What Are Recommendations in a Customer Data Platform and How Do They Work?

Recommendations in a customer data platform (CDP) are product or content suggestions generated automatically from each customer's browsing and shopping behaviour, then delivered on websites and in emails. They work when your customer data from every touchpoint — website, email, and in-store — is unified in one place, so the platform can match the right product to the right person at the moment they are most likely to act. If your data sits in separate tools and no single system knows what a customer viewed, bought, or abandoned, recommendations will stay generic.

What "Recommendations" Means Inside a CDP

A recommendation is not a manually curated list of bestsellers. In a CDP it is an output of the platform's understanding of each individual:

• Input: behavioural signals (pages viewed, products browsed, past purchases, email engagement) plus audience membership built from unified customer data.
• Action: the platform scores and selects products for that specific visitor or subscriber.
• Output: personalised placements on homepages, category pages, and product pages, or product blocks inside emails.

The distinction matters because it changes who does the work. Instead of a merchandiser choosing what to show across a catalogue, the platform generates suggestions per person and updates them as behaviour changes.

How Customer Data Feeds Recommendations

Recommendations are only as good as the data behind them. The platform's role is to unify data that customers generate at every touchpoint and shape it into audiences that reflect who those customers really are.

A practical example: a retailer with both physical stores and an online shop. Purchase data lives in the store systems, browsing data lives on the website, and email engagement lives in the email tool. Once these are unified, a customer who bought in-store but browses online can receive recommendations informed by both — not just by whatever the website happens to know.

This is the mechanism behind the XL-BYG case: the retailer previously relied on product directors to manually choose recommendations across 200,000+ products. With a personalisation engine and CDP, recommendations are now delivered automatically and in real time, and customer audiences sharpen targeting across the range. As Helene Birk put it, "Our recommendations are now much more accurate without spending any time on them, and they have lifted our conversion rate considerably."

Website Recommendations vs. Email Recommendations

Both use the same underlying customer data, but they serve different moments.

Website recommendations suit visitors who are already engaged and deciding what to look at next. Email recommendations suit subscribers who are not on your site at that moment, so the email itself has to carry the personalisation.

Combining Recommendations With Behavioural Triggers

Recommendations become more powerful when they are attached to a moment of intent rather than a fixed schedule. Behavioural triggers send automated emails the moment customer behaviour calls for it — for example, an abandoned cart or a price drop — and recommendations fill those emails with the specific products that matter to that person.

The logic: a trigger identifies when to send, and recommendations decide what to show. An abandoned-cart email without recommendations is a reminder; with recommendations it can surface the abandoned item plus relevant alternatives.

How to Tell Whether Recommendations Are Working

Track metrics that connect recommendations to revenue, not just engagement:

• Conversion rate — the platform reports +273% conversion rate with recommendations.
• Average order value — reported at +37%.
• Unique products per order — reported at +66% more unique products per order, a sign recommendations are broadening baskets rather than repeating the same items.
• Email revenue — reported at 102% increase in email revenue.
• Revenue per email — Imerco reports €1.25 revenue per email and a ~10x increase in click-to-open rate.
• Triggered email performance — Imerco reports a 74% open rate on triggered emails.

The Imerco case shows the combined effect: purchase data from 165+ physical stores and the online shop is unified into precise audiences, then paired with behavioural email triggers so each customer receives a personalised message rather than a generic newsletter.

What to Check Before Choosing a Platform

• Does it unify data across all your touchpoints, including offline? Recommendations built on partial data will underperform.
• Can it serve both website and email placements from the same customer profiles, or will you need to stitch tools together?
• Does it support behavioural triggers so recommendations can be tied to intent moments like cart abandonment or price drops?
• Can merchandisers still intervene — boosting specific products or building curated feeds for the right audiences — when business priorities require it?
• What results does it report, and on which metrics? Conversion rate, AOV, and revenue per email are the ones that connect to commercial outcomes.

If your current setup requires manual product selection across a large catalogue, or your website and email personalisation run on separate data, that is the clearest signal a CDP-driven recommendation engine would change your results.