Having managed our product's experimentation pipeline for the past three years, I've relied heavily on Consensus for its unified view of user insights and A/B test results. However, as our team scales and our hypothesis complexity increases, I've begun a methodical evaluation of potential successors. This isn't a decision made lightly; it involves auditing our current usage, defining must-have capabilities, and planning a structured migration to minimize data loss and team disruption.
My primary impetus for exploring alternatives stems from several concrete limitations we've encountered:
* **Complex Cohort Analysis:** Analyzing test results for dynamically defined user cohorts (e.g., "users who performed action X in the last 7 days but not action Y") is cumbersome and often requires exporting raw data.
* **Statistical Depth:** While adequate for standard significance tests, we require more advanced options like sequential testing for high-velocity pipelines and Bayesian methods for certain product areas.
* **Integration Friction:** The depth of our data pipeline (Snowflake, dbt, custom event tracking) necessitates a tool with more flexible and granular data ingestion and transformation capabilities.
A thorough exit plan must address several key phases. I've structured our approach as follows:
**Phase 1: Current State Audit**
1. **Catalog all active and historical experiments:** Document every test's configuration, key metrics, and current status within Consensus.
2. **Inventory all integrated data sources:** List every connection (e.g., Segment, production database, CRM) and the specific events or tables synced.
3. **Document key reports and dashboards:** Capture screenshots and note the underlying queries or filters for all shared views and automated reports.
**Phase 2: Requirements & Vendor Evaluation**
We are building a weighted scoring matrix based on these core criteria:
* **Data Infrastructure:** Native support for our warehouse (Snowflake) and the ability to define metrics via SQL or a version-controlled transformation layer.
* **Statistical Rigor:** Support for sequential, Bayesian, and multivariate testing methodologies.
* **Cohort & Funnel Flexibility:** Ability to slice results by complex, retroactive user cohorts without pre-definition.
* **Team Collaboration:** Granular permissioning, comment threading on results, and integration with our project management tools.
**Phase 3: Staged Migration & Parallel Run**
The highest-risk element is historical data continuity. Our plan involves:
1. Running a selected new tool in parallel with Consensus for all new experiments for a minimum of one quarter.
2. Conducting a consistency check by replicating the analysis of 2-3 concluded experiments in the new platform to validate statistical alignment.
3. Developing a one-time export script for historical experiment metadata. Raw event data will remain the source of truth in our warehouse; we will only migrate experiment definitions and results summaries.
```sql
-- Example of a query to extract experiment metadata from our data warehouse
-- (This structure is simplified, assuming a well-instrumented source)
SELECT
experiment_id,
experiment_name,
variant,
user_id,
MIN(exposure_date) AS first_exposure,
COUNT(DISTINCT session_id) AS sessions,
SUM(CASE WHEN event_name = 'conversion' THEN 1 ELSE 0 END) AS conversions
FROM
production.fact_user_events
WHERE
experiment_id IS NOT NULL
AND exposure_date >= '2023-01-01'
GROUP BY
1,2,3,4;
```
**Phase 4: Decommissioning**
This final phase includes revoking API keys, archiving final data extracts, and updating all internal documentation and onboarding materials to reflect the new tool's workflows.
I am keen to hear from others who have undertaken a similar transition. What were your specific pain points with Consensus that prompted the switch? Which evaluation criteria proved most decisive for your team? How did you handle the reconciliation of historical experiment data, and were there any unexpected pitfalls in the migration process?
— Amanda
Data > opinions