Farfetch's case for building your own experimentation platform
Blog post from GrowthBook
Farfetch built an in-house experimentation platform, Fabs 2.0, after finding that its hybrid approach of internal tools and an external vendor created performance problems, inconsistent results, and additional analysis work. The platform routes every experiment through a deeply integrated feature-toggling system connected to customer segmentation, tracking, content, recommendations, and messaging, allowing technical and nontechnical teams to conduct tests without code injection. Its experimentation center of excellence focuses on enabling teams through shared hypothesis templates, peer-review clinics, open learning sessions, coaching, and a knowledge base rather than centrally executing tests. Farfetch measures success through a “learning rate” rather than a win rate, defining failure as poorly designed experiments while treating disproven hypotheses as useful outcomes. The company applied this approach to develop its Inspire recommendation engine, which initially underperformed against an external market leader but improved through hundreds of iterative tests over two years and ultimately replaced the vendor. The account argues that building an internal platform is most appropriate when experimentation technology and proprietary data are strategically important and an organization has the technical capacity to maintain it.
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