Case study deck · 8 slides
DTC & ecommerce
Illustrative scenarioA mid-market DTC brand
Illustrative scenario based on our methodology, not a specific client engagement. Figures are representative targets, not claimed results.
The challenge
The brand had bought a capable recommendation engine. It sat unused. Merchandisers could not see why products were surfaced, no one owned the weekly tuning, and the homepage hero got all the attention while the high-frequency surfaces were ignored.
The technology worked. The operating model around it did not.
The approach
On the Durable AI Index this was high impact, low stickiness. The fix was not a better model, it was a workflow the merchandising team would actually run.
We focused the effort on the surfaces shoppers hit every session, product pages, cart, and lifecycle messaging, and made the system legible so merchandisers would trust it.
What we built
Results
Illustrative scenarioFocus on the two metrics that compound for DTC
Illustrative target: lift in average order value
A tuning cadence the team owns, not a set-and-forget tool
Legible recommendations merchandisers actually use
Representative of the outcome this approach targets for a DTC brand. Not an actual client result.
How it stuck
Because the merchandisers helped shape the workflow and could see why the system recommended what it did, they ran the weekly tuning themselves. The tool stopped being a dashboard nobody opened and became part of how the team merchandises.
“A recommender the team does not trust is shelfware. We designed the workflow and the transparency first, then the lift followed.”
Gaurav Bhushan Sharma, 10dem
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