Experimentation’s build-vs-buy debate is asking the wrong question

Product teams traditionally choose between building in-house experimentation platforms, which provides control but requires substantial engineering and maintenance resources, and purchasing standalone tools, which accelerate testing but can isolate experiment data from behavioral analytics. A growing third option integrates experimentation, feature flagging, and product analytics in one platform, reducing data silos and allowing teams to define audiences, measure primary and secondary effects, and investigate user behavior from a shared source of truth. The text argues that data location and workflow integration are as important as cost, implementation time, and ownership when evaluating experimentation approaches. It highlights Step, a financial platform for teens and young adults, which consolidated experiment exposure, warehouse metrics, and product analytics in Mixpanel, enabling decisions within a single dashboard; after testing a redesigned user experience, Step reported a 14% increase in customers making it their primary bank account.

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