Brainstorm how to optimize email engagement

You are responsible for lifecycle email programs and need to lift on-site engagement that can be attributed to email. a) Pick a single primary objective metric plus 2–3 guardrail metrics (for example, unsubscribe rate, spam complaint rate, session depth). b) Produce at least 10 specific interventions spanning targeting, timing, content, and system levers (such as send-time optimization, subject/body variants, frequency caps, personalized recommendations, reactivation cohorts, triggered versus batch sends, pre-header tests, multi-subject holdouts, copy length, AMP/email actions); for each one, estimate the expected lift and the risks. c) For your two strongest ideas, design rigorous experiments: control/holdout setup to measure incremental lift compared with no-email and compared with the status quo, traffic allocation, deliverability controls (bounces, spam placement), contamination mitigation, sequential testing or MDE-based duration, multiple-comparison handling, and long-term retention readouts. d) List the instrumentation/data you need (deliveries, opens, clicks, device, locale, user email eligibility, prior activity) and explain how you will detect cannibalization with push/notifications. e) Prioritize using RICE/ICE and propose a safe ramp if early wins move the primary metric but harm a guardrail. Overview: This question tests a data scientist’s ability in lifecycle email optimization, measuring incremental on-site engagement attribution, experiment design, metric selection, and deliverability/guardrail trade-offs within Analytics & Experimentation.