Understanding User Behavior To Inform Product Changes

While it can be easily believed that customers are the ultimate experts about their own needs, there are ways to gain insights and knowledge that customers may not be aware of or able to articulate directly. While customers are the ultimate source of truth about their needs, product managers can complement this knowledge by employing a combination of research, data analysis, and empathetic understanding to gain a more comprehensive understanding of customer needs and expectations. The goal is not to know more than customers but to use various tools and methods to gain insights that can lead to building better products and delivering exceptional user experiences. ➡️ User Research: Conducting thorough user research, such as interviews, surveys, and observational studies, can reveal underlying needs and pain points that customers may not have fully recognized or articulated. By learning from many users, we gain holistic insights and deeper insights into their motivations and behaviors. ➡️ Data Analysis: Analyzing user data, including behavioral data and usage patterns, can provide valuable insights into customer preferences and pain points. By identifying trends and patterns in the data, product managers can make informed decisions about what features or improvements are most likely to address customer needs effectively. ➡️ Contextual Inquiry: Observing customers in their real-life environment while using the product can uncover valuable insights into their needs and challenges. Contextual inquiry helps product managers understand the context in which customers use the product and how it fits into their daily lives. ➡️ Competitor Analysis: By studying competitors and their products, product managers can identify gaps in the market and potential unmet needs that customers may not even be aware of. Understanding what competitors offer can inspire product improvements and innovation. ➡️ Surfacing Implicit Needs: Sometimes, customers may not be able to express their needs explicitly, but through careful analysis and empathetic understanding, product managers can infer these implicit needs. This requires the ability to interpret feedback, observe behaviors, and understand the context in which customers use the product. ➡️ Iterative Prototyping and Testing: Continuously iterating and testing product prototypes with users allows product managers to gather feedback and refine the product based on real-world usage. Through this iterative process, product managers can uncover deeper customer needs and iteratively improve the product to meet those needs effectively. ➡️ Expertise in the Domain: Product managers, industry thought leaders, academic researchers, and others with deep domain knowledge and expertise can anticipate customer needs based on industry trends, best practices, and a comprehensive understanding of the market. #productinnovation #discovery #productmanagement #productleadership

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.

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

The excruciating mistake I made in my first year as an L7 Product Manager at Google:👇 𝗢𝗯𝘀𝗲𝘀𝘀𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗱𝗲𝗰𝗸𝘀 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗲𝗿𝘀 𝘄𝗲𝗿𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗵𝗲𝗿𝗲 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝘁𝗼 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗺𝗲. Starting with market analysis and competitive matrices is the fastest way to build the wrong thing. Instead, understand your users by: • Shadowing customer support calls (I heard pain points I'd never see in data) • Running weekly user interviews (even if your calendar is packed) • Actually using your product daily (I found 3 critical bugs this way) • Mapping their full journey (not just the happy path) Once you make users your north star, you stop building what looks good in slides. Because PMs who master user empathy outperform strategists every time. 🙌