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ThinnAiQ
Industry Solutions

AI Solutions for Retail & E-commerce

Show each shopper what fits. Hold the right stock. Stop fraud early.

Increase in Revenue
10–30%Increase in Revenue
Reduction in Operating Costs
20–40%Reduction in Operating Costs
Improvement in Inventory Turnover
15–25%Improvement in Inventory Turnover
Accuracy in Fraud Detection
99%+Accuracy in Fraud Detection

Industry Challenge

Shoppers move between store, app and marketplace in the same day. Stock sits in the wrong place. Carts are left behind, prices shift hourly, and fraud takes a share of the rest.

AI Opportunities

  • Show each shopper what fits them, on every channel
  • Move price and offers with demand and stock
  • Plan demand and hold the right stock per store
  • Catch fraud in the payment flow, not in the monthly report
  • Answer routine buyer queries at any hour of the day
  • Get numbers by the day, not by the month

Our AI Solutions

  • Personalisation Engine

    Show each shopper the products they are likely to buy.

  • Demand Forecasting

    Plan stock by SKU and store, so shelves stay full.

  • Dynamic Pricing

    Prices move with demand, stock and what rivals charge.

  • Inventory Optimisation

    Right stock, right place, fewer stockouts and less dead cover.

  • Customer Support AI

    Bots that answer common queries at any hour and hand over the rest.

  • Fraud Detection

    Flag odd payments as they happen, not in a monthly report.

Top AI Applications

  • Product Recommendation
  • Market Basket Analysis
  • Abandoned Cart Prediction
  • Demand Forecasting
  • Price Optimisation
  • Sentiment Analysis
  • Fraud Detection & Prevention
  • Store Performance Analytics

Why Retail & E-commerce Is Ready for AI

Retail has the shortest feedback loop of any sector we work in. A change to recommendations shows in days, a price rule in hours. That makes it the easiest place to prove AI value honestly, and the easiest place to fool yourself, since season and promotions muddy almost every before-and-after count.

So we insist on a holdout test. Part of your traffic keeps the current experience while the rest sees the new one. Lift is then measured against a live control, not against last month. It takes longer to report. It is also the only number worth acting on.

What We Need From You

You almost certainly have most of this already. Gaps are workable — they change the sequence, not the feasibility.

  • Order and transaction history, ideally 12 months or more
  • Product catalogue with categories and attributes
  • Customer IDs where you have them, for repeat-buyer work
  • Web or app events: views, cart adds, drop-offs
  • Stock and movement records by store
  • Your promotion calendar, so the model can tell cause from coincidence

How an Engagement Runs

  1. 1

    Instrument and audit

    We check what is actually being tracked before modelling anything. Broken or partial event tracking is the most common reason retail AI under-performs.

  2. 2

    Segment and model

    Shopper and product segments come first. One model across very different buying habits averages the signal away.

  3. 3

    Holdout test

    Deployed to a share of traffic with a real control group, and measured over a full buying cycle rather than a week.

  4. 4

    Roll out and monitor

    Full rollout with drift checks, since catalogue and shopper mix change every month in retail.

E-commerce Retailer case study
Case Study

E-commerce Retailer

Challenge

Carts were left behind, stock was planned by hand, and costs kept rising.

Our Solution

We built product recommendations, demand plans and stock cover in one system.

Increase in Conversion Rate
22%Increase in Conversion Rate
Reduction in Stockouts
30%Reduction in Stockouts
Reduction in Operating Costs
18%Reduction in Operating Costs

Expected Impact

  • Revenue Growth

    More orders, bigger baskets, and shoppers who come back.

  • Operational Efficiency

    Less planning by hand and fewer daily spreadsheet edits.

  • Cost Reduction

    Less dead stock, fewer stockouts, less hand work.

  • Better Experience

    Each shopper sees what suits them, on whichever channel they use.

  • Data-Driven Decisions

    See what sold today, not what sold last month.

Retail & E-commerce AI — Common Questions

Useful results usually start at a few months of orders, and more helps. Below that we can still run content and attribute rules, which need no behaviour data at all. We switch to behaviour models as the orders build up.

Ready to Grow Retail Sales with AI?

Tell us your channel mix and your stock pain. We will start with one of them.

Book a Free Consultation