Design an A/B test for a recommendation algorithm

Question

Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.

Codemia · Accepted Answer

Start by framing the problem clearly:

1. **What question are we answering?** Be specific about the hypothesis or business question.

2. **What data do we need?** Identify the data sources, time period, and relevant dimensions for segmentation.

3. **What methodology?** Descriptive analysis, causal inference, predictive modeling, or experimentation?

4. **What are the success criteria?** How will we know if the analysis is actionable?

**Exploratory Analysis**: Start with aggregate trends, then segment by key dimensions (platform, country, user cohort, channel). Look for heterogeneous effects.

**Experiment Design**: If running an A/B test, define: randomization unit, primary metric, guardrail metrics, sample size (power analysis), and duration (account for weekly seasonality).

**Causal Reasoning**: When experiments aren't possible, use quasi-experimental methods: difference-in-differences, regression discontinuity, or instrumental variables.

**Statistical Rigor**: Report confidence intervals, not just point estimates. Check for practical significance alongside statistical significance.

**Potential Biases**: Acknowledge survivorship bias, selection bias, novelty effects, and network effects. Discuss how they might affect conclusions.

**Actionable Recommendations**: End with clear, specific recommendations. "We should do X because the data shows Y, which we expect to improve Z by N%." Include caveats and next steps.

Design an A/B test for a recommendation algorithm

MongoDB

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Defining the Analysis Framework

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