Paid acquisition without a margin view
Symptom
Customer acquisition cost can look acceptable while discounts, returns and fulfilment costs leave little contribution margin by channel, campaign or product.
Ecommerce Intelligence Lab
Where AI can improve your ecommerce margin per order
Your store already produces the signals: orders, returns, sessions, ad costs and email activity. We help you test the few AI workflows most likely to improve margin without replacing the systems your team runs.
4 minutes · results on screen · no email needed
The challenges we see
Four patterns come up in almost every one of the online stores we work with. If one sounds familiar, that is usually where to start.
Returns hidden in SKU and size detail
Symptom
Return reasons, variants and acquisition sources sit in different reports, so teams react after margin has already been lost.
Merchandising decisions made by hand
Symptom
Collections and search results depend on manual rules, so product demand, stock position and margin priorities are hard to balance.
Lapsed customers found too late
Symptom
The team can see who has stopped buying, but not which customers to contact first or when a reminder is worth testing.
Practical AI uses
Four uses we would consider first. Each one runs on data most teams already have.
Use case 01
Search, collection and product ranking
- What it does
- Ranks products and suggests searches using purchase, browse and catalogue signals, with rules for margin, stock or merchandising priorities.
- Data it needs
- 6–12 months of orders, sessions and catalogue attributes; stock or margin fields where available.
- Time to first value
- 6–8 weeks
Use case 02
Product-page and checkout diagnosis
- What it does
- Shows where drop-off differs by device, traffic source, product or payment outcome, then ranks tests against the current funnel.
- Data it needs
- Funnel events, device and source fields, product context and payment failure logs where available.
- Time to first value
- 4–6 weeks
Use case 03
Creative and campaign testing
- What it does
- Turns approved product claims and winning ad patterns into test variants, then compares performance by campaign and product before budget moves.
- Data it needs
- Ad exports, product feed, creative library and margin inputs where available.
- Time to first value
- 4–8 weeks
Use case 04
Reorder and lapse prioritization
- What it does
- Scores customer groups by likely reorder or lapse, so email or SMS teams can test timing and offer rules on a defined cohort.
- Data it needs
- Order history with customer IDs, consent status and email engagement.
- Time to first value
- 8–12 weeks
How we implement
Four steps, with a decision point after each. You can stop after any of them and keep what was built.
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Diagnose
Review orders, returns, ad costs, product data and analytics with the people who own each workflow.
You get: a ranked use-case list, baseline, decision rule and data-gap log
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Pilot
Run one workflow on one channel or category against a baseline agreed before launch, with a holdout or comparison where practical.
You get: a working workflow, measurement readout and go / no-go recommendation
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Integrate
Connect approved inputs to the store, ad or messaging workflow, add review rules and train the team.
You get: a production workflow with access, QA and rollback notes
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Scale
Extend the tested workflow to another category or channel, with monitoring for drift, margin and team adoption.
You get: a rollout playbook your team can own
Expected outcomes
These are estimates, not client results. We confirm or revise each one against your own baseline during the diagnosis.
- Estimate only; assumes enough comparable traffic for a clean test, a clear baseline and changes to page content or funnel friction.
- Estimate only; assumes returns are concentrated in identifiable SKUs, variants or reasons and that expectation-setting or merchandising changes can be tested.
- Estimate only; depends on hours logged during diagnosis, the number of collections or campaigns and how much approval remains manual.
- Estimate only; assumes pilot and integration cost plus monthly incremental contribution margin are measured separately, excluding media spend and unplanned platform work.
Questions store owners and ecommerce operators ask
Something else on your mind? Ask us directly.
Do we need a data warehouse before we start?
No. A first diagnosis can start from exports or approved API access from your store, ad, analytics and email systems. We will show which fields are usable and which gaps affect the pilot.
Can you work with Shopify, WooCommerce or a custom store?
Often, yes. We start with the order, product, session and event data your setup can provide, then connect or write back only to the systems and workflows you approve.
Who owns the models and the data?
You do. Models, code and data stay in your environment or your cloud account. We don't reuse your data to train anything for other clients.
How much of our team's time does a pilot take?
Plan for about three hours a week from the ecommerce owner, plus a short setup session with whoever manages the store platform, analytics or data access.
What if the pilot misses its target?
We agree the target and baseline before we start. If the pilot falls short, you get a written explanation of why, and you are not committed to the next phase.
Reviewed by Faruk Karagöl, CTO ·
Find out which use case fits your store
18 questions, about 4 minutes. You see your score and top three gaps before we ask for anything.
