Recommendation Engines and Personalisation
Product and content suggestions shown to a visitor or customer, built from your own catalogue and their real behaviour, tuned to your margins and stock position rather than to a generic bestseller list.
What This Generates
Personalised suggestions shown at the point a customer is browsing or after they buy, built from your catalogue and their actual behaviour — not a static list of your most popular products shown to everyone.
Where Recommendations Show Up
Website and App Product Pages
Suggestions shown alongside the product a visitor is already looking at.
Post-Purchase and Cart Suggestions
Relevant add-ons shown at checkout or after an order completes.
Email and WhatsApp Follow-Up Offers
Suggestions sent as a follow-up based on past purchase behaviour.
Content or Course Suggestions
For education or media businesses, relevant content suggested based on what a person has already engaged with.
What Recommendations Are Tuned Against
- Your margin and stock position, not popularity alone.
- Seasonal and category relevance to what's being browsed.
- Avoiding repeated suggestions of something a customer already bought and won't need again soon.
Building It
Catalogue and Behaviour Data Review
Checking what catalogue and behavioural data is available to build from.
Recommendation Logic Build and Testing
Building and testing the logic against your actual catalogue.
Placement Integration
Fitting recommendations into your site, app or messaging channel.
Ongoing Tuning as Catalogue Changes
Adjusting recommendations as products, stock and margins change.
When a Small Catalogue Doesn't Need This
A small catalogue with only a handful of products doesn't have enough variety for personalisation to add much. Simple manual cross-sell rules work better at that scale.
Frequently asked questions
Do we need a large catalogue for this to work?
Some minimum variety is needed for personalisation to add value over manual rules — we assess your catalogue before recommending this route.
What drives the cost of a recommendation engine?
The size of your catalogue and how many channels the recommendations need to appear in.
What drives a recommendation engine's build timeline?
How much historical browse and purchase data is available to train recommendations against.
Does this work with our existing e-commerce platform?
Yes, it is built to integrate with the platform or app you already sell through.
Who owns the recommendation logic?
You do — the logic, tuning rules and behavioural data stay yours.
What data do we need from your catalogue?
Catalogue data and a history of purchase or browsing behaviour to train the recommendations against.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
- No obligation
- We reply the same working day
- Your details stay private