WW (Weight Watchers)
Planning Tools
WW Planning Tools
Client: WW (Weight Watchers)
Timing: Q2 2020 – Q4 2020
Our team aimed to support members who needed to make decisions about what to eat, and when. Over the course of a few months, the team put together a few methods to learn how to support those needs.
My role:
- Lead designer focusing on product and design strategy.
- Tasks included design direction socialization, product vision, prototyping & usability testing, and alignment facilitation in close partnership with data science, analytics and research teams.
Checking the results
Looking back at what we learned
Emails of direct member feedback
Avg App engagement
- Food data was organized in a very siloed & manual way limiting the experience for shopping lists, ingredient based search, eating suggestions and more (Case study link).
- Member navigation issues increased as the experience expanded (Case study link).
- In the moment support tested very successfully.
- Ingredient based recipe search received thousands of requests for more recipes and ingredients to be supported.
- Eating suggestions became one of the most engaged with experiences app wide.
- Looking forward support showed mixed results.
- Members leaned on the experience to locate their favorite foods and recipes.
- Food imagery made it easier for members to locate the foods they were looking for.
- Members experienced navigation issues find & re-finding parts of the experience.
- Pre-made meal plans got a lot of positive qualitative feedback, but required better infrastructure to scale.
- Shopping lists got a lot of positive qualitative feedback, but required deeper food data work to scale.
Next steps
- Opportunity to better support members conducting the same kinds of searches over time.
- Opportunity to apply ingredient based search to app wide experience.
- Opportunity to provide deeper looking forward support through a more integrated experience app wide.
The Task
Opportunity for
better support
Qualitative research identified members were struggling with making in the moment & forward looking food decisions.
Based on those member studies, the team identified an opportunity to better support members’ eating decisions
- For short-term planners – Enabling them to understand what they can eat in the moment.
- For long-term planners – Enabling them to define what they intend to eat beforehand so they can follow a schedule.
The Challenge
Will uncovered needs
translate to action?
- There was a lot of qualitative research highlighting member needs, but no quantitative data to support those insights.
- We established a goal to gather missing quantitative data by testing the broad scale efficacy of members’ desired planning support.
- Brainstorming sessions with our analytics and data science partners helped us determine which member problems we could solve for through data and which behaviors we felt most confident focussing on.
The Hurdles
Lacking the right data
The biggest hurdle was data:
- In order to be able to provide the right kind of support for members, our team needed recipe metadata that didn’t exist.
- For any given recipe, we needed to know what ingredients were included which was no easy thing to tackle even with some advanced data science models applied to our data set.
- A data science ingredient model built could provide a minimal amount of recipes + ingredients for the experience.
The Solution
Beta testing separately
and collectively
We built a set of individual beta experiences that could be used in combination – enabling us to measure both at the macro and micro level.
The majority of the experiences focussed on more in the moment support while the connection between them focussed on support for those that want to look forward.
In the moment
Find recipes based on specific ingredients
In the moment
Lean on pre-built meal plans of varying lengths and preferences
In the moment
Get suggestions on foods to eat
Looking forward
View & manage what you plan to eat all in one place
Looking forward
Access a shopping a list based on your planned recipes
