Feature Adoption Analysis

So many product teams work on new features they believe will be a game-changer for users. But how do you really know if a feature will be adopted by users? This is where UX research comes in. As UX researchers, we can help identify the probability of feature adoption by digging deep into user needs, behaviors, and expectations. Here are some ways we measure and predict feature adoption: 1. User Interviews and Surveys: By speaking directly to users, we can gauge their interest in a new feature. Through surveys or interviews, we explore how they might use the feature, what problems it would solve for them, and how it fits into their current workflows. These qualitative insights give us an early understanding of potential adoption barriers. 2. Usability Testing: A feature may seem like a great idea on paper, but how do users actually interact with it? Conducting usability tests on prototypes allows us to see whether users understand the feature, how intuitive it is, and where they might get stuck. If the feature feels cumbersome, adoption rates will likely be lower. 3. Task Success Rate: This metric allows us to measure how easily users can complete tasks using the new feature. A low success rate indicates friction, and users are less likely to adopt a feature if it doesn’t make their experience easier. 4. User Journey Mapping: By mapping out the user journey, we can see where the new feature fits into the overall user experience. Does it make sense within the flow of their tasks? Are there unnecessary steps or points of confusion? A smooth, integrated feature is more likely to be adopted. 5. A/B Testing: Once a feature is live, we can run A/B tests to see if it’s driving the desired behavior. Does the feature increase engagement or task completion compared to the previous version? These quantitative insights allow us to measure real-world adoption and refine the feature based on user interactions. 6. Feature Feedback: After a feature is released, gathering feedback is key. By monitoring user comments, satisfaction scores, and support tickets, we can understand how users feel about the feature. Are they using it as intended? Are there any pain points that need addressing? As UX researchers, our role is to validate whether a feature truly meets user needs and fits within their daily tasks. We can predict adoption rates, identify potential issues early, and help product teams make informed decisions before launching a feature. How do you measure feature adoption in your research?

I recently got an inside look at how Chipper Cash increased feature adoption by 194%, driven by user research. Chipper Cash is a cross-border fintech company offering peer-to-peer payments. They had launched crypto transfers, but adoption was low. Here’s what they did: 1. Break down the core questions To get to the root of the problem, they started with: – Are users familiar with crypto? – Do they know Chipper Cash even offers it? – What’s stopping them from trying it? 2. Launch an in-product survey They built a multi-part in-product survey to answer those questions. These typically get ~30% response rates—far better than traditional surveys. 3. Review insights in real time One finding stood out: 58% of users didn’t even know the feature existed. Open-ended responses revealed something deeper—many users lacked basic knowledge of crypto, which explained the hesitation. 4. Take action Instead of jumping straight to more marketing, they launched targeted awareness efforts and a crypto education initiative. 5. Keep iterating The team continues to monitor adoption data and uses the same playbook to surface new opportunities. Chipper used Sprig to run this process end-to-end and drove a 194% increase in feature adoption. 📊 Real user insights. Real impact.

How do SaaS PMs decide which features to sunset? Here's how I went about it => I queried for 2 metrics per feature: ✅ Adoption: What portion of the customer base uses it? ✅ Utilization: How frequently is it used? Step 1: Adoption Rates 🔹 Adoption rate of a feature =  %age of accounts that have used it in a period of time (quarterly/monthly). Notes: 🔸 If you pull adoption rates for the entire customer base, you'll get misleading numbers. There are always features that may be preferred by only a specific segment of the audience. So, slice the results across audiences that make sense. 🔸 Depending on your product, you may want to segment by persona, problem category or firmographics for B2B (e.g. company size, industry, geography). Classify adoption has high/low. Features with poor adoption rates will warrant further investigation. Step 2: Utilization Rates Utilization is meant to track the frequency of usage i.e. how much a feature gets user per account. Utilization rate =  Find the # of times a feature is used per account. Then, take the median of all the values. Why median? Averages often skew this result. Medians, although not perfect, offset outliers better. Once you have the utilization rates for each feature for the month, normalize the values with respect to each other and classify them as high or low. Step 3: Plotting Plot this utilization against adoption to get a 2 by 2 matrix with 4 possibilities. 👉 High Adoption & High Utilization These are usually core features that fall on the critical path of the product. Ex: In a CRM, it'd be "Create Contact". 👉 High Adoption & Low Utilization Features like admin settings or permission control usually fall in this category - everyone uses them but not that frequently. However, high-effort features that are poorly designed also sometimes fall in this quadrant. 👉 Low Adoption & High Utilization Delighter or performance features that are used by power users fall here. Ex: Macros in Excel. However, this could also indicate an issue with discoverability of the feature. Adoption could be low because it's hard to find. 👉 Low Adoption & Low Utilization These could be useful 1-time features that are strategically critical e.g. porting over objects from a specific third-party tool during onboarding. OR these are indicative of features that are truly struggling - usually these are candidates for sunsetting. With this plot in hand, [1] Reason whether the feature's quadrant is in line with expectations. [2] If not, inspect if there is an awareness, user education, solution design or sales problem limiting adoption or usage? [3] If needed, revisit "the why" behind the feature with the team and re-validate with customers. [4] (Optional) Ideate if the feature can be pivoted to something useful. [5] If all else fails, shortlist for sunsetting. -- How do you nominate features for sunsetting?

One of the most common mistakes teams make when evaluating early product features is asking users whether they like an idea and treating the answer as evidence. Decades of behavioral research and very practical product research work show that this is a weak signal. People are generally bad at predicting what they will use, adopt, or pay for in the future, especially when there is no cost, effort, or tradeoff attached to their answer. That is why early feature evaluation should focus on behavior rather than belief. When a feature is only a concept, a smoke test can already tell you a lot. Exposing users to the idea through a landing page, announcement, or waitlist and observing whether they click or sign up answers a very specific question. Is this worth building at all, not whether it sounds good in theory. When an idea becomes clickable, fake door tests bring the decision closer to real behavior. Placing a realistic entry point inside the product and observing who actually tries to use it shows intent in context. The power of this method comes from the fact that users believe the feature is real at the moment of interaction. Transparency afterward is essential, but the action itself is the signal. For complex or technically risky features, especially AI, automation, or recommendation systems, Wizard of Oz prototyping allows teams to observe natural behavior before automation exists. Users interact with what looks like a fully functional system, while a human performs the work behind the scenes. This reveals expectations, decision making, and breakdowns that are invisible in abstract discussions. Concierge MVPs go one step further by making the human involvement explicit. Here, the value is delivered manually, often in a high touch way, to see whether users actually engage, return, and benefit. If people do not use or value the service when friction is low and quality is high, automation will not fix the underlying problem. Across all of these approaches, the principle is the same. Early feature evaluation should not ask people what they like. It should watch what they do when a real opportunity to engage is placed in front of them.

We shipped 5 major updates in June alone. But... here's the brutal truth every founder needs to face: shipping quickly without adoption is expensive guessing. I used to think shipping fast was enough. Send the email blast, update the changelog, announce it on social media—surely our power users would find these features, right? Wrong. Last week, I was screensharing with one of our biggest advocates—someone who uses Supademo daily, refers customers, and genuinely loves our product. As I walked through a workflow, I casually mentioned our sandbox mode feature. Her response shocked me: "Wait, you have sandbox demos?" This feature had been live for months. She'd received three emails about it. It was in our changelog. But she had no idea it existed. That's when it hit me—it’s not just about building valuable features. The problem is we're assuming users will magically find them or find them intuitive. So here are tweaks we’re putting in place to cast a wider adoption net: 1️⃣ Mandated monthly 3 min video recaps for the 3-5 features everyone should know (this video) 2️⃣ Layering in-context product tours for core features (powered by Supademo): passively displayed via subtle tooltips or badges users can action on (when ready) 3️⃣ Continuing with biweekly product update posts and occasional emails for interested users Incredibly hard problem to solve, but we're trying to maximize ways for users to naturally discover our latest features and benefits — at their own pace and in the moment they’re ready (vs. an intrusive product tour popup). Founders and operators: what are you doing to drive feature adoption in an era of shipping fast?

Small Features -> Big Impact A trap in developing product is often thinking TOO big. Sometimes all your customers want are small, but critical things. We launched our new Dashboard 2.0 earlier this year which saw mediocre adoption by our users. This baffled us, cause the new Dashboard did everything the old dashboard did - except it was like 100x faster. In digging deeper, we found two things: 1. We removed some small things from the old dashboard that people seemed to love. 2. We added some new functionality that was good, but not great. We took that info and delivered 5 micro-enhancements to the dashboard this week: 1. Ability to custom pick your own "quick links". This was a new feature, but we hard assigned the top 5 quick links we knew people used. But so many users wanted this to be customizable. 2. Flexible dashboard date picker, allowing people to view their dashboard reports using common relative dates (last month, this quarter, etc) or any relative date range they chose. 3. More streamlined check-in flow. It's always good to save some time for commonly used things, no? 4. Added back customer headshots to reports. A simple oversight that was a big customer complaint - we want to see our customer headshots. 5. Added back barcode dashboard checkin support. For the minority of our gyms who use barcode scan checkins, we broke that functionality on our new dashboard. These 5 micro-changes took one short sprint to develop and resulted in a 50% increase in dashboard enablement in the last week - and drove adoption up to almost 61%. If you know me, I love swinging for the fences when it comes to product - but I have come to really appreciate small effort - large value drivers.

Feature launches don’t drive growth. Adoption does. That’s one of the biggest takeaways from my conversation with Roy Frenkiel, Director of Product at Uber Eats. In Europe, many restaurants use their own couriers—outside the Uber app. This created a major blind spot: no live tracking, no direct messaging, and more failed deliveries. This caused a spike in defect rate—the percentage of orders that go wrong, including "never received" deliveries. One bad experience made customers 10x more likely to churn. To fix it, the team launched a QR-code solution to bring external couriers into the Uber ecosystem. The product shipped. But adoption? Just 1.5%. That’s where Ops came in. The operations team partnered with local restaurants—offering training and creating incentives (like discounted marketplace fees) to drive behavior change and adoption. With their help, adoption took off—and defect rates dropped significantly. More companies are realizing that to truly shift deeply ingrained behaviors—especially in complex, multi-market environments—product alone isn’t enough. When EPD (Engineering, Product, Design) teams integrate tightly with Operations, they unlock outcomes that actually stick—driving real-world behavior change, retention, and sustainable growth. See the full article here: https://lnkd.in/grfcGS-2

The greatest threat to your valuation isn’t churn. It’s adoption debt hiding behind ‘green’ dashboards. Most founders don’t see it because the top-level metrics look healthy: - Logins → green 🟢 - Renewals → green 🟢 - Support volume → green 🟢 - Feature adoption → RED 🔴 And that last line quietly drags NRR down quarter after quarter. In this market, investors aren’t rewarding top-line ARR. They’re rewarding expansion efficiency. When customers never touch the features tied to higher tiers, NRR drops below 100% and your valuation multiple compresses by 30–40%. This is why adoption can’t live in a CSM playbook anymore. It belongs in pricing, product, and board-level strategy: - Define which features drive expansion - Measure adoption long after onboarding - Tie usage to commercial outcomes - Treat expansion as an intentional path, not a pleasant surprise Your valuation isn’t about how many customers you keep. It’s about how many customers you grow. Fix adoption debt, & your NRR and valuation will follow. ♻️ Repost to help founders catch adoption debt early. 🔔 Follow Ali Mamujee for more insights on growth & strategy.

Most product teams celebrate the product launch. They shouldn't. Here’s what usually happens: A team ships a shiny new feature. They high-five. Then sprint straight into building the next one. But features don’t create value just by existing. That’s exactly what happened on a team I worked with years ago. We launched a brand new feature that we thought everyone would love — a huge engineering effort. But weeks later, sales didn’t pitch it. Support didn’t know how to explain it. And users? Confused, or unaware it even existed. We built it. But it never landed. And here's why: 🎯 Real impact happens after the launch. If you’re not enabling GTM teams to sell it… If you’re not helping support teams explain it… If you’re not learning what’s working and what’s not… You’re not done. You’re just getting started. The shift? From: "We shipped it—what’s next?" To: "We shipped it—how do we make it stick?" Here’s how: ✅ Empower internal teams -> Arm GTM with positioning, use cases, and objection handling -> Run enablement sessions with real customer scenarios- > Provide internal FAQs and demo scripts that evolve with feedback ✅ Track adoption and feedback -> Track the key metrics that matter -> Capture qualitative insights from sales calls and support tickets -> Segment feedback by persona to uncover hidden blockers ✅ Reinvest—or ruthlessly cut what’s not working -> Double down on features driving real outcomes -> Sunset or simplify features that confuse or underdeliver -> Use a “feature scorecard” to guide resource allocation Final thought: Launch is step 1. Stickiness is the real finish line. -- 👋 I’m Ron Yang, a product leader and advisor. Follow me for insights on product leadership + strategy.