Handle sales pressure with analytical integrity
Overview
This question evaluates a data scientist's skills in analytical integrity, causal inference, experimental design, stakeholder communication, and change management when pressured to convert a correlation into policy.
Interview Scenario: Call Volume vs. Win Rate — Causation vs. Correlation
You support Sales as a data scientist. Leadership observed a positive correlation between call volume and win rate and wants to mandate 2× calls starting next week, asserting it will increase wins. You know the analysis was correlation-only and likely confounded by deal stage and rep mix.
Describe how you would:
(a) Push back diplomatically while maintaining partnership, including specific phrasing you’d use in the meeting and in a written summary.
(b) Propose a safe, low-cost follow-up (e.g., staggered rollout or quota-neutral pilot) that respects quarterly targets, including eligibility, guardrails, and success metrics.
(c) Set expectations on timeline and data quality (instrumentation checks, definitions, and an SLA for reporting) before launch.
(d) Align stakeholders (VP Sales, RevOps, frontline managers) and document decision criteria if results are null or negative.
(e) Handle pressure to publish directional wins mid-pilot without sufficient evidence.
Solution
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