The goal is to allow the user rating recipes more intuitively, to help the user feeling more motivated to rate recipes, and to encourage the user speak out their opinions.

At Marley Spoon, enhancing the recipe rating feature was pivotal for both user engagement and culinary development. Recognizing that user feedback directly informs our culinary team's decisions on recipe refinement and selection, we aimed to create a more intuitive and motivating rating experience. By decoupling the "done cooking" and "rate recipe" actions, and allowing users to rate recipes at their convenience, we sought to foster a seamless feedback loop. This initiative not only empowers users to share their opinions effortlessly but also ensures that Marley Spoon remains their preferred choice for quality meal experiences.

After the launch on May 26, Marley Spoon's event count increased from approximately 400 events to a peak of nearly 1,000 events by early June, marking a growth of around 150% in less than two weeks.

Following the initial growth spike, Marley Spoon consistently maintained daily event counts between 700 and 1,000, showing a steady engagement level without significant drops (a variation of only about 10-15%).

Despite Dinnerly’s rapid rise in engagement starting in late July, Marley Spoon’s event count remained strong, retaining approximately 80-90% of its peak performance from the initial growth phase.

How do we plan and implement the most impactful user research methods before launch?

Which user challenges should we tackle first? After exploring multiple pain points, we chose “rating” as the main focus.

How do we engage product managers and developers from the start to refine ideas quickly? Leveraging Agile sprints allowed us to iterate on UI designs in real time.

How can an ideation workshop swiftly align the team on the optimal user journey while spotlighting important considerations?

How do we craft solutions that balance brand identity, user needs, and feasibility—ready for testing and implementation?

What can both qualitative and quantitative insights teach us post-launch, and how do we use those findings to drive continuous improvements?