Explain switch to product analytics and company fit

# 1) 90-second opener: Why Attentive + why DS → Product Analytics (sample talk track) - Why Attentive: I’m drawn to durable, measurable value. Messaging is a high-intent, first‑party channel with line-of-sight to revenue and a hard compliance bar—exactly the kind of environment where disciplined experimentation wins. Attentive’s scale, deliverability moats, and merchant focus mean small product changes can create outsized ROI. - Why transition: I moved from model‑centric DS to product analytics to own business levers end‑to‑end—problem framing, experiment design, decisioning, and post‑launch adoption. I want to be accountable for shipping outcomes, not just algorithms. - Problems I want to own: subscriber growth and trust (opt‑in quality, double opt‑in completion), message relevance and incrementality (RPM, CTR/CVR, holdouts), and onboarding time‑to‑value for merchants. - End‑to‑end example: I led a sign‑up unit redesign: reframed the question (growth vs. list quality), defined success (incremental subscribers and 7‑day unsubscribe), ran a staged ramp with guardrails, shipped globally, and drove +9% list growth with stable unsubscribes. - How I measure success: business impact first—incremental revenue per message (RPM_inc), merchant retention/expansion, subscriber health (unsub/complaints), and cost to serve. Model metrics (AUC) support these, but the scoreboard is dollars, activation, and trust. # 2) Trading rigor for speed: decision, guardrails, and executive communication - Situation: One week before a peak sales event, design proposed a new on‑site SMS sign‑up experience predicted to lift capture. A full A/B across merchants would miss the window. - Choice: Prioritize speed with a smaller, sequential test and staged rollout. - Approach: - Predefine the decision: ship if capture rate lift ≥ +5% and 7‑day unsubscribe delta ≤ +0.3 pp. - Sample: 30 merchants spanning traffic tiers; 25% traffic to variant; CUPED to reduce variance; sequential monitoring (alpha=5%, beta=20%). - Guardrails: immediate rollback if complaint rate > 0.1%, deliverability dips > 1 pp, or legal opt‑in artifacts missing. - Ramp plan: 25% → 50% → 100% over 3 days if thresholds hold; kill switch and auto-revert baked into config. - Communicating risk to a director: - One‑slide TL;DR: upside (+6–10% capture) vs. bounded downside (max −0.2% net weekly sign‑ups under worst case, limited to 25% traffic for 48 hours). - Expected value framing: EV = p(uplift) × incremental subs − p(no effect) × test exposure cost; plus a risk matrix across compliance/deliverability. - Clear ownership and timeboxes: who watches dashboards hourly, who flips the switch, and what “red” looks like. - Outcome: Shipped in time for peak; realized +8.4% capture with stable 7‑day unsub; scaled safely. Why this works: You convert missing rigor into explicit guardrails, bound the blast radius, and communicate in business terms (EV, downside limits) instead of statistical jargon. # 3) 20‑minute call, interviewer 10 minutes late: structure for speed - Objective: Deliver value in ~10 minutes without derailing flow. - Structure (BATON: Bottom line, Agenda, Test assumptions, Options, Next steps): 1) Bottom line first (30–45s): concise answer to their ask with a clear recommendation and metric. 2) Two closed‑ended clarifiers (30s): “Primary success metric is incremental RPM, not raw revenue—correct?” “Hard guardrail on daily unsub is +0.3 pp—yes?” 3) Approach summary (3–4 min): data needed, method, constraints, timeline. Use three headlines, not a tour of details. 4) Risks & guardrails (1–2 min): name the riskiest assumption and your mitigation; define a stop‑loss. 5) Align on decision and owners (1–2 min): who decides, by when, what artifact you’ll send (1‑pager) and the next meeting. - Tactics to keep momentum: - Speak in headlines; push detail to follow‑up doc. - Convert open questions into either/or choices they can confirm quickly. - Periodically label-check: “If we hit +5% RPM_inc with unsub stable, we ship—agreed?” # 4) Three Attentive product metrics for the first 90 days Choose a balanced set: growth, efficiency, and trust. - 1) Subscriber list growth rate (LGR) - Why: Top‑of‑funnel for all downstream revenue; quality matters. - Definition: LGR = new opted‑in subscribers / site sessions (or active audience), segmented by source. - Leading indicators to instrument: - Sign‑up unit view → submit → double opt‑in completion. - Cost per subscriber (if paid sources), source mix, mobile vs. desktop. - Time‑to‑first message receipt. - 2) Incremental revenue per message (RPM_inc) - Why: Measures efficiency and true lift, not just attribution inflation. - Definition: RPM_inc = (Revenue_treatment − Revenue_holdout) / Delivered messages. - Leading indicators: - Delivered rate, click‑through rate (CTR), conversion rate (CVR) within the attribution window. - Personalization usage flags (dynamic content, segments, send‑time optimization). - Send frequency per subscriber per week; fatigue curves. - 3) Subscriber health and trust - Why: Sustains deliverability and long‑term LTV under strict carrier/compliance constraints. - Definitions: - Daily unsubscribe rate, complaint rate, carrier filtering rate, hard/soft bounces. - Leading indicators: - Early unsubscribe hazard (Day 0–7), frequency caps adherence, recency‑based saturation. - Content quality proxies (spam terms, MMS vs SMS mix), quiet hours violations. Pitfalls: Optimizing raw revenue can mask churn via opt‑outs; always pair RPM with health. Attribution windows inflate metrics; prefer holdouts or geo/switchback when possible. # 5) Pushing back on a solution with data (STAR) - Situation: Sales/growth wanted to double weekly send frequency to close a revenue gap. - Task: Assess whether higher frequency would be incremental or just pull‑forward revenue while degrading subscriber health and deliverability. - Actions: - Built a hazard model of unsubscribe as a function of send frequency, recency, and content type; identified steep churn beyond 3 msgs/week for mid‑engagement cohorts. - Ran a 10% holdout by cohort to estimate incrementality, not just last‑click attribution. - Constructed scenarios: +2 sends/week produced +11% attributed revenue but only +3% incremental, with +0.6 pp unsub and higher carrier filtering risk. - Narrative to influence: “Short‑term dollars vs. trust ledger.” Framed the trade as protecting deliverability and long‑term LTV. - Proposed alternatives: frequency increase only for high‑propensity segments; dynamic content for others; send‑time optimization; strict stop‑loss on unsub (+0.3 pp) and complaint rate. - Result: Shipped targeted frequency policy (+8% incremental revenue), unsub flat, filtering down 0.2 pp; policy adopted as default with automated guardrails. Why it worked: You reframed success to incremental value and subscriber trust, quantified risks, and offered a safer path to the same goal. # 6) Two incisive questions for the director - Guardrails vs. growth: What non‑negotiable thresholds do we treat as hard stops (e.g., daily unsubscribe delta, complaint rate, carrier filtering), and where are we willing to take calculated risk to accelerate revenue per message? - Next growth unlock: Over the next 12 months, what’s the biggest bottleneck to merchant ROI—deliverability capacity, attribution signal loss, or content relevance—and where should data science invest first (e.g., causal measurement, send‑time optimization, AI‑assisted creative)? # Quick templates you can reuse - Decision guardrail template: Define primary metric, minimum detectable lift, max acceptable harm, ramp stages, rollback triggers, and monitoring owners. - Alignment close: “Primary metric X, guardrail Y, decision by Z, owners A/B, artifact: 1‑pager with baseline, plan, risks. If we hit X and respect Y, we ship—agreed?”