to highlight, or messages to test, MaxDiff helps you choose wisely when you can’t do everything at once.
• Stakeholder opinions are conflicting. When internal debates about priorities can’t be resolved through argument, customer data settles the question. MaxDiff provides quantitative evidence for decision-making.
• You need to differentiate in a crowded market. If competitors are all saying similar things, MaxDiff reveals which specific claims will break through. Often, the winning messages are ones companies overlook because they seem “obvious” or “not unique enough.”
• You’re optimizing for a specific audience segment. Generic research about “customers in general” often produces generic insights. MaxDiff works best when you recruit participants who precisely match your target customer profile.

The methodology has limitations worth noting. It requires careful setup, and you need to know which options to test before you start.

If you don’t include the right benefits in your initial list, you won’t discover them through MaxDiff.

It also works best with a reasonably sized set of options (typically 5-15 items).

And the results tell you about relative importance, not absolute importance; everything could theoretically matter, but MaxDiff reveals the hierarchy.

How to use MaxDiff findings in your optimization strategy

Once you have MaxDiff results, the application extends beyond simply reordering homepage elements. The insights should inform your entire digital experience.

Your messaging architecture should reflect the importance hierarchy. High-scoring benefits deserve prominent placement, repetition across pages, and detailed explanation. Low-scoring benefits can either be removed or repositioned as supporting rather than leading messages.

Your testing roadmap should prioritize changes based on MaxDiff findings. If customer reviews scored highest in your study, test different ways of showcasing reviews before you test other elements. Let the data guide your experimentation priorities.

Your content strategy should emphasize what customers care about. If service guarantees scored highly, create content that explains the guarantee in detail, shares stories of when it was honored, and addresses common concerns. Build your editorial calendar around the topics MaxDiff revealed as important.

Your sales enablement should align with customer priorities. If the research showed that prospects value licensing credentials, make sure your sales team knows to emphasize this early in conversations. Create collateral that highlights the trust signals that matter most.

The most effective companies use MaxDiff as one tool in a broader research program. They combine it with qualitative research to understand why certain benefits matter, behavioral analytics to see how users interact with different messages, and continuous testing to validate that the predicted preferences translate into actual conversion improvements.

Turning guesswork into growth

The SaaS company we worked with started with a dozen possible messages and no clear sense of which would build trust most effectively with B2B buyers. After the MaxDiff analysis, they had a data-backed hierarchy that let them confidently restructure their homepage and broader messaging strategy.

This is the power of asking prospects the right questions in the right way. Not “do you like this?” which produces inflated scores for everything. Not “rank these 11 items,” which overwhelms participants and produces unreliable data. But rather, through repeated forced choices, revealing the true importance of each element.

If you’re struggling with similar prioritization challenges (too many options, limited space, stakeholder disagreement about what matters), MaxDiff analysis might be the tool that breaks through the noise. It transforms subjective opinion into statistical evidence, letting your prospects vote on what will actually convince them to choose your platform.

Ready to discover which messages actually resonate with your customers? The Good’s Digital Experience Optimization Program includes research methodologies like MaxDiff analysis to help you prioritize changes based on real customer preferences, not guesswork.

The post MaxDiff Analysis: A Case Study On How to Identify Which Benefits Actually Build Customer Trust appeared first on The Good.

The problem is that a freelance consultant and an enterprise project manager might both use your collaboration tool, but they have completely different goals, workflows, and definitions of success.

We recently worked with a leading enterprise SaaS company facing exactly this challenge. Their product served everyone from solo creators to enterprise teams, but the experience treated everyone the same.

Users logging in to track a single client project got bombarded with team collaboration features. Power users managing complex workflows couldn’t find advanced capabilities buried in generic menus.

Users felt overwhelmed by irrelevant features, engagement plateaued, and retention suffered.

The solution wasn’t another traditional segmentation model. It was intent-based segmentation; a framework for grouping users by what they’re actually trying to accomplish, then personalizing the experience to match those specific goals.

Understanding behavioral segmentation and where intent fits

Before diving into how to build intent-based clusters, it helps to understand where this approach fits within the broader landscape of user segmentation.

Most SaaS companies are familiar with demographic segmentation (company size, industry, role) and firmographic segmentation (ARR, team size, geographic location). These approaches tell you who your users are, but they don’t tell you what they’re trying to do, which is why they often fail to predict engagement or retention.

Behavioral segmentation takes a different approach. Instead of looking at static attributes, it focuses on how users actually interact with your product.

Behavioral segmentation divides users based on engagement. This includes actions like feature usage patterns, open frequency, purchase behavior, and time-to-value metrics.

While not the only way to do things, behavioral segmentation is widely regarded as more effective than demographic segmentation alone, with plenty of research backing it up.

Within behavioral segmentation, there are several approaches:

• Usage-based segmentation looks at frequency and intensity of use.
• Lifecycle segmentation tracks where users are in their journey.
• Benefit-sought segmentation groups users by the outcomes they want to achieve.

Intent-based segmentation sits at the intersection of all three.

It identifies clusters of users who share similar goals and workflows, then maps those patterns to create a more personalized experience.

Intent-based clusters answer the question: “What is this user trying to accomplish right now?”

In a recent client engagement that inspired this article, this distinction mattered. They had mountains of usage data showing which features people clicked, but no framework for understanding why certain feature combinations existed or what job users were trying to complete.

They knew “Business Professionals” used their tool, but that category was so broad it offered no actionable insights. A marketing manager building campaign timelines has completely different needs than a legal team tracking contract approvals, even though both might be classified as “business professionals.”

Intent-based clustering gave them that missing layer of insight.

Case study: How to spot the need for intent-based segmentation

Let’s talk more about the client engagement I mentioned. This is a great case study for when to use intent-based segmentation.

We work on a quarterly retainer for these clients with our on-demand growth research services. So, when they mentioned struggling with how to personalize experiences and improve retention, we opened up a research project that same day.

The team could see that certain users logged in daily and used five or more features. Great, right? Not really. When we dug deeper, we discovered something critical. Heavy feature usage didn’t predict retention. Some power users churned while casual users stuck around for years.

The issue wasn’t the quantity of features used; it was whether those features aligned with what users were trying to accomplish. A user coming in weekly to update a single client dashboard showed higher retention than someone exploring ten features that didn’t match their core workflow.

The symptoms: What teams told us was broken

During our stakeholder interviews, we heard the same frustrations across departments:

From product: “We know the top five features everyone uses, but that doesn’t help us understand why they’re using them or what to build next. Two users might both use our template feature, but one is building client proposals while the other is standardizing internal processes. They need completely different things from that feature.”

From marketing: “Our segments are too broad to be useful. ‘Business Professional’ could mean anyone from a solo consultant to an enterprise VP. When we send educational content, we can’t make it relevant because we don’t know what problem they’re trying to solve.”

From customer success: “We can see when someone is at risk of churning because their usage drops off, but we can’t predict it before it happens. By the time we notice, they’ve already decided the product isn’t right for them. We need to understand intent earlier so we can intervene proactively.”

From UX research: “Users think in terms of tasks, not features. They don’t say ‘I want to use the dependency mapping tool.’ They say, ‘I need to make sure the design team finishes before development starts.’ But our product talks about features, not outcomes.”

The underlying problem: Missing the ‘why’

What became clear was that the organization had plenty of data about behavior but no framework for understanding intent. They could answer questions like:

• How many people use feature X?
• What’s the average session duration?
• Which users log in most frequently?

But they couldn’t answer the questions that actually mattered:

• What are users trying to accomplish when they use feature X?
• Why do some users stick around while others churn?
• What combination of goals and workflows predicts long-term retention?

This may sound familiar. They have data about behavior but lack context about intent. Without understanding the users’ different definitions of success, they use generic personalization that recommends “similar features” and misses the mark entirely.

The four-phase framework for creating intent-based user clusters

Based on our work with the enterprise SaaS client, we developed a systematic framework for building intent-based clusters from scratch.

The process has four distinct phases, each building on the previous one.

Think of this as a directional guide rather than a rigid formula. You can adapt the scope based on your resources and organizational complexity.

Phase 1: Capture institutional knowledge and identify gaps

Most organizations have more customer insight than they realize; it’s just scattered across teams, buried in old reports, and locked in individual team members’ heads. The first phase consolidates that knowledge and identifies what’s missing.

1. Conduct cross-functional interviews

Start by interviewing stakeholders who interact with users regularly: product managers, customer success, sales, support, and marketing. For our client, we conducted interviews with seven team members from UX research, growth, engagement, marketing, and analytics.

The goal isn’t consensus. You want to uncover patterns and contradictions instead. Focus your conversations on these questions:

• How do you currently describe different user types?
• What patterns have you noticed in how different users engage with the product?
• What data exists about user behavior that isn’t being used to inform decisions?
• Where do personalization efforts break down today?
• What questions about users keep you up at night?

These conversations surface institutional knowledge that never makes it into documentation.

Capture everything. The contradictions are especially valuable—they reveal where teams operate with different mental models of the same users.

2. Audit existing research and reports

Next, gather relevant user research, data analysis, and customer insight. For our client, we analyzed eight existing reports, including retention data, cancellation surveys, user studies, engagement patterns, and conversion analysis.

Look for:

• Marketing segmentation models (usually demographic-heavy)
• User research studies (often small sample, rich insights)
• Behavioral analytics reports (feature usage patterns, cohort analysis)
• Customer journey maps (theoretical vs. actual paths)
• Support ticket analysis (pain points and use cases)
• Cancellation surveys (why users leave)

Pay attention to gaps between how different teams think about users. Marketing might segment by company size, while product segments by feature usage. Neither is wrong, but the lack of a unified framework means teams optimize for different definitions of success.

3. Define hypotheses about intent-based variables

Based on interviews and research, develop hypotheses about what variables might define user intent. This is where you move from “who uses our product” to “what are they trying to accomplish.”

For our client, we identified several dimensions that seemed to correlate with intent:

• Primary workflow type: Are users managing team projects, client deliverables, or personal tasks?
• Collaboration patterns: Solo work, small team coordination, or cross-functional orchestration?
• Usage frequency: Daily operational tool or periodic project management?
• Success metrics: Speed (quick task completion) vs. thoroughness (detailed planning and tracking)?
• Document complexity: Simple task lists or multi-layered project hierarchies?

The goal is to create testable hypotheses that can be validated in Phase 2.

Phase 2: Validate clusters through user research and behavioral analysis

Once you have initial hypotheses, Phase 2 tests them against real user behavior and feedback. This is where hunches become data-backed insights.

1. Develop provisional cluster groups

Transform your hypotheses into provisional clusters. For our client, we identified six distinct intent-based clusters. Let me illustrate with a fictional example of how this might work for a project management SaaS tool:

Sprint Executors: Users focused on rapid task completion and daily standup workflows. They need speed, simple task updates, and quick team coordination. Think startup teams moving fast with lightweight processes.

Client Project Coordinators: Users managing multiple client engagements simultaneously with strict deliverable timelines. They need client visibility controls, progress tracking, and professional reporting. Think agencies and consultancies.

Cross-Functional Orchestrators: Users coordinating complex projects across departments with dependencies and approval workflows. They need Gantt views, resource allocation, and stakeholder communication tools. Think enterprise program managers.

Personal Productivity Optimizers: Users treat the tool as their second brain for personal task management and goal tracking. They need customization, recurring tasks, and minimal collaboration features. Think solopreneurs and executives.

Seasonal Campaign Managers: Users with predictable high-intensity periods followed by dormancy. They need templates, bulk operations, and the ability to archive/reactivate projects easily. Think retail operations teams or event planners.

Mobile-First Coordinators: Users who primarily access the tool from mobile devices for field work or on-the-go updates. They need streamlined mobile experiences and offline sync. Think field service teams or traveling consultants.

Each cluster gets a descriptive name that captures the user’s primary intent, not just their behavior. “Sprint Executor” tells you more about what someone is trying to do than “high-frequency user.”

2. Conduct targeted user research

With provisional clusters defined, recruit users who fit each profile and conduct interviews to understand:

• Their primary use cases and goals when they first adopted the tool
• How they discovered and currently use the product
• Their typical workflows from start to finish
• What defines success in their role
• Pain points and unmet needs
• How they decide which features to explore
• What would make them cancel vs. what keeps them subscribed

For our client, we conducted three to four interviews per cluster, totaling around 24 user conversations. This gave us enough coverage to validate patterns without drowning in data.

The insights were eye-opening. We discovered that one cluster had the fastest time-to-value but the lowest feature adoption. They found what they needed immediately and never explored further. Another cluster showed the highest retention but needed the longest onboarding. They invested time up front because the tool was critical to their workflow.

3. Analyze behavioral data to confirm patterns

User interviews reveal what people say they do. Behavioral data shows what they actually do. Cross-reference your clusters against:

• Feature usage sequences (which tools appear together in sessions)
• Time-to-value metrics by cluster (how quickly do they get their first win)
• Retention and churn patterns (which clusters stick around)
• Upgrade and expansion behavior (which clusters grow their usage)
• Support ticket themes (which clusters need help with what)
• Feature adoption curves (how exploration patterns differ)

For our client, the data revealed critical differences. The “Sprint Executor” equivalent had fast initial adoption but plateaued quickly. They found their core workflow and stopped exploring.

The “Cross-Functional Orchestrator” cluster showed slow initial adoption but deep engagement over time. They needed to learn the tool thoroughly to unlock value.

These patterns weren’t visible in aggregate data. Only by segmenting users by intent could we see that different clusters had fundamentally different paths to retention.

4. Build detailed cluster profiles

For each validated cluster, create a comprehensive profile that becomes the foundation for personalization. For example:

Cluster name: Sprint Executors

Primary intent: Complete daily tasks quickly with minimal friction and maximum team visibility

Most-used features:

• Quick-add task creation
• Board view for visual sprint planning
• Mobile app for on-the-go updates
• Real-time team activity feed

Typical workflow patterns:

• Morning standup with task assignments
• Throughout the day: quick updates and status changes
• End of day: marking tasks complete and planning tomorrow

Behavioral flags that identify this cluster:

• Creates 5+ tasks in first week
• Returns daily within first 14 days
• Uses mobile app within first 7 days
• Rarely uses advanced features like Gantt charts or dependencies

Retention drivers:

• Speed of task completion
• Team visibility and accountability
• Mobile accessibility

Churn risks:

• Tool feels too complex for simple needs
• Feature bloat is making core actions harder to find
• Forced upgrades to access speed-focused features

Personalization opportunities:

• Streamlined onboarding focused on quick task creation
• Mobile-first feature discovery
• Templates for common sprint workflows
• Integrations with communication tools

These profiles become the single source of truth that product, marketing, and customer success can all reference.

Phase 3: Develop indicators and personalization strategies

The final phase connects clusters to action. This is where the framework moves from insight to implementation.

1. Create behavioral flags for cluster identification

Most users won’t self-identify their intent at signup. You need to infer cluster membership from behavioral signals early in their journey. The key is identifying flags that appear within the first 7-14 days. It should be early enough to personalize the experience before users decide if the tool is right for them.

For reference, the “Sprint Executor” cluster in our fictional example:

• Created 5+ tasks in first week
• Logged in on 4+ separate days in first 14 days
• Used mobile app within first 7 days
• Board or list view used more than timeline/Gantt view (80%+ of sessions)
• Invited at least one team member within first 10 days
• Never explored advanced dependency features
• Average session length under 10 minutes

Versus the “Client Project Coordinator” cluster:

• Created 3+ separate projects within first week (indicating multiple clients)
• Used folder or workspace organization features within first 5 days
• Set up client-specific permissions or external sharing settings
• Created custom views or reports within first 14 days
• Longer average session times (20+ minutes per session)
• Uses professional or client-specific terminology in project names
• High usage of export or presentation features

The goal is to find the minimum viable signal set that reliably predicts cluster membership. Start with more flags and refine over time based on which actually correlate with long-term behavior.

One critical finding from our client work: early behavioral flags predicted retention better than demographic data.

A user who exhibited “Client Project Coordinator” behaviors in week one showed 40% higher 90-day retention than the average user, regardless of their company size or job title.

2. Map personalization opportunities to each cluster

With clusters and flags defined, identify specific ways to personalize the experience across the user journey:

Onboarding sequences: Tailor the first-run experience to highlight features that match user intent. Show Sprint Executors how to set up their first sprint board, not the full feature catalog with Gantt charts and resource allocation tools they don’t need.

In-app messaging: Trigger contextual tips based on usage patterns. When a Client Project Coordinator creates their third project with similar structure, suggest project templates to save time.

Feature discovery: Recommend next-step features that align with cluster workflows. For Sprint Executors who’ve mastered basic task management, introduce the mobile app and integrations with their communication tools—not complex dependency mapping.

Content and education: Send targeted educational content that addresses cluster-specific goals. Client Project Coordinators get tips on professional reporting and client permissions. Sprint Executors get productivity hacks and team coordination strategies.

Upgrade paths: Present pricing tiers and feature upgrades that match cluster needs. Don’t push team collaboration features to Personal Productivity Optimizers who work solo and won’t use them.

Support prioritization: Route support tickets differently based on cluster. Client Project Coordinators might get priority support since they’re often managing paying clients. Seasonal Campaign Managers might get proactive check-ins before predicted busy periods.

For our client, this mapping revealed opportunities they’d completely missed. One cluster had been receiving generic “explore more features” emails when what they actually needed was advanced security capabilities for compliance requirements. Another cluster kept churning at the end of trial because onboarding emphasized features they’d never use instead of the speed-focused tools that matched their workflow.

Phase 4: Develop test concepts to validate impact

Turn personalization opportunities into testable hypotheses. Don’t roll everything out at once. Start with high-impact, low-effort tests that prove the value of intent-based segmentation.

For our client, we proposed several test concepts structured to validate clusters quickly and build organizational confidence in the framework. Here are a few examples.

Example Test 1: Intent-Based Onboarding Survey

Background: The organization lacked a way to identify user intent at the critical moment when users were most open to guidance: right after signup, but before they’d formed opinions about product fit.

Hypothesis: By asking users to self-identify their primary goal during their first meaningful session, we can segment them into actionable clusters that enable personalized feature discovery and messaging, resulting in improved 3-month retention rates by 5-10%.

Test design: During the first session (after initial signup but before deep engagement), present a brief survey asking: “What brings you here today?” with options that map directly to identified clusters:

☐ Coordinate my team’s daily work (Sprint Executors)

☐ Manage multiple client projects (Client Project Coordinators)

☐ Organize complex cross-functional initiatives (Cross-Functional Orchestrators)

☐ Track my personal tasks and goals (Personal Productivity Optimizers)

☐ Plan seasonal campaigns or events (Seasonal Campaign Managers)

☐ Update projects while on the go (Mobile-First Coordinators)

☐ Something else (with optional text field)

Then immediately personalize their first experience based on their response: Sprint Executors see a streamlined task creation tutorial, Client Project Coordinators get guidance on setting up client workspaces, etc.

Success metrics:

• Primary: 3-month retention rate by selected cluster (looking for 5-10% lift)
• Secondary: Survey completion rate (target: >80%), feature adoption aligned with cluster (target: 20% lift), time to first value-generating action
• Guardrails: No negative impact on day 2 or day 7 retention

Acceptance criteria for “winning test”:

• Survey completion rate >80%
• 60% of users select a pre-set option (vs. “something else”)
• Statistically significant retention lift in at least one cluster
• No degradation in key engagement metrics

Acceptance criteria for “learning test”:

• 40% of users select “something else” (suggests clusters don’t match user mental models)
• No statistically significant difference in retention (suggests clusters exist, but personalization approach needs refinement)

Audience: New paid subscribers on first day, trial users converting to paid, reactivated users returning after 30+ days dormant. Start with 25% of eligible users to minimize risk.

Timeline: 90 days to measure primary retention metric, but early signals (completion rate, feature adoption) available within 14 days.

Example Test 2: Cluster-Specific Feature Recommendations

Background: Generic in-app messaging had low click-through rates (<5%) and wasn’t driving feature adoption. Users felt bombarded by irrelevant suggestions.

Hypothesis: For users who match behavioral flags within the first 14 days, triggering personalized feature recommendations will increase feature adoption by 20% and engagement depth by 15%.

Test design: Identify users by behavioral flags, then trigger targeted in-app messages at contextually relevant moments:

• Sprint Executors see mobile app download prompt after completing 5 tasks on desktop: “Update tasks on the go: get the mobile app”
• Client Project Coordinators see reporting features after creating third project: “Impress clients with professional progress reports”
• Cross-Functional Orchestrators see dependency mapping after creating complex project: “Map dependencies to keep cross-functional teams aligned”

Success metrics:

• Primary: Feature adoption rate for recommended features (target: 20% lift vs. control)
• Secondary: Overall engagement depth (features used per session), message click-through rate
• Guardrails: No increase in feature abandonment (starting but not completing flows)

Audience: Users who match cluster behavioral flags within first 14 days. Test one cluster at a time to isolate impact.

Timeline: 30 days to measure feature adoption impact.

Example Test 3: Retention Email Campaigns by Cluster

Background: Generic “tips and tricks” email campaigns had 8% open rates and weren’t moving retention metrics. Content felt irrelevant to most recipients.

Hypothesis: Segmenting email campaigns by identified cluster will improve email engagement by 50% and show a measurable correlation with retention.

Test design: Replace generic weekly tips emails with cluster-specific content:

• Sprint Executors: “5 ways to speed up your daily standup” / “Mobile shortcuts that save 2 hours per week”
• Client Project Coordinators: “How to impress clients with professional project reports” / “3 ways to give clients visibility without overwhelming them”
• Personal Productivity Optimizers: “Build your second brain: advanced filtering techniques” / “Automate your recurring tasks in 5 minutes”

Send to users identified through either the onboarding survey or behavioral flags. Track engagement and retention by cluster.

Success metrics:

• Primary: Email open rates (target: 50% lift), click-through rates (target: 100% lift)
• Secondary: Correlation between email engagement and 90-day retention
• Guardrails: Unsubscribe rates remain stable or decrease

Audience: Users identified as belonging to specific clusters either through survey responses or behavioral flags, minimum 14 days after signup.

Timeline: 6 weeks for initial engagement metrics, 90 days for retention correlation.

Post-test analysis framework

For each test, we established a clear decision framework:

If “winning test”:

• Roll out to 100% of eligible users
• Begin development on next phase of personalization for that cluster
• Use learnings to inform tests for other clusters
• Document what worked to build organizational playbook

If “learning test”:

• Analyze all “something else” responses for missing clusters or unclear framing
• Review behavioral data to see if clusters exist, but personalization was wrong
• Iterate on messaging, timing, or format
• Decide whether to retest with refinements or try different approach

If negative impact:

• Immediately roll back to the control experience
• Conduct user interviews to understand what went wrong
• Reassess cluster definitions or personalization approach
• Consider whether the cluster exists but needs a different treatment

The key to successful testing is starting small, measuring rigorously, and being willing to learn from failures. Not every cluster will respond to every type of personalization, and that’s valuable information. The goal isn’t perfect personalization immediately; it’s continuous improvement based on what actually moves metrics.

Intent-based segmentation mistakes and how to avoid them

Based on our experience implementing this framework, here are the mistakes that will derail your efforts:

1. Starting with too many clusters

More isn’t better. Six well-defined clusters are more useful than fifteen overlapping ones. You need enough clusters to capture meaningfully different intents, but few enough that teams can actually remember and act on them. Start with 4-6 clusters and refine over time. If you find yourself creating clusters that differ only slightly, you’ve gone too granular.

2. Confusing demographics with intent

Job title, company size, or industry might correlate with intent, but they don’t define it. We’ve seen solo consultants behave like “Cross-Functional Orchestrators” and enterprise teams behave like “Sprint Executors.” Focus on what users are trying to accomplish, not who they are on paper.

3. Creating overlapping clusters

Each cluster should be distinct in its primary intent and workflow patterns. If you’re struggling to articulate how two clusters differ behaviorally, they’re probably the same cluster with different labels. Test this by asking: “If I saw someone’s usage data, could I confidently assign them to one cluster?”

4. Ignoring edge cases entirely

Some users will span multiple clusters or switch between them based on context. That’s fine. The framework should accommodate primary intent while recognizing that users are complex. A user might primarily be a “Client Project Coordinator” but occasionally use “Personal Productivity Optimizer” features for their own task management. Don’t force rigid categorization.

5. Skipping the validation step

Your initial hypotheses will be wrong in places. User research and behavioral data keep you honest and prevent confirmation bias. We’ve seen teams fall in love with theoretically elegant clusters that don’t actually exist in their user base, or miss entire segments because they didn’t fit the initial hypothesis.

6. Treating clusters as static

User intent evolves. Someone might start as a “Personal Productivity Optimizer” and grow into a “Client Project Coordinator” as their business scales. Review and refine your clusters quarterly based on new data, product changes, and market shifts.

7. Personalizing too aggressively too soon

Start with high-confidence, low-risk personalization (like targeted email content) before you completely diverge user experiences. You want to validate that clusters behave differently before you build entirely separate onboarding flows.

8. Forgetting to measure impact

Intent-based segmentation is valuable only if it improves outcomes. Define success metrics upfront (e.g., retention lifts, engagement depth, upgrade rates, support ticket reduction) and track them by cluster. If personalization isn’t moving these metrics, refine your approach.

Making intent-based segmentation work for your organization

The framework we’ve outlined works across product categories and company sizes, but implementation varies based on your resources and organizational maturity.

If you have limited data: Start with Phase 1 and Phase 2, using qualitative research to define clusters before investing in behavioral infrastructure. You can manually tag users based on interview responses and onboarding surveys, then personalize through targeted emails and customer success outreach. As you grow, build the data systems to automate cluster identification.

If you have rich behavioral data but limited research capabilities: Reverse the order. Start with data patterns and validate through targeted interviews. Look for natural groupings in your analytics that suggest different workflow types, then talk to representative users from each group to understand their intent.

If you’re a small team: Don’t let perfect be the enemy of good. Start with 3-4 obvious clusters based on your highest-level workflow differences. The founder of a 10-person startup probably has a better intuitive understanding of user intent than a 500-person company with siloed data. Write down what you know, test it with a few users, and start personalizing.

If you’re a large enterprise: The challenge is getting organizational alignment, not defining clusters. Use Phase 1 to surface where teams already operate with different mental models, then use data to arbitrate. Create executive sponsorship for the new framework so it becomes the shared language across product, marketing, and CS.

The key is starting somewhere. Most companies know their one-size-fits-all approach isn’t working, but they keep personalizing around the wrong variables that don’t actually predict what users are trying to accomplish.

Intent-based segmentation reorients everything around the question that actually matters: What is this user trying to accomplish, and how can we help them succeed at that specific goal?

Turn insights into retention that drives revenue

Understanding user intent is just the first step. The real value comes from translating those insights into personalized experiences that keep users engaged and drive measurable revenue growth.

At The Good, we’ve spent 16 years helping SaaS companies identify their most valuable user segments and optimize experiences around what actually drives retention. Our systematic approach to user segmentation goes beyond frameworks. We help you implement experimentation strategies that prove which personalization efforts move the needle on the metrics your board cares about.

Plenty of companies struggle to implement segmentation that’s actually actionable. They end up with beautiful personas gathering dust or broad categories that don’t inform product decisions.

Intent-based segmentation is different because it connects directly to behavior you can observe and experiences you can personalize.

If you’re struggling with generic experiences that fail to resonate with different user types, or if you know your segmentation could be better but aren’t sure where to start, let’s talk about how intent-based segmentation could transform your retention strategy and drive revenue growth.

The post The Exact Framework We Used To Build Intent-Based User Clusters That Drive Retention For A Leading SaaS Company appeared first on The Good.

Fritz O’Connor, former VP of Marketing at Ironman 4×4 America, exemplifies this approach. With over two decades of experience spanning manufacturing, sales, and marketing leadership, Fritz has developed a methodology for building high-performing organizations that deliver results consistently, even in challenging circumstances.

A marketing leader built for manufacturing

Fritz’s career journey reads like a masterclass in understanding customers across different industries. Starting in the printing and paper industry, he cut his teeth in structured sales training programs that taught him the fundamentals of professional sales and business operations.

“I’ve spent my entire career in sales and marketing roles. Almost exclusively in the manufacturing sector for companies that make stuff,” Fritz explains. This foundation in manufacturing would prove invaluable throughout his career, giving him deep insight into the complexity of bringing physical products to market.

His two-decade tenure at GE further refined his skills across diverse business environments. “We always used to say we can work in any industry, anywhere in the world, and still get paid by the same company,” he recalls. This experience working across plastics, appliances, and GE Corporate gave him a unique perspective on how great companies operate at scale.

But it was during his time at GE Corporate that Fritz discovered what would become his career-defining framework: differential value proposition (DVP). Working in a marketing consulting role with virtually every business in GE’s global portfolio, he helped launch this customer-centric approach to messaging and positioning throughout the organization.

This systematic approach to understanding and serving customers became foundational to Fritz’s ongoing success.

Implementing systems and frameworks that take teams from features to solutions

Originally coined by the founder of Valkre Solutions, Jerry Alderman, the DVP framework transforms how companies think about customer messaging and competitive positioning. Fritz became a master at implementing this methodology across diverse organizations.

“What are you offering? Be it a product or service that is better than the customer’s next best alternative,” Fritz explains. This might seem simple, but the implications are profound. Rather than competing on features or price, DVP focuses on solving customer problems in ways that competitors simply cannot match.

The challenge, as Fritz learned during his GE implementation, is that DVP represents a fundamental shift in thinking. "Every business, product, or service has a value proposition, but not every value proposition is differential. So many companies have the same value proposition. The white space is that differential part."

"It's about switching thinking from a feature to a benefit. For example, a blue appliance is not a differential value proposition. It's a feature."

Fritz teaches teams to make this shift by leading with problems and solutions.

"It's how it makes the consumer or customer's life better, how it solves that problem. You have to identify what the problem is. You have to articulate how you can fix that problem in a different way, better than anybody else."

This shift from features to solutions requires teams to understand their customers' actual problems, not just their stated needs.

For leaders, this translates directly into more effective product messaging, clearer value propositions, and ultimately, higher conversion rates.

Overcoming the "this is how we've always done it" challenge

One of Fritz's biggest career wins (and ongoing challenges) centers around implementing the Differential Value Proposition (DVP) methodology across organizations. The implementation at GE became both a success story and a learning experience in change management.

"As you can imagine, anytime you try and launch a new process in a company the size of GE, you can be met with resistance. Especially when you're coming out of corporate."

This resistance taught Fritz a crucial lesson about implementing change: "I don't view that as a challenge or a stumbling block, but as a fantastic and wonderful opportunity because when you flip those people, they become your biggest proponents."

His approach centers on listening first, then demonstrating value in the stakeholder's own language. "It's a listening journey. You've gotta understand what the challenges are that of the people with whom you're working, whether it's an external customer or an internal customer."

"Proactively listen and walk in the shoes of the people I'm working with. When I'm trying to introduce something as significant as DVP or other business tools."

This listening approach helps identify the real challenges and resistance points, making it possible to address them effectively.

The foundation: accountability, responsibility, and challenge

But having the right frameworks isn't enough. Fritz learned that execution depends on creating the right team culture. He is quick to credit his teams as the backbone of his successful projects, and one of the ways he supports them is with clear organizational principles.

"I have a few underlying business principles that I've gained along the way that are the foundational threads for me," Fritz explains. "One is, any team I work with or works for me, my job is to make them as successful as possible."

This people-first approach manifests through three guiding principles:

• Accountability: Holding yourself and your team responsible for deliverables and outcomes
• Responsibility: Taking ownership of significant business challenges
• Challenge: Embracing difficult problems that create meaningful business impact

"The way I do that is through three guiding principles, which are accountability, responsibility, and challenge," Fritz notes. "I want to be entrusted with significant responsibility that is helping to solve a significant business challenge."

These principles translate into a simple but powerful operational mantra: deliver on time, complete with excellence.

"I know those all sound like buzzwords, but they're not meant to be. And we don't treat them as such. We treat them as very simple guiding principles to keep us focused."

Putting it all together at Ironman 4x4

When Fritz joined Ironman 4x4 America, he found the perfect opportunity to apply all of these frameworks.

Ironman 4x4 is a global company that sells off-road parts and accessories for 4x4 vehicles (lift kits, suspension parts, bumpers, etc.). They have been around since the 1950s, but were new to the United States, so Fritz had the opportunity to find new ways to market their complex "fitment" products, or parts that must work with specific vehicle makes and models. This complexity creates both technical and marketing challenges that Fritz's team had to solve systematically.

His sales background gave him an invaluable perspective on marketing effectiveness. "If you spend any time in sales, that means you're around customers, whether those are B2B or B2C customers. And you learn what's important to them."

This customer proximity taught him the critical principle of "show me, don't tell me." Rather than relying on feature lists or industry awards, effective marketing demonstrates value through customer experiences and outcomes.

"We always, in both sales and marketing, it's easy to get into the trap of just talking, talking, talking, describing stuff, talking about features and benefits. Talking about the industry's best. Nobody cares about your industry. They care about how your product or service is going to impact them."

The key to marketing complex products, Fritz knew, is understanding how customers think about their problems. Rather than leading with technical specifications, the focus should be on the customer's end goal and the emotional drivers behind their purchase decisions.

Fritz emphasizes the importance of demonstrating value rather than just describing it: "Really, visual storytelling, video storytelling, placing the customer in the scene so they understand your value. That ability comes from firsthand experience of seeing that happen in the sales arena."

A data-driven website replatforming

His POV shaped everything he was involved in at Ironman 4x4 America, from new product introduction processes to website optimization. Fritz implemented structured new product integration toll gates with clear deliverables and cross-functional accountability, ensuring every product launch was executed with precision across creative, digital, and channel marketing.

His customer-centered thinking and frameworks proved essential when his team tackled a complex website migration from an outdated platform to Shopify. The project was based on their understanding that a website change was necessary to better serve their audience and increase ecommerce sales.

Working with The Good on a DXO Program, the Ironman 4x4 team executed the redesign and replatforming with data-driven methodology. Rather than relying on opinions about what the site should look like, they embraced rapid prototyping and continuous testing.

"Any decision made without data is just an opinion, right?" Fritz notes, referencing CEO Luke Schnacke's philosophy.

"We try to be very data-driven, which is why it was so important for us to work with The Good, to get that data and share it with the team managing the website replatforming so that they were making data-driven decisions on design and functionality."

They didn’t wait for a “perfect website” to figure out what customers wanted. They tested and got feedback throughout the entire process to make sure they were developing the right ideas.

"I realized we were never going to do it perfectly," Fritz recalls. The team was getting bogged down in opinions about checkout processes, product customizers, and overall site design. "We could end up using half our development budget on building something that doesn't perform."

"Ultimately, we agreed to launch and then test the heck out of it. We didn't want to overburden the development pipeline with projects that don't have a financial impact."

This represents a fundamental shift in thinking. They went from trying to build the perfect site to building a testable foundation for continuous improvement.

The beauty of working with The Good in this situation, Fritz explains, was "the rapid prototyping, the test and learn. We could very quickly get feedback and iterate and then test and learn again."

Multiplying results through partnership

Leveraging an external partnership accelerated progress beyond what internal resources could achieve alone and held the team accountable to the frameworks and goals of staying user-centered and data-driven.

"If you're not an expert, I would recommend doing a website project with a company like The Good. It wasn't a cost, it was an investment," Fritz emphasizes. "And I think that Ironman 4x4 is the beneficiary of the investment that they made with The Good as they migrated over to Shopify and learned about what customers would like."

The partnership enabled intentional, studied testing with proper dependencies and measurable results tracking.

"That whole test and learn methodology is done in a very structured, deliberate way. Making changes in a waterfall, with the proper dependencies articulated, and then tracking the measurable benefits of changes, and then tweaking accordingly from there."

This approach breeds confidence because it's entirely data-driven, removing guesswork from critical business decisions.

Lessons for marketing and sales leaders

For marketing and sales leaders looking to build similar operational excellence, Fritz's approach provides a roadmap: start with principles, understand your customers deeply, make decisions based on data, and never underestimate the power of strategic partnerships to unlock potential.

Start with principles, not tactics

Before implementing any marketing or optimization program, establish clear guiding principles. Fritz's framework of accountability, responsibility, and challenge provided a foundation that influenced every decision and created lasting organizational change.

Understand your customer's next best alternative

Move beyond feature-benefit messaging to understand what your customers would do if your solution didn't exist. This "next best alternative" thinking is the foundation of truly differential value propositions.

Convert resistance through understanding

When facing organizational resistance to change, focus on understanding stakeholder concerns rather than pushing solutions. Meet people where they are and demonstrate value in their language.

Embrace data-driven decision making

Resist the temptation to rely on opinions or best practices. Instead, create structured testing methodologies that let customer behavior guide optimization decisions.

Invest in external partnerships strategically

Recognize when external expertise can accelerate progress. The right partnerships provide capabilities and perspectives that internal teams may not possess, ultimately delivering better results faster.

Starting an optimization journey

Fritz's approach to building and scaling teams, including Ironman 4x4's US marketing operations, demonstrates how principled leadership, customer-centric thinking, and strategic partnerships can create sustainable competitive advantages.

"There's no obstacle too big that can't be overcome with data and optimization, right?" Fritz states emphatically. "The whole point of being data-driven and optimizing is to get time back and to become more efficient."

His advice for other leaders facing similar challenges?

"Get to yes. Figure out how to do it. Don't say, this is why I can't do it. Say this is how I'm going to do it. Here are things I need to do in order to do it. Then hold yourself accountable. Make it happen. Do it."

The secret, according to Fritz, lies in celebrating small wins that compound over time: "Little steps, I always like to say, celebrate the little wins. Go after the little wins because they compound on one another and then all of a sudden you're gonna look back and go, holy mackerel, I can't believe I am where I am."

The secret is consistency: "And it starts with data as your foundation and optimization as the accelerator."

For ecommerce leaders looking to build similar operational excellence, Fritz's framework provides a proven template: establish clear principles, understand customer problems deeply, make data-driven decisions, and never underestimate the power of strategic partnerships to accelerate growth.

Ready to optimize your ecommerce experience with data-driven methodology? Learn more about The Good's Digital Experience Optimization Program and discover how strategic partnerships can unlock your growth potential.

The Good helps ecommerce brands like Ironman 4x4 optimize their digital experiences through research-backed testing and strategic partnerships. Our team combines deep technical expertise with proven methodologies to deliver measurable results for growing brands.

The post Fritz O’Connor Stays User-Centered and Leads with Data During Uncertain Times appeared first on The Good.

This is where a specialized conversion rate optimization (CRO) agency becomes invaluable. The right partner doesn’t just run A/B tests. They bring a systematic approach to understanding user behavior, validating hypotheses with research, and implementing changes that compound over time.

But the CRO landscape is crowded with agencies that promise results yet deliver vanity metrics. Some treat optimization as an add-on service to their broader marketing offerings. Others focus exclusively on quick wins without building sustainable testing programs.

We’ve analyzed the top conversion rate optimization agencies based on their methodologies, specializations, proven results, and approach to client partnerships. Whether you’re a SaaS company trying to reduce churn or a DTC brand looking to improve ROAS, this list will help you identify which agency aligns with your specific needs and growth stage.

What makes a great CRO agency?

Before diving into our rankings, it’s important to understand what separates the best CRO agencies from the rest. Based on industry research and analysis of top-performing optimization programs, effective agencies share these characteristics:

Specialization matters

Agencies that focus exclusively on conversion optimization typically outperform those treating CRO as one service among many. They’ve invested in proprietary methodologies, tools, and team expertise that generalist agencies can’t match.

Research that drives testing

The best agencies don’t guess which tests to run. They use qualitative and quantitative research to understand user behavior before building hypotheses. This approach leads to higher test win rates and more impactful improvements.

A focus on retention, not just acquisition

While many agencies obsess over increasing conversion rates, top performers optimize the entire customer lifecycle from awareness through retention and referral.

Transparent communication and collaboration

You should expect regular updates, clear reporting on what’s working (and what isn’t), and genuine partnership rather than vendor-client dynamics.

Proven track record with similar businesses

An agency that’s delivered results for B2B SaaS companies may not be the right fit for high-consideration ecommerce brands, and vice versa.

With these criteria in mind, let’s explore the agencies leading the field.

1. The Good

What they do: The Good is a digital experience optimization consultancy specializing in SaaS and ecommerce brands. Unlike traditional CRO agencies that focus narrowly on conversion rate, The Good zooms out to look at full digital journeys and find opportunities for improvement in all key metrics at all stages of the funnel.

Why they stand out: With over 16 years of industry-leading experience, The Good has generated hundreds of millions in additional revenue for clients, including Adobe, The Economist, Xerox, and MillerKnoll. Their Digital Experience Optimization Program goes beyond standard A/B testing to include continuous research, strategic roadmapping, and cross-functional optimization across the entire customer journey.

What makes The Good particularly effective is their specialization. Digital experience optimization isn’t an add-on; it’s the only service they offer. This focus means their team brings deep expertise in research methodologies, behavioral psychology, and experimentation frameworks that generalist agencies can’t match. The founder and CEO is renowned CRO expert Jon MacDonald, who has written multiple best-selling books on the topic.

Their approach centers on three pillars: research, strategy, and experimentation. Rather than jumping straight to testing, they invest in understanding user behavior through qualitative research, analytics, and customer feedback. This foundation allows them to build hypotheses that drive measurably better outcomes.

Notable results:

• The Economist: 5% increase in paid subscription conversions
• Xerox: 18.9% revenue increase on product page visits
• Autodesk: 11% growth in subscriptions
• IDX: 20% increase in B2C registrations
• Helium 10: 12% increase in registrations

2. Conversion

Conversion is a CRO agency that works with major brands like Microsoft, Meta, Domino’s Pizza, and Dollar Shave Club. They specialize in large-scale A/B testing programs combined with UX research and personalization.

Their business runs on an Infinity Experimentation Process, which is a structured, repeatable methodology designed for organizations with significant web traffic and complex customer journeys.

3. Invesp

Founded in 2006, Invesp is one of the earliest dedicated CRO agencies. They excel at building structured, strategic roadmaps for businesses at the beginning of their optimization journey. They help companies understand where to start, what to test, and how to prioritize efforts for maximum impact.

4. SiteTuners

Founded in 2002, SiteTuners focuses on removing distractions and optimizing websites for lead generation. They work mainly with B2B companies and service businesses where the goal is form completions or demo requests rather than ecommerce transactions.

They take a practical approach to optimization by diving deep into analytics to uncover exactly where users lose interest, then make clear, measurable changes to improve performance. Their methodology focuses on filtering out unqualified traffic and boosting lead quality, not just quantity.

5. CRO Metrics

CRO Metrics specializes in A/B testing programs backed by statistical analysis and user behavior research. They emphasize proper test design, statistical significance, and learning from both winning and losing tests to build cumulative knowledge about what drives conversions. They help establish testing infrastructure, build experimentation roadmaps, and train internal teams on optimization best practices.

6. Speero

Speero (formerly CXL Agency) is an experimentation agency specialized in building and scaling experimentation programs, with a focus on embedding testing culture across product and marketing teams.

Their experimentation operating systems (XOS) enable teams to make faster, better decisions. Their work focuses on the “retention economy” with the goal to drive long-term customer value rather than just short-term conversion wins.

7. KlientBoost

KlientBoost combines conversion rate optimization with PPC management, ensuring the traffic you’re paying for actually delivers results. They focus on rapid testing of landing pages and conversion funnel elements and usually handle high testing velocity for companies that need quick results and continuous iteration.

8. Conversion Fanatics

Conversion Fanatics blends conversion rate optimization with social media marketing insights. They follow Kaizen principles, making small, meaningful improvements that compound over time.

Their integration of social insights with conversion optimization is particularly valuable for ecommerce brands that rely heavily on social commerce and influencer marketing.

Alternatives to CRO agencies

While working with a specialized CRO agency offers significant advantages, it’s not the only path to better conversion performance. Here are three alternatives worth considering:

Hiring an Independent CRO Consultant

For companies with limited budgets or extremely low traffic, an independent consultant can provide strategic guidance and hands-on expertise at a lower cost than agency retainers.

Pros: More affordable than agencies (typically $150-$300/hour), flexible engagement terms, and direct access to senior-level expertise. Many consultants bring experience from top agencies and can help you avoid common mistakes.

Cons: Limited bandwidth compared to full agency teams, may lack resources for complex technical implementation, and you’ll still need internal team members to execute recommendations.

Building an In-House CRO Team

Organizations committed to long-term optimization programs often build internal teams that combine research, design, analytics, and development capabilities.

Pros: Deep institutional knowledge of your product and customers, direct alignment with company goals, and cumulative learning that stays within your organization. In-house teams can respond quickly to opportunities and integrate optimization thinking across departments.

Cons: Significant investment required to hire, train, and retain specialized talent. A complete optimization team typically includes researchers, designers, analysts, strategists, and developers—full staffing costs often exceed $500,000 annually for a mid-sized team. Without agency experience and an external perspective, in-house teams may hit plateaus or fall into pattern thinking.

Using Optimization Platforms and Tools

Several platforms now offer AI-powered testing and optimization capabilities that can reduce the need for dedicated agency support.

Pros: Lower cost than agencies or full in-house teams, easy to implement, and continuously improving through machine learning.

Cons: Tools execute tactics but don’t provide strategy. They lack the qualitative research, user understanding, and creative problem-solving that experienced optimization teams bring. You still need someone who understands experimentation methodology, statistical significance, and conversion psychology to get meaningful results.

Before you make a decision, find out where your optimization program stands

Before committing to any agency partnership, it’s valuable to understand your current optimization maturity and identify the highest-leverage opportunities for improvement.

The Good’s 5-Factors Scorecard provides a comprehensive assessment of your optimization program based on insights from top product marketing and ecommerce teams. In just 18 questions, you’ll receive a custom action plan that identifies where to focus your efforts for maximum impact.

Ready to take the next step toward conversion excellence? Get your personalized 5-Factors Scorecard or contact The Good’s team to explore how the Digital Experience Optimization Program can help you unlock sustainable growth through research-backed experimentation and strategic optimization.

The post The Best CRO Agencies for Growing SaaS Companies and DTC Brands appeared first on The Good.

“The fundamental shift that people have to make is that you’re no longer a data collector. You’re a data connector,” says Ari Zelmanov, former police detective and current research leader. In Ari’s view, as teams get leaner and tools get better at executing research tasks, the job of the researcher becomes standing up repositories, socializing learning mechanisms, and creating the systems that empower organizations to act on good information.

We spoke with research leaders who've successfully made this transition, transforming their teams from siloed specialists into customer-centric learning cultures. Their approaches varied, but one theme was clear: when you empower others to answer their own questions, you don't diminish your value, you multiply it.

The d word holding us back

Before diving into solutions, there's an elephant we need to address: Democratization. Many researchers worry that democratizing research will lead to poor methodologies, incorrect conclusions, or devalued expertise. But Ari feels the argument is nye.

"The only people arguing about democratization are researchers," says Ari. "Nobody else is arguing about it. We're infighting about something that we have zero control over. It's happening."

I tend to feel like anyone arguing about democratization is missing one critical point: customer centricity isn't just one person's job.

Anton Krotov, Researcher in an organization of over 10,000 people, was in the fortunate position of being very trusted by his colleagues. So much so that they believed research could answer all of their questions.

“I had already established a reputation. I was fortunate that I didn't need to sell the value of research. Quite the opposite. People came to me with too many requests. They believed research could do everything for them. I needed to set up boundaries.”

Overwhelmed with requests from colleagues, Anton realized that the solution wasn't saying no—it was saying yes in a different way. Rather than becoming a bottleneck, Anton chose to become a bridge.

Connect teams through shared intelligence

Good intelligence is the responsibility of many disciplines, not just research. To get answers quickly, Ari's teams use what he calls the "Moneyball" approach to research, a framework that prioritizes speed and accessibility over methodological purity:

"Product teams are incentivized to move fast. So, how do you make research fit into that in a way that makes sense? We built something called Moneyball Research. It's super simple: start with what you know. It could be in your repository, it could be what you know. Then you go to what data is accessible within 24 to 48 hours. That's usually internal analytics, CSAT tickets, NPS, sales conversations, and tribal knowledge. Then—and only then—do you go to primary research."

This approach shifts conversations away from methods and focuses instead on what teams need to know and how confident they need to be. "Then it's up to the researcher to be the doctor. Diagnose that, determine how they're going to collect that evidence given the time, money, and level of rigor."

René Bastijans, lead researcher at a growth-stage startup, has found creative ways to loop colleagues into data collection. His sales team is trained to lightly survey prospects during sales calls and report back to the wider team.

"We've trained our sales team to ask for specific data and enter it into Salesforce. Researchers and the product team have access to these data, and therefore, sales has allowed us to keep a pretty good pulse on the market."

This creates a healthy feedback loop that keeps everyone abreast of evolving user needs while extending the research team's reach without expanding headcount.

Invite colleagues into the research process

While it might seem counterintuitive to share methodologies and research responsibilities, successful research leaders see democratization as an opportunity rather than a threat.

To remove research bottlenecks, Anton ran internal workshops to upskill his colleagues on doing their own research. This proactive approach to education focused on tailoring training to his colleagues' specific needs: "I try to cover the cases that will be really applicable, so I don't offer any cookie-cutter material and don't go much into theory. It's really tailored to their day-to-day work."

The key is meeting people where they are and giving them tools that fit their contexts. Not everyone needs to become a master researcher, but many can learn to conduct basic customer interviews or query data effectively.

Brittany Lang, UX Research Manager and M.S. in Information Science, uses project reviews as a time to cultivate a shared point of view and continually refine her thinking.

“Before we socialize research plans, I usually take a look at it, or I have someone else on my team take a look at it. It doesn't have to be your manager that's reviewing something, but can someone give you feedback?

It's nice when coworkers leave comments and I can see what other people on the team have said and we can agree or challenge, and then have a discussion about it. I also learn in those moments too. When I'm looking at how members of my team have reviewed other work, where they're coming from and their perspective, I learn a lot from them in those moments.”

Facilitate low-risk learning

It takes more than a few ambitious researchers to imbue a company’s culture with a learning mindset, which is why rituals and learning programs are so important.

Anton’s employer formalized this approach to building safe learning environments through a program called "Gigs for Growth," a repository of side projects from different departments where employees can apply to work on learning opportunities outside their typical scope.

"It's like a company green light that you can work on learning during your full-time gig and outside of your typical work scope. Something that you would never otherwise be able to touch in the company."

Under this program, researchers can support QA engineers, sales can support marketing, and everyone gets exposure to new perspectives that inform their primary roles. "You get some really new experiences that otherwise you wouldn't be able to."

At The Good, we like to build regular, low-stakes opportunities for knowledge sharing and skill development. One of our approaches at The Good is a ritual called "Random Question of the Week." During another bi-weekly meeting, team members share client questions that stumped them or that they felt they could have answered better.

These conversations help build shared perspectives that then get turned into artifacts:

• FAQ entries for brief, punchy answers
• Articles for long-form perspectives
• Policies or SOPs that outline ways of working

The result is that teams become more aligned, can answer tough questions on the spot, and save time by referring to their collective knowledge instead of rehashing the same discussions.

Another effective ritual is "Critique & Share" sessions, where team members bring questions, websites they admire, or work they're developing to get fresh perspectives from colleagues who haven't been deep in the weeds of a particular project.

Maggie Paveza, Senior Strategist at The Good, shares that it has helped her break the ice when building a shared P.O.V.

"It's pretty informal and often we're not showing our own work, so it feels less intimidating to ask your team members, 'why do you think this competitor is using this strategy,' than if it were your own work," explains Maggie.

The power of being a data connector

"The fundamental problem that research as an industry has is we've been myopically focused on the front end of the equation," says Ari. "Data collection, statistical significance, theoretical saturation—insert whatever fancy academic word you want in here. But the real power comes on the back end of the equation."

That back end is about connection, synthesis, and empowerment. When researchers shift from being data collectors to data connectors, they don't lose their expertise; they amplify it.

As Anton puts it, "Where soil is right, then you can do things. Praise people for when they do things great. You can learn from mistakes, you can learn from success."

The goal isn't to turn everyone into a researcher. It's to create an environment where customer insights flow freely, where good questions get asked by many disciplines, and where learning happens continuously rather than in bursts.

Making the shift

Building a customer-centric learning culture doesn't happen overnight, but it starts with understanding where your organization is open to change and being constructive about how you facilitate it.

Look for teams and individuals who are already curious about customers. Find the places where people are asking good questions but lack the tools or confidence to find answers. Then meet them there with the right combination of education, tools, and support.

"At the end of the day, it's about empowering decision-making," says Ari. And in a world where customer expectations evolve quickly and research teams are lean, that empowerment might be the most valuable thing researchers can provide.

The post From Data Collector to Data Connector: Embracing Research Democratization appeared first on The Good.

But then the metrics start to roll in. Conversion rates are flat. Churn is up. Customer satisfaction scores haven’t budged.

Sound familiar? You’re not alone.

Most SaaS companies are stuck in a feature factory, churning out functionality users don’t want, don’t use, or actively avoid. While your competitors are optimizing the core experiences that drive growth, you’re polishing the peripheral features.

Here’s the uncomfortable truth: You’re probably building the wrong things.

The hidden cost of feature bloat

Low-impact features aren’t just harmless additions to your product; they’re silent growth killers. Every hour spent building or optimizing a feature that doesn’t move the needle is an hour stolen from something that could grow your business.

But what exactly makes a feature “low-impact”? It’s not about whether the feature works or whether someone, somewhere, might find it useful. Low-impact features are those that:

• Address edge cases rather than core user needs
• Generate minimal usage after launch
• Don’t correlate with key business metrics like retention or expansion revenue
• Create more complexity than value

According to research by UserPilot, the average core feature adoption rate is 24.5%. That means more than 75% of features might as well not exist from a user perspective.

When a SaaS company prioritizes those extra features, it is likely suffering from feature bloat.

Feature bloat is costly for your team, your users, and your business. An excess of features creates complexity and detracts from your product’s core value. Sometimes, feature bloat can actually prevent your product from doing its main job.

The cost of feature bloat develops quickly. Some examples include:

Development opportunity cost: While your team builds that quirky reporting dashboard that three power users requested, your core onboarding flow continues to hemorrhage trial users.

User experience degradation: Every new feature is another decision your users have to make, another item in the navigation, another potential source of confusion. Research from the Nielson Norman Group shows that feature bloat directly correlates with decreased user satisfaction and other industry experts agree. Jared Spool calls it experience rot and often highlights the inevitable complexity creep and user experience decline that occurs when teams add features without ruthless prioritization.

Technical debt accumulation: Low-impact features still need maintenance, bug fixes, and updates. They create dependencies that slow down future development and increase the risk of breaking changes.

Low-impact features don’t just waste resources; they actively prevent you from building high-impact ones.

Consider a hypothetical case of a B2B SaaS platform that spent six months building an advanced scheduling feature requested by their largest enterprise client. The feature worked beautifully for that one client, but sat unused by 98% of their user base. Meanwhile, their core product suffered a 60% drop-off rate during onboarding. This was a fixable problem that could have doubled their conversion rate.

The real kicker? That scheduling feature became a maintenance burden, requiring updates every time they changed their core platform. What started as a “quick win” became an ongoing resource drain.

Warning signs you’re in the feature bloat trap

It isn’t always easy to identify if and when you’re prioritizing low-impact features. Here are some of the common red flags that might make you think twice about how you’re building your roadmap:

• Lack of data: Decisions based on gut feeling rather than data-driven insights can easily lead to prioritizing the wrong things.
• The squeaky wheel syndrome: Your roadmap is driven by whoever complains loudest, not by what data shows you should build.
• Internal politics: Sometimes, features are prioritized based on the influence of certain stakeholders rather than their actual value to the user or the business.
• Fear of risk: High-impact features often involve more risk and uncertainty. Teams might opt for safer, less impactful options to avoid potential failures.
• Shiny object syndrome: New feature ideas consistently trump optimization of existing functionality, or the allure of new and trendy features can sometimes overshadow the importance of addressing core user needs.
• Short-term focus: A focus on immediate gains can lead to neglecting long-term strategic goals and prioritizing quick wins over sustainable growth.
• The metrics disconnect: You can’t clearly articulate how each planned feature connects to business outcomes like revenue, retention, or user satisfaction.
• Poor prioritization framework: Without a clear and consistent framework for evaluating and prioritizing features, it’s easy to fall into the trap of prioritizing the wrong things.
• The “just one more thing” mentality: Features keep getting added to releases because they seem small and easy.

The longer your team functions in the trap of any of these situations, the harder it is to change the behavior. So, if this resonates, try to get your team on board to shift behavior and implement some of the strategies we outline below.

A better way: Data-driven prioritization

The solution isn’t to stop building features, it’s to build and optimize the right ones. This means establishing clear criteria for what constitutes “high-impact” before you write a single line of code.

Start with the outcome, not the output

Instead of asking “What features should we build?” ask “What user behaviors drive business growth, and how can we encourage more of them?”

Implement continuous user research

Don’t just collect feature requests, use them as an opportunity to understand the underlying problems. Continuous research that includes things like regular user interviews, behavioral analytics, and feedback loops can help you distinguish between what users say they want and what actually drives value.

Continuous research also allows you to test assumptions before implementation. Including rapid testing in your workflow can help you get fast, early feedback on concepts from real users for better direction.

Let the data guide decisions

Base your prioritization decisions on data from user research, analytics, and market analysis so that you can focus on what users truly need and what will drive the most significant impact.

Use prioritization frameworks consistently

Tools like RICE (Reach, Impact, Confidence, Effort) or the ICE (Impact, Confidence, Ease) scoring model help you compare feature ideas objectively. The specific framework matters less than using one consistently.

At The Good, we use the ADVIS’R Prioritization Framework to guide our optimization strategy.

Measure everything

For every feature you build, define success metrics upfront. If you can’t measure whether a feature is working, you can’t determine if it’s worth the investment.

Consider the indirect impact

Sometimes, a feature might not directly impact a North Star metric but could have a significant indirect impact. For example, improving the onboarding experience might not immediately increase conversion rates but could lead to higher user retention and lifetime value in the long run.

Focus on your most valuable users

Part of building and optimizing the right features means understanding your users. If you haven’t, conduct a step-by-step user segmentation study to help identify your highest-value users. Then you can tailor feature prioritization and optimization to their use case before moving on to other segments. A feature that’s high-impact for one segment might be low-impact for another.

Embrace the power of “no”

The most successful product teams are ruthless about saying no to good ideas so they can say yes to great ones. Create explicit criteria for what doesn’t make the cut. It’s okay to say “no” to features that don’t align with your strategic goals or offer significant value.

Moving beyond the feature bloat factory

Breaking free from the low-impact feature trap requires discipline, but the payoff is substantial. Companies that master prioritization don’t just build better products; they build products faster, with fewer resources, and with much better business outcomes.

The goal isn’t to build everything your users request. It’s to understand what truly drives value and relentlessly focus on that.

Your roadmap should be a strategic weapon, not a wishlist. Every feature should earn its place through clear evidence that it will move the metrics that matter.

Stop building features. Start building value.

Struggling to identify which features truly drive growth? Our Digital Experience Optimization Program helps SaaS companies cut through the noise and focus on changes that move the needle.

The post The Biggest Roadmap Mistake: Prioritizing Low-Impact Features appeared first on The Good.

Despite this being a proven economic model, companies are rarely focusing their effort on that 20%.

It’s not because they don’t want to; it’s because it is easy to get wrapped up in not losing a single sale, to the point that you are spreading yourself too thin.

If you focus your energy and product improvements on the highest-value user segment, you will see greater returns for less work.

In this article, we’re sharing the study we recently ran for a client that helped us identify their most valuable user segments and prioritize improvements to meet their needs.

What are user segments?

User segments are groups within a customer base who share similar characteristics, behaviors, or values.

They are created with user segmentation, which researches those commonalities and divides your audience into distinct groups. You can then tailor experiences, personalize messaging, and focus optimization efforts on their specific needs.

Common types of user segments

User segments can be divided based on different traits. The type of segmentation you use will vary based on your use case and goals. Here is a quick overview of common user segments.

Why companies optimize for the wrong segments

When we run prioritization exercises, one of the most common mistakes we see is companies focused on segments of users based on volume. If the segment has more users, they automatically believe it deserves more attention.

This reflects one of the three common prioritization mistakes:

1. Volume bias: Prioritizing segments with the most users rather than the most value
2. Squeaky wheel focus: Optimizing for the users who complain the loudest
3. Recency fallacy: Focusing on the latest acquisition channel or user cohort without evaluating their actual value

The uncomfortable truth is that your most valuable segments may not be your largest, your loudest, or your newest.

Conducting a segmentation study step by step

At The Good, we’ve developed a systematic approach to identify and prioritize your most valuable user segments. Here’s how it works.

Step 1: Set your goals

Before you start analyzing data, segmenting users, and prioritizing, you need a clear understanding of your project goals. In most cases, they will look something like this:

• Identify and quantify subsets of user segments based on use cases
• Understand the potential value of known segments
• Identify features and benefits that are most important on a per-segment basis
• Find opportunities to improve the engagement of high-value users

These goals can be turned into the key research questions of your study.

Step 2: Identify valuable behaviors beyond revenue

Your most valuable user segments, of course, need to drive revenue, but there are other indicators to consider when prioritizing who you are building/optimizing for.

Current value metrics, future value indicators, influence value, and cost-to-serve factors will all influence the overall value of a user segment.

• Current value metrics: Revenue generated, subscription tier, feature usage, team size
• Future value indicators: Growth trajectory, expansion potential
• Influence value: Referral behavior, advocacy impact
• Cost-to-serve factors: Support requirements, implementation complexity, churn risk, acquisition cost

Identifying and tracking these metrics and scoring segments based on this information will help paint a more holistic picture of value. Some segments might not be your biggest revenue drivers today, but they represent significant future opportunities, so you may choose to optimize for them instead of your current biggest spenders.

Step 3: Collect qualitative and quantitative data

Once you’re clear on goals and value metrics, you’re ready to start collecting data for your segmentation analysis. Gathering a multidimensional data set will help you better understand users as the complex humans they are. Types of data that will help your analysis will include:

• Usage patterns: Frequency, features used, time spent in the product
• Transactional data: Revenue contribution, plan type, upgrade/downgrade history
• Behavioral signals: Engagement with key activation points, referral behavior
• Acquisition source: Channel origin, customer acquisition cost, time to convert
• Demographic/firmographic data: Company size, industry, role

Most of this data will be sourced from your main quantitative collection tool, such as Google Analytics or your product analytics. But for a truly effective study, you need to supplement all this information with qualitative context. Surveys, session recordings, or user tests can help you better understand why your users are doing what they do.

Step 4: Conduct factor analysis to identify value drivers

Group your data together into a reduced number of independent factors that represent the underlying themes within the dataset. This will help identify value drivers that differentiate your user segments.

For example, in a recent segmentation project, we discovered distinct value factors that formed natural segment groupings:

• Efficiency seekers: Primarily valued time savings and streamlined workflows
• Integration power users: Heavily utilized connections to other tools in their stack
• Data-driven optimizers: Focused on analytics and performance insights
• Scale-focused operators: Needed enterprise features and team collaboration

Understanding these value drivers helps you move beyond simple demographic segmentation to truly understand what motivates different user groups.

Step 5: Apply cluster analysis to form actionable segments

Once you’ve identified the key value drivers, use cluster analysis to group users with similar characteristics. Usually, 3-7 distinct segments emerge from the exercise.

These segments often cross traditional demographic lines, revealing unexpected patterns. For example, power users might not be enterprise customers as you assumed, but mid-market companies with specific workflow needs.

This is also the time to start looking for natural clusters of behavior that indicate high-value segments. Considering this, when you’re analyzing user clusters, look for key differentiators like:

1. Usage frequency: Daily users vs. weekly vs. monthly
2. Feature utilization: Which user flows are most common for each segment
3. Value perception: What features each segment values most highly
4. Growth potential: Which segments show increasing usage over time

Step 6: Quantify segment value and opportunity size

The inputs from your data, factor, and cluster analyses will produce outputs of your high-value segments.

Here’s an example of that workflow so far. The data (survey themes collected) on habits, values, and use cases were the inputs for the factor and cluster analyses. That resulted in segments around the frequency of product use, customer values, and reason for use.

For each potential high-value segment, revisit the value metrics you established in step 2 of the process. Calculate the relevant metrics to ensure you’re not just following hunches but making data-backed decisions about where to focus.

The most valuable segments often show strength across multiple metrics, not just in current revenue. For example, a segment with moderate current revenue but excellent retention and high referral rates may be more valuable than a high-revenue segment with poor retention.

You’ll also start to see how your most valuable segments differ from your hypotheses. Maybe it’s not defined by company size but by a specific usage pattern. As a specific example, imagine users who perform at least 3 exports per week AND invite 2+ team members within the first 30 days are 4.5x more likely to upgrade to the enterprise tier within 6 months.

This kind of insight could transform your priorities, focusing on making these specific actions easier and more intuitive, rather than spending time/money on creating new features for other segments.

Step 7: Map segments to specific opportunities

The final step is to leverage your knowledge about high-value users to focus optimization efforts. Now, you can connect your segment analysis to concrete optimization opportunities. A few thought starters for this process:

1. What actions correlate with long-term success for this segment?
2. Where do users in this segment typically struggle?
3. What capabilities does this segment need but doesn’t have?
4. What value propositions connect most strongly to this segment?

You’ll end up with a list of optimization opportunities. To prioritize those efforts and start building a roadmap, we recommend scoring them across these dimensions on a 1-10 scale, then calculating a weighted score that reflects your company’s specific situation and constraints.

1. Potential revenue impact: How much additional revenue could optimizing for this segment generate?
2. Implementation effort: How difficult would it be to implement changes for this segment?
3. Time to results: How quickly can you expect to see meaningful outcomes?
4. Strategic alignment: How well does focusing on this segment align with your long-term business strategy?

For example, if you’re under pressure to show quick wins, you might weigh “time to results” more heavily. If you’re planning for long-term growth, strategic alignment might carry more weight.

This will be the start of your roadmap for optimization efforts, ensuring that you focus resources on the right opportunities for your most valuable segments.

Focus on your highest-value segments first, then gradually expand your optimization efforts to secondary segments once you’ve captured the initial value. Always consider potential cross-segment impacts when making changes.

Drive growth with user segmentation and prioritization

As your product and market evolve, so will your user segments. What constitutes a high-value segment today may shift as you introduce new features or enter new markets.

We recommend evaluating your user segments quarterly, with a more comprehensive review annually or whenever you experience significant business changes.

Remember, the path to scaling your SaaS business isn’t through trying to please everyone with generic optimizations. It’s through deeply understanding which user segments create the most value and deliberately focusing your limited resources on enhancing their experience.

Ready to identify and prioritize your most valuable user segments? The Good’s Digital Experience Optimization Program can help you discover untapped growth opportunities through expert research, strategic insight, and data-driven experimentation. Contact us to learn more about how our team can help your SaaS business scale faster.

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