Liv Up — Shopping List optimization on a Foodtech
Overview
Product Designer — discovery through handoff, working alongside a PM and a data analyst.
- Problem
- The catalog grew from about 100 to over 400 items, but repeat customers kept buying the same frozen meals and never saw the rest.
- What I did
- Explored where to surface new products without slowing down a "get my usual and go" routine, and landed on a shopping list with discovery built in.
- Result
- In an A/B test, users bought 20% more variety. Over six months, their average order was 13% higher.
Problem_
Liv Up built its reputation on curated, healthy frozen meals. During COVID we identified a demand to include traditional grocery items, but they weren’t selling as much as internal research suggested they would.
When they were bought, the new items were well-reviewed — but they weren’t being bought enough.
From previous data we knew that users who purchased a greater variety of items had significantly higher retention rates. So low variety was both a revenue problem and a churn signal.
Before - Around 100 items
Frozen meals
After - Over 400 items
- Frozen meals
Produce
Dairy
Meats & Seafood
Bakery and sweets
Pantry
Drinks
Discovery_
With help from our data analyst first I looked into who was buying the new products.
New customers were buying them just fine. But when we looked into our users who had been active for six months or more, we noticed a high rate of repeat purchases: the same items, month after month. Moreover, when they did buy different items, these tended to come from categories similar to those they had already purchased.
After a few months, their purchasing preferences seemed to have crystallized.
Interviews_
I ran interviews with repeat purchasers looking for patterns across users with similar purchase habits. My goal here was to find out how users decided what to buy and how they use our app to find their items.
What all of them had in common is: once they knew what items they liked they stopped navigating through our catalog to discover new items. They knew where their favorite items where so they went straight to their respective categories or searched directly for them on our search bar. Some of them even just copy pasted items from their last order, made a few adjustments and called it a day.
So we thought, when do we present new products to repeat buyers without feeling intrusive or complicating their shopping and checking out routine to the point of raising churn rates?
The behavior that made them valuable customers, high intent and low friction,
also made the expanded catalog invisible to them.
Solution_
At first we explored a Favorites page
It gave users a place to save regular buys, with new items alongside. But a page built around past purchases reinforced the tunnel vision we were trying to break, and new items felt out of place.
Tried moving the position of recommendations
Instead of a generic section at the bottom, recommendations now sat inline, next to the item they related to. Someone who always bought a chicken dish would see a new protein suggested right beside it.
Then pivoted to a shopping list that encourages the discovery of new products
We renamed the section from “Favorites” to “Shopping list”, shifting the mindset from “things I like” to “things I'm going to buy”. An empty list now starts seeded with items from past orders, not a blank screen.
Added shortcuts to help navigating
Discovery shortcuts (New arrivals, Recommended, Previously bought, Best reviewed) gave users clear paths into the new catalog without forcing them to explore.
At last, we added a search field
Users could find items for their shopping list or cart without leaving the page, streamlining the whole experience.
Results_
Through A/B testing, users with the shopping list enabled bought a 20% greater variety of items, the metric we were designing for.
Over the following six months, that variety showed up in the basket: users who adopted the shopping list had a 13% higher average ticket. On purchases with a completed order, sessions ran 2 minutes longer on average, much of it spent on the shopping list page.