Understanding User Behavior In Subscription Services

One of the biggest challenges in UX research is understanding what users truly value. People often say one thing but behave differently when faced with actual choices. Conjoint analysis helps bridge this gap by analyzing how users make trade-offs between different features, enabling UX teams to prioritize effectively. Unlike direct surveys, conjoint analysis presents users with realistic product combinations, capturing their genuine decision-making patterns. When paired with advanced statistical and machine learning methods, this approach becomes even more powerful and predictive. Choice-based models like Hierarchical Bayes estimation reveal individual-level preferences, allowing tailored UX improvements for diverse user groups. Latent Class Analysis further segments users into distinct preference categories, helping design experiences that resonate with each segment. Advanced regression methods enhance accuracy in predicting user behavior. Mixed Logit Models recognize that different users value features uniquely, while Nested Logit Models address hierarchical decision-making, such as choosing a subscription tier before specific features. Machine learning techniques offer additional insights. Random Forests uncover hidden relationships between features - like those that matter only in combination - while Support Vector Machines classify users precisely, enabling targeted UX personalization. Bayesian approaches manage the inherent uncertainty in user choices. Bayesian Networks visually represent interconnected preferences, and Markov Chain Monte Carlo methods handle complexity, delivering more reliable forecasts. Finally, simulation techniques like Monte Carlo analysis allow UX teams to anticipate user responses to product changes or pricing strategies, reducing risk. Bootstrapping further strengthens findings by testing the stability of insights across multiple simulations. By leveraging these advanced conjoint analysis techniques, UX researchers can deeply understand user preferences and create experiences that align precisely with how users think and behave.

Look at what they do, not just what they say. User behavior is how users interact with and use software. It includes things like: → how people navigate the interface → which features people use most often → the order in which people perform tasks → how much time people spend on activities → how people react to prompts or feedback Product managers and designers must understand these behaviors. Analyzing user behavior can enhance the user experience, simplify processes, spot issues, and make the software more effective. Discovering the "why" behind user actions is the key to creating great software. In many of my sales discussions with teams, I notice that most rely too heavily on interviews to understand user problems. While interviews are a good starting point, they only cover half of the picture. What’s the benefit of going beyond interviews? → See actual user behavior, not just reported actions → Gain insights into unspoken needs in natural settings → Minimize behavior changes by observing discreetly → Capture genuine interactions for better data → Document detailed behaviors and interactions → Understand the full user journey and hidden pain points → Discover issues and opportunities users miss → Identify outside impacts on user behavior Most people don't think in a hyper-rational way—they're just trying to fit in. That's why when we built Helio, we included task-based activities to learn from users' actions and then provided follow-up questions about their thoughts and feelings. User behaviors aren't always rational. Several factors contribute to this: Cognitive Biases ↳ Users rely on mental shortcuts, often sticking to familiar but inefficient methods. Emotional Influence ↳ Emotions like stress or frustration can lead to hasty or illogical decisions. Habits and Routine ↳ Established habits may cause users to overlook better options or new features. Lack of Understanding ↳ Users may make choices based on limited knowledge, leading to seemingly irrational actions. Contextual Factors ↳ External factors like time pressure or distractions can impact user behavior. Social Influence ↳ Peer pressure or the desire to conform can also drive irrational choices. Observing user behavior, especially in large sample sizes, helps designers see how people naturally use products. This method gives a clearer and more accurate view of user behavior, uncovering hidden needs and issues that might not surface in interviews. #productdesign #productdiscovery #userresearch #uxresearch

Most companies ask the wrong question about churn When subscription companies (B2C or B2B) come to us, one of the first things they ask is: 👉 “What are the features or journeys that lead to churn?” It feels like the obvious place to start. But after analyzing this across hundreds of companies, here’s what we found: Churn is rarely caused by one dramatic moment. Not by rage clicks. Not by a dead-end screen. Not even by one bad experience. When we run causal analyses on user behavior vs. churn, the results are almost always weak. Don't get me wrong - qualitative feedback can highlight frustrating moments and other reasons. But the quantitative data tells a different story. The truth is simple (and uncomfortable): Most users churn - especially new ones - because they never saw the value in the first place. They didn’t build a habit. They never had a reason to come back. So instead of asking “why do people churn?” — flip the question: 👉 “Why do people retain?” That’s where the real answers lie. At Loops, we’ve learned to focus on: 1. Identifying the features & journeys that drive short- and long-term retention  Use different analyses for new user retention vs exisiting users (2nd week retention vs week-over-week retention) 2.For new users: doubling down on activation - finding the causal aha and habit moments (how many times they complete a certain user journey to retain). 3. As retention/churn are lagging metrics, focus on engagement. Identify the biggest drivers to improve engagement. Connect feature usage to engagement metrics like days used, sessions completed, time spent, and the number of actions performed. So yes, churn is technically the opposite of retention. But if you want to actually move the needle, sometimes you should ask “why did they stay?” and not just "what did they leave?".

Most teams are just wasting their time watching session replays. Why? Because not all session replays are equally valuable, and many don’t uncover the real insights you need. After 15 years of experience, here’s how to find insights that can transform your product: — 𝗛𝗼𝘄 𝘁𝗼 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗥𝗲𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗥𝗲𝗽𝗹𝗮𝘆𝘀 𝗧𝗵𝗲 𝗗𝗶𝗹𝗲𝗺𝗺𝗮: Too many teams pick random sessions, watch them from start to finish, and hope for meaningful insights. It’s like searching for a needle in a haystack. The fix? Start with trigger moments — specific user behaviors that reveal critical insights. ➔ The last session before a user churns. ➔ The journey that ended in a support ticket. ➔ The user who refreshed the page multiple times in frustration. Select five sessions with these triggers using powerful tools like @LogRocket. Focusing on a few key sessions will reveal patterns without overwhelming you with data. — 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲-𝗣𝗮𝘀𝘀 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 Think of it like peeling back layers: each pass reveals more details. 𝗣𝗮𝘀𝘀 𝟭: Watch at double speed to capture the overall flow of the session. ➔ Identify key moments based on time spent and notable actions. ➔ Bookmark moments to explore in the next passes. 𝗣𝗮𝘀𝘀 𝟮: Slow down to normal speed, focusing on cursor movement and pauses. ➔ Observe cursor behavior for signs of hesitation or confusion. ➔ Watch for pauses or retracing steps as indicators of friction. 𝗣𝗮𝘀𝘀 𝟯: Zoom in on the bookmarked moments at half speed. ➔ Catch subtle signals of frustration, like extended hovering or near-miss clicks. ➔ These small moments often hold the key to understanding user pain points. — 𝗧𝗵𝗲 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 + 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Metrics show the “what,” session replays help explain the “why.” 𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 Gather essential metrics before diving into sessions. ➔ Focus on conversion rates, time on page, bounce rates, and support ticket volume. ➔ Look for spikes, unusual trends, or issues tied to specific devices. 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗪𝗮𝘁𝗰𝗵 𝗟𝗶𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 Organize sessions based on success and failure metrics: ➔ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗖𝗮𝘀𝗲𝘀: Top 10% of conversions, fastest completions, smoothest navigation. ➔ 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗖𝗮𝘀𝗲𝘀: Bottom 10% of conversions, abandonment points, error encounters. — 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗥𝗲𝗽𝗹𝗮𝘆 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 Make session replays a regular part of your team’s workflow and follow these principles: ➔ Focus on one critical flow at first, then expand. ➔ Keep it routine. Fifteen minutes of focused sessions beats hours of unfocused watching. ➔ Keep rotating the responsibiliy and document everything. — Want to go deeper and get more out of your session replays without wasting time? Check the link in the comments!