Avoid Common Pitfalls in User Research: From Personas to Psychological Insight

Having participated in many user interviews, I experienced the opposite perspective as a participant for the first time, prompting reflection on common misconceptions in user research and how to stay user‑centric without being solely user‑driven.

Misconception 1: Creating Personas Through Q&A

Alan Cooper introduced the persona concept as a virtual representation of real users. While the theory is sound, practitioners often rush to classify and label users, producing overly simplistic and inaccurate traits because people exhibit many facets that vary across contexts and self‑perception.

To build effective personas, conduct pre‑research. For a social reading product, gather data from user accounts—profile, join date, activity level, interests, book lists, purchases, social networks—and supplement with their blogs or micro‑blogs. This groundwork enables focused interview questions rather than starting from scratch.

Misconception 2: Surveying User Behavior Instead of Psychology

Interviewers frequently ask for percentage distributions or frequency statistics, which often result in guesswork. Users may not track exact usage times, and they care more about experience than raw numbers. For reading apps, usage frequency depends more on content (what books are being read) than on the product itself. Quantitative questions should be backed by actual user data or logs.

Research should uncover users' motivations, needs, emotions, and stories before behavior occurs. Helpful prompts include: how users discover the product, how they describe it to others, which features are must‑have versus nice‑to‑have, when they recall and locate the product, and perceived differences from competitors.

Misconception 3: Confusing Individuality with Commonality

An individual’s traits are personal and cannot represent all users, yet personal insights often reveal universal patterns. User research is an art of extracting shared characteristics from individual experiences.

For example, a user prefers a platform that allows easy text copying for note‑taking, revealing two psychological pain points: note‑taking and comprehensive resources. Must‑have features are typically common across users, while nice‑to‑have features reflect diverse individual preferences.

Distinguishing individuality from commonality yields findings that are not anecdotal but provide robust evidence for product decisions.

Sample interview questions for self‑research include:

Describe how you discovered each book in your reading list (search, bookstore, social recommendation, WeChat, events, etc.).

Compare this product with competitors—what stands out, and what are the shortcomings?

Would you recommend the product to friends, and how would you describe it?

Under what circumstances do you recall and use the product?

Demonstrate a planned new feature and observe which excites you most.