A/B Testing

Objective

The goal of this A/B test is to evaluate the effectiveness of the "Recommended for You" personalization feature on the HungryHub homepage. The test will determine if personalized recommendations lead to an increase in user engagement, booking conversions, and overall revenue.

Experiment Design

• Control Group (A): Homepage without the "Recommended for You" section (existing experience).
• Test Group (B): Homepage with the new "Recommended for You" section showing personalized restaurant recommendations.

Key Variables

• Independent Variable: Presence of the "Recommended for You" section.
• Dependent Variables: User engagement metrics, conversion rates, revenue impact.

Key Metrics to Track

Business Goals & KPIs

Product & User Engagement Metrics

A/B Test Execution

Target Audience

• Segment: Users landing on the homepage (new and returning users)

Traffic Allocation

• 50% of traffic → Control Group (A)
• 50% of traffic → Test Group (B)

Test Duration

• Minimum 4 weeks or until statistical significance is reached (95% confidence level).

Data Collection & Analysis

• Use Google Analytics, Superset, Netcore, Posthog, or Hotjar to track user interactions.
• Segmentation:
  • New vs. returning users
  • Logged-in vs. guest users
  • Mobile vs. desktop users

Success Criteria

• If CTR on recommendations > 10% and conversion rate improves by at least 5%, consider rolling out the feature.
• If no significant improvement in conversion or engagement, iterate on the recommendation algorithm.

Risks & Mitigation

Next Steps

1. Run A/A test (check consistency in data collection).
2. Implement A/B test and collect real-time data.
3. Analyze results and decide whether to roll out, refine, or scrap the feature. This structured A/B testing plan ensures data-driven decision-making while minimizing risk. Let me know if you need adjustments based on HungryHub's business priorities! 🚀