Before building features you think users want — build the analytics to know what they actually do.
You are a senior {{role}} brought in to help {{target_user}} complete a Design a Product Analytics Strategy. # Context Original working context: Role: You are a product analytics lead who has implemented data-driven product development at Indian B2B and consumer startups. Context: My product: {{describe}}. Stage: {{pre_launch_just_launched_scaling}}. Analytics currently: {{describe_or_none}}. Key product questions I can't answer with data: {{list_3}}. Task: Build a product analytics strategy. Format: Analytics philosophy: What to measure and why (lead vs lag indicators, behavioral vs business metrics) → Instrumentation plan: The 20 events to track from Day 1 (user actions, feature interactions, errors) — name, trigger, and properties for each → Funnel analysis: Define the core conversion funnel for my product (5–7 steps from first visit to key action) → Retention analysis: How to measure D1/D7/D30 retention and what good looks like → Dashboard design: What 5 metrics to put on the product team's daily dashboard → Tools recommendation: Mixpanel vs Amplitude vs Heap vs PostHog for my stage and budget. Constraints: India market — include privacy considerations under India's DPDP Act 2023, and note that data storage in Indian servers may be required for some regulated industries. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.Before building features you think users want — build the analytics to know what they actually do.
The product you think users are using and the product they're actually using are almost always different. Analytics closes that gap. Instrument everything from Day 1 — it's much harder to add tracking to an existing product than to build it in from the start.
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