Our recommendation model showed that a new sorting algorithm the platform engineering team was shipping would reduce click-through rate by about 12%. I put together a detailed analysis with the statistical breakdown and shared it in their weekly sync. The team disagreed — they thought my data didn't account for latency improvements. I pulled additional cohort data, re-ran the analysis, and escalated to our shared director who reviewed the findings. The team ultimately rolled back the change after the director weighed in.
Our recommendation model flagged that a sorting change the platform engineering team was shipping would drop click-through rate by 12% in the enterprise segment. When I presented the finding, they pushed back — their latency data showed meaningful performance gains and they didn't trust that I'd accounted for it. Instead of escalating, I set up a working session where I walked through my methodology and explicitly built their latency metric into a joint impact scorecard. That reframe shifted the conversation from whose data was right to what outcome we both wanted for the customer. We agreed on a phased rollout with a shared holdout group. The click-through risk played out as predicted, and the team voluntarily paused the full launch. No director needed.