A/B testing is a crucial method for optimizing user experiences and validating data-driven hypotheses. In this post, we explore how to conduct effective A/B testing using Snowplow, covering traffic segmentation, assignment logic, and integrating third-party tools.

Q: Can I run A/B tests with Snowplow?

Yes, Snowplow provides the necessary infrastructure to track and analyze A/B tests effectively. While Snowplow doesn’t offer built-in traffic splitting, it can capture user assignment and exposure events, allowing you to evaluate experiment outcomes across the entire user journey.

Q: How can I implement traffic splitting in Snowplow?

To assign users to control or treatment groups, you need to define assignment logic. Tools like Mojito provide frontend traffic splitting with minimal overhead and built-in Snowplow integration. Example setup:

// Mojito: Assign user to variant

const variant = mojito.assign({ experimentId: "exp123", userId: user.id });

‍

// Track assignment in Snowplow

snowplow('trackSelfDescribingEvent', {

  schema: 'iglu:com.mycompany/experiment_assignment/jsonschema/1-0-0',

  data: {

    experimentId: 'exp123',

    variant: variant

  }

});

Alternatively, backend assignment tools like PlanOut provide robust decisioning logic that can be integrated with Snowplow through self-describing events.

Q: What are some recommended tools for A/B testing with Snowplow?

• Mojito: Lightweight, frontend-focused, and source-controlled for easy experimentation.
  
  
• PlanOut: Open source framework from Meta for backend and frontend assignment logic.
  
  
• Optimizely: Commercial tool with extensive frontend support and Snowplow integration.
  
  

Q: How do I track A/B test exposure and outcomes?

To analyze A/B test performance, track the following events:

• Assignment Event: Captures user assignment to a specific variant:
  
  

CREATE TABLE derived.experiment_assignments AS (

  SELECT

    e.event_id,

    e.user_id,

    e.experiment_id,

    e.variant

  FROM atomic.events e

  WHERE e.event_name = 'experiment_assignment'

);

• Conversion Event: Captures outcomes associated with each variant:
  
  

CREATE TABLE derived.conversion_events AS (

  SELECT

    e.event_id,

    e.user_id,

    e.variant,

    e.conversion_value

  FROM atomic.events e

  WHERE e.event_name = 'conversion_event'

);

Q: How can I analyze A/B test results in Redshift?

Use SQL to join assignment and conversion tables for outcome analysis:

SELECT

  a.variant,

  COUNT(c.event_id) AS conversions,

  AVG(c.conversion_value) AS avg_conversion_value

FROM derived.experiment_assignments a

LEFT JOIN derived.conversion_events c ON a.user_id = c.user_id

GROUP BY a.variant;

Final Thoughts

Snowplow’s event-driven architecture makes it an ideal platform for conducting A/B tests with precision. By leveraging tools like Mojito, PlanOut, and Optimizely, data teams can implement robust traffic splitting and track outcomes in a unified data set, driving actionable insights for product optimization.

Stay tuned for more advanced A/B testing strategies and analytical frameworks in upcoming posts.