Product: Unimeal / My role: Head of Product & UX / 2021-2022

How we redesigned recipe swapping and improved completion rate

Product: Unimeal / My role: Head of Product & UX / 2021-2022

How we redesigned recipe swapping and improved completion rate

Product: Unimeal / My role: Head of Product & UX / 2021-2022

How we redesigned recipe swapping and improved completion rate

⚠️ Problem:
Users who dislike a recipe in their personalized plan can swap it for another, but the "Swap" screen is unsorted and hard to navigate. This makes it difficult to find suitable alternatives, negatively impacting user satisfaction and overall product metrics.

⚠️ Problem:
Users who dislike a recipe in their personalized plan can swap it for another, but the "Swap" screen is unsorted and hard to navigate. This makes it difficult to find suitable alternatives, negatively impacting user satisfaction and overall product metrics.

⚠️ Problem:
Users who dislike a recipe in their personalized plan can swap it for another, but the "Swap" screen is unsorted and hard to navigate. This makes it difficult to find suitable alternatives, negatively impacting user satisfaction and overall product metrics.

Research

We planned to test grouping dishes by useful themes for the user. To do this, we conducted a quantitative study to identify the most important dish parameters for users.

To conduct the study, we created a survey and showed it to Unimeal users who had used the recipe swap feature.

We first asked an open-ended question to gather unbiased responses and uncover new insights. Then, we followed up with a matrix question based on our prior research.

The open-ended question didn’t provide new insights, but it confirmed our choices for the next question. 😎

We then calculated the weighted average of responses to rank the parameters based on user ratings.

Unfortunately, I can't show the full image due to confidentiality reasons and my commitment to the signed NDA 🤷‍♂️

Solution

In the first iteration, we grouped the recipes based on the most important parameters for users.

In the second iteration, we focused on one of the most important parameters — ingredients.

We combined this with insights from open responses and previous research, leading to the implementation of a search by recipe name and ingredients.

Conclusion

We learned the most valuable parameters users consider when choosing a dish. The research helped us make more informed decisions and changed our perspective on the importance of specific parameters.

Main Outcome: Increased meal swap conversion.