A spec wall, not a buying path
The legacy flow asked for tire size up front. Anyone who didn't know it bounced before seeing a single product.
Sears Auto
Matching drivers to the right tires by how they drive.
We transformed a complex tire search into a five-step guided experience, using IBM Watson to match real driving habits with personalized recommendations.
0-stepguided flow from habit to recommendation
1stquestion is how you drive, not your tire size
3×more qualified matches surfaced per visit
4%driving-profile dimensions scored live
The challenge
Buying tires shouldn’t start with a part number.
Most tire sites assume you already know your size, load index, and speed rating. Drivers don’t. Sears needed a path that met people where they were.
Recommendations no one could explain
Generic "best seller" lists gave no reason a tire fit this driver, so nothing built the confidence to buy.
Habits are messy inputs
Climate, commute length, mileage and priorities had to resolve into a clean, ranked result a shopper would trust
The system we built
From driving habits to a ranked match.
We built the recommendation as a conversation, not a filter. Each answer narrows the field and feeds Watson a richer profile, so by the final step the shopper sees a short, ranked list of products..
Five-step intake into structured driving profile
Watson model scores product fit per drive
Ranked results with a reson for every match
Add-to-cart handoff into the Sears catalog
Outcomes
A buying path people finished.
qualified matches / visit
flow most shoppers complete
It began with the question every tire dealer asks first: How do you drive? The technology simply helped scale that conversation.
product outcome, Sears Auto recommendation flow
Shoppers who'd have bounced at “enter your tire size” now reached a result they understood and a reason to buy the one at the top.
Stack
Start a build
Have a decision flow worth getting right?
Tell us the hard problem. You'll talk to the senior engineers who'll scope and ship it.