Impact of electronic data on user behavior

Your transaction data can predict your personality. A study of 6,408 users and 4.5 million transactions showed that financial behavior correlates with Big Five traits Models can predict them with up to 64% AUC. Most predictable: Conscientiousness, Neuroticism 💡 Least predictable: Openness (too abstract for spending data) Examples of behavioral signals: 1. Conscientious users → consistent spending, Square Cash usage, beauty and clothing categories 2. Neurotic users → discount stores, low-value purchases, reduced category diversity Every prediction was backed by interpretable logic using rule extraction and counterfactuals. 🧠 91% of users had a unique explanation for their label. This wasn’t pattern matching at the group level. It was individual-level reasoning. From spending frequency to category diversity, it’s possible to derive: – Personality traits (Conscientiousness, Neuroticism, Extraversion) – Behavioral consistency and volatility – Emotional stability indicators – Self-control and planning tendencies Power of data and the direction for behavioral products. Explainable, adaptive, and grounded in real user activity.

Your data knows more about you than you think and it's not just about privacy We all know our phones track us. But few of us realise what that data really says—and what companies can do with it. In this episode of Lancefield on the Line, I speak with Professor Sandra Matz, psychologist, data scientist and author of Mindmasters, about the surprising power and peril of our digital footprints. This is one of the most stimulating and disturbing conversations I’ve had on the show. Here are the top five takeaways: 1. Digital footprints go beyond what you post; they include everything from your GPS data to how often your phone runs out of battery. 2. Machine learning can infer your personality, values, and mental health from these "behavioural residues", often more accurately than people close to you. 3. There are significant benefits, such as early detection of emotional distress or AI-powered mental health support when used ethically. 4. Federated learning is a game-changer, allowing AI to help you without harvesting your data. Trust becomes built-in, not blind. 5. The Evil Steve Test is essential for leaders; if your data practices were used by someone with bad intentions, would they still feel ethical? This conversation will make you think differently about your phone, your organisation, and yourself. Tune in and explore how to lead and live with greater ethical awareness in the age of data.

Every product team strives to understand their users, but traditional methods like surveys, interviews, and usability tests only tell part of the story. They capture what users say - but not always what they do. The real insights lie in their actions, and that’s where clickstream analysis changes the game. Clickstream data is the digital trace of user behavior - where people click, how long they stay on a page, the paths they take, and where they drop off. At first glance, it seems like just a collection of numbers, but hidden in that data is a story - a real, unbiased view of how users interact with a product. For UX researchers, this kind of data is invaluable. It helps uncover behavior patterns that might not surface in traditional research. It highlights friction points, moments of hesitation, and places where users disengage. It shows what features are actually being used versus what people say they use. It helps measure the impact of design changes and track engagement over time. But analyzing clickstream data requires more than just counting clicks. The key is going beyond the surface and asking the right questions: What patterns separate engaged users from those who leave? When do people tend to drop off, and what factors contribute to it? How do different types of users interact with the same experience? Can we predict future engagement based on past behavior? To answer these kinds of questions, we used multiple methods: - Tracking engagement trends helped us understand how user behavior evolved over time. - Forecasting future engagement used time-series analysis to predict upcoming trends, revealing whether engagement would remain stable or decline. - Predicting user behavior leveraged machine learning to anticipate which users were likely to continue engaging and which might churn. - Estimating dropout risk with survival analysis pinpointed the moments when users were most likely to disengage, helping identify critical intervention points. Clickstream analysis isn’t a replacement for usability research, but it adds another layer to how we understand user behavior. Usability testing tells us why people struggle with a design, but clickstream data shows where and when those struggles happen in real-world use. Together, they create a more complete picture of digital experiences. UX research has always been about understanding people, and in a world where user interactions generate more data than ever, clickstream analysis helps see beyond what users say and into what they actually do.

Ever wondered why some checkout pages feel like they were designed just for you? NORBr’s article shows that a tailored checkout is built on smart data and precise design tweaks. Imagine this: You add items to your cart, and your checkout page will display the payment options you actually use. It even highlights shipping choices that match your past behavior. The result is a smoother, more engaging process that slashes cart abandonment and boosts conversions. But what exactly makes a checkout feel so personal? Is it just a matter of color and layout or is there a deeper strategy at play? We reveal that it is all about leveraging user data. Businesses can predict which options will make you feel comfortable by analyzing how you shop. They then display familiar offers that cut friction and build trust without you even realizing it. Consider the impact of a minor tweak: A checkout page that rearranges options based on your previous orders or a payment method that appears precisely when needed. These minor changes can lead to big jumps in conversion rates. Here is the twist: How do these tailored tweaks work behind the scenes? What data do companies collect to shape this experience? Can these personalized touches genuinely change the way you shop online? The article explains that even minor adjustments can lift your entire e-commerce game. When shoppers see options that match their habits, they feel understood. That sense of familiarity nudges them to complete the purchase. There is more to discover about how these strategies are evolving. How will future checkouts adapt as technology and consumer behavior change? What hidden elements might we see that will make online shopping even more intuitive? Stay tuned. The next chapter in e-commerce success may reveal secrets that could revolutionize your checkout experience and turn a standard process into a truly tailored journey.