Snowplow

Problem: Experiment results live in a separate tool from product analytics and revenue data, making it hard to see how variations truly impact downstream behavior and LTV. Solution: Convert streams experiment and variation exposure into Snowplow as structured events, aligned with your existing event taxonomy and product tracking. All data lands in the same warehouse tables. Outcome: Teams can join experiments with signups, feature usage, and revenue in one model, revealing which variants drive higher activation, retention, and LTV—not just on-site conversions.

Problem: Standard A/B reports stop at surface metrics like CTR or form submits, hiding how different variations influence long-term user journeys and behavior across sessions and devices. Solution: Convert + Snowplow tag each user with experiment and variation exposure in the event stream. Analytics teams build cohorts and path analyses in their BI tool using this data. Outcome: You uncover which experiences lead to healthier long-term journeys—fewer support tickets, more feature adoption, better retention—and prioritize winning patterns across the funnel.

Problem: Built-in experimentation dashboards use generic attribution windows and logic that don’t match your internal models, causing mistrust in test outcomes. Solution: With experiment data in Snowplow, data teams apply their own attribution rules, lookback windows, and incrementality models directly in the warehouse or modeling layer. Outcome: Stakeholders trust experiment readouts because they’re based on the same attribution logic as the rest of the business, enabling confident rollout decisions and budget shifts.

Problem: Strict privacy, governance, or data residency rules make it risky to rely on black-box experimentation tools that store user-level data outside your controlled stack. Solution: Convert sends only the experiment and variation metadata you choose into your Snowplow pipeline, where all user-level data is stored and governed in your own infrastructure. Outcome: You meet compliance requirements while still running robust A/B tests, with full visibility and auditability of what is collected, where it’s stored, and how it’s used.

Problem: Multiple teams run tests with different tools and naming conventions, leading to fragmented reports and no single source of truth for experimentation performance. Solution: The Snowplow integration uses structured events and a consistent schema for Convert experiment exposure, which can be standardized in your central data models. Outcome: Marketing, product, and growth teams all report on experiments from the same warehouse tables, improving comparability, governance, and the ability to roll up impact across brands or markets.

Problem: Personalization engines often ignore experiment history, missing the chance to tailor experiences based on which variants users have already seen or responded to. Solution: Convert sends experiment and variation data into Snowplow, where it can feed real-time or near-real-time user profiles and decisioning models that your personalization stack consumes. Outcome: You can suppress losing experiences, reinforce winning ones, and trigger tailored journeys based on past test exposure, increasing relevance and conversion without extra tracking hacks.