PRODUCT STRATEGY / MIXED METHODS / QUANTITATIVE RESEARCH
What customers need from performance analytics.
Building off multiple studies to understand what makes search performance analysis difficult and which improvements matter most.
Customers had data, but struggled to trust, interpret, and act on it
Led a mixed-methods research program spanning customer interviews, survey design, competitor analysis, and synthesis.
A prioritized view of customer problems and opportunities for the roadmap
The opportunity
Performance analysis is central to merchandising and search optimization, yet customers often found it hard to understand what the data meant. The research question was not simply which software features people wanted, but what prevented them from acting on their analytics data to improve their search.
How I approached it
I conducted qualitative interviews to examine how customers monitored performance, investigated anomalies, and combined information from multiple analytics tools. Those findings shaped a follow-up survey of 53 participants that quantified challenges and potential features we could add to our product. I used prior research and competitive analysis alongside the interview and survey findings to determine which customer needs to prioritize, then mapped those priorities to the next two quarters of planned product work.
HOW THE RESEARCH BUILT TOWARD A DECISION
01 / EXPLORE
Customer interviews
Understand how people evaluate performance and where decisions break down.
02 / QUANTIFY
Survey · n=53
Measure the relative importance of challenges and potential improvements.
03 / PRIORITIZE
Roadmap comparison
Identify where planned capabilities aligned with customer needs—and where gaps remained.
Research approach shown without confidential feature details.
What the research revealed
Customers needed to understand where performance numbers came from, investigate unexpected changes, and predict the effects of adjustments before making them. Many checked their search data against other analytics tools, making deeper analysis slow and fragmented. The survey highlighted opportunities to simplify how teams manage the settings that control search results, spot performance problems earlier, and make better decisions with clearer visualizations and practical recommendations.
Why it mattered
The research translated broad frustrations into a ranked set of opportunities. Comparing customer priorities with planned capabilities revealed substantial alignment as well as specific gaps, giving product teams a more concrete basis for discussing what to prioritize next. The result was not just a list of feature requests, but a clearer picture of the decisions customers were trying to make and the evidence they needed to make them.
Selected details have been generalized to protect confidential research.