AI readiness for retail and e-commerce companiesEvery sale is recorded. The customer isn’t.
The till and the checkout don’t tolerate ambiguity about what sold. Everything around them does: one shopper as four profiles, a product with three codes, a return recorded in finance but not in the store. Personalisation, forecasting and pricing plays all depend on those joins.
Starts with a 30-minute call. Commits you to nothing.
The AI on the table
The AI plays proposed in retail and e-commerce.
What vendors and plans put forward in retail and e-commerce, what each play needs from the data, and the gap that holds it back. In a real Read, each call rests on your Truth Score™ (a score out of 100 for how far your data can be trusted) and the records behind it. The calls here are an illustrative read, not a benchmark.
- Blocked
- Needs
- One shopper across store, email and loyalty
- Held back by
- Several profiles per shopper, conflicting consent
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Demand and stock forecasting
“Order the right stock for every channel”
What demand forecasting needsNeeds work- Needs
- Sales by product across channels, with stock-outs
- Held back by
- Marketplace sales missing from the ERP history
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Price and promotion optimisation
“Optimise markdowns and promotions”
What pricing and rebate optimisation needsNeeds work- Needs
- Net prices after promotions and returns
- Held back by
- Promotions never tagged on the order
-
Customer service agent
“Answer order and returns queries automatically”
What customer service automation needsDeploy now- Needs
- Tickets tied to orders and customers
- Watch for
- Guest-checkout tickets tied to no order
-
Repeat purchase and lapse prediction
“Win back lapsing customers”
What customer churn prediction needsNeeds work- Needs
- Purchase history tied to one customer
- Held back by
- Guest orders and duplicate profiles
- Needs work
- Needs
- Returns linked to the original order
- Held back by
- Refunds issued with no matching return
Illustrative example, not a client. Your calls depend on your records.
The systems estate
The systems behind the plays.
Written for retailers, consumer brands and e-commerce businesses selling through a web store, marketplaces and, in many cases, physical shops, with an ERP or inventory system, an email platform and a support desk. Each system is built to be right about one thing. None was built to agree with the others, and the plays above all run across more than one.
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Web store
Orders, shoppers, returns
ShopifyAdobe Commerce
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ERP and inventory
Products, stock, purchasing
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Email and SMS
Profiles, consent, campaigns
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Support
Tickets, returns queries
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Finance
Ledger, refunds, payouts
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Data warehouse
Channel and marketing data
Typical, not exhaustive. Anything that exports to CSV or Excel can be included.
The gaps
Every system looks clean on its own. The truth is in the gaps between them.
One record traced across your web store, the email platform and the ERP: each system right about its own part, and the three still disagree. Then the gaps that hold the plays back. Which of them exist in your data is what a Truth Score Read finds out.
Where the plays sink
-
Store ↔ Email
The same shopper as several email profiles
SinksPersonalisation and lapse prediction
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Marketplace ↔ ERP
Marketplace orders summarised, not itemised, in the ERP
SinksDemand forecasting
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Store ↔ Finance
Refunds in finance with no return in the store
SinksFraud detection and net pricing
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Email ↔ Store
Consent that differs between the store and the email platform
SinksPersonalisation, and compliance
Gaps to test in the sector, not client findings.
Regulation
The rules that shape which plays you can run.
Kept short and honest. This is not legal advice: we evidence what your data can support; your counsel decides what the rules require.
GDPR and ePrivacy
Personalisation depends on consent for marketing and, in many cases, for tracking. Where consent records disagree between systems, the strictest one has to win.
Personalised pricing
EU consumer law requires you to tell customers when a price has been personalised on the basis of automated decision-making.
Card data
Payment card data sits under PCI DSS. We never need it: the exports we ask for exclude card details.
The EU AI Act
Most retail AI sits in the lower-risk tiers, with transparency duties for chatbots. Manipulative techniques are prohibited outright.
Your plan, tested
Got a plan that names these plays?
Send it with your exports. A Truth Score Read tests the AI in it against your own records, within 5 working days of complete exports. Go further with an AI Investment Review: every bet ranked, your plan tested play by play, and a roadmap to hand to your own team.
- 01
Send the plan, if there is one
The business plan, growth strategy, value creation plan or the vendor proposal. We pull out the AI plays in it and the targets that lean on them.
- 02
Send exports from two or more systems
Usually web store and ERP and inventory to start. Spreadsheets count. Under NDA, using the field list we send.
- 03
Test each play against the records
Each play mapped to the data it needs. Our engine matches the customers, people, deals and invoices across your exports to see whether that data is there and agrees, and every red flag is checked by hand against sample records.
- 04
Get the verdict, play by play
A signed memo: your Truth Score, a Leverability read marking each play Deploy now, Needs work or Blocked with the reason in your own records, and a verdict: sign, fix first or walk away.
What you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Not a software company? The sample is one, but the method doesn’t change; only the systems do. For a retail and e-commerce business the same match runs across your web store, the email platform and the ERP, and every red flag is checked by hand the same way.
Questions
AI in retail and e-commerce, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWe have millions of orders. Can you handle that volume?
Volume is not the constraint; agreement between systems is. We agree the scope on the call: a recent period, or one channel or region, enough to show whether the systems agree.
Our web store isn’t on your system list. Can it be included?
Yes. Every major store platform exports orders, customers and products. If it exports, it can be compared with the ERP, the email platform and finance.
Why is personalisation so hard to get ready?
Because it depends on identity and consent, and those are the two things most likely to differ between the store, the email platform and the CRM. Both are checkable from exports.
Do you need card or payment data?
No. The exports we ask for exclude card details. Order, refund and payout records are enough for every play on this page.
Before retail and e-commerce AI gets funded, find out what your data can carry.
Exports from two or more systems, plus the plan or proposal if there is one. A signed verdict, play by play, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.