Wiki
Tracking Plans
How the podcast archive frames tracking plans as shared event-instrumentation rules for product, growth, analytics, and engineering teams.
Related Wiki Pages
Definition
A tracking plan records the rules for product instrumentation. It lists the events a product should collect and the properties attached to those events. It also records data types, owners, and collection context.
Product, growth, analytics, and engineering teams use the plan before they implement event tracking. Everyone should know what a product action means before the data reaches dashboards, experiments, or activation tools.
Arpit Choudhury gives the clearest DataTalks.Club definition in How to Build a Data-Led Growth Stack. At 13:34, he starts the growth stack with a tracking plan. Teams document each event and event property before they collect the data. They also document user and account properties, data types, semantics, and ownership.
Tracking plans overlap with data governance and schema agreements because they set rules at collection time. They prevent downstream teams from inheriting ambiguous event names, duplicated events, missing properties, or product metrics that nobody can trace back to a real action.
Common Definition
Across the relevant episodes, a tracking plan is less a spreadsheet and more a shared agreement. The team decides which product moments matter, names those events, defines the properties, and records where the event should fire. Then engineers instrument the product and analysts use the same definitions in product analytics, funnels, experiments, and data activation.
Arpit ties this to data skepticism at 10:45-18:27 in the data-led growth episode. A data-led professional should know where data comes from and should question whether it’s accurate. A tracking plan gives that person the context to investigate a signup spike. The spike may come from real users, fake accounts, a client-side button click, or a server-side completion event.
Natalie Kwong explains the downstream reason in ETL vs ELT and the Modern Data Stack. At 17:55-21:22, she discusses raw storage, guardrails, and governance. A tracking plan gives product events enough meaning for warehouse layers and dbt models. Data marts and BI work use the same definitions later.
Guest Differences
Guests agree that teams need explicit event definitions, but they start from different problems.
Arpit starts from data-led growth. He wants product and growth teams to collect the right behavior data, analyze it, and then activate it. Support, sales, engagement and product tools use the same data. His tracking-plan discussion at 13:34-28:52 focuses on event choice and event properties. He also covers collection location and engineer feedback (data-led growth episode).
Natalie starts from the modern data stack. Her episode doesn’t center tracking plans, but it explains why product events need structure before they become warehouse data. Raw ingestion, transformations, governance, and cleanup all depend on knowing what data means (modern data stack at 15:30-21:22 and 43:02-43:45).
Mehdi OUAZZA gives the adjacent platform version in Scaling Data Engineering Teams. At 23:26, he discusses Kafka schemas and schema registries. He also covers allowed changes and change review. That’s not product analytics tooling, but it supports the same principle. Teams need explicit agreements before event data becomes a dependency for other teams.
Event Naming
Event naming is the first visible part of a tracking plan. At 24:43, Arpit uses SaaS examples such as signup, email verification, and project creation. He also mentions teammate invitations, task creation, client creation, and invoice creation (data-led growth at 24:43). The event name should tell analysts which product action happened, while the properties explain the context.
Arpit’s signup example shows the naming problem. A signup event can mean a
clicked button, a submitted form, an email verification, or a completed server
record. At 27:00, he separates client-side and server-side events. Client-side
events work well for intent signals such as clicks and page interactions, while
server-side events work better for completed business actions. A tracking plan
should make that distinction explicit.
Teams also need property names and types. At 13:34, Arpit includes event properties and user properties in the plan. He also includes account properties and data types. Those details let analysts segment a funnel by acquisition channel or plan type. They can also use account size, device, or source without reverse-engineering the event later.
Product Analytics
Tracking plans feed product analytics because product analytics tools need consistent event names and properties. Arpit places product analytics after collection and storage. At 22:50-30:03, he moves from the tracking plan into collection and warehouse storage. Product analytics and BI use the same documented product events. Teams can then study acquisition, activation, retention, and engagement (data-led growth episode).
This also connects tracking plans to experimentation. In Product Analytics and A/B Testing, Jakob Graff explains that experiments need randomization and assignment tracking. They also need stable metrics and power analysis.
At 24:44-37:44, those concerns depend on knowing which events mark assignment and exposure. Teams also need clear outcome events. A tracking plan doesn’t replace experiment design, but it gives experiment metrics a cleaner event base.
Data Quality
A tracking plan is also a data-quality control. It reduces duplicate event names, inconsistent casing, and vague meanings. It also reduces missing owners and undocumented capture points.
Arpit’s fake-signup example at 18:27 shows the operational value. When a metric spikes, the team needs to know which event fired and where it came from. The team also needs properties that can explain the source (data-led growth episode).
That quality work continues after collection. Natalie describes raw-data guardrails and governance at 17:55-21:22 in the modern-stack episode. She also recommends cleanup of unused data at 43:02. Product events therefore need both front-door documentation through the tracking plan and downstream controls in data quality and observability, data observability, and DataOps.
Teams can start with a spreadsheet or document when the event set is small. Arpit still recommends creating the plan before instrumentation. At 20:47, he mentions Avo, Iteratively, and TrackPlan as collaborative tracking-plan tools. Those tools help teams discuss taxonomy and event quality. Engineers still need to implement the events and confirm where each event should fire.
Reverse ETL And Activation
Tracking plans become more important when product events leave dashboards and drive operational systems. Arpit defines activation at 30:03 as making product data available in support, sales, engagement, and product experiences. Support teams can see customer usage before replying to a ticket. Sales teams can prioritize accounts. Growth teams can trigger messages or personalize onboarding (data-led growth episode).
Reverse ETL sends warehouse data back into operational systems. At 37:25, Arpit names Census, Hightouch, and Grouparoo for this job.
Natalie gives the data engineering version at 35:42-39:06 in the modern-stack episode. She describes warehouse tables moving back into source systems or business tools. In both cases, bad event definitions can become bad customer-facing actions.
Customer data platforms create a related path. At 38:20, Arpit describes CDPs as bundled systems for collection, segmentation, and activation. A tracking plan still matters in that setup because the CDP can only segment and activate the events the team defined and collected.
Governance And Ownership
Tracking plans need owners because event definitions change as products change. Arpit includes ownership in the tracking-plan definition at 13:34 and returns to team structure at 46:13-56:08. Data engineers, analysts, analytics engineers, and product operations all touch the stack. Documentation and data literacy decide whether new team members can interpret the events (data-led growth episode).
Governance starts with a small set of decisions. The team needs to decide who can add an event, who reviews the name and properties, and which engineer owns implementation. It also needs a product or analytics owner who confirms the meaning. When an event changes, the team needs a notification path. Without those answers, a tracking plan can drift into stale documentation while the product keeps changing.
Mehdi’s Kafka discussion gives a useful platform analogy. At 23:26 in the scale-up episode, schemas and schema registries help teams control event structure and allowed changes. Product tracking plans do the same kind of work for analytics events. They make change review explicit before downstream models, funnels, experiments, or reverse ETL syncs depend on the event.
Related Pages
These pages cover the concepts that tracking plans define or support.