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In the early days, you can run A/B tests using Google Optimize and a Spreadsheet Calculator. But as your team grows, this breaks.

You will have collisions (two tests running on the same page). You will have data latency. You will have people peeking. To solve this, mature data teams build an XP (Experimentation Platform).

1. The Architecture of an XP

An XP is not just a UI; it is a pipeline. It automates the statistical rigor we learned in Modules 1-11.

2. Build vs. Buy?

This is the classic dilemma. Do you build your own (like Netflix/Uber) or buy a tool (like Optimizely/Statsig)?

Buy (Optimizely, Statsig, Eppo)

• Pros: Instant setup. No maintenance. Advanced stats built-in (CUPED, Sequential Testing).
• Cons: Expensive at scale. Hard to integrate with custom internal metrics.

Build (Internal Tool)

• Pros: Fully customizable. Deep integration with your Data Warehouse.
• Cons: You need a dedicated Data Engineering team to maintain it. If the pipeline breaks, testing stops.

Recommendation: Buy first. Only build when you have >50 engineers and >100 tests per year.

3. The Cultural Shift

Building the tool is the easy part. Building the culture is hard. An XP is useless if Product Managers ignore the results.

Conclusion

You have now completed the A/B Testing Masterclass. You have moved from simple hypothesis generation to statistical power analysis, and finally to platform architecture.

Remember: The goal of A/B testing is not just to lift conversion rates. It is to reduce the cost of being wrong. By building a rigorous testing engine, you give your company the freedom to take big risks safely.