What do you do if your user insights and user research are not providing valuable information?

If your user insights and user research are not providing valuable information, consider the following steps to address the situation: 1) Review Research Methods 2) Refine Research Questions 3) Target the Right Audience 4) Increase Sample Size 5) Diversify Data Source 6) Collaborate with Stakeholders 7) Iterate and Experiment 8) Seek External Expertise

A common root cause for failed or invalid user insights is when you let your own biases get in the way of actually understanding the customers. This is typically manifested by user research being done with leading and biased questions. For instance asking a customer why they think "feature X is good" implies that you expect them to tell you it is good, and also you imply that feature X is important to you. Try to ask open ended questions like "what would you like of feature X for it to be beneficial to you".

Something important is to re-think the types of questions you’re asking. When designing your research, always note WHY you’re asking each and every question and ways to rephrase the question to get more answers

It sounds simplistic, but if you are not getting insights that are valuable there must be something wrong with the process. You can change the format, the user types or segments, the tools that you choose to collect the information .. there is an unlimited amount of things to vary. Having said all this, sometimes you might not find what you are looking for but you are still getting very valuable insight. Question your prior assumptions or have a look at the data through a new perspective.

- When user insights and research lack value, I'd reassess research goals to ensure alignment with product objectives. - For instance, if user feedback on a new feature is vague, I'd reevaluate research goals to focus on specific user behaviors or pain points. - By reframing goals to seek actionable insights, such as understanding why users abandon the feature, I can gather more valuable information to drive product improvements.

Analyse if the tools and techniques are not right or your questions are not right. The root cause analysis findings will give an idea about the next steps to take corrective actions.

- If current research methods fall short, I'd diversify approaches by combining quantitative and qualitative methods. - For example, supplementing analytics data with user interviews or usability testing provides a more comprehensive view of user needs. - By leveraging different methods, I can uncover insights that may have been overlooked and gain a deeper understanding of user behaviors and preferences.

Reviewing your favorite products astutely can be incredibly illuminating, revealing patterns and choices that offer a lot to learn from. While specific features can spark ideas, focusing on the competitor's market and the landscape they operate in as a whole can help you better understand where your product currently stands. Even products that seem distant from or irrelevant to your use case can help you generate ideas for your own product or look at it from a fresh lens. There's a reason we've coined terms like "Netflix of books".

When there's limited information about the product and it's in the MVP phase, a "Lean UX" approach is beneficial. Methods like design sprints, in short cycles such as a week, allow for iteration based on Understanding, Diverging, Deciding, Prototyping, and Testing. For early-stage products, this provides valuable insights without the need for extensive development effort. For established products, which often feel stagnant and as if "there's nothing new to do," techniques like "Jobs to be Done" help to understand the tasks users are trying to accomplish with your product and the outcomes they wish to achieve, paving the way for new insights.

I've been using generative AI to explore data that was previously more difficult to analyze, such as support conversations, free text entered into the product itself, and other forms of commentary. Mass analysis of text written by users has been very helpful in identifying jobs to be done, sources of frustration, intentions, and other aspects that help unlock pathways for product evolution.