A comprehensive guide to Mapping Cognitive biases to a Customer Product Journey: Behavioral Science Insights for Researchers, Analysts and PMs alike.
In recent decades, behavioral scientists, cognitive psychologists, and many alike have delved deep into understanding human cognition and behavior. Scholarly works have, for instance, uncovered a plethora of biases, fallacies, and cognitive concepts that significantly impact human decision-making processes. Drawing from these academic fields, UX Researchers, PMs, and Analysts can gain deeper insights into user behavior, anticipate cognitive pitfalls, and design user experiences that are engaging, productive, and delightful.
Understanding biases or cognitive filters is crucial for consumer-facing product development as they shape daily user interactions and perceptions. Unfortunately, the process of identifying and mapping biases to customer journeys and developing data-driven mitigation strategies is a difficult and time-consuming one. In this guide, I aim to reference scholarly works on human behavior to provide a process guide, relying on raw customer data to exemplify an end-to-end product cycle.
Literature Review tl;dr
One of my favorite works on cognition is “Thinking, Fast, and Slow” by Daniel Kahneman and Amos Tversky. (Still consider them the most brilliant cognitive psychologists of my time) Kahneman brilliantly elucidates the interplay between 2 modes of thinking that apply to human mind; the intuitive system 1 and thinking system 2. System 1 operates automatically, quickly, and intuitively, relying on heuristics and mental shortcuts to make decisions with minimal effort. System 2 operates more deliberately and analytically, involving deeper reasoning and information evaluation. If you have analyzed data, you would know that system 1 is dominant in daily decision making and is more, unfortunately, more susceptible to biases.
Daniel Simons and Christopher Chabris explore limits of attention and perception, revealing significant implications of inattention blindness for user experience design. Intentional blindness refers to the failure to notice unexpected stimuli when attention is focused on a particular task or object. For instance, in any given digital interaction, a user is likely to overlook critical information if it is not adequately highlighted or presented in a way that captures attention effectively. Recognizing this inherent nature may allow designers to avoid user frustration, confusion or errors in user interactions.
Dan Ariely’s “Predictably Irrational.” Provides experiments on irrational decision-making, studying the role of heuristics and biases in shaping choices, from loss aversion to the decoy effect. Loss aversion bias occurs when an individual places greater weight on avoiding losses than acquiring equivalent gains. In this context, loss aversion can divert focus towards perceived risks or losses, potentially, overshadowing other relevant information. A not so popular but important bias is one that occurs when the introduction of an irrelevant option (decoy) influences a choice between two other options. Known as the decoy effect, the individual’s attention and perception of value are distorted allowing for suboptimal decision-making. The decoy effect is crucial for designers and decision-makers because their decision on product option presentation can significantly affect user attention and decision outcome.
More brilliant works on Cognitive Concepts to consider during product discovery and design include Daniel Gilbert’s exploration of affective forecasting in “Stumbling on Happiness” which identifies how individuals often mis-predict their future emotional states leading to decision-making errors. Similarly, Elizabeth Loftus on false memories sheds light on the malleability of human memory and its implications for perception and judgment. Linguistic anthropologist Deborah Tannen examined how language styles and communication patterns influence social interactions and decision-making processes. Her work on conversational styles sheds light on the nuances of language user and its impact on user feedback, individuals’ interpretations and judgements.
Source to template and full-text including the process guide, select bias definitions, example scenarios, and a case study drawing on Pearson customer reviews: https://www.figma.com/file/ZGTSgB97oUqDXb037PY5gX/identifying-user-biases-from-qualitative-data?type=whiteboard&node-id=0-1&t=CanWsUXOeMIbcK8U-0
By recognizing that cognitive processes such as attention, perception, memory among others shape user interaction and decision outcome, UX researchers, analysts, and consumer-facing roles should feel empowered to create more intuitive, engaging, and effective user experiences. Through the systematic identification and mitigation of biases, product and strategic teams can navigate the complexities of human cognition and decision-making, ultimately crafting products that resonate with users on a deeper level.
