Work

Case study deck · 9 slides

DTC & ecommerce

Illustrative scenario

Personalization that merchandisers actually run

A mid-market DTC brand

Audit & TeardownProcess Redesign & BuildAdoption & Enablement

Illustrative scenario based on our methodology, not a specific client engagement. Figures are representative targets, not claimed results.

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The challenge

What was broken

The recommender was live everywhere within six weeks and technically correct within eight, then became furniture. Merchandisers stopped opening the console because it gave a confidence score, not a reason, so the safe move was to quietly override and move on. Multiply that weekly across every category and the rank order decayed into something nobody trusted or bothered to improve.

Average order value, indexed, in the eight months after the recommender went live but before reason codes and weekly tuning
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The approach

Scored on the Durable AI Index

We scored every surface on the Durable AI Index and found the effort was inverted: the hero banner had a design review and a CMO agenda slot on a surface shoppers glance at once a session, while product pages, cart and SMS, hit five to twenty times a visit, ran untouched vendor defaults. So we moved the effort to match the traffic.

DURABLE AI INDEXImpact85Feasibility78Stickiness8041at startafter redesign
High impact, low stickiness. The gap was the workflow, not the model.
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What we built

The system, not just the model

We redesigned the workflow around the model, then built the pieces that make it run every day.

  1. 1

    Reason-code layer replacing bare scores with plain-English rationale

  2. 2

    Weekly tuning console scoped to PDP, cart and lifecycle messaging

  3. 3

    Override-tracking dashboard feeding manual corrections back to the model

  4. 4

    Reasoned recommendations extended into post-purchase email and SMS

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Inside the build

Where the design hours went vs. where the shoppers were

Each surface plotted by how often shoppers hit it against how much design effort it received before the rebuild.

Hero design hrs/mo: 38PDP/cart on default: 8 moTuning attendance: 95%

Tools & systems

recommendation enginereason-code surfacingmerchandiser tuning UIemail/SMS lifecycle platformoverride-tracking dashboard
Homepage hero
Product pages
Cart
Post-purchase / lifecycle
actively tunedleft on defaultrare per session · hit every session
Shopper frequencyDesign/personalization effort
05 / 09

Results

Illustrative scenario

What good looks like

AOV + repeat

Focus on the two metrics that compound for DTC

5–12%

Illustrative target: lift in average order value

Weekly

A tuning cadence the team owns, not a set-and-forget tool

Trusted

Legible recommendations merchandisers actually use

This is an illustrative case built to represent a pattern we see repeatedly in mid-market DTC engagements, not a real client's measured results. The numbers below are representative, not audited figures from a named company.

AVERAGE ORDER VALUE (INDEXED)100Baseline109After lift
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How it stuck

Adoption is the deliverable

The habit that stuck is unglamorous: fifteen minutes every Monday, three merchandisers, one screen, reading reason codes before anyone overrides. It survived because a merchandiser could repeat the reasoning to their own boss without paraphrasing a black box, and the hero banner didn't get worse, it just got right-sized to a quarterly review.

050100month 6This engagementTypical pilot
Weekly tuning-session attendance, typical unowned tool vs. the reason-coded workflow, over 12 months
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The banner got the design reviews because the CMO looks at it every morning. The cart page got the sales.

Gaurav Bhushan Sharma, 10dem

Gaurav Bhushan Sharma, 10dem
08 / 09

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