What Is Customer Data and How Do You Use It to Personalise Experiences?

Updated

Customer data is the record of how individual customers behave and transact with your business — pages browsed, emails opened, products bought in-store or online. You use it by unifying those records into one platform, shaping them into audiences, and feeding those audiences into personalisation such as product recommendations, triggered emails, and merchandising. This applies to any business whose customers interact across more than one channel; if all your customer activity happens in a single system you rarely touch, the payoff is smaller.

What counts as customer data

Customer data is behavioural and transactional information tied to a person or account. It is not the same as aggregate business metrics.

Type Example Tied to an individual?
Customer data A shopper viewed three product pages, opened two emails, bought once in-store Yes
Analytics data Site-wide traffic, bounce rate, average session length Usually aggregated
Business data Revenue, inventory levels, margin No

The distinction matters because personalisation depends on knowing who did something, not just how many people did it.

Where customer data comes from

Customers generate data at every touchpoint. Common sources:

  • Website browsing — pages viewed, categories explored, products compared
  • Email engagement — opens, clicks, and which products drew attention
  • In-store purchases — transactions rung up at physical locations
  • Online transactions — orders, cart contents, purchase history

A retailer with both physical stores and an online shop accumulates these in separate systems by default. Imerco, for example, unified purchase data from 165+ physical stores and its online shop to build audiences from both.

Why unifying across touchpoints matters

Data sitting in separate systems gives you a partial view. A customer who browses online but buys in-store looks like two different people — or like a browser who never converts.

Bringing sources together in one platform lets you shape them into audiences that reflect who your customers really are. That accuracy is the input to everything downstream: if the audience is wrong, the recommendations and triggers built on it are wrong too.

XL-BYG previously had product directors manually choosing recommendations across 200,000+ products. After unifying data in a CDP with a personalisation engine, recommendations became automatic and real-time. Helene Birk's summary: "Our recommendations are now much more accurate without spending any time on them, and they have lifted our conversion rate considerably."

From unified data to personalised experiences

Once data is unified, it feeds personalisation in several concrete ways.

Recommendations

Personalise homepages, category pages, and product pages with suggestions grounded in each visitor's browsing and shopping behaviour. The same logic applies inside email: product recommendations for each subscriber, built into your existing email platform.

Triggers

Send automated emails the moment behaviour calls for it — an abandoned cart, a price drop — turning high-intent moments into conversions. Imerco combined behavioural email triggers with unified audiences and reported a 74% open rate on triggered emails and €1.25 revenue per email.

Merchandising

Choose specific products to boost, or build curated product feeds shown across websites and emails to the right audiences. This is where unified data and manual judgement work together rather than replacing each other.

Reported outcomes

Raptor's published results from customer deployments include:

  • 102% increase in email revenue
  • 52% purchase completion rate
  • +273% conversion rate with recommendations
  • +37% average order value
  • +66% more unique products per order
  • ~10x increase in click-to-open rate (Imerco)

Treat these as vendor-reported figures from specific customers, not guarantees. Your results depend on your data volume, channel mix, and how well your audiences are built.

What to check before investing

  • Do you have data in more than one place? If website, email, and store data live in separate systems, unification is the prerequisite for everything else.
  • Do you have enough behavioural volume? Recommendations and triggers need activity to learn from.
  • Which use case comes first? Abandoned-cart triggers are typically simpler to launch than full on-site recommendation engines.
  • How will you measure it? Conversion rate, average order value, and email revenue are the metrics referenced in the results above.

Pricing and plan details are not covered here — check the vendor's pricing page for current terms.