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 just published some research on what actually drives successful AI rollouts in engineering teams – and the findings might surprise you. We followed 500+ developers through their AI rollouts, combining telemetry data, surveys, and interviews to understand what separates successful adoptions from lackluster ones. Here's what we found: 1️⃣ The tool matters less than how you enable. Same AI tool, completely different adoption curves depending on organizational practices. One company got to 90% adoption in weeks; another plateaued at 20%. 2️⃣ 27% more PRs merged – but 19% more out-of-hours work. Engineers are shipping more, but they're also working longer hours. The qualitative data points to delivery pressures, not excitement about AI. 3️⃣ Code quality can actually improve with AI – if you make it a goal. Many teams saw PR sizes balloon (a warning sign). But one organization kept PR sizes down by 8.5% even with high AI usage. Their differentiator? Clear expectations about maintaining quality, plus a strong code review culture. 4️⃣ Your AI super-users (often high-performers) have already figured out what will take others months to learn. And your team wants to learn from peers, not just formal training or from executives. Create systems to capture and scale their practices. The bottom line: Leadership actions determine AI outcomes more than the technology itself (especially as model performance and tools converge) The full research dives into adoption patterns, quality metrics, wellbeing impacts, and practical recommendations from super-users. We built a feature in Multitudes off the back of this research to help other organizations apply these findings in their own context. Huge props to the whole team for shipping this before EOY! Massive thanks to Culture Amp, Mable, Eucalyptus and Pleo for being our research participants :)) And big thanks to Nathen Harvey, Kelly Blincoe, Thomas Fritz for their research and academic guidance along the way.

Structured rollout boosts Copilot adoption and satisfaction by 20% AI Adoption: Old Lessons, New Opportunities - The more I explore the successes of AI, the clearer it becomes: the fundamentals of change management are as relevant as ever. The twist? With AI, the stakes are higher, the pace is faster, and the challenges more amplified. Adopting AI is change on steroids, requiring not just structured rollouts, but also robust training, clear messaging, and unwavering support from senior leadership. When these elements come together, the results are nothing short of transformative. Executive Summary A structured rollout and robust onboarding process are the secret ingredients to maximizing the impact of AI tools like GitHub Copilot. Companies that invest in pilot programs, training, and support see higher adoption rates, greater satisfaction, and measurable boosts in engineering velocity. In one case study, developers with structured onboarding reported 17% higher satisfaction rates, proving that a thoughtful approach pays dividends in utilization and ROI. Key Points 1. Structured Rollouts Deliver Results: Rolling out Copilot in phases, starting with a well-supported pilot group, resulted in an 81% satisfaction rate—17% higher than teams without structured onboarding. Satisfaction directly correlates with increased tool usage and overall productivity gains. 2. Training and Support Are Game-Changers: Interactive learning events, dedicated support channels, and regular check-ins help developers fully embrace Copilot’s capabilities. These efforts boost satisfaction and adoption, ensuring organizations get the most value from their investment. 3. Best Practices Drive Adoption: To maximize Copilot’s impact, provide hands-on training, create spaces for community sharing, and offer regular usage nudges. This approach not only drives adoption but fosters a culture of continuous learning and innovation within teams. Article Link --> https://lnkd.in/gxjQM66m Author - Abi Noda

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

Part 4: What Good Change Really Looks Like — Adoption, Activation, and the Hard Part of Digital Transformation! We’ve all been there. The platform is live. The AI engine is in place. The dashboards are beautiful. But no one’s using it. Or worse—people are using it wrong. Here’s the truth I’ve learned over and over again: Transformation doesn’t fail in the design. It fails in the adoption. And adoption isn’t about a big announcement or a training deck—it’s about trust, behavior change, and making sure what we build actually fits how people work. Here are a few principles that can help: 🔹 Change champions aren’t a buzzword—they’re the glue. Identify trusted employees across regions or functions to act as embedded advocates. These individuals can test early, share success stories in team forums, and coach others hands-on—making adoption feel more peer-driven than top-down. 🔹 Leadership can’t just approve—it has to participate. Encourage execs and managers to model the new tools during business reviews or day-to-day decisions. A single team lead running a planning session using the new dashboard sends a clearer message than any email blast. 🔹 Train the process, not just the tech. Design enablement around “how this helps me do my job better”—not just “what buttons to click.” Walkthroughs like “how to prep for a forecast review in 10 minutes” or “how to handle exceptions faster” resonate far more than feature overviews. 🔹 Personalized onboarding > one-size-fits-all. Tailor your rollout by role. A finance analyst cares about variance, a sales manager about trending, and an ops lead about exceptions. Deliver just enough context to help them act quickly and confidently. 🔹 Build feedback loops into the rollout. Set up simple ways to gather input and adapt—like Slack channels, feedback buttons, or short check-in surveys. Monitor usage, flag common drop-offs, and adjust fast. Showing that feedback turns into action builds trust quickly. I’ve said it before: launching the tech is the easy part. The hard part—and the real work—is getting people to trust it, use it, and embed it into how they work. That’s where the value lives. And that’s where transformation actually happens. #DigitalTransformation #ChangeManagement #Adoption #Leadership #TechEnablement #AI #ProductDelivery #DigitalStrategy P.S. Nothing beats a good team lunch to bring people together. At the end of the day, transformation is about people—sharing ideas, building trust, and yes… passing the biryani (Google it, it’s worth it). 😊

We just surveyed nearly 100 TA leaders about AI adoption in recruiting, and the data points to one conclusion: 2026 is going to be a sea change year. Right now, only 2 AI use cases have crossed into mainstream adoption (>50%): Job Design (63%) and Job Posting (68%). But in the next 12 months, based on stated implementation plans, FIVE more features will cross that adoption threshold: → Job Posting AI: 68% → 85% → Job Design AI: 63% → 78% → AI Resume Scoring: 44% → 62% → Outbound Recruiting AI: 44% → 58% → Interview Scheduling: 37% → 55% → Candidate Rediscovery: 33% → 51% This is exactly what you'd expect to see in early-stage technology adoption—where most organizations (78.5%) are at Level 1 AI maturity, where AI drafts and recommends, but humans make all decisions. What's changing? The barriers that kept adoption concentrated at Level 1 are falling: • Legal frameworks are emerging • ROI is increasingly proven (AI is driving improvements in recruiting team satisfaction, hiring team satisfaction, candidate satisfaction, as well as improvements in productivity and speed) • Strategic playbooks are being shared The AI Maturity Brief breaks down: ✓ Which features are crossing mainstream adoption and why ✓ What early adopters are reporting in terms of benefits ✓ The 5-level maturity model and how to advance ✓ Implementation roadmaps based on risk-adjusted approaches 📊 Read the full report: https://lnkd.in/gVzqxR9Q 📝 TA leaders, contribute to the research (4-min survey): https://lnkd.in/gVzqxR9Q Where is your organization on the maturity curve? #TalentAcquisition #AIinRecruiting #FutureOfWork

The quickest path to building a product people love starts with slowing down and seeing what they truly value. Early on, Boon, I thought I knew exactly what features solved our customers’ hiring problems. I built a product that looked perfect on the drawing board. Had this conversation with an early customer that opened my eyes to something completely different. We were discussing their referral program challenges when they shared that they valued different features than the ones I was most excited about. I had been designing a comprehensive suite of advanced tools, but what they actually needed was something much simpler - they just wanted their employees to engage more consistently with their referral process. This was eye-opening. All the while, we had been creating solutions for problems our customers hadn't even encountered yet, overlooking the basics right in front of us. We began watching what customers actually used and built more of that. Our adoption rates improved as a result of this approach. The simple features we had once viewed as "basic" turned out to drive the most value. For example, customers used our reward management system in a manner that differed from our expectations. We observed how they naturally managed rewards and rebuilt the system to align with their actual workflow. Now we build features based on observed usage, not just what we think is impressive. When customers guide your roadmap, two things happen: 1. You build what people actually use, not just what demos well 2. Adoption flows naturally because you're improving existing behaviors When you feel tempted to add more bells and whistles, pause. Watch how users actually engage with your product first. Your role is to identify what works and improve it.