Product analytics
How to design event taxonomies that scale across multiple products while preserving the ability to analyze product specific behaviors.
Designing scalable event taxonomies across multiple products requires a principled approach that preserves product-specific insights while enabling cross-product comparisons, trend detection, and efficient data governance for analytics teams.
August 08, 2025 - 3 min Read
A robust taxonomy starts with a clear governance model that defines who can modify event definitions, how changes propagate, and how to handle legacy data. Establish naming conventions that reflect user intent and system actions, while avoiding overly granular fragments that balloon maintenance overhead. Invest in a core set of high-level event families—such as engagement, conversion, and error events—so analysts can pivot across products without relearning the same vocabulary. Document the rationale behind each category, provide examples, and maintain a changelog for transparency. This foundation supports scalable analytics by reducing ambiguity, accelerating onboarding, and enabling consistent reporting across the entire product portfolio.
To keep scalability intact, design events with stable identifiers that survive UI changes and feature rewrites. Separate event type from metadata, allowing product teams to attach product-specific attributes without fragmenting the core schema. For instance, track a general “purchase” event with shared fields like amount and currency, while product-specific details such as SKU or loyalty tier live in a flexible attributes payload. Emphasize backward compatibility and versioning so older dashboards continue to function as new events emerge. This approach minimizes data gaps while preserving the ability to slice the data by product line or feature set.
Build a core taxonomy, then extend with controlled product-specific signals.
Start by defining a small, stable core taxonomy that can support cross-product analysis, then progressively expand with product-specific extensions. Map each event to a user journey stage and quantify the business value it reveals, ensuring stakeholders agree on the interpretation of metrics. Create a plugin-like mechanism that lets new products adopt the existing core taxonomy while injecting their unique signals through optional namespaces. This balance between universality and customization helps maintain comparability across products while preserving the richness of product-level insights. Regular reviews prevent drift and keep the taxonomy aligned with evolving priorities.
When introducing product-specific signals, isolate them in dedicated namespaces or attribute blocks. This keeps the universal schema clean and reduces the risk of collisions between fields from different products. Define strict data types, value scopes, and permitted enumerations to enforce data quality at the source. Provide validation rules and automated tests to catch schema violations before data lands in your warehouse. Encourage product teams to justify new attributes with concrete analytics use cases, which helps prioritize changes that deliver measurable value and avoid taxonomy bloat.
Use governance, versioning, and documentation to protect your taxonomy's integrity.
A practical design principle is to treat events as verbs and attributes as adjectives associated with a product instance. Use a consistent event naming pattern such as verb-action, object, and outcome, and standardize the order of fields. This makes it easier to write reusable queries and dashboards across products. Establish a catalog of allowed attributes with clear definitions and examples, so teams know which signals are appropriate for each event. By enforcing consistency, analysts can compare conversion paths, retention patterns, and engagement swings across a heterogeneous product lineup without inventing new data schemas for every release.
Complement a scalable taxonomy with a robust data dictionary and lineage tracing. Track the origin of each event, the teams responsible for its creation, and how it has evolved over time. Implement telemetry that logs changes to schemas, mappings, and job pipelines to prevent silent regressions. Provide visualizations that show how product changes affect analytics results, enabling faster root-cause analysis when metrics diverge. This discipline helps governance while preserving the ability to drill down into product-level detail whenever needed.
Enforce contracts, validation, and stakeholder alignment across teams.
As products diverge, create clear boundaries in the taxonomy for shared versus product-specific signals. Shared events should remain consistent across the portfolio, while product-specific events live in isolated namespaces. This separation reduces cross-talk and preserves the interpretability of dashboards. It also enables teams to evolve their features independently without forcing a global schema overhaul. The result is a scalable framework that supports both broad portfolio metrics and deep product analytics, delivering insights at the right level of granularity for stakeholders.
Leverage schemas and data contracts to formalize expectations between product and analytics teams. Use interface definitions to specify required fields, optional attributes, and validation constraints. Employ automated schema enforcement at data ingestion points to catch anomalies early. Regularly sync with product managers to align on upcoming changes and to review the impact on downstream analyses. By codifying agreements, you reduce frictions during releases and maintain trust in the analytics outputs used for strategic decisions.
Regularly review, prune, and evolve the taxonomy with stakeholder input.
When scaling, adopt a tiered approach to event collection that matches business needs with technical feasibility. Start with a minimal viable set of events that guarantee core insights, then layer on richer signals as product complexity grows. Use sampling and aggregation strategically to manage volume without sacrificing the quality of cross-product comparisons. Maintain a preference for openness: document decisions, share dashboards, and invite feedback from a diverse set of stakeholders. This openness accelerates consensus and reduces the likelihood of divergent interpretations across teams.
Maintain a living taxonomy by instituting periodic health checks and sunset policies. Monitor usage patterns to identify underutilized events that could be pruned or retired, and reallocate resources to signals with higher analytic value. Establish a formal review cadence that includes data engineers, product leads, and BI analysts. The reviews should assess alignment with strategic goals, data quality, and the practicality of extending the taxonomy to new products. A disciplined refresh process keeps the framework relevant without becoming cluttered.
In a multi-product context, the ability to analyze product-specific behaviors hinges on disciplined segmentation. Define segments that reflect both product identity and user behavior, ensuring they map cleanly to the taxonomy's event blocks. Use segment templates to promote consistency while allowing twisting filters for product nuances. Provide example analyses that show how a single behavior manifests differently across products, highlighting where shared patterns converge and where divergence demands separate interpretation. This clarity enables teams to derive actionable, product-aware insights without sacrificing cross-product comparability.
Finally, invest in tooling that supports governance and exploration. A well-designed catalog with search, tagging, and lineage visualization helps teams discover relevant events quickly and understand their origins. Integrate data quality checks, version control for schemas, and alerting for schema drift. Equip analysts with notebooks or dashboards that demonstrate best-practice queries across products. By combining governance with powerful exploration capabilities, organizations sustain scalable analytics that reveal both universal trends and unique product stories.
