DoorDash builds an explore-exploit ML model to select the best merchant image for each consumer
DoorDash's image selection was static — every consumer saw the same single image per merchant, often the best-selling item's photo, which could be an unrepresentative side dish rather than an entree and did not adapt to individual consumer preferences.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Image pool assembly
A pool of six images — five top-selling items plus a header image — is assembled for each merchant per user session.
Tools used
multi-arm bandit algorithmUCB
Outcome
The Image EnE explore-exploit model selects the most compelling image from a pool of six per merchant per user session, and an A/B test showed improvement in new restaurant trials while maintaining conversion rate and order frequency.
What failed first
An image rotation experiment improved clicks and new restaurant trials but hurt overall homepage conversion — fresh images attracted interest and click-throughs, but consumers were confused when previously-rejected merchants reappeared differently, generating friction that reduced conversion.