The Power of Data-Driven Decision Making: Avoiding 7 Common Pitfalls in User Research
As a product manager, I’ve learned that making informed decisions requires more than just intuition. It demands a deep understanding of your users and their behaviors. Recently, I embarked on a research project to improve our triaging feature, and I encountered several pitfalls that could have led to inaccurate conclusions. In this article, I’ll share my experience and highlight the importance of avoiding these common mistakes in user data research.
Starting with the Right Questions
Before diving into product enhancements, it’s essential to define the questions you want to answer. Our team asked three key questions: How many Pro customers actively use our issues feature? What’s the distribution of triaging among these customers? And are there outliers in the way users triage certain issue types or statuses?
The Importance of Segmentation
When analyzing user behavior, it’s crucial to consider different tiers of users. In our case, we focused on Pro customers, as they have access to advanced features like AI-powered issue detection. By scoping our data to Pro customers, we gained a better understanding of their triaging habits.
Identifying and Isolating Outliers
Outliers can significantly distort your data, leading to incorrect conclusions. We discovered that a small group of super-users were triaging issues at an alarming rate, skewing our results. By removing these outliers, we got a more accurate picture of our users’ behavior.
Analyzing Cohorts Separately
Different user cohorts often exhibit distinct behaviors. By analyzing our Pro customers separately from our super-users, we uncovered insights that might have been lost in a combined analysis.
Considering Multiple Vectors
When examining user data, it’s essential to consider different vectors of seemingly simple stats. For instance, instead of just looking at the number of users who triage issues, we explored how many users triage issues of each type and to each severity level.
Drawing Hypotheses and Documenting Findings
After identifying exceptional data points, we drew hypotheses to explain these findings. It’s crucial to document these hypotheses and share them with colleagues to gather feedback and refine our understanding.
Collaboration and Continuous Learning
User research is a collaborative effort, and documenting findings helps to build upon previous research. By sharing our results with colleagues, we can identify blind spots, ask better questions, and create more informed hypotheses.
By avoiding these seven common pitfalls in user data research, you can ensure that your product decisions are driven by accurate insights and a deep understanding of your users’ needs. Remember, data-driven decision making is a continuous process that requires ongoing research, collaboration, and refinement.