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.

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 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.

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.

The post How to Identify Your Most Valuable User Segments and Prioritize Accordingly appeared first on The Good.

Perhaps no corner of the product industry is witnessing democratization more than UX research.

Despite “research” working its way into the job descriptions of more and more disciplines, experienced, high-caliber researchers will always have their place in industry. Whether it’s to supplement your team’s capacity, tap into deep expertise, or get an objective outside perspective, research vendors are valuable for a host of reasons. But between traditional agencies and the recent increases of independent and fractional labor, how do you know you’re talking to someone with the chops to execute at a high level?

We asked product research experts Hannah Shamji and Jon MacDonald how to spot a great research vendor. Read on to hear their perspective on what “green flags” to look for when vetting your next research partner.

They Ask a Healthy Number of Questions

Most experienced researchers have chosen the wrong method at least once in their careers. And the outcome is always disappointing. “Picking the wrong research method leaves you with results you effectively can’t use, and is a huge waste of resources,” says Jon MacDonald, Founder and CEO at The Good.

Fortunately, that painful lesson has its upside in learning value. Careful to avoid diving headfirst into a low-utility approach, experienced researchers ask plenty of questions before jumping into execution. This assures they understand both the problem space and how the research will be actioned on.

“If they are asking questions, it tells you they want to understand the business context,” says Hannah Shamji, former psychotherapist turned customer researcher. “If they’re just jumping in and not really scoping things out, it’s probably a sign they’re not the right fit.”

That heavy lifting up front helps shape a clear scope, but the conversation is more than just a learning exercise. A strong vendor will then massage the methodology to fit the business challenge. “I think it’s important to not lead with a method unless you have a very clear diagnosis,” says Hannah.

Jon agrees. “A good researcher will avoid a cookie-cutter approach,” says Jon. It’s why his team kicks off every project with a conversation designed to uncover nuance, align on business goals, and extract the institutional knowledge embedded within the team. It’s a process Jon calls “diagnosing before prescribing.” And it’s why The Good doesn’t respond to RFPs.

“If a scope is completely mapped out before involving a vendor, we often find that it’s poorly suited to yield the outcomes they’re after,” says Jon.

By forming scope through a collaborative process that starts with a conversation, research vendors are well-equipped to help craft an approach that’s appropriate and effective.

They Can Walk You Through the Tradeoffs

If you’re at the stage where you’re vetting research vendors, you probably have some idea of how to get the job done, i.e., through a survey or customer interviews. But Hannah warns that because research is so “accessible-sounding,” it’s common to chat with clients who start out asking for one form of research but really need another.

From Hannah’s point of view, a true expert will help you navigate the tradeoffs of one method vs another. They’ll help you understand how an approach impacts your time, budget, and expected outcomes. “There are a lot of easy, accessible go-tos like running a survey and talking to customers, but there are so many other forms of research that can close the gap,” Hannah says.

“The difference between an executioner and consultants is that if you want someone to do, that’s a slightly different hire. If you want someone who will help you navigate the tradeoffs, it’s a different conversation.”

Jon agrees.

“Our clients love chatting through their needs with us because we’re really good at helping them outline the constraints and requirements of the task at hand and figuring out where to get the most leverage. We’re a thought partner. So by being brought in early enough, we can help them think through what they need to learn with new research versus where we can rely on historical or secondary research.”

In an ideal world, we would execute at the perfect balance of depth, speed, and cost. But at the speed of business today, most contexts leave us wanting for either time, budget, or rigor. A good research vendor will help you navigate the tradeoffs and make an informed decision.

“Sometimes you need to be scrappy, sometimes you need to go deep,” Hannah explains. "Being able to juggle your timeline and adapt the methods to your needs is key. Not everything needs significant rigor.”

As such, Hannah recommends being up front with your vendor and communicating what your priorities are—being honest about your budget, when you can act on the findings, who’s involved, and what’s in your power to change (versus what authority lives on another team). This context will help your vendor deliver “just enough research.”

They Are Flexible in Their Collaboration Style

For Hannah, research services are best done in a way that meets the team where they are at. That tailored collaboration style is what Jon calls a “one size fits one” approach.

As such, our experts believe a strong research vendor tailors their engagement to the company's needs, understanding that research roles can shift depending on the stage of the business. "Depending on who’s involved, I think about research differently," Hannah says. "There are certain stages where it’s not helpful to bring in a vendor with a buttoned-up process."

For instance, Hannah finds that early-stage founders seeking product-market fit may benefit more from hands-on coaching than outsourced research, “so they can stay close to the data and be at the frontline of it.”

For those early-stage founders, Hannah recommends working with a partner who will open up their process or even take a more coaching-based approach. That way, the feedback loops are faster and the learnings are gathered first-hand. “You want to own the process yourself and minimize the gap between learning and doing.”

This manifests in conversational snapshots of the data as it’s rolling in. "Sometimes I will drip out the findings as I get them because I know they need to move," Hannah notes. “I’ve had sales [people] jump on a call with me in the middle of me doing research because they just want to ask some questions to fill in the gaps with what I’m learning.”

For Jon, it’s about figuring out how involved a partner wants to be. “Some people want email updates almost daily, others just want a report in their inbox when things are wrapped. We try to work in a way that gives them their desired level of input and transparency.” This kind of adaptability ensures that research remains a business enabler rather than a bottleneck.

How To Choose The Right Research Partner

Choosing the right research vendor isn’t just about credentials or experience; it’s about fit.

To set yourself up for success, look for vendors who:

• Ask the right questions and diagnose problems before prescribing solutions.
• Can communicate tradeoffs to determine a path forward that fits your needs
• Are flexible in their collaboration style, tailoring their approach to the company’s stage and objectives.

By keeping these green flags in mind, businesses can ensure they partner with a research vendor who will deliver value, not just data.

The post Three Green Flags to Look For in a Research Vendor appeared first on The Good.

Judd Antin perhaps put the value of user experience research (UXR) best:

“When research makes a product more usable and accessible, engagement goes up, and churn rates go down. Companies need that for the bottom line. Users get a better product. Win-win.”

Still, the latest reports put typical UX research staffing at a ratio of one researcher to every 50 developers. Perhaps as a result of this imbalance, research roadmaps can be excessively long and often fail to respond to the real-time needs of product specialists.

“Getting on a roadmap can be very tricky,” says Heidi Dean, Principal Product-Led Growth Manager at Adobe. “With a limited amount of internal resources shared across a matrixed environment, you sometimes have to rely on outside help to get the insights you need.”

When DIY Just Won’t Do

In the era of “founder mode,” many product managers (PMs) and product marketing managers (PMMs) are circumventing internal resources and doing ad-hoc research themselves. And while the DIY approach can be a solution to long lead times, it’s not always feasible. A careful combination of training, tooling, and time is required for non-researchers to do their own research.

Take, for example, moderated user interviews and usability studies. “It’s a skill set that I’ve had to try and hone,” says Dean. With permission, desire, and training, Dean has upskilled in the methodologies, but she’s conscious that not everyone in a product role has the time or opportunity to do so. “Sometimes it’s hard to find the time to recruit and talk to customers,” she says.

It’s not just the lack of formal training preventing PMs from DIY-ing it. Access barriers and time also play a significant role.

While formal research teams are generally equipped with tools like Lyssna, Rally, and Pendo, many research tools operate on a per-seat basis. As a result, access to “seats” is often tightly guarded—and PMs are often left off the roster.

These access barriers can make ad-hoc projects hard to streamline. In previous roles, Dean has seen this play out as a permission-seeking exercise that manifests in added up-time for even simple projects. “There’s a lot of overhead that comes with getting access to a system like that. It can be a heavy lift.”

Add to that the challenge of fitting research into an already-packed schedule, and the barriers to DIY research can feel unsurmountable. “It’s not an easy thing to slot into existing work and commitments,” says Dean.

Using Outside Experts to Supplement Research

Luckily, with support from firms like The Good, you don’t need to be an expert in user testing to get quick insights. PMs with already-packed research roadmaps and busy schedules hire outside experts like us to cut the line and get results quicker.

“Using part of our budget to gain customer insights has been invaluable for decision-making. The insights from user research have helped us unlock new opportunities and validate hypotheses,” says Dean.

The impact isn’t just doing more with less, but doing it reliably faster, says Software Director of Product Marketing Gabrielle Nouhra, who leverages The Good for UX research, rapid testing, and on-site experimentation, and thinks of The Good as “an extension to the product team.”

“The speed at which we obtain actionable findings has been impressive. We are receiving rapid results within weeks and taking immediate action based on the findings, unlike past survey research that often took much longer to yield insights.”

The Multiplying Force of Long-term Partners

Operating somewhat behind the scenes, outside vendors can be a multiplying force that enables product managers, according to Dean. “Your team’s work is additive to our roadmap and helps us meet the demands of our stakeholders looking for customer insights.”

If a good research partner amplifies their impact, why aren’t more product teams leveraging research vendors?

It all comes down to cost and time.

Whether it’s familiarizing them with your business model, metrics, or past insights, standing up a relationship with a new vendor is work, and the process is imprecise. “You try to do the best download that you can, but things are always going to get missed or misinterpreted,” says Dean.

That investment cost is why Dean says that when comparing a long-term partner to one on retainer, “there’s no comparison.”

“When you work with somebody long term, they learn your products, the organization and your stakeholders. They understand the pain points that you’re dealing with, and then you just develop a shorthand.”

Retainer relationships mean time saved, which is why, in Dean’s view, dollars spent with a long-term partner go a lot farther. “Using a partner to help with our research needs has been an efficient use of our resources,” she says.

That manifests in not just time saved but a less arduous process altogether. “It’s streamlining things. It makes everything easier. I can get a lot more done using you guys than even I can with my team.”

Assuring the Success of the Relationship

Once you’ve found the right partner and begun building a backlog of research, the results are compounding. Partners with historical product knowledge can mitigate the pain of reorgs by retaining institutional knowledge.

They can also act as a scaffolding to support new hires. Nouhra knows this firsthand. When she was onboarded to a new role, her first task was to review existing research. Having both a catalog of existing, high-quality research and a partner at The Good who could walk her through it has been an empowering resource that enabled her to dive in quickly.

“You brought me up to speed on so much when I joined—beyond test results and our catalog of research, you were able to share what product updates had been proposed, which were implemented, and what the tradeoffs were to get them live. This gave me a headstart with my product and cross-functional teams.”

Knowing that all good things take time, we asked Dean and Nouhra for their tips for a lasting, high-impact partner. Here’s what they said.

Invest in Up-front Relationship Building

While Nouhra raves about the time-savings of a good partner, she cautions that the dividends are born of an up-front investment. Acting purposefully at the outset can set you up for success. “Take the time to invest in the upfront so that you can reap the benefits of the partnership down the line.”

“Having a partner that's always by your side, you've already done the investment. You can actually get a lot more out of it in the short term because they know the background, and they know your customers, and they know your site experience.”

Include Your Vendor in the Scoping Conversations

When other internal stakeholders are involved, Dean recommends letting the vendor in early, even during the scoping phase. That way, they can ask clarifying questions and quickly speak about the budget implications of various methodologies. It’s an approach that saves time, and it helps identify assumptions and biases that might otherwise arise if the conversation stayed internal, according to Dean.

“When the vendor asks questions, it can draw out the unspoken details. It comes across as ‘I want to make sure we do the best work for you guys.’ So there's a built-in trust that we're all trying to get to, and there's a joint exercise of figuring out what that is.”

Establish a Client-side Conduit

To assure mutual success, Dean recommends assigning a single person to be responsible for mitigation in the event of an issue with regard to the vendor-stakeholder relationship.

While it’s possible that no issues will arise, in Dean’s view, just having someone client-side own the relationship makes her stakeholders feel supported. “They know that I'm personally vested in their success. I'm not just throwing them over the fence to a vendor.”

Getting Started

The benefits of user research in SaaS are proven and they aren’t singular. Conducting frequent, consistent research delivers compounding results. Your whole organization can benefit from the learnings if you pass user insights between development, sales, marketing, product, and more.

But, if like many product leaders, conducting your own research gets put on the back-burner due to competing initiatives, ditch the one-off engagement approach.

To get the results you’re looking for, you need to commit to a long-term research partner. Invest time and resources upfront, and you’ll be rewarded with insights that will propel growth. The longer you engage with the right partner, the easier it will be to glean more insights and, in turn, improve your product experience, marketing, and more.

If you want to understand if The Good might be that long-term partner for your business, get in touch. We start with a thorough audit of your current practices and digital experience to ensure you get everything you need (and nothing you don’t) from working with us. Check out our program and get in touch.

The post What is The Strategic Value of Ongoing User Research in SaaS? appeared first on The Good.

Analytics data, user interviews, and surveys are helpful in their own ways, but there is powerful insight in observing people use the site or app.

This gives you a clear, comprehensive, and unbiased view of their experience.

How do you get this valuable view? With a tool like Hotjar.

Hotjar is one of our favorite research tools. It’s a staple of our workflow and a key way we develop insights to optimize our clients’ digital experiences.

Sometimes, new clients will ask us to use their preferred tool, but we usually resist. That’s how confident we are in Hotjar’s value. Right now, it’s the best heat mapping software on the market for professional researchers.

We’d like to take a moment to explain what makes Hotjar so great and how it helps us create better experiences for our clients. We’ll also address a common criticism of Hotjar’s platform.

What is Hotjar?

Hotjar is an analytics tool that helps digital brands understand how users interact with their websites. It provides insights into user behavior through visual representations so you can identify areas for improvement and enhance the overall user experience.

Unlike traditional analytics tools that offer simple numerical data, Hotjar provides visual feedback through heatmaps, session recordings, surveys, user interviews, and feedback polls.

These tools let you see exactly how users navigate your site, where they click, and how far they scroll. This information helps you make data-driven decisions to optimize your website, which ultimately leads to a better digital experience for everyone (not to mention higher conversion rates and increased revenue for you).

What are Heatmaps, Scroll Maps, and Click Maps?

Before we dig into why Hotjar is the best heatmap software, let’s get an understanding of the tool’s primary value.

If you look at analytical data, it can seem like conversions just happen on their own. But in reality, there are dozens of little variables that affect how and when your visitors decide to take action.

For instance, a visitor might read some content, explore some images, or watch a video on the page before finally taking the next step. These “footprints” can provide key insight to help you optimize the experience and drive more conversions.

Unfortunately, you can’t get this kind of data out-of-the-box in Google Analytics. (That isn’t to say Google Analytics is a bad tool, but it doesn’t provide everything you need.) And if you have customer tracking set up that tells the fuller story, all that can do is tell you what’s happening. It won’t show you.

Therefore, you need specialty tools to show exactly what your visitors do on your site: heatmaps, scroll maps, click maps, and session recordings.

Heatmaps: Where People Pay Attention

A Nielsen eye-tracking study made pretty big waves when it proved what we all suspected: people don’t read on the web. We scan.

In fact, we scan in a fairly predictable F-shaped pattern. We start on the far left-hand side, scan to the right, and then drop down and to the left to repeat.

The result is that some spots on the page get the majority of our attention. Other spots are basically ignored.

That Nielsen study is an example of a heatmap. It shows us where users focus their attention. We can use it to learn whether design elements are effective and how to optimize the page.

Areas that receive a lot of attention are shown in warmer colors, like red and orange, and areas that receive little attention get cooler colors, like green and blue.

For instance, consider the following two images. When the baby is facing forward, the face receives the majority of the reader’s attention (indicated by the hot red spot). The title and text are far “cooler,” meaning they get less attention.

But look what happens when the baby faces the content. The attention on the face gets transferred to the text.

The direction of a face is a simple visual cue, but we wouldn’t see its effect without the help of the heatmap.

Obviously, this is a simple example. It’s not always so cut-and-dry. However, it shows us that heatmaps help us understand what our users are paying attention to. Armed with that information, we can create an experience that meets their needs and our goals.

Scroll Maps: Whether People Consume Your Content

You design long, beautiful pages. But does anyone read them? Do they actually make the experience better for your users?

Scroll maps help us understand where people scroll to on a page and how long they spend there. These maps use the same hot-cold color grading as heat maps. If users spend a lot of time in one area, the map shows it as red or orange. If they never scroll to a part of the page at all, it gets the super-cold blue.

Check out the following scroll map example. Essentially, this map tells us that no one scrolls below the fold.

Suppose this page’s juiciest offer is below the fold. In this case, most users will never see it because they don’t have a reason to explore further.

Does this mean information farther down the page is less valuable to users? Not necessarily. The following scroll map shows a page that’s almost entirely hot, meaning users care about all of the content.

Scroll maps are another powerful tool to help you optimize your pages. Like heatmaps, they tell you what users care about and offer insight into improving your pages.

Click Maps: Whether People are Close to Converting

The click is one of our most valuable signals because it represents engagement with the content. In some cases, a click indicates a prized conversion.

If people click your call to action, it’s a sign that your page is well-optimized. If they click elsewhere, it means they find something else more valuable or need more information.

Click maps show where someone clicks on your page. They reveal whether your users are interested in what they’re looking at.

Let’s look at an example. In this click map, you’ll notice most of the clicking takes place around the selector tabs on the left (represented by the warm zone). There’s also some clicking on the menu and the logo.

This map indicates that the page is working as intended. Users interact with the intended components and then explore other areas of the site.

Click tracking is part of Hotjar’s heat-mapping feature, but it doesn’t just show you where the click happens. You also get to learn where the user moved their cursor. This is another layer of user behavior that makes us love Hotjar.

5 Features That Make Hotjar the Best Tool for Researchers

Now that you understand Hotjar’s value offering let’s explore what specifically makes it the best tool on the market.

1. Separate Instances for Each Map

Having access to lots of different types of data is great, but some tools pump them all into the same report, which paints a muddy picture and makes accurate analysis difficult.

We love that Hotjar provides heatmaps, scroll maps, and click maps in separate instances with clear markings. This separation helps our team focus on the information we’re looking for so there are no misunderstandings.

2. Filters for Session Recordings

Session recordings typically take a while to sort through, especially if you have many of them. You can watch them at speeds faster than real-time, but they still take a while to watch.

This means we end up spending a lot of time watching dozens of irrelevant recordings for each page, often without learning anything valuable. It’s a major time suck.

Fortunately, Hotjar lets us filter our recordings to reduce the number of sessions we’re forced to watch. We can quickly drill down to the sessions that have the most impact on whatever we’re trying to learn.

Here’s a list of all the filters you can apply to your bank of session recordings.

• Path/URL – Explore where users have or haven’t navigated. You can focus on viewed pages, specific landing pages, exit pages, or traffic channels.
• Session – Refine data based on broader details about the session, such as new/returning users, country, duration, or page count.
• Behavior – This includes actions performed/experienced by users during the session, such as clicks, events, rage clicks, entered text, refreshed page, U-turns, or errors.
• User Attributes – Sessions from specific users based on custom attributes you’ve passed to Hotjar from your data.
• Technology – Refine collected session data based on technology used during a session, such as device, screen resolution, browser, operating system, or Hotjar user ID.
• Feedback – Filter sessions where a user submitted feedback through a feedback widget or Net Promoter Score widget.
• Experiment – Explore sessions based on inclusion in an experiment.
• Date Filter – Filter sessions based on relative or custom date ranges.

Our favorite filters include device type, landing page, pages visited, duration, relevance (engagement), and new vs returning user.

3. Keyboard Shortcuts for Quick Navigation

When you’re watching session recordings, Hotjar enables you to stop/play or go forward/back using the keyboard. This is a huge time saver, letting you bounce around recordings quickly to find the information you need.

Some competitors allow this kind of movement, but their buttons are small and out of the way. You have to click them manually, which takes your attention away from the video. As far as we know, no one else provides keyboard shortcuts so you can zip through the recording with ease.

4. Quickly Find Usability Issues

There’s only so far people will go to find what they need on your website or in your app. If your digital experience is hard to use, you’ll struggle to convert visitors. It’s simple logic.

In a HubSpot survey, 76% of respondents said the most important factor in a website’s design is the ability to find what they’re looking for.

Using Hotjar is a great way to identify usability issues that prevent users from taking the next step, whether that’s completing a purchase, opening a new user account, consuming content, or whatever else they need.

For instance, if you notice users opening your menu and hovering around without clicking, it tells you they couldn’t locate something they wanted. Maybe it’s worth testing different menu structures to facilitate a better experience.

5. Identify Moments of “Rage”

Sometimes, users become so frustrated that they click repeatedly to make the site work. This is often caused by slow page speed, confusion, or broken elements.

These are serious moments of frustration that you must avoid.

We like that Hotjar’s click maps can show you where users rage clicked. This helps us focus on the biggest causes of frustration in their experience.

Any rage-click issues you identify are easy wins. Solve them quickly before other users experience the same frustration.

Downsampling: A Common But Misguided Criticism of Hotjar

Whenever you consider analytics tools, you’ll likely read complaints of downsampling. Some tools use it. Tools that don’t use it often plaster it over their marketing as a point of value.

Downsampling refers to the process where a tool shows you a random percentage of your total session data instead of the full 100%.

Many analytics tools use downsampling for their free or lower-priced tiers and then encourage you to upgrade to higher-priced options to get access to 100% of your data. Essentially, this means that lower-tier accounts never see some of their data.

Downsampling can pose problems for detailed and precise metrics, such as conversion rates, in tools like Google Analytics. Calculating these nuanced numbers requires a complete understanding of the total number of visitors and sessions. Any reduction in the data can skew the results.

However, when using Hotjar for heat mapping, the situation is different.

Heatmaps are primarily used to identify patterns of user behavior on your website. Whether you’re looking at 100% of your sessions or a subset of sessions, the trends and themes that emerge from the data are usually consistent.

Some tools claim to be superior because they don’t downsample, but in our opinion, this really isn’t a concern when it comes to heat mapping. The fear of missing out on data is often exaggerated to encourage users to switch to more expensive plans.

Even with a sample of data, Hotjar’s ability to visualize user interactions, such as clicks and scrolls, allows you to make informed decisions about your website optimizations.

Go with Hotjar for Reliable Insights

Hotjar provides reliable and valuable data to help you understand user behavior patterns. The insights you gain are still robust and actionable. The app is simple to use for beginners and pros alike.

Hotjar is what we use to optimize sites like Pendleton, The Economist, and Fully. If you want to empower yourself (and your organization) with the best information to optimize your digital experience, Hotjar is the way to go.

You can sign up for a free account here.

The post This Is The Best Heatmap Software For Researchers (Yes, It Downsamples, And That Is OK) appeared first on The Good.

Amidst the plethora of apps and algorithms promising to streamline processes, there’s an often overlooked hidden gem—a tool deeply rooted in psychology and human behavior.

Welcome to the world of heuristics, where problem-solving meets intuition to build better digital experiences.

Heuristics, or mental shortcuts, could be the key to unlocking optimal performance in your role and your digital property.

In this article, we explore how these cognitive shortcuts pave the way for smoother, more intuitive user interactions. We unravel the significance of each of our six Heuristics for Digital Experience Optimization and offer actionable strategies for enhancing user experiences.

What are heuristics?

Heuristics are mental shortcuts used to solve problems quickly and effectively. They allow people to speed up analysis and make informed, efficient decisions.

Our brains are wired to take shortcuts and make quick decisions. So, heuristics play a crucial role in how customers navigate and perceive digital experiences.

How do heuristics apply to digital experience optimization?

By understanding the mental shortcuts your customers rely on, you shift the focus squarely onto their experience. Ensuring we understand and adhere to those shortcuts aids users in quickly and successfully accomplishing their goals. At the same time, actively removing barriers that interfere with these heuristics builds a subconscious level of trust with your customers.

We unpack more of these elements in our book, Behind the Click, but fundamentally, heuristics in digital experience optimization are a way to frame common optimization challenges and turn them into a trustworthy experience that:

• Feels familiar
• Does what they say
• Functions intuitively

Feels Familiar

From the classic navigation menu to the ever-present search bar at the top right, there’s a certain rhythm to digital experiences. Customers have developed a strong expectation of how websites and apps should function.

When a digital experience adheres to these established norms, customers feel a sense of familiarity and control. This subconsciously reduces friction and makes them more receptive to your company.

Does What They Say

Customers crave predictability and transparency in their digital experiences. Honoring a promise—whether about your pricing structure, refund policy, or product features—is essential.

Unexpected fees, convoluted purchase processes, or hidden terms and conditions violate the customer’s trust. Be upfront about all costs and keep the interactions straightforward to build confidence and credibility with your customer base.

Functions Intuitively

Intuitive design is crucial, especially for SaaS products. Users are already familiar with countless digital platforms, so don’t force them to relearn fundamental workflows for your product.

Leverage common design patterns and visual design cues. When your product functions in a way that feels natural, customers can focus on the value you provide rather than the mechanics of using the interface.

The Heuristics for Digital Experience Optimization

The Heuristics for Digital Experience Optimization are a tool developed at The Good to theme common optimization issues and opportunities with the user at the center of analyses.

These heuristics can guide your strategy and help you build digital journeys that feel familiar, do what they say, and function intuitively, as mentioned above.

The six heuristics are:

1. Priming & Expectation Setting
2. Trust & Authority
3. Ease
4. Benefits & Unique Selling Points
5. Directional Guidance
6. Incentives

Let’s take a look at each heuristic in more detail. We’ll cover what it is, how it manifests, and optimization examples of how you might adjust your digital experience to account for these heuristics and any barriers impeding users from accomplishing their tasks seamlessly.

Heuristic #1: Priming & Expectation Setting

Set users up for success by clarifying how the interface will perform, what actions users should take, and what they can expect.

Violations of this heuristic may manifest as:

• Unmet Expectations
• Poor Priming
• Unclear System Status

To adhere to the priming and expectation setting heuristic, you can apply a tactic like explicitly mentioning free shipping early in the journey to reduce cart abandonment rates or sharing estimated delivery dates. Doing so targets the poor priming violation.

Share Estimated Delivery Dates

Get as specific as possible with shipping dates on both PDPs and checkout pages.

You can do this in a few ways. First, you can list the date the item will arrive instead of giving a nonspecific range. “Standard Shipping 3-5 Business Days” becomes “Standard Shipping: Arrives by February 24.”

Here’s an example of estimated delivery dates. Notice the “Delivery Options” box in the bottom right corner.

Alternatively, you can add a zip code search option, where users can type in their location, and your website will provide estimated delivery dates (EDDs).

Setting clear expectations for EDDs can reduce customer anxiety, improve purchasing confidence, and even reduce the workload of your customer support team.

Heuristic #2: Trust & Authority

Establish and maintain perceived trust, authority, and security throughout the digital experience.

This is critical because issues like bugs or anything that violates users’ sense of trust can lead to disengagement. Building trust enhances users’ confidence in the website while violating it can lead to abandonment.

Violations of this heuristic may manifest as:

• Poor Usability
• Comparison Shopping

To follow the trust and authority heuristic, mitigate bugs, build trust by featuring social proof, or consider adding additional educational “how it works” content for complex products.

Highlight Positive Reviews

While brands can say a lot of great things about themselves, they can be more effective, more relatable, and more believable when real customers are singing their praises.

Featuring positive reviews can build user confidence to make a purchase decision and increase user trust.

This is especially great for brands with high-price point products (bikes, furniture) or products with trust-reducing user-dependent variables (makeup: compatibility with dark skin, shoes: true to size fit).

Offer Guarantees

Guarantees can help prime users to make purchasing decisions and incentivize them to purchase. They give users a feeling that the brand is making a commitment to them.

Highlighting guarantees in a quickly scannable way can increase a sense of trust, reduce decision paralysis, and highlight the value of a product.

Highlighting guarantees is great for sites that have high-value items (mattresses, bikes) and/or brands with trust-reducing user-dependent variables (dress fit, color match).

Add a How-it-works Model

Describing “How it Works” for some business models and/or features can give users the context and confidence that they need to understand competitive differentiators like price and quality.

Doing so for complex products will boost user trust, encourage buy-in to the brand, and instill purchasing confidence.

Heuristic #3: Ease

Ensure your interface is easy to use, including aspects like information architecture, navigability, and seamless functionality.

Violations of this heuristic may manifest as:

• High Interaction Cost
• Heavy Cognitive Load
• Content Fatigue

Making a website easy ensures that users won’t abandon it due to its complexity. It also offers better accessibility to diverse audiences. You can address the ease heuristic by reducing content or building in clear navigation elements like a mega menu.

Add a Mega Menu

Adding a mega menu can show the breadth of products, provide directional guidance, and increase visits to PDPs. This can ease product discoverability.

Mega menus are great for brands with a wide range of product offerings and/or multiple sub-categories.

Truncate Long Lists & Copy

Large amounts of copy or long lists can overwhelm users and create additional cognitive load.

Truncating long lists and copy can improve directional guidance, help users differentiate products better, and increase the likelihood of purchase.

Note that it’s not just about adding a “Read more” and hiding ineffective copy behind a click. It is sometimes necessary to bring in a copy expert to rewrite product copy entirely, focusing on decreasing the work to read and increasing the value for the user.

Heuristic #4: Benefits & Unique Selling Points

Highlight the benefits and unique features of products or services to persuade users to purchase them here instead of elsewhere.

Violations of this heuristic may manifest as:

• Low directness
• Attentive/intentional reading

To address this heuristic, consider testing factors like faster shipping times or highlighting product quality.

Add Quality Tiles

Brands often over-rely on homepages to sell the brand and product pages to sell a product’s features. Few users make it to all of a website’s pages (home, category page, product page), leaving users with knowledge gaps about brand positioning and product benefits.

Displaying quality tiles within collection pages can increase engagement, help users connect with brand values, and reiterate purchase incentives.

This is great for brands that have strong value propositions (sustainability, luxury) and selling points (hand-made, organic) that will connect with users.

Note that each quality tile variant could focus on a different theme, such as sustainable business practices in one, quality in another, and incentives like free shipping and returns in another.

Add Value Proposition to a Banner

Global promotion banners aren’t just for sales. They can be utilized to quickly communicate brand values, product benefits, and key differentiators with simple microcopy.

Showcasing positioning, brand values, and key differentiators in banners can increase engagement and decrease adds to cart.

Heuristic #5: Directional Guidance

Support users in finding and discovering what they need through visual hierarchy, way-finding, and guiding them to the next best step in their journey.

Violations of this heuristic may manifest as:

• Low Visibility/Low Discoverability
• Low Findability

This is particularly helpful for users who may need extra assistance in decision-making. Think of them as your friend who never knows where they want to go for dinner. We’re offering them an easy guide to follow, directing users toward desired actions or outcomes.

You can address the directional guidance heuristic with improvements like predictive search or sort order.

Use Predictive Search

Increasing the use of search is a great way to encourage intentional browsing, but often, users need a helping hand to guide them to relevant products or pages.

Featuring popular or relevant products in search suggestions can improve product discoverability, increase the helpfulness of search, and help users quickly and easily navigate the site.

Deeper customizations might include featuring different products based on user segment, search terms entered, seasonality, or geographic area of the site.

Change Sort Order

Sort orders often default to standard settings that don’t support user goals.

Testing alternative default sort orders (by popularity, by price) can help users quickly discover the products that are right for them and improve directional guidance.

Heuristic #6: Incentives

Provide additional motivation, confidence, and urgency for users to make a purchase. Ideally, incentives encourage a user to convert today rather than at a later date.

Violations of this heuristic may manifest as:

• Abandonment
• Comparison Shopping

You can address this heuristic by offering things like expedited shipping for VIP members, promotional offers, or guarantees.

Create Value-Based Promotions

Instead of discounting to incentivize purchases, which can ultimately devalue your product, consider a promotion that adds value. For example, buy-one-get-one-free, free shipping when you reach a minimum purchase amount, or a free gift with purchase.

Suggest Bulk Orders

Nobody likes running out of their favorite product. There are plenty of ways to incentivize adding products to the cart with tactics like cross-sells, upsells, or product bundling.

Suggesting shoppers stock up is a good way to increase AOV and secure long-term brand loyalty. This can be particularly effective if you have a shipping threshold.

How The Heuristics for Digital Experience Optimization Can Inform Your Strategic Roadmap

One of the most powerful ways to turn these six heuristics into an actionable improvement plan for your digital property is to use them to inform your strategic roadmap.

Armed with user research, identify common patterns or pitfalls that your users are experiencing. Then, group those patterns by the Heuristics for Digital Experience Optimization we covered above.

You can then prioritize the themes based on their potential impact on performance and develop a plan to test improvements. The whole process is outlined in more detail in our article on theme-based roadmaps, so I highly recommend checking that out if you’re looking to turn conversion challenges into opportunities.

The power of heuristics is being able to strategically and efficiently identify your digital challenges in a way that is centered on the user experience. At the end of the day, if your customer is getting stuck in your digital journey, you need to find out where and smooth out their path to purchase. Until that happens, not much else matters.

If you want to do just that and would like expert support in the process, take a look at our Digital Experience Optimization Program. The custom program gives you access to an entire team of researchers, strategists, designers, and developers so you can build a better website, app, or digital product.

The post How To Solve Common Optimization Issues Using Heuristics (With Examples) appeared first on The Good.

• “But doesn’t this Chia Pet require constant watering?”
• “Surely I’m going to have to sharpen this Ginsu knife again and again.”
• “Are you really saying that if I’ve fallen and I can’t get up, all I need to do is press a button? That seems too easy.”
• “I just need to clap to turn the lights on? Surely there’s a switch I need to flip.”

When it comes to selling anything, there will always be objections to overcome. Customers have reservations and questions that keep them from purchasing. It’s a normal part of any shopping experience.

This is nothing new. Before David Ogilvy became an advertising legend, he earned his stripes by selling pressure cookers. He wrote about the lessons he learned in a book titled “The Theory and Practice Of Selling The AGA Cooker.”

In the book, he taught prospective salesmen how to overcome objections such as “It’s too big for my kitchen” and “I’m only renting my present house,” both common objections in the 1930s.

And while Ogilvy’s book may not have been a bestseller, it gives us at least two valuable insights into overcoming objects.

1. You need to know the objections if you’re going to overcome them
2. Once you know the objections, you can meet them head-on

These two insights are even more critical for digital products since you’re not usually talking with customers face-to-face and hearing their objections. You need a proven strategy for unearthing potential objections and then overcoming them.

This article aims to help with that. We’ll cover:

• The primary types of customer objections
• How to identify the objections of your specific customers
• How to overcome those objections

What Are Customer Objections?

At the risk of stating the obvious, let’s ensure we’re all on the same page regarding customer objections.

Customer objections are concerns that cause them to hesitate (at best) and abandon (at worst) during the digital purchase experience.

People want to be sure they’re making the right choice. We’ve all been burned by products that seemed too good to be true—the “amazing” deal that turned out to be a dud or that trendy product made out of inferior materials.

With hundreds of years of snake oil and used car salesmen informing customer opinions, you need to be willing to meet customers where they are by addressing objections head-on.

Why You Need To Understand Your Customer’s Objections

Every customer objection is friction on the path to purchasing. Most customers are risk-averse; therefore, the more objections they have, the more risk they feel when purchasing, and the less likely they are to hit that “Buy” or “Subscribe” button.

And here’s the bottom line: overcoming objections isn’t an end in itself, but it is a way of ultimately improving the customer experience and increasing sales.

Let’s be clear though, it’s not enough to merely address generic objections. You need to address the specific objections of your customers. Some objections are unique to your customers and tied specifically to your products and company, so any “list of customer objections” won’t suffice. You must conduct your own research to understand your unique customers.

And make sure you do this early and often! As Leslie Ye at HubSpot notes:

“Nothing is more dangerous to a deal than letting sales objections go unaddressed until the final stages. The longer the buyer holds an opinion, the stronger that opinion usually is – and the harder you’ll have to fight to combat it.”

Identify objections and address them early on, and you’ll be on your way to optimizing your digital sales funnel.

How To Identify Your Unique Customer Objections

It’s easy to think that you know your customers and their objections, but unless you actively study your audience, there’s a good chance that there are dozens of objections you’re unaware of.

Consider a few of these strategies to uncover your customers’ unique concerns.

#1 – Research

There are several ways you can conduct audience research to help identify the specific objections they have. Some effective methods include surveys, customer interviews, and analyzing website data. For a deeper dive into customer research strategies, check out our e-book on the topic.

But when it comes to understanding customer objections, here are a few relevant considerations:

Conduct User Research
User research is a structured way to find out why users take certain actions. It uncovers user behaviors, motivations, and pain points as they interact with your website or digital product. Going beyond gut feelings or assumptions, it uses a variety of methods to glean actionable insights directly from users. This knowledge enables you to create a product or website that truly caters to customer needs and expectations.

User research is the umbrella term that user testing falls under. User research can also refer to other research methods, such as focus groups, interviews, and surveys.

Beyond understanding customer objections, the biggest benefits of user research include:

• Getting outside the jar
• Knowing what to improve (instead of guessing)
• Providing better customer-centric experiences

Collect User Behavior Data
Installing a tool like Hotjar on your site allows you to see visual reports of your top site pages, and see what content users interact with. Heatmaps can add context to site analytics like time on page, exit pages, and funnel dropoff data. This helps uncover what content on your site demands the attention of users and what might be overlooked.

Determine Your Net Promoter Score (NPS)
NPS is a management tool that allows you to determine how loyal your customers are. Scores range from -100 (everyone is a detractor) to +100 (everyone is a promoter). It’s essentially a metric that measures your overall relationship with your customers.

NPS is typically calculated based on how customers respond to a single question: How likely are you to recommend our company/product/service to a friend or colleague?

Anything over a 9 is considered a promoter, those under 6 are considered detractors, and those between 7-8 are considered passives.

Net Promoter Score = % of Promoters – % of Detractors

After the customer responds, they are typically asked a series of open-ended survey questions to bring clarity to their answer.

These questions can include:

• How did you first hear about our company/product?
• What are the three biggest things you dislike about our products?
• How can we improve your experience?
• What features do you value the most?
• How would you describe our products to a friend?
• What are our products missing?
• What are three things that almost stopped you from using our products?

By asking these types of highly specific questions, you can get a good sense of the common objections your customers have.

#2 – Chat

If you have a chat function on your site, you’re sitting on a gold mine when it comes to determining customer objections. You can methodically go through the chat logs and highlight the specific questions, objections, and problems that come up repeatedly. Then, you can compile those objections and create a plan for answering them.

This is also a good opportunity to address whether your chat adds to or interrupts the customer experience. While it can offer great insights into your customers’ main concerns, it shouldn’t interrupt the shopping experience by popping up without being requested.

#3 – Feedback Form On Your Website

When do customers typically use feedback forms? When they encounter a problem. The information submitted through these forms can be incredibly helpful in identifying points of friction in the sale process and addressing follow-up questions. Are there common problems your customers are mentioning in feedback forms? Those are objections to overcome.

#4 – Customer Service Reps

Your customer service representatives are on the front lines of customer interactions and will have a good sense of the common problems customers encounter and typical sales objections. Tap into their experience to identify the consistent customer objections that occur. While some of what they say will certainly be anecdotal, it can give you a broad picture of what your customers feel.

#5 – Social Channels

People tend to share very positive and very negative experiences on social media platforms. Closely monitoring social media channels allows you to identify those who’ve had negative experiences and personally interact with them to discover their pain points. For those who share positive experiences, you have the opportunity to ask them specifically what made their experience so good and leverage their feedback as social proof.

#6 – Brand Feedback On Third-Party Sites

Third-party websites that house reviews, testimonials, recommendations, and other similar content can give you valuable insight into customers who have had negative experiences on your site. These sites also usually allow you to engage with the customer by replying to the review, asking for further clarification, and offering to fix any problems.

How To Overcome Customer Objections (Step-By-Step)

Once you’ve determined the specific objections your customers have, you can begin to address them systematically.

Typically, objections fall into one of three categories:

1. Risk – The customer is concerned that the cost of the product may not be worth the value it provides.
2. Quality – The customer is concerned that the product may be low quality, and thus not provide a satisfying experience.
3. Relationship – The customer is concerned that the company selling the product (in this case, you) is of questionable character and may provide poor service.

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How Do You Overcome Each of These Customer Objections?

#1 – Work To Reduce The Perceived Risk

Perceived risk is subjective and will vary from customer to customer, though there are numerous ways you can reduce the amount of risk they feel. Each of these strategies involves, in some fashion, reframing the conversation to demonstrate that the risks are minimal and the benefits significant.

In most cases, some or all of these strategies will be used in concert with each other.

• Prove that the value of the product exceeds the costs and potential risks. This should be your overarching strategy when it comes to reducing the perceived risk. You want the customer to understand that the value your product provides far exceeds the risks. Because value depends on several factors (quality, benefits, relation to competitor products, etc.), you need to understand the specific risks that concern them and then show how the value of your product speaks to each of their risks.
• Re-frame the cost. When a budget-conscious person looks at your product, they are primarily aware of one thing: cost. This one thing can overshadow almost everything else about your product. You can overcome this objection by reframing the cost in terms of the value it will bring to their life. The phrase, “You can’t put a price on your health,” is a common example of this. Yes, it may cost a lot for a medical procedure, but the value of feeling well far outweighs the cost. The same approach applies to your physical or digital product. Yes, your product costs a certain amount, but compared to the value it brings, it’s worth the price.
• Highlight the benefits. There’s a huge difference between highlighting your product’s features and benefits. Features are things like the materials it’s made from, the different things it can do, etc. It slices, it dices, it makes Julienne fries. Those are features. Benefits, on the other hand, are the way the features improve the person’s life. A customer can easily see the features of a product on your site. What they can’t necessarily do is connect the dots between those features and how they will bring value to them. Your mobile phone battery case offers a 40% overall increase in battery charge. That’s a feature. This extended battery life translates into 9 hours of not needing to worry whether your battery will die. That’s a benefit. Focus on benefits over features.
• Offer a guarantee. Few things do more to set customers at ease than a guarantee. If they know they can get their money back without any hassle, their sense of risk will be greatly reduced. Nordstrom has built their reputation on allowing returns for the entire life of their products. If you bought a backpack in 6th grade and want to return it 20 years later, they’ll let you do it. This greatly reduces the risk people feel when purchasing their products.
• Give sufficient product details. This should go without saying, but we’re going to say it nonetheless. At a minimum, you should give customers enough product details to make an informed decision. If customers have to do significant research just to determine product details, it’s unlikely you’ll make the sale.

#2: Make The Quality Of The Product Or Service More Apparent

One of the chief concerns of every customer is the quality of your product or service. They want to know they’re making a wise purchase that will provide value over the long run. There are several simple ways to highlight the quality of what you offer.

• Highlight your service and support. By drawing attention to your outstanding customer service, you demonstrate that you’re committed to the customer beyond the sale. You genuinely want them to get value from your product and are willing to dedicate time and resources to help them. Companies like Zappos and Trader Joe’s have built hugely loyal customer bases due to their passionate commitment to outstanding customer service. For the customer, this acts as a safety net of sorts. They know that if something goes wrong, they can easily get the problem fixed.
• Highlight abilities to customize or personalize. If your product can be customized in any way, that should be highlighted to potential customers. This gives them the assurance that the product will be exactly what they want, and to their specifications. Additionally, customization typically indicates more individual attention given to creating each product, as opposed to cranking them off an assembly line.
• Highlight values that will appeal to your customers. Depending on your product, your customers will have certain things they value. For example, if you’re selling handcrafted leather bags, your customers will probably value craftsmanship. If you’re selling electronics, speed will be a key value. Supplement buyers value the purity and organic nature of their purchases. Do whatever you can to highlight those particular values on your site and in conversations with customers. This can be instrumental in overcoming objections.
• Create high-quality supplemental content. One of the cheapest ways to overcome objections is to create high-quality, high-value supplemental content on your site that will help your customers. For example, if you sell coffee beans, an in-depth guide on creating the perfect cup of coffee with a French Press will serve your potential customers and demonstrate your commitment to your product.

#3 – Build Relationships and Care For Your Audience

Perhaps most importantly, you want to demonstrate that you truly care for your audience. Potential customers want to know that you’re not going to take their money and disappear. If you can build relationships with your customers, you’ll retain them for the long run and increase your Customer Lifetime Value.

Some simple ways to build relationships and care for your audience are:

• Show testimonials. Testimonials from satisfied customers demonstrate both the reliability of your product or service and just how much you care about your customers. This goes a long way toward establishing trust with and overcoming the objections of potential customers. Why does Amazon show the overall customer product rating immediately under the product title? Because they know that customers trust the opinions of other customers. Adding testimonials and reviews to your site can go a long way toward overcoming objections.
• Show case studies. By putting successful case studies on your site, you demonstrate that: 1) You have a history of helping customers succeed and 2) You are committed to building outstanding relationships with your clients. Case studies also help minimize the risk potential customers might feel. It shows them that numerous other customers have used your product or service successfully.
• Create loyalty programs. There’s a reason loyalty programs have long been a staple of brick-and-mortar stores: they work. When you reward people for being loyal customers, they keep coming back, which then allows you to build a relationship with them. The more you nurture that relationship, the fewer objections they have and the more likely they are to buy from you.

Common Sales Objections For SaaS & Ecommerce Companies

How do these specifically manifest for SaaS and ecommerce companies? Let’s take a look.

SaaS Common Sales Objections

Cost Concerns:

• Objection: Some customers may be hesitant to subscribe to a service once they see the price and perceive that the cost is too much. A lack of budget is one of the most common types of objections.
• Resolution: Your pricing page needs to communicate the value of your SaaS product effectively to address any price objections. The pricing page should have clear, customer-friendly language, simple layouts, and be free from any misleading marketing tactics. Provide flexible pricing plans, free trials, or discounts for longer commitments.

Integration Challenges:

• Objection: Another common cause for concern when it comes to SaaS products is the complexity of integrating the solution into existing systems. Prospects don’t want to disrupt their processes by worrying about the compatibility of the product with their system.
• Resolution: Address their concerns by highlighting integration processes and providing customer testimonials showcasing successful integrations. You can even take it one step further by offering dedicated customer support or integration assistance, showing that they don’t have to worry about anything because you’ll be there to help them along. Sharing case studies of similar businesses that have successfully integrated your solution can also show them the service in action.

Data Security Concerns:

• Objection: It’s perfectly understandable for potential customers to be cautious about storing sensitive data in the cloud. Security breaches happen all the time, and they have no guarantee that their data will be safe.
• Resolution: Assure your customers of your company’s robust security measures. Provide compliance certifications if necessary and highlight encryption protocols. Offer data privacy guarantees and share information on how your SaaS solution keeps customer data safe.

Limited Customization:

• Objection: Customers want a solution or service that fits their needs. If your SaaS solution appears too rigid and not customizable, they may hesitate to use your service.
• Resolution: Make sure to showcase the flexibility of your platform by detailing customization options and the ability to tailor the solution to meet specific business needs. Providing examples of how other businesses have successfully personalized the platform can encourage them further.

Ecommerce Companies Customer Objections

Shipping Costs and Times:

• Objection: Common objections in sales are high shipping costs or extended delivery times. Customers don’t like surprises. When you add a high shipping cost on top of the product they arepurchasing, chances are they will abandon their cart. 
• Resolution: Time is money, and your customers want to know when their purchases will arrive on their doorstep. Get as specific as possible with your delivery dates. Many ecommerce stores have also discovered that incorporating free shipping as part of their strategic plan enables them to sell more goods and earn more profits.

Product Quality Concerns:

• Objection: Shoppers want to know they’re getting their money’s worth, but it can be difficult to determine the quality of a product through a website.
• Resolution: You can convey a sense of quality and craftsmanship by provisioning detailed product descriptions. Describe the materials used, the care that went into its construction, and any unique characteristics that set it apart. Supplement this with high-resolution images and videos that allow customers to closely examine details. Customer reviews and social proof are incredibly powerful – highlight those that specifically mention the product’s quality and durability.

Product Fit and Sizing Concerns:

• Objection: Customers hesitate to buy clothes (and other size-sensitive items) online because they can’t try them on first. Nobody wants the hassle of returning something that doesn’t fit.
• Resolution: Eliminate their uncertainty by providing detailed product descriptions, high-quality images, and customer reviews. User-generated photos showing the product on different body types provide valuable visual context. Make exchanges and returns easy to build confidence. Consider offering a satisfaction guarantee or warranty to reassure customers about the quality of your products.

By understanding and effectively addressing these objections, SaaS and e-commerce companies can build trust, improve customer satisfaction, and ultimately increase conversion rates.

See Things Through The Eyes Of Your Customers

Ultimately, overcoming objections is about seeing things through the eyes of your customers. It’s about understanding the reservations, hesitations, and questions they have, empathizing with those concerns, then seeking to overcome those objections. Overcoming the objections of your customers is key to improving your digital customer experience and increasing sales.

Remember, the best kind of customer relationship is based on trust. People who trust you are far more likely to buy from you. But as with any relationship, building trust takes time and action. By taking action to identify customer objections and then taking time to answer them, you put yourself in a position for success.

If you need help identifying and overcoming your customer objections, contact us.

The post How to Discover and Resolve Your Customer Objections appeared first on The Good.