Customer Driven Development : What, Why & How?
summary
Presents concrete A/B testing practice: a fill‑in hypothesis template, choosing net rentals per day as a primary metric, and using stages / first‑pixel triggers to remove noise. Shows runtime math (example: 30% baseline, 20% minimum uplift, six variants, 200 daily visitors → ~28 days) and explains statistical power, confidence intervals and the danger of statistical ghosts (stopping early gives false positives). Recommends chopping big features, planning follow‑ups, and basing hypotheses on data; cites running 100 experiments, 10% success.
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chapters (10)
- 0:00Introduction to customer-driven development
- 1:13Why focus on the customer
- 2:18What is A/B testing
- 4:46Foundations of a good A/B test
- 6:09Writing a strong hypothesis
- 10:20Choosing the right metrics
- 13:31Runtime of an experiment
- 17:52Eliminating noise in data
- 21:27Avoiding statistical ghosts
- 24:36Follow-up experiments and recap
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