Describe a situation where key business stakeholders challenged or disagreed with your data-driven recommendation. How did you structure your analysis, communicate uncertainty and assumptions, and ultimately influence the decision or adjust your approach based on their feedback?

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VirtualInterview.AI · Accepted Answer

I once led pricing analysis for a new mid-tier subscription. My recommendation, based on historical elasticity and A/B tests, was to price at $39 instead of the $29 that marketing favored. My model suggested we’d see ~8–10% lower conversion but 18–22% higher ARPU, with net revenue +9% over 6 months.

Marketing pushed back hard, arguing the higher price would damage brand perception. Instead of debating, I broke the analysis into three parts: what we know (elasticity from past tests), what we’re assuming (that behavior would generalize to this segment), and what we don’t know. I translated uncertainty into best/likely/worst-case revenue ranges and explicitly showed the overlap with their $29 scenario. We agreed on a 4-week experiment: 50% of traffic at $29, 50% at $39, with pre-defined decision rules. The test showed +7.5% net revenue at $39 and no significant change in NPS. With that evidence, marketing supported rolling out the higher price globally.

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