How Grubhub tests big product bets before they ship
Blog post from GrowthBook
Michal Lenik describes Grubhub’s experimentation approach as an upstream, design-led process focused less on traditional A/B testing and more on testing small cohorts, prototypes, and concepts with real users before broad releases. Her teams position design as a business function that helps define solutions early, using AI to accelerate prototyping and research synthesis while allowing designers to focus on design engineering, craft, and product strategy. Examples include a post-order review screen that was gradually tested to reduce customer-care contacts without hurting orders, and merchant-dashboard concepts that shifted direction after merchants preferred a simpler experience over more metrics. Grubhub defines success metrics across design, product, engineering, and business outcomes, then combines quantitative results with follow-up qualitative interviews to understand user behavior and improve underperforming features. Lenik argues that organizations should intentionally decide whether to de-risk products through rapid production iteration or more extensive upstream validation, based largely on how quickly their engineering teams can respond to feedback.
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