Survey and interview signals mapped needs, return barriers, and decision context.
Independent integrated case study · Product marketing × analytics
BGMI: player insight to retention strategy.
A single player decision story: use behavioral evidence and primary research to identify the experience barrier, then turn that signal into positioning and a measurable reactivation plan.
1,257player survey responses
50player interviews
3.7 vs 6.8satisfaction by error frequency
Context & role
Context: In a crowded battle-royale market, the team needed to decide whether to emphasize new content or fix the core experience for returning players. My role: Independent product marketer and analyst responsible for research design, data synthesis, segmentation, positioning, and the measurement plan.
The product question
What was making players less likely to return-and how could a product and marketing team turn that finding into a credible retention motion rather than a generic campaign?
One system: evidence to action
The project deliberately joined the analytical layer to the marketing layer. Research was not the deliverable; it was the input to a sharper product promise and an accountable launch plan.
Behavioral and satisfaction patterns isolated reliability as the priority.
Executive views translated the evidence into an easy-to-read decision narrative.
Segments, positioning, and retain–resurrect–recruit plays made the finding usable.
Analytical readout
Reliability before feature expansion
- Connected behavioral signals with the 1,257-response survey and 50 interviews.
- Used SQL and Python to validate patterns and prepare analysis-ready views.
- Users with high error frequency rated satisfaction at 3.7/10 versus 6.8/10 for low-error users.
Marketing translation
A promise players can believe
Built behavioral segments and decision-profile personas, then anchored the return story in reliability, competitive integrity, and player-created content-not a short-lived promotion.
Recommendation & outcome
Prioritize reliability work and lead reactivation with proof of a more dependable competitive experience. The marketing strategy becomes more credible because it is rooted in the actual experience barrier; the analytics work becomes more valuable because it changes an operating decision.
Product decision
Repair the core loop first.
Sequence reliability improvements ahead of new-feature investment.
GTM decision
Make the return promise specific.
Target lapsed segments with proof-led messaging and creator participation.
What I would measure next
Track reactivation rate, error frequency, first-week gameplay, weekly engaged payers, creator-content participation, and 30-day return retention by segment. That connects the product fix, message, and commercial outcome in one measurement system.
SQLPythonTableauSurvey analysisUser interviewsSegmentationPositioningRetention strategy