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.

Here’s what they’re missing: the most successful product-led companies don’t just follow the playbook. They rewrite it based on what their actual users reveal through experimentation.

While everyone else copies best practices, companies that layer experimentation into their PLG strategy are discovering the specific insights that accelerate their growth. In a world where everyone has access to the same tactics, the ability to learn about your own users (and do it faster) becomes a moat.

Companies like Booking.com, Netflix, and Amazon didn’t achieve their dominance by following conventional wisdom. They made experimentation central to their success, running thousands of experiments annually to optimize their user experience. And you don’t need their resources to adopt their approach.

What is product-led growth?

Product-led growth is a strategy that emphasizes the product itself as the primary driver of customer acquisition, conversion, and retention.

Traditionally, companies have relied on sales and marketing tactics to create leads and drive customer adoption. Ads and websites had to do most of the selling, and the onus was on the potential user to read ads, navigate websites, choose between feature matrices, and, at times, go through a complicated sales process (on or off-site).

In a product-led growth model, companies remove as many obstacles as possible to acquiring free registered users. This approach often involves offering a free or freemium version of the product, allowing users to experience its value before committing to a paid subscription.

If the experience is good enough to keep them using it, and the paid features are valuable enough, then the hope is that users will ultimately convert into paying customers. In this way, the product serves as the main vehicle for customer acquisition and expansion.

Just like test driving a car, they let you test drive their product and discover the value on your own, before making a purchase decision.

Companies that successfully implement a product-led growth strategy often benefit from increased customer loyalty, higher conversion rates, lower customer acquisition costs, and sustainable long-term growth.

The shift from “launch and learn” to “test and learn”

Plenty of companies, between product-market fit and scale, run their growth strategies on a “launch and learn” philosophy. They build features based on hunches, ship them to users, then analyze the results afterward. This approach can work, but when operating on a product-led growth model, product decisions carry outsized impact. The product experience influences pretty much every KPI from acquisition to retention.

When you launch first and learn later, you’re essentially gambling with your users’ experience. Every poorly conceived feature, every friction point, every missed opportunity represents lost revenue and potentially churned customers. More importantly, it represents wasted development resources that could have been deployed more strategically.

This is where experimentation comes in. Instead of “launch and learn,” companies can shift to “test and learn.” This means experimentation and analysis of results happen pre-launch, not after. Changes are validated with real users before full implementation, minimizing risk and maximizing ROI.

Experimentation before implementation gives you an understanding of real customer behavior and clearly indicates how you can repeat results by uncovering the why behind those behaviors.

How experimentation amplifies PLG success

Experimentation is only helpful to a product-led growth strategy when it is done right. So what are some of the ways to implement that will amplify PLG success?

1. Systematic optimization across the customer journey

The most effective approach to PLG experimentation uses frameworks like ROPES (Registration, Onboarding, Product, Evangelize, Save) to systematically optimize each stage of the customer experience. Rather than randomly testing features, successful companies identify specific levers within each stage and experiment systematically.

For example:

• Registration phase: Testing form length, social proof elements, and value propositions
• Onboarding phase: Experimenting with tutorial formats, progress indicators, and time-to-value optimization
• Product phase: Testing feature discoverability, UI changes, and user flow improvements
• Evangelize phase: Optimizing sharing mechanisms, referral programs, and viral loops
• Save phase: Testing retention tactics, upgrade prompts, and churn prevention strategies

This systematic approach ensures that experimentation efforts are strategic rather than scattered, creating compounding improvements across the entire user journey.

2. Accelerated learning through parallel testing

Traditional A/B testing approaches test one hypothesis at a time, which can drastically slow your learning velocity. Advanced PLG companies run multiple experiments simultaneously across different parts of their product experience, dramatically increasing the rate at which they gather insights.

The key to successful parallel testing is ensuring experiments don’t interfere with each other. As Natalie Thomas, our Director of UX and Strategy, explains: “It’s important to look at behavior goals to assess why your metrics improved after a series of tests. So if you’re running too many similar tests at once, it will be difficult to pinpoint and assess exactly which test led to the positive result.”

Successful parallel testing requires:

• Creating testing roadmaps that cover independent product areas
• Building small, cross-functional teams assigned to each area
• Establishing clear metrics and success criteria for each test
• Implementing proper statistical controls to avoid interference

3. Rapid experimentation for faster innovation

Speed matters in PLG. Market opportunities disappear quickly, and user expectations evolve constantly.

So, one of the main objections to implementing an experimentation strategy is that testing cycles often take weeks or months to complete. But high-performing PLG companies have found ways to cut this time in half without losing statistical rigor. Key strategies include:

Supplementing A/B Tests with Rapid Testing: Not every hypothesis requires a full A/B test. Qualitative research, user interviews, and rapid prototyping can validate concepts quickly before investing in development.

Modular Testing Approaches: Instead of starting from scratch each time, successful teams create reusable components like design templates, testing frameworks, and analysis processes to reduce setup time.

AI-Powered Research: Using artificial intelligence as a research assistant to speed up data collection, user recruitment, and insight generation.

Prioritization Frameworks: Implementing systematic prioritization (like the ADVIS’R framework) to ensure high-impact experiments get fast-tracked through the process.

4. Data-driven feature development

Experimentation helps PLG companies avoid the biggest roadmap mistake: prioritizing low-impact features. Instead of building what seems logical, experimentation reveals what actually drives user behavior and business metrics.

This is particularly important as you scale beyond basic PLG practices. When you’re competing with other product-led companies, the quality of your feature decisions becomes a key differentiator. Companies that systematically test and validate features before full development consistently outperform those that rely on intuition.

The most successful approach combines quantitative testing with qualitative insights. This means not just measuring what users do, but understanding why they do it. This deeper understanding enables teams to build features that truly resonate with users rather than features that just check boxes.

5. Building an experimentation-first culture

An outcome of adding experimentation to a product-led growth strategy is that it will help build the practice into your company culture. To do that, you can follow a few key steps.

Start with infrastructure

Before you can effectively use experimentation to support PLG, you need the right infrastructure. This includes:

• Testing platforms that can handle both simple A/B tests and complex multivariate experiments
• Analytics systems that provide real-time insights into user behavior
• Data pipelines that connect user actions to business outcomes
• Collaboration tools that enable cross-functional teams to work together effectively

Establish clear processes

Successful experimentation requires discipline. Teams need clear processes for:

• Hypothesis formation and validation
• Test design and statistical planning
• Resource allocation and project management
• Results analysis and decision-making
• Knowledge sharing and organizational learning

Foster cross-functional collaboration

The most impactful experiments often come from unexpected sources. Engineers closest to the code understand technical constraints and opportunities. Designers see user experience friction points. Customer success teams hear directly from users about pain points.

Creating space for these diverse perspectives to contribute to experimentation efforts often leads to breakthrough insights that no single team would discover independently.

The compound effect of systematic experimentation

What makes experimentation so powerful for PLG companies is its compound effect. Each successful experiment doesn’t just improve one metric. It teaches you something about your users that informs future experiments.

Over time, this creates an accelerating cycle of improvement. Companies that have been systematically experimenting for years possess a deep, nuanced understanding of their users that newcomers can’t easily replicate. This understanding becomes a sustainable competitive advantage.

Moreover, experimentation capabilities themselves improve with practice. Teams get faster at designing tests, more sophisticated in their analysis, and better at translating insights into action. The infrastructure and culture that support experimentation become organizational assets that compound over time.

Experimentation as your PLG multiplier

Product-led growth without experimentation is like driving with your eyes closed. You might reach your destination, but probably not efficiently, and certainly not safely. Experimentation transforms PLG from a collection of best practices into a systematic approach to user-centered product development.

The companies that win in today’s competitive SaaS landscape aren’t just those with the best products; they’re those that can consistently improve their products based on real user insights. They’ve made experimentation not just a tactic, but a core organizational capability.

Ready to transform your PLG strategy with systematic experimentation? The Good specializes in helping product-led companies build experimentation capabilities that drive sustainable growth.

Our Digital Experience Optimization Program combines strategic frameworks like ROPES with hands-on experimentation support to help you uncover the specific insights your business needs to scale. Let’s explore how experimentation can accelerate your growth →

The post How Does Experimentation Support Product-Led Growth? appeared first on The Good.

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

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

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

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

Organic growth spurs feature parity

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

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

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

The four strategic surface types every product leader should know

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

1. Replica surfaces

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

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

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

2. Utility surfaces

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

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

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

3. Accessory/companion surfaces

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

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

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

4. Growth lever surfaces

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

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

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

What it looks like to intentionally limit feature parity

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

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

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

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

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

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

1. Let platform economics shape your strategy

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

Mobile considerations:

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

Web/desktop considerations:

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

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

2. Build where your users engage

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

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

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

3. Design for authentication, not attribution

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

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

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

4. Match your tools to your strategy

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

Web-Focused Tools:

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

App-Optimized Tools:

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

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

5. Define success differently for each surface

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

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

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

6. Start with purpose, not capability

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

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

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

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

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

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

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

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

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

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

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

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

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

A hot topic in the 2010s, it argues the merits of shipping fast against the merits of validating pre-launch. Over time, it’s been argued under different names like “test everything” or “founder-led growth.” But it all boils down to the same question: Is it better to validate before or after launching?

Every so often, it’s worth revisiting these hotly debated topics to ground ourselves (and our products) in strategic decision-making.

We have much more data, knowledge, and tech than when the debate started. So, let’s take a look at where the “test and learn” vs “launch and learn” stands and how to make your own decision on which approach is best.

Defining “test and learn” and “launch and learn”

First, full transparency. We’re big advocates for a “test and learn” culture.

As one of the first players in conversion rate optimization, The Good coined many strategies that support experimentation-led growth. We wholeheartedly believe that all ideas are hypotheses to be tested.

However, we also understand that everything has nuance, and there is no one-size-fits-all approach to product optimization. Each method has merits, depending on the business context.

Here’s how “test and learn” and “launch and learn” stack up in an apples-to-apples comparison.

Test and learn

Definition: Test and Learn is an iterative approach focused on experimentation, where hypotheses are tested through small-scale trials before broader implementation. It emphasizes data collection and analysis to inform decisions.

Methodology:

• Establish clear hypotheses before testing.
• Conducts controlled experiments (e.g., A/B testing) to evaluate specific variables.
• Utilizes metrics to assess performance against predefined KPIs.

Objectives:

• Minimize risk by validating ideas before full-scale rollout.
• Foster innovation through iterative learning, allowing teams to pivot based on results.
• Provide insights into customer behavior and preferences.
• Refine products or marketing strategies based on empirical data.
• Encourage a culture of continuous improvement and adaptation based on feedback.

Challenges:

• Time-consuming nature of extensive testing cycles.
• Potential for analysis paralysis if not managed properly.

Launch and learn

Definition: Launch and Learn focuses on quickly deploying products or features to the market, with the understanding that adjustments will be made based on real-world user feedback post-launch.

Methodology:

• Rapidly launch new offerings.
• Implement a feedback loop to continuously gather user input post-launch.
• Leverage insights to inform subsequent iterations.

Objectives:

• Accelerate time-to-market for new products or features.
• Gather immediate insights from actual users.
• Get faster identification of market needs based on user experience.
• Flexibility to pivot based on immediate user reactions.

Challenges

• Difficulty in managing customer expectations post-launch.
• Risks potential negative user experiences.

4 considerations when picking the right approach for your product

So, which one is better?

As with most things in optimization (and the world), the answer is “it depends.”

While there is no blanket approach to experimentation, there are some important considerations that can help guide your decision.

1. Stage of growth

Whether you choose a “test and learn” or “launch and learn” approach often depends on your company size and resource availability. Let your stage of growth be the primary guide for which experimentation approach you use.

For companies looking to find product-market fit, a “launch and learn” approach is often executed to get fast, real-time feedback. But to take your business from product-market fit to scale, it’s crucial to move past product-led growth best practices and take a “test and learn” approach.

When you are just starting to implement PLG practices, you may rely on hunches or best guesses. But as you grow, experimentation should happen pre-launch.

2. Risk level

Another consideration when picking an experimentation strategy is the level of risk associated with the changes or launch.

For example, if you’re working on a feature or journey that impacts the core user experience, you should always “test and learn” prior to launch. It would be a pretty big risk to “launch and learn” something broken in the core product experience and suddenly see your churn rate skyrocket.

However, if you’re launching a fix for a feature that is already broken, finding a quick, usable solution is more important than adhering to a strict “test and learn” approach.

3. Confidence level

Product management leader Marc Abraham advocates for a confidence check before launch to understand how much or little testing is needed.

He outlines the confidence levels as:

• “High Confidence: Our confidence in the upcoming release is high because we tested it thoroughly internally, have launched a similar product or feature before, or if there’s an issue the fallout will be small.
• Low Confidence: Our confidence in the upcoming release is low because we haven’t fully tested it, it’s based on new technology, or creates a totally new user experience.”

These are great guidelines for getting started. And if you are still unsure, you can perform what Emma Leyden calls a “gut check.”

“Your ‘gut check’ can be done in low-effort ways. It won’t give you the most confident answer, but something as simple as showing a design to friends and family before you launch can teach you a lot.”

While product intuition is important, remember we all have our biases. Sometimes, it’s hard to see our products from different perspectives, which is why testing or validating your ideas prior to launch is essential.

4. Product nature

Build it, and they will come!

That’s the motto of many “launch and learn” advocates, and rightfully so. If there is no product built in the first place, there is nothing to learn about.

But that’s only true if what you’re launching is simple and functional.

The complexity (or simplicity) of the product/feature can be a major consideration when deciding on your experimentation approach. Complex, high-investment products should use “test and learn” to validate the user experience and also support your investments pre-launch.

Whatever you choose, make sure you learn

While we’re champions of “test and learn,” we know that time-crunched growth leaders don’t always have that luxury. The most important takeaway is to never launch and leave.

Regardless of the approach, the goal should always be to learn. Collect and analyze both quantitative and qualitative data and use those insights to iterate.

Abraham says, “I view releasing something without learning from it as a cardinal sin. It’s very important to continuously learn from real users and actual usage (or not) about your key hypotheses.”

Experiment-led growth

If you’re ready to move from product-market fit to scale and would like to improve your experiment-led growth practices, The Good can help.

We build a culture of experimentation within SaaS companies and spur growth through better UX across the product lifecycle.

Our methodologies discover untapped opportunities and improve KPIs, including registration, activation, engagement, monetization, expansion, and win-back.

The post Is “Test and Learn” or “Launch and Learn” Better?  appeared first on The Good.

But, as more companies adopt the methodology for their tools, PLG strategies become table stakes rather than competitive differentiators. The best practices that might have helped you stand out a few years ago are now run-of-the-mill.

So, how do you make sure your product-led growth efforts stand out, improve the user experience, and go beyond the typical best practices you see day-to-day?

What are the product-led growth best practices?

Freemium and free-trial pricing models have spurred the movement toward product-led growth.

Because the purchase happens later in the customer lifecycle, the user evaluation period is longer and more thorough. Users can evaluate the product for what it offers, how it solves problems, and its ease of use. Instead of relying on marketing messages and sales calls to make a purchase decision, the user evaluates and engages with the product before converting.

This simplifies the recipe for success. If a SaaS tool provides more value than it costs, the user will convert.

There is a lot of literature out there on foundational strategies for PLG. Generally, the industry’s product-led growth best practices include specific tactics related to the following:

• Develop a product-first company culture so that the whole organization is focused on delivering the best product experience.
• Emphasize free trial or free accounts in marketing and sales to increase registrations.
• Minimize friction during sign-up with a clear, personalized, and engaging onboarding experience.
• Make it clear to freemium or free trial users what they are missing out on and what they will get by converting to a paid account.
• Prioritize account expansion over net new users with plan upgrades and customer marketing strategies.
• Gather customer feedback, review user behavior, and conduct testing to measure and improve the product experience.

While all of these are true and valuable, they will only get you so far, and it’s hard to know how to actually make them happen.

Best practices are for beginners

One of our favorite mottos at The Good is that “best practices are for beginners.” Yes, it is important to stick to foundational truths in SaaS optimization work: stay user-centered, establish consistent research practices, iterate your way to success, etc. But, to scale your SaaS organization, you need to go further.

There are many reasons for this, including:

• Best practices are tethered to the past, but your tool is not
• What works for your competition won’t necessarily work for you
• Sticking religiously to best practices holds you back from making data-backed improvements
• Prescribing solutions without diagnosing challenges sets you up for failure

Best practices can be a good starting point for companies looking to dip their toes into product-led growth or optimization, but they’re like training wheels. Once you’ve mastered them, they quickly cap how much you can scale. True growth demands a more tailored approach.

In his book, Opting In To Optimization, Jon MacDonald notes, “Above-average businesses—the ones converting their target customers in droves—are learning in real-time from every click and movement of their current users.”

As established, the ultimate goal of product-led growth is to leverage the product itself to improve acquisition, conversion, and retention metrics. So, how do you actually make that happen once you already have the foundational elements in place?

How to leverage PLG to go from product-market fit to scale

For companies that have outgrown best practices and are ready to scale, here are a few ways to take your product-led growth strategies to the next level.

1. Review the ROPES framework and identify levers you haven’t pulled

First, start by looking at the big picture with the ROPES framework.

The ROPES framework was designed by our team to help product-first SaaS leaders think about, optimize, and improve the end-to-end customer experience. It goes deeper than simple best practices and keeps the product at the center of everything you do by covering the user journey from registration to cancellation. Ultimately, it helps product-led companies:

• Define key stages in the customer journey
• Identify important metrics to measure each stage
• Understand the elements, forces, and factors that help or hinder engagement

Looking at your organization through the lens of the ROPES framework provides context to what makes a great product experience, what levers you can pull at each stage of the customer journey to improve acquisition, conversion, and retention metrics, as well as who on a SaaS product team should be leading the phase.

Once you have the foundational elements of product-led growth in place and are hitting a plateau, review the ROPES framework and identify which areas you aren’t fully leveraging to encourage engagement.

2. Optimize your team

Next, take a look at your team structure and ensure that the right people are leading the right stages of optimization. For example, the registration phase should be driven by the marketing team in collaboration with UX designers, while the product stage should be owned by the product team.

But don’t let this hold you back from letting cross-team collaboration happen. It’s important as a product leader to:

• Bridge the gaps and translate messages across teams
• Stay open-minded and ready for the unexpected
• Bring in people who might not always be part of the ideation phase but can offer a lot of valuable input

That’s because creativity doesn’t just come from the top.

Emma Leyden, product leader from IDEO, Title Nine, and more, says, “I have a deep belief that everyone is creative. I think that engineers are some of the most creative people in any organization. When I say that, CEOs look at me shocked, but engineers are closest to the work and want to ship products that will actually be used, so they have a good idea of what should be built.”

Leveraging your entire team to bring creative new approaches to PLG and allowing the right teams to drive their stages of the product forward allows for the proper balance of collaboration and ownership.

3. Deepen your feature moat

If you’ve reached a plateau in your PLG strategies, it might be time to dig into your feature moat.

A feature moat is when a product offers such unique and superior product features that the competition can’t quickly replicate them. There’s literally a gap—a moat—that your competitors will be scrambling to cross.

Think of it like this: If your product is a great solution, it will change the lives and work of your users. Their needs and preferences change. They develop new problems that you’re positioned to solve. Each solved problem represents a widening moat between you and your competitors.

How do you create this advantage? By continuing to drill deep into user needs and pain points even after you’ve achieved product-market fit.

Don’t rest, satisfied that you’ve learned enough about your users. Continue to leverage generative and evaluative research to uncover new insights into their behavior and needs. Ultimately, this is key to developing a customer experience that evolves with the user.

4. Transition from “launch and learn” to “test and learn”

When you are just starting to implement PLG practices, you may rely on hunches or best guesses. But as you grow, experimentation should happen pre-launch.

You are transitioning from “launch and learn” to “test and learn.”

Even in scenarios where you need to launch quickly, you should at least perform what Emma Leyden calls a “gut check.”

“Your ‘gut check’ can be done in low-effort ways. It won’t give you the most confident answer, but something as simple as showing a design to friends and family before you launch can teach you a lot.”

As a good rule of thumb, Emma encourages having some kind of user research scheduled every week, even if it’s as simple as letting someone see or use the prototype of a product and voicing their thoughts aloud.

While product intuition is important, it’s important to keep in mind we all have our biases. Sometimes, it’s hard to see our products from different perspectives, which is why testing or validating your ideas is essential.

5. Ditch generic benchmarks

Benchmarks are like best practices. They are a great starting point for companies looking to set goals, but for most SaaS companies, they are practically meaningless. We discuss the problems with benchmarking in our article “Why Industry Benchmarks are Bullshit,” but it comes down to this:

• Competitor data can be unreliable, inaccurate, or simply made up.
• Even niched-down industry data still contains too much noise.
• Your products, market conditions, pricing strategy, channel mix, and/or customer groups are just too different to control against even a true competitor.
• Goal-setting, testing, and learning are better alternatives to industry benchmarks.

Essentially, benchmarks are too simplistic to be useful, especially if you’re looking at only one metric, like conversion rate. And even if you match your competitor’s metric, it’s not like you’re going to stop optimizing your experience. You always want that number to improve.

So what’s the alternative? Instead of benchmarking against competitors, we recommend the recipe that works for top companies: setting strong data foundations, checking your assumptions about your audience and their behavior, and building a research practice.

Ditch generic benchmarks. Measure yourself against top optimization teams and identify high-impact areas for improvement instead.

6. Circulate your research across the organization

SaaS leaders with PLG foundations can improve how and when they share customer research to move from product-market fit to scale. Even if you have already established an ongoing research practice, to take this to the next level, improve how customer data and insights are circulated across teams.

When you skip this step, the disconnect between great research and doing something about the insights holds you back from building a user-centered culture and slows innovation.

To fully capitalize on customer insights:

• Give your research a home: Organize data into digestible, prioritized recommendations for teams across your company rather than overwhelming them with raw data.
• Identify patterns and form insights: Regularly circulate customer research to key stakeholders through internal newsletters, reports, or collaborative tools to align, identify areas for improvement, and uncover insights.
• Generate potential improvement ideas to address insights: Use shared insights as a foundation for brainstorming and decision-making across marketing, product, sales, and support teams.

By creating clear channels for sharing and proactively acting on research, SaaS leaders drive growth beyond what PLG best practices alone can achieve.

7. Try new free-to-paid conversion strategies

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

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

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

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

Here are some strategies to consider for improving free-to-paid conversions:

• Remind users to upgrade early and often
• Drive users to value quickly
• Present gated features near free features
• Make your calls to action clear and consistent
• Be thoughtful about which features are gated
• Make free users aware of their trial time
• Offer a great onboarding experience
• Use paywalls to demonstrate paid features
• Clearly label your paid features

Remember, these are just ideas. Tailor them to your audience based on the issues you’ve identified and your proprietary user research.

8. Extend your capabilities with external support

Whether exploring new features, testing improvements, or mitigating risk, effective product-led growth teams use research at every stage of the product lifecycle.

Yet, research departments are often under-resourced, with typical staffing ratios at one researcher for every 50 developers. This imbalance leads to long research roadmaps that struggle to address the immediate needs of product teams.

In response, SaaS teams rely on external support to supplement their efforts and move beyond best practices to real, sustainable growth. One-off research projects can help, but sophisticated organizations find the most effective partners to work with them long-term.

Heidi Dean, Principal Product-Led Growth Manager at Adobe, says, “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.”

Integrating a specialized firm like The Good to come in and work on projects without much uptime can exponentially increase the user insights you receive and, in turn, the impact you can have on your organization.

Don’t get stuck at best practices

When you don’t push yourself past the comfortable, known best practices, you hold yourself back from scaling your SaaS tool.

If you recognize that you have reached that plateau, hopefully, this article has provided some inspiration for the next steps and areas in which you can focus your energy.

It can be tough to read the label from inside the jar, and if you want to get a fresh perspective to help you scale, reach out to our team. We bring years of experience optimizing SaaS user experiences and providing expert consulting for SaaS product teams.

After a short call to ensure a good mutual fit, we’ll get started supporting your product-led growth efforts with research, strategy, and experimentation.

The post Product-led Growth Best Practices Will Only Get You So Far appeared first on The Good.

Basically, the client was concerned that they would build something suboptimal that we would just change later. Why not build it properly the first time, right?

Unfortunately, it’s not that simple. No optimizer can look at an unfinished site and definitively say, “No, that isn’t right; do it this way.”

That’s because your optimization program should operate differently at different times. How you approach optimization depends on where you are in the maturity of your organization.

This means some activities won’t feel like optimization (such as building a data foundation) but are actually critical to the process.

That begs the question: What does optimization look like at each stage? How do you know what to focus on?

New Thinking: The Competencies of Successful Digital Brands

Before we start plotting optimization activities along a timeline, let’s first understand what optimization actually looks like. Many people think optimization is as simple as testing button colors and stock photos, but it’s quite deeper than that.

After studying hundreds of optimization experts at some of the biggest companies, we identified five competencies that influence the success of digital products. We call it the 5-Factors Scorecard. Here are those competencies:

• Data Foundations: Goals, ownership, and good data form the backbone.
• User-Centered Approach: A comprehensive roadmap and a high-context approach.
• Resourcing: Resources support adequate capabilities and pace.
• Toolkit: A variety of tools for planning, measurement, and protocols.
• Impact & Buy-In: Tools and practices increase relevance and perceived efficacy.

Teams that excel in these 5 Factors are 60% more likely to meet their annual performance targets and twice as likely to rank “excellent” in a Net Promoter Score (NPS).

Essentially, if you have these five competencies, you stand a good chance of meeting your optimization goals.

Want to know if your organization exhibits these 5 Factors? Take the 7-minute quiz to get your 5-Factors Scorecard and learn where to invest to improve your digital experience.

What to Focus on During Your Optimization Program

Now you’re probably wondering when you should invest in those competencies or when the best time would be to build these competencies. Well, it depends on your position in the optimization program.

If you’re just starting optimization for the first time, your activities will be radically different than if you were six years into the process.

Let’s look at what optimization means at different points in your journey.

Early in Your Optimization Process: Build Data Foundations & Adopt A User-Centered Approach

As you first start to take optimization seriously, you should be focused on problem identification. Your goal is to uncover where users find friction in their experience with your site or app.

Identifying and eliminating obvious moments of friction is one of the easiest ways to improve the user experience and boost conversions. It’s also the quickest.

We address this in two ways:

1. Building a solid data foundation. You need advanced analytics that come from an accurate source of truth you can trust. Your data should be hygienic, meaning it’s tracked properly and accessible at will to whoever needs it.
2. Adopting a user-centered approach. It’s important to place the needs, preferences, and abilities of users at the forefront of the optimization processes. Work to understand their perspective, behaviors, and goals so you can design a product that’s intuitive and enjoyable to use.

In fact, you can start thinking about data collection and the user-friendliness of your digital property before you start designing.

As you consider features for your site or app, ask yourself how you’ll collect and report the data. Then be sure to configure your features to support a good user experience (unless that makes things harder for your internal operations).

Midway Through the Optimization Process: Expand Your Toolkit

Once the foundation of your optimization program is ready, it’s time to expand your toolkit. This requires three critical elements: Prioritization, research, and experimentation.

Prioritization

First, you’ll need a prioritization framework. There are a lot of options here, but most optimizers use frameworks like RICE, ICE, or PIE. There is no best prioritization framework. The right framework for you depends on your culture.

Research

Second, you’ll need to start conducting a lot of research. You’ll need to perform generative research (to understand your audience) and evaluative research (to understand the effectiveness of your proposed optimizations).

Experimentation

Finally, you’ll need two kinds of experimentation.

On-site experiments take the form of A/B tests. This kind of testing is powerful but complex to execute and requires a significant volume of traffic and conversions. It’s also not great at comparing radically different changes.

Off-site testing methods are important as well, such as card sorting, preference testing, tree testing, and first-click testing. These tools are efficient and often more appropriate than on-site testing. For instance, suppose you want to drastically change your menu. It usually makes more sense to run a tree test offline than to try to A/B test it.

Later in the Optimization Process: Measure Impact & Improve Buy-In

As you conduct experiments and start to get results, you’ll need to measure impact and use your optimizations as evidence to get ongoing leadership buy-in. Teams with strong buy-in from their leadership tend to foster a culture of optimization, get better budgets, and have the best results.

This may not seem like optimization work, but without active participation from your leadership and an organization-wide culture of optimization, there’s no optimization program at all, so it needs to be taken seriously.

Optimization at Specific Moments

Now that you see how your optimization work changes depending on the maturity of your optimization process, let’s look at some specific moments in your organization’s journey. This will help you understand how those different competencies apply to different stages.

Designing or Launching a Digital Property

During the launch phase and immediately after launch, leverage optimization to validate or invalidate your ideas before they go into development. This saves time and money and helps set you up for greater success when you launch.

At this time, it’s smart to conduct interviews with potential users. Ask them about their needs and preferences. If possible, show them options for elements of your site to gauge their future usage.

Rebranding or Redesigning

During a rebrand or redesign, you’ll want to conduct user research and testing. Have real people use your site to discover its flaws and give their feedback. You might also find it helpful to have them use both versions of your site (the old and the redesign) to tell you which they prefer.

It’s also a good time to study your competitors. What features and systems work well for their digital properties? Their success doesn’t guarantee success for you (even if your audiences overlap perfectly), but it can be a good starting point.

It’s also the right moment to ensure your data foundation and toolkit are up to par. Do you have the right resources in place to run a thorough optimization program on the new design?

At Specific Times or Seasons

Depending on your industry or niche, there may be certain times of the year that make sense to dig deep into optimization.

In ecommerce, for example, brands tend to juggle their optimization program around the holiday season. Some brands prefer to take their foot off the optimization pedal during Q4 in order to focus on holiday campaigns and then pick things up again once the madness wanes.

In our opinion, however, Q4 is a great time to optimize for ecommerce. You have more sessions and data than normal, your site is full of high-intent visitors, and you get a broader group of shoppers (not just your usual audience).

Non-ecommerce brands often have similar “seasons” as well. A newspaper gets more visitors during an election cycle. A wedding planning app sees more sales in the summer. A SaaS brand gets more signups after a big industry convention.

Furthermore, you may have to consider internal politics. If your department gets its funding in July, that may be the right time to kick off a yearly optimization plan. If your company is in the middle of a hiring boom, it might be best to wait until you know who’s on your team before getting ambitious with optimization.

When to Bring in an Optimization Team

As you can see, true optimization is a foundational process that starts early and digs into several areas of your organization. So when is the right time to bring in an external optimization team?

Let’s return to those five competencies. If you have them, there’s a good chance your optimization work will move the needle for your brand. But you probably don’t have all of them…

It’s smart to bring in a digital experience optimization team for the competencies you lack. For instance, you may have the skills to build a data foundation but aren’t sure how to prioritize experiments. In this case, you would set up your data collection process on your own and then bring someone in to develop a prioritization approach that you carry forward.

Can you hire an optimization team to provide all five competencies? Yes, and it’s best to bring in a team to provide the tools you need to carry your optimization program forward.

Custom Optimization for Your Company

Every digital experience is different, and so every optimization program will be different. How you approach optimization depends on where your company is in its lifecycle.

If you want to maximize user engagement and revenue, you must consider the entire digital experience—from the way you collect data to how you prioritize and run experiments to the very culture of your organization. We call this Digital Experience Optimization (DXO).

DXO brings the pieces you need to complete an optimization puzzle and build a better digital product. Our team can amplify your impact with the tools, techniques, and expertise that you just can’t find in a single hire. Together, we’ll build a strategy and tactical roadmap that will set you on the path toward a well-optimized digital experience that delights users and serves your goals.

Learn more about our Digital Experience Optimization Program.

The post What to Focus on at Each Stage of Your Optimization Practice appeared first on The Good.

There are over 50 factors that influence conversion rates across 8 categories. Some are within our control, and some are outside. On-site factors are only half of the equation.

This means you can’t look at another company’s conversion rate to measure your own success. The effort is futile.

Does that mean comparisons are bad? Of course not. Instead, you need to look at your own specific users and then compare yourself on multiple metrics (in a more nuanced fashion) against a recipe for high performance.

So, what’s that recipe? We figured it out by asking hundreds of the highest-performing digital optimizers.

In this article, we’re going to talk briefly about why benchmarking doesn’t work. Then we’ll introduce you to a better way to measure the efficacy of your optimization efforts: a 5-Factors Scorecard.

Why Traditional Benchmarking Is Ineffective

Industry benchmarking is the obvious starting point for anyone who wants to take optimization seriously. You don’t know what your goals should be, so you look to your competitors. It’s like looking at the crowd when you aren’t sure how to behave. We want to anchor ourselves to something.

Unfortunately, comparing your conversion rate to other companies, or even to your industry as a whole, is practically meaningless.

We discuss the problems with benchmarking in our article “Why Industry Benchmarks are Bullshit,” but it comes down to this:

• Competitor data can be unreliable, inaccurate, or simply made up.
• Even niched-down industry data still contains too much noise.
• Your products, market conditions, pricing strategy, channel mix, and/or customer groups are just too different to control against even a true competitor.
• Goal-setting, testing, and learning are better alternatives to industry benchmarks.

Essentially, benchmarks are too simplistic to be useful, especially if you’re looking at only one metric like conversion rate. And even if you match your competitor’s metric, it’s not like you’re going to stop optimizing. You always want that number to improve.

So what’s the alternative? Instead of benchmarking against competitors, we recommend the recipe that works for top companies: setting strong data foundations, checking your assumptions about your audience and their behavior, and building a research practice.

A Better Way to Measure Success

Instead of sticking to traditional website benchmarking, let’s explore a better way to score your performance. We call it the 5-Factors Scorecard.

We conducted a study of hundreds of optimization experts to uncover the key factors that influence the success of digital products.

Based on their responses, we identified five competencies that set high-performance teams apart. Teams that excel in these 5 Factors are 60% more likely to meet their annual performance targets and twice as likely to rank “excellent” in customer satisfaction.

• Data Foundations: Goals, ownership, and good data form the backbone.
• User-Centered Approach: A comprehensive roadmap and a high-context approach.
• Resourcing: Resources support adequate capabilities and pace.
• Toolkit: A variety of tools for planning, measurement, and protocols.
• Impact & Buy-In: Tools and practices increase relevance and perceived efficacy.

So, armed with your 5-Factors Scorecard, you’ll know where to invest to improve your digital experience and what steps you need to take to grow your impact in your organization. Take the quiz to get your 5-Factors Scorecard and keep reading to find out more about the 5 Factors.

The 5 Factors of Digital Success

The 5 Factors emerged from our study of hundreds of digital leaders on what drives the success of their optimization teams.

Here’s what our respondents look like:

• Respondents hail from over a dozen countries in ecommerce, digital media, SaaS, and B2B services, though most came from the US.
• 52% are consultants and 48% are client-side optimizers.
• 52% of respondents are seated primarily in a marketing role, but we also spoke with strategists, data scientists, designers, engineers, editors, and more.

After analyzing their responses, we identified a clear narrative about what makes optimization teams successful. We’ve distilled these learnings into the 5 Factors.

The highest-performing digital leaders share these five common factors:

1. Data Foundations

No optimization team is complete without access to a solid layer of data. Anything from basic pageview and page path data to time-on-site, bounce rates, top-selling products, geographic or demographic information, and more advanced business analytics.

What makes for good data?

• It comes from an accurate source of truth you can trust.
• It’s hygienic, meaning it’s being tracked properly.
• It’s accessible at will to whoever needs it.

Many organizations are operating in less-than-ideal conditions when it comes to data. Building a healthy data flow you can trust should be your first step to building a strong optimization program.

Key Takeaway: A solid data foundation allows you to align your strategies with actual user behaviors and needs. Ensure data accuracy and accessibility to make informed decisions based on reliable insights.

2. User-Centered Approach

The most successful digital leaders adopt a user-centered approach that places the needs, preferences, and abilities of users at the forefront of the optimization processes.

This approach involves understanding the users’ perspectives, behaviors, and goals to create products that are intuitive, usable, and enjoyable.

Our respondents typically have:

• A data-backed understanding of their visitors’ demographics (age, gender, location)
• A data-backed definition of users’ entry context (channels, device types, landing pages, etc.)
• A comprehensive roadmap that is based on user challenges (rather than executives’ opinions)

Key Takeaway: Understanding user behaviors and goals helps organizations design experiences that increase engagement and conversions. Prioritize users to create products and services that are intuitive and enjoyable to use.

3. Resourcing

Optimization is often used as a shorthand for on-site experimentation. In reality, it’s a much bigger tent. Successful optimization programs require multiple people with diverse expertise. Moving through the phases of the optimization process requires research, data analysis, design, and engineering, among other disciplines.

Marketing is the most plentiful expertise on optimization teams. Over 50% of our respondents work in marketing-related positions.

Still, most teams get by with just five or fewer hours of support per week from other disciplines. Engineering plays the smallest role: some organizations have no engineering support at all. Generally, this is due to a lack of buy-in from higher-ups.

That lack of optimization resourcing comes with its setbacks. Teams with high marks for annual performance or high NPS had at least 5 hours or more, on average, of each necessary discipline.

Key Takeaway: Assemble or outsource a team with varied skill sets to address different aspects of the optimization process effectively. Adequate support lets you move through optimization phases smoothly, leading to faster iterations and better outcomes.

4. Building a Toolkit

The most successful digital leaders have a variety of tools for planning, measurement, and protocols. Having a proven toolkit is crucial as it keeps you moving relentlessly forward toward your goals.

Your toolkit requires three critical tools: Prioritization, research, and experimentation.

Prioritization

Most successful optimizers agree that their goals are clear and they understand their organization’s prioritization process. 73% have a clear goal and 57% say they use a standardized process when project planning. (To be fair, some optimizers admit to struggling with prioritization in cases where those decisions are made at a higher level.)

Optimizers use a patchwork of prioritization processes, but they usually start with a prioritization framework, such as RICE, ICE, or PIE. The right framework for your organization depends on your culture. Impact, investment cost, customer satisfaction, and speed are the top prioritization factors.

Other unique prioritization factors, such as political effort or legal compliance, can be useful as well, depending on your needs.

One thing is true for sure: “One-size-fits-one.” There is no best prioritization framework. The key is finding the one that works for you, aligning your entire team around it, and following it relentlessly.

Research

The best optimizers conduct a lot of research. This falls into two categories: 1) Generative research helps you understand your audience. 2) Evaluative research helps you understand if your solutions work.

Over 70% of the respondents conduct generative research methods at least a few times per year. This includes techniques like data analysis, customer interviews, surveys, and heatmap analysis. 60% of respondents conduct evaluative methods like task completion analysis and sentiment testing.

Experimentation

On-site experimentation is the favored validation method for the best optimizers. The use of A/B testing in production has increased. This is a powerful method, but it comes with challenges: it’s complex to execute, requires a significant volume of traffic and conversions, and can easily be done incorrectly.

Leaders can expand their toolkit with other off-site validation methods, such as card sorting, preference testing, tree testing, and first-click testing. These tools are often faster, less expensive, and more appropriate than on-site experimentation methods.

Key Takeaway: Prioritization frameworks keep you focused on high-impact initiatives. Make use of evaluative research and off-site experimentation techniques to validate your changes.

5. Impact and Buy-In

The optimization teams that create the biggest impact tend to have strong buy-in from their leadership. Leadership buy-in delivers sufficient budget and people resources and creates a culture that values experimentation and incremental change.

Organizations with leaders that don’t bring an optimization mindset see reduced impact. Even though most of the respondents to our survey conduct in-house experimentation, only about half say their leaders cite experiments, consumer research, and user experience research as influencing their decisions.

Key Takeaway: Strong leadership buy-in creates a culture that values optimization and ensures sufficient resources are allocated to optimization efforts. Buy-in is a key ingredient to driving meaningful impact.

How To Know Where You Stand: The 5-Factors Scorecard

You don’t need to score perfectly in all 5 Factors to get an edge. Even the top performers still had room for improvement.

The first step is just understanding where you stand today.

This scorecard automatically measures you against the highest-performing teams to expose what stands between you and digital excellence. To evaluate your 5 Factors and how you stack up against top performers, take this short assessment.

Armed with your 5-Factors Scorecard, you’ll know where to invest to improve your digital experience and what steps you need to take to grow your impact in your organization.

It’s far more informative than a traditional benchmark. Take the quiz to get your 5-Factors Scorecard.

The 5 Factors vs. Benchmarking

What makes the 5-Factors model more informative than benchmarking? Let’s look at the advantages.

Focus of Assessment

The 5-Factors model focuses on identifying key factors that contribute to success in digital optimization programs. It emphasizes elements that the most successful optimization teams have in common.

Benchmarking, however, only compares your organization’s performance against industry standards or competitors. It looks at the output of success, not what can be done to create that success for yourself.

Level of Detail

The 5 Factors provide a detailed framework for assessing performance across multiple dimensions. It offers specific areas for improvement and guidance on enhancing digital optimization efforts.

While benchmarking provides valuable insights into overall performance compared to industry standards, it doesn’t offer granular guidance on specific aspects of your optimization program or how to improve them.

Customization

The 5 Factors allow you to tailor your optimization strategies based on your unique goals, challenges, and resources. It offers flexibility in prioritizing areas for improvement and adapting approaches to suit your needs.

Benchmarking, on the other hand, provides standardized comparisons against industry norms or competitors, which may not always capture the nuances of those organizations’ strategies or objectives. There just isn’t enough context.

Continuous Improvement

The 5-Factors model emphasizes continuous improvement by focusing on iterative processes, ongoing learning, and adaptation to changing circumstances. It encourages organizations to evolve their optimization strategies over time to stay competitive and meet evolving user needs.

While benchmarking provides a snapshot of performance at a specific point in time, it may not inherently promote continuous improvement unless organizations actively use benchmarking data to drive change and innovation.

Get Your Own 5-Factors Scorecard

Curious about where you rank among the 5 Factors?

Get your own 5-Factors Scorecard by taking a quick assessment. Your scorecard gives you direction to make positive changes in your optimization program by highlighting the areas that need improvement.

The onus is then on you to implement those changes. If you need support, just get in touch.

The post An Alternative To Website Benchmarking: The 5 Factors Of Digital Success 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.

And there is nothing more frustrating to a business than losing out on a sale because their website was optimized for one audience and, in turn, alienated a whole second subset of shoppers.

We see this a lot for ecommerce brands that have a direct-to-consumer and wholesale operation. They are selling to shoppers purchasing for personal use AND to big businesses who are then going to resell their product.

It’s a great opportunity for the brand, expanding its reach and diversifying channels. However, the two audiences are on completely different customer journeys, making it difficult to create a digital experience that delivers to both.

We’ve gone deep into how to manage (and prevent) channel conflict. But, we still feel there is something left unsaid: How can brands specifically optimize their online experience for both a B2B and B2C audience?

In this article, we’ll look at a step-by-step approach for catering to your unique users.

Let’s dive in.

Understand Your Audience

As a business straddling the line between B2B and B2C sales, it can seem impossible to deliver a seamless website experience for both. But that isn’t the case if you take the time before diving into the intricacies of dual-targeting strategies to understand your data.

Note: If you’re a reader of our content, the first step being an analysis of your audience and data shouldn’t come as a surprise. It’s a foundation for all of the optimization strategies we’ll recommend later on.

A good first step is to check your Google Analytics. Specifically:

• Review Audience by Page: Take a look at the distribution of your audience across different sections of your website. Analyze page-specific data to identify where both B2B and B2C users are engaging or where one audience may play a bigger role.
• Review Traffic Source by Page: Traffic sources play a pivotal role in shaping user intent. Review how users arrive at specific pages so you can better align the content on your site with their expectations.
• Review Revenue Source by Audience: Identify where the majority of your revenue is coming from so that you can make informed decisions on prioritization and resource allocation for each audience.

Reviewing your ecommerce analytics reports will help you determine the traffic and revenue split between B2B and B2C.

Map The Customer Journey For Each Segment

Once you have an idea of your audiences and how they engage with different parts of your site, you can outline the customer journey of each. This helps you make informed decisions on when to prioritize one audience over the other and put together a unique treatment for each.

A few good questions to answer in this stage are:

Do you have a primary audience?

Establish if one audience generates significantly more revenue than the other. Or if there is one that is always shopping online while the other generally prefers print or in-store. This is a great prioritization tactic.

Where do you need to have messaging and content that speaks to both audiences? Where can you prioritize one audience over the other?

This helps to identify key touchpoints where messaging and content need to resonate with both B2B and B2C audiences. It also gives context so you can strategically prioritize one over the other (where it makes sense) based on the significance of each segment.

Where in the customer journey do you divert B2B traffic to a different section of the site?

Also, as you look at the customer journey, you can pinpoint where diverting B2B traffic makes the most sense. Usually, you can uncover specific touchpoints to redirect the different shoppers for more tailored experiences. This ensures that users encounter content and features relevant to their needs, optimizing their journey and increasing their likelihood to purchase.

Hypothesize Areas for Improvement

With all of your context, you’re set up to start hypothesizing areas for improvement on your website.

We’ve optimized hundreds of millions in revenue for clients and their digital properties, so while strategies will depend on what you uncover, there are some common areas for optimizing the B2B and B2C challenge and a few examples that can help get you started.

Homepage

The homepage serves as a crucial touchpoint for both audiences. Find ways to guide users to their intended website pages with consistent design but unique stories and content that resonate with both.

Here is a great example from Old World Christmas, whose digital team works with The Good to optimize their direct-to-consumer (DTC) site while keeping their robust retailer network in mind.

By reimagining a homepage module that directs users to register as a retailer, they showcase similar messaging and content that both B2B and B2C customers can appreciate, while still providing a clear call-to-action for potential partners.

This creative approach ensures a consumer-friendly presentation while still addressing the vital B2B audience.

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Menu Navigation

Consider menu navigation sections that cater specifically to B2B and B2C users.

Ecommerce companies can learn from SaaS in this regard. Distinct functionalities may be more appealing for each audience type, so SaaS companies reflect this right away in the menu.

QualtricsXM, for example, shares specific use cases for their product right in the menu and also highlights who that use case is best for.

While ecommerce brands will likely choose to take a softer approach, there is value in seeing how a menu can be laid out so users know exactly where to click next to get to the right content for them.

The office supply company, Staples, has a dedicated “For Business” section prominently displayed in their navigation. Clicking on the option expands it and shows users the option to explore wholesale or B2B programs that Staples offers.

Landing Pages

Segmentation is a powerful tool to tailor your website experience for different audiences. Consider creating distinct landing pages based on user profiles.

This allows you to prioritize the segment that contributes the most to your revenue without neglecting the other. On landing pages, you can craft messages that resonate with the distinct needs of B2B and B2C audiences.

Let’s take a look at how tailored messaging can enhance the user experience and align with the goals of each segment.

Take Brooklinen, a brand that caters to DTC and wholesale customers through distinct landing pages, as an example.

For their DTC audience, Brooklinen prioritizes ease of purchase.

Their landing page is representative of a well-designed ecommerce site. A ‘Shop by Category’ option can be found directly under the fold.

They also highlight enticing “Best Sellers” in the next section. Browsing feels effortless, guiding customers toward adding to their carts.

On the B2B side of things, the spotlight shifts from shopping to partnering with Brooklinen.

They do away with product listings on this landing page, focusing instead on their two programs: Trade and Hospitality. Each program is tailored to specific business needs.

CTA buttons are different, with “Apply Now” and “Log In” being the call-to-action on the B2B landing page.

Test and Validate (Or Invalidate) Ideas

Once you’ve hypothesized areas for improvement, you’ll be excited to start improving your website. But, before implementing changes, make sure you test changes with your audiences.

The goal is to collect feedback to validate or invalidate hypotheses and refine your strategies based on real user insights. This is a crucial step in the process that can help your site be more user-centered, save you resources, AND increase conversions.

Depending on your time and the change you’re testing, we would normally recommend A/B testing or a form of rapid testing.

Once you confirm that your ideas will positively impact the user experience you can go ahead and implement them, feeling confident in your decision and how it will improve your online experience.

Improve B2B Sales Without Hindering Online Purchases

The key to improving your B2B sales without hindering online purchases is to make sure each user’s journey is optimized for their unique requirements.

Continuously adapt and refine your strategies, and your business will thrive by catering to the needs of both B2B and B2C audiences.

A few tips to remember before I sign off:

• Prioritize based on revenue but consider the unique needs of each audience.
• Strategically divert B2B traffic at critical touchpoints in the customer journey.
• Tailored messaging and design enhancements can significantly impact user engagement.
• Regular testing and evaluation are essential for refining strategies and ensuring ongoing success.

With that in mind, you can embrace the duality of B2B and B2C sales, viewing it not as a challenge but as an opportunity for growth.

And if you want to start optimizing for both B2B and B2C, contact us.

The post How To Optimize Your Website For Both DTC and Wholesale appeared first on The Good.