Product Analytics Implementation Plan

You're Probably Tracking the Wrong Events

A PM at a Series B fintech company once showed me their Mixpanel dashboard. 847 tracked events. "Button Clicked." "Page Viewed." "Modal Opened." Eight hundred and forty-seven events, and they still couldn't answer the question: "Why are users dropping off after the first deposit?"

The problem wasn't their analytics tool. It was the absence of a tracking plan.

The Data You Collect Shapes the Decisions You Make

Most teams instrument analytics reactively. Someone asks a question, nobody can answer it, so they add a tracking event. Over months, this produces a graveyard of disconnected data points that describe what happened without explaining why.

Amplitude's 2024 Product Analytics Benchmark found that the median B2B SaaS company tracks over 500 events, but only 12% of those events are referenced in any dashboard or analysis within a given quarter. The rest is noise — expensive noise, if you're paying per event.

A tracking plan flips the approach. Instead of starting with "what can we track?" it starts with "what decisions do we need to make?" Then you work backward to the data required. If your biggest question is "why do trial users not convert?", your instrumentation plan should map every step of the trial-to-paid journey with properties that capture context: plan type, feature usage depth, time in trial, and interaction patterns.

Gibson Biddle, former VP of Product at Netflix, talks about the "proxy metric" concept — measurable numbers that correlate with what you actually care about. A good analytics implementation plan identifies these proxy metrics and instruments them precisely, rather than carpet-bombing your product with generic click trackers.

How This Prompt Helps

This prompt creates a structured analytics implementation plan tailored to your product and analytics tool. You provide your product context, key user flows, and top questions — and it produces a complete event taxonomy, property definitions, user attributes, and a funnel structure that answers the questions you actually care about.

The output includes naming conventions, property schemas, and even suggested dashboard layouts. This means your engineering team can implement it directly without ambiguity about event names or property formats.

When to Reach for This

• You're implementing analytics for the first time and want to do it right instead of bolting events on ad hoc
• Your existing analytics are a mess — hundreds of events, no naming convention, and you still can't answer basic product questions
• You're migrating to a new analytics platform (say, from GA4 to Amplitude) and want a clean start
• A new major feature is launching and you need a tracking plan before engineering starts instrumentation
• Your data team keeps asking product to define what events and properties they need

What Good Looks Like

A strong analytics plan has a clear naming convention (e.g., Object Action format like Article Viewed, Subscription Started), properties that capture meaningful context (not just that something happened but the circumstances around it), a mapping from business questions to specific events and funnels, and a plan for data validation. You should be able to hand it to an engineer and have them instrument it without a single follow-up question.

Sources

• Product Analytics Benchmark Report 2024 — Amplitude
• The Art of Product Strategy: Proxy Metrics — Gibson Biddle