Use cases & best practice Building in-house analytics DIY Product Analytics Project Checklist A practical checklist for building and maintaining in-house product engagement analytics infrastructure.
This isn’t a scare tactic — it’s a sanity check. If you plan to build your own product analytics infrastructure, here’s what you need to think through.
You can use this as article as a project planning aid, a resourcing reference, or a budget justification tool.
Each section below outlines a part of the solution. Inside each section, you’ll find a series of tasks that cover:
What to build
What to document; and
What to maintain
The Tasks column outlines the things you’ll need to consider and build. The Notes column includes some must-have items and some questions to kickstart your thinking. If you get stuck or would like some advice, you’re welcome to connect with the Accoil team through support .
Task Notes Define events to track (page views, button clicks, feature usage, etc.) Is it standardised across platforms (web, mobile, API)? Choose tracking method Segment, RudderStack, custom SDK, server events? Instrument event collection in product codebase Who owns the schema? Who reviews it? Build or configure ingestion pipeline If not using Segment: set up HTTP collectors, queues, storage buffers. Handle high event volume Will require buffering (Kafka/Kinesis) and scaling plan. Validate no event loss How do we know events aren’t dropping? Version control of event schema What happens when a new feature changes how an event fires?
Task Notes Define transformation logic Flattening, denormalizing, joining, aggregating. Build ETL / ELT pipeline Airbyte, dbt, custom scripts — where does logic live? Set up data warehouse Snowflake, BigQuery, Clickhouse, Postgres — with access controls. Handle schema evolution What happens when event shapes change? Monitor data latency Are we ingesting hourly, daily, near real-time? Document transformations So that others can interpret the results correctly.
Task Notes Define core product metrics DAU, WAU, MAU, retention, activation, frequency. Define engagement score logic What counts as “engaged”? Multiple tiers? Weighted actions? Support trial vs. paid user cohorts Different engagement patterns, different thresholds. Configure user traits How are traits defined, stored, and kept up-to-date? Track changes in traits over time Are historical traits versioned? Segment customers effectively By plan, lifecycle stage, team size, product area, etc. Create clear metric definitions Accessible glossary for all stakeholders. Handle feature rollouts & metric updates Who updates metrics when features ship or change? Enable metric versioning To track evolution of definitions and usage.
Task Notes Determine financial data source(s) Stripe, Chargebee, NetSuite, custom billing DB. Sync financial data into warehouse Securely, and on an acceptable refresh schedule. Define MRR / ARR logic What’s included? Discounts? Refunds? Multi-year deals? Tie financial data to product usage By account, workspace, or user? What’s the join key? Manage data sensitivity and access Who can query revenue data? What audit logs are in place?
Task Notes Identify systems to push data into Slack, Intercom, Salesforce, HubSpot, Jira, Zendesk, Notion, etc. Define data sync logic What data, how often, and under what conditions? Build integration connectors or scripts Or evaluate and embed an integration platform. Manage API keys & OAuth tokens Where are they stored? How are they rotated? Who has access? Monitor rate limits & retries Handle failures gracefully and alert someone. Align data models Does each tool understand the product concepts (user, plan, activity)? Test integration logic end-to-end Simulate edge cases and alerting logic.
Task Notes Classify data sensitivity What’s PII? What’s internal-only? Implement data encryption At rest and in transit. Set up access control Role-based, logged, auditable access to data and dashboards. Monitor access logs Who accessed what, and when? Ensure SOC 2 / GDPR / HIPAA alignment (as applicable) Especially for financial, healthcare, or regulated use cases. Store secrets securely Prefer vaults to plaintext config. Periodically audit data handling processes And rotate credentials on schedule.
Task Notes Build dashboards For PMs, CS, marketing, leadership, etc. Make dashboards contextual & explainable Can people trust and interpret what they’re seeing? Document each chart/metric Purpose, source, filters, caveats. Enable segmentation and filtering Per cohort, timeframe, feature, lifecycle. Support live alerts to Slack/email What thresholds matter? Who should be notified? Determine dashboard ownership Who updates them when things change?
Task Notes Set ownership model Who owns the system — eng, data, product ops? Set up alerting/monitoring For ingestion failures, schema mismatches, query issues. Establish SLAs How fresh should the data be? Who handles incidents? Plan for outages and recoveries What happens if the pipeline goes down mid-week? Set maintenance calendar For schema reviews, refactors, tool upgrades. Track and prioritise technical debt Event naming debt, duplicate metrics, undocumented queries.
Task Notes Estimate infra cost (storage + compute) Cloud spend will grow with usage unless managed. Track 3rd-party tool costs Segment, Looker, Metabase, dbt Cloud, etc. Assess build-vs-buy ROI Cost of engineering time + ownership vs. platform fees. Get stakeholder alignment Does everyone agree on the plan, and what’s being measured? Secure ongoing budget & resourcing This isn’t a one-and-done project.
Most analytics projects start small — a dashboard here, a metric there. But over time, the need for accuracy, flexibility, integration, and reliability grows. And unless you’ve planned for it from the start, the stack begins to crack.
This checklist isn’t to scare you — it’s to help you plan. If you’re building your own product analytics infrastructure, you deserve to go in with eyes wide open.
