Approaches To Enhance User Experience Based On Behavior

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.

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!

I just spoke with Elijah Woolery and Aarron Walter of the Design Better podcast about the hidden forces that drive product adoption and behavior change. Here's what product managers and growth leaders need to know: 🧠💡 Humans don't act rationally, and the environment affects behavior more than attitudes, preferences, or beliefs. This isn't just theory—it's the foundation of effective product design. A few insights worth noting: 🔄 Your biggest competitor isn't who you think. It's the status quo—what users are already doing. The biggest predictor that I'll exercise today is whether I exercised yesterday. 👁️ Don't ask users what they want; watch what they do. Brazil's stock exchange thought their users needed better information about expiring bonds. The problem? People don't remember expiration dates from 10 years ago. By focusing on the behavior (reinvestment) rather than awareness, we increased bond reinvestment 5X. 🎯 For truly successful product engagement, focus on what I call "uncomfortably specific key behaviors" rather than abstract metrics like retention or engagement. At One Medical, we increased bookings by 20% not by asking people to "get care" (who thinks that way?) but by recommending a specific doctor. ✨ Your users don't come in with fixed preferences—you help create them. The Significant Objects Project sold junk shop items on eBay with compelling stories, turning $50 worth of items into $3,500. As a product leader, it's your job to help users understand value, not assume they already know it. ⏱️ Present bias is real: Chime switched from "save money on overdraft fees" (future benefit) to "get paid two days earlier" (immediate benefit)—and saw dramatically better conversion. I run Irrational Labs, a behavioral economics consultancy with Dan Ariely, where we apply these principles to help products drive meaningful behavior change. What hidden forces are affecting your product experience? Listen to the full conversation here: https://lnkd.in/efB6FD_6 #BehavioralEconomics #ProductDesign #GrowthMarketing

During meetings with stakeholders, we often hear about 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒃𝒐𝒖𝒏𝒄𝒆 𝒓𝒂𝒕𝒆𝒔, 𝒊𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈 𝒓𝒆𝒕𝒆𝒏𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒑𝒕𝒊𝒎𝒊𝒛𝒊𝒏𝒈 𝒄𝒐𝒏𝒗𝒆𝒓𝒔𝒊𝒐𝒏 𝒇𝒖𝒏𝒏𝒆𝒍𝒔. If you're feeling confused and overwhelmed about how to do all of this, you're not alone. Here's something for those new to the world of metric-driven design. Trust me, your designs can make a real difference :) 𝗙𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗶𝗿𝘀𝘁, 𝗴𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀 𝗔𝗡𝗗 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 → Talk to real users. Understand their pain points. But also, grab coffee with the marketing team. Learn what those metrics mean. You'd be surprised how often a simple chat can clarify things. 𝗠𝗮𝗽 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗹𝗼𝘄 → Sketch it out, literally. Where are users dropping off? Where are they getting stuck? This visual approach can reveal problems you might miss otherwise and which screens you need to tackle. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗶𝗺𝗽𝗹𝗲, 𝘀𝘁𝘂𝗽𝗶𝗱 (𝗞𝗜𝗦𝗦)→ We've all heard this before, but it's true. A clean, intuitive interface can work wonders for conversion rates. If a user can't figure out what to do in 5 seconds, you might need to simplify. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 → Trust isn't built by security badges alone. It's about creating an overall feeling of reliability. Clear communication, consistent branding, and transparency go a long way. 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 → Transform mundane tasks into engaging experiences. Progress bars, thoughtful micro-animations, or even well-placed humor can keep users moving forward instead of bouncing off. Remember, engaged users are more likely to convert and return, directly impacting your key metrics. 𝗧𝗲𝘀𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗽𝗲𝗮𝘁 → Set up usability tests to validate your design decisions. Start small - even minor changes in copy or button placement can yield significant results. The key is to keep iterating based on real data, not assumptions. This approach improves your metrics and also sharpens your design intuition over time. 𝗗𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹 → While it's tempting to create something totally new, users often prefer familiar patterns. Research industry standards and find data around successful interaction models, then adapt them to address your specific challenges. This approach combines fresh ideas with proven conventions, enhancing user comfort and adoption. Metric-driven design isn't about sacrificing creativity for numbers. It's about using data to inform and elevate your design decisions. By bridging the gap between user needs and business goals.

For years, companies have been leveraging artificial intelligence (AI) and machine learning to provide personalized customer experiences. One widespread use case is showing product recommendations based on previous data. But there's so much more potential in AI that we're just scratching the surface. One of the most important things for any company is anticipating each customer's needs and delivering predictive personalization. Understanding customer intent is critical to shaping predictive personalization strategies. This involves interpreting signals from customers’ current and past behaviors to infer what they are likely to need or do next, and then dynamically surfacing that through a platform of their choice. Here’s how: 1. Customer Journey Mapping: Understanding the various stages a customer goes through, from awareness to purchase and beyond. This helps in identifying key moments where personalization can have the most impact. This doesn't have to be an exercise on a whiteboard; in fact, I would counsel against that. Journey analytics software can get you there quickly and keep journeys "alive" in real time, changing dynamically as customer needs evolve. 2. Behavioral Analysis: Examining how customers interact with your brand, including what they click on, how long they spend on certain pages, and what they search for. You will need analytical resources here, and hopefully you have them on your team. If not, find them in your organization; my experience has been that they find this type of exercise interesting and will want to help. 3. Sentiment Analysis: Using natural language processing to understand customer sentiment expressed in feedback, reviews, social media, or even case notes. This provides insights into how customers feel about your brand or products. As in journey analytics, technology and analytical resources will be important here. 4. Predictive Analytics: Employing advanced analytics to forecast future customer behavior based on current data. This can involve machine learning models that evolve and improve over time. 5. Feedback Loops: Continuously incorporate customer signals (not just survey feedback) to refine and enhance personalization strategies. Set these up through your analytics team. Predictive personalization is not just about selling more; it’s about enhancing the customer experience by making interactions more relevant, timely, and personalized. This customer-led approach leads to increased revenue and reduced cost-to-serve. How is your organization thinking about personalization in 2024? DM me if you want to talk it through. #customerexperience #artificialintelligence #ai #personalization #technology #ceo