Building customer profiles with UK open data: a step-by-step guide

In our previous post, we explored how freely available UK open data can add a powerful layer of insight to your customer records. In this post, we'll make that concrete. Starting with three fictional customers who have nothing more than a name and a postcode, we'll walk through five open data sources and show how each one sharpens the picture.

Starting point: what do we actually know?

Here's our base dataset — the kind of thing you might export from a CRM or e-commerce platform.

What can we determine about these customers? Not a huge amount. We don't even know their gender. All we really know is the area they're from — if you happen to be familiar with UK postcode regions. Any attempt to segment this data would leave all three customers in a single undifferentiated group, treated identically by any marketing campaign.

Let's change that.

Layer 1: ONS Census Data — who lives here?

The Office for National Statistics publishes socio-demographic classifications at the postcode level. By matching our customers' postcodes against this data, we can overlay a set of demographic attributes.

Suddenly our customers look very different from one another. Customer 1 lives in an area characterised by retired city dwellers. Customer 2 is in a prosperous suburban area with high employment and above-average qualifications. Customer 3 falls somewhere between — an area of older working-age residents approaching retirement.

The ONS provides detailed pen portraits for each grouping, describing typical traits like car ownership, industry of employment, and ethnic mix. These descriptions can be used to derive further attributes tailored to a specific business or product category.

What we can now infer:

Already, we have a basis for differentiated marketing. The messaging, timing, and channel strategy for a retired customer should look quite different from what works for a working professional in their forties.

Layer 2: Land Registry — what does their home tell us?

The Land Registry records every property transaction in England and Wales. By matching postcodes, we can understand the property market in each customer's area.

House price acts as a useful proxy for income. Customer 2 lives in an area where the average property price exceeds £1.5 million — placing them firmly in a high-income bracket. Customer 1's area, by contrast, has modest property values consistent with a lower-income retired population. Customer 3 sits in the middle.

Updated customer picture:

For a luxury brand, Customer 2 is clearly the primary target for premium products. But this data is equally useful for avoiding wasteful marketing — there's little point pushing high-end products to customers whose area profile suggests they won't convert.

Layer 3: EPC Data — how big is the household?

Energy Performance Certificate data, published by the Department for Levelling Up, Housing and Communities, includes property floor area and room counts.

This complements the Land Registry data and helps distinguish between expensive but small city properties and genuinely large family homes. Customer 2's 12-room, 396 m² property almost certainly houses a family. Customer 1's compact three-room property suggests a single person or couple.

Updated customer picture:

For household-centric products — furniture, DIY supplies, home appliances — room count is particularly powerful for re-targeting. A customer who has bought one set of curtains for a 12-room house may well need eleven more.

Layer 4: Ofcom Connectivity Data — how do we reach them?

Ofcom publishes broadband speed data by area, which has direct implications for how you communicate with customers.

Customer 3's broadband speed is essentially inhibitive for any media-rich content. Video testimonials, interactive web experiences, streaming product demos — none of these will reach them effectively. Physical mail or simple email campaigns would be far more appropriate. Customer 2, by contrast, has excellent connectivity and could be engaged through video, social media, and rich digital content.

Updated customer picture:

This kind of insight prevents wasteful spending. There's no point serving video ads to someone who can barely load a webpage.

Layer 5: Met Office Weather Data — when should we reach them?

Weather data might seem like an unusual addition to a customer profile, but it enables a genuinely useful form of contextual marketing. By appending local weather forecasts to customer records, you can time campaigns based on conditions.

A footwear brand might trigger a rain boot campaign for Customer 3's area when wet weather is forecast. A garden furniture retailer might push promotions to Customers 1 and 2 during a sunny spell. Weather-triggered marketing drives relevance — it reaches people at the moment a product feels most useful.

The complete picture

Starting with nothing but a name and postcode, we've built a layered profile for each customer that tells us their likely age bracket, employment status, probable income level, household size, how to reach them, and even when conditions are right for certain products.

None of this required a single form field, survey question, or cookie. It's all derived from publicly available, regularly updated open data.

Combining open data with your own

Open data is most powerful when it's used alongside your internal transactional and interaction data. Consider Customer 2: their open data profile suggests a high-income family in a well-connected suburban area. If your own records show they've made a single mid-range purchase via your website after clicking through from a social media ad, you can start to build a much richer hypothesis.

They have the means to buy premium products. They live in a large home with scope for multiple purchases. They're digitally engaged. Their single mid-range purchase isn't a sign of low value — it's a starting point. The open data tells you there's significant headroom.

Equally, combining the two data sources can flag inconsistencies worth investigating. If your internal segmentation labels a customer as "high value" based on one large purchase, but open data suggests they live in a low-income area, that's a signal — perhaps they were buying a gift, or perhaps your segmentation needs adjusting.

Key takeaways

Open data is freely available, well-maintained, and remarkably powerful when structured correctly. The datasets used in this example represent just a tiny subset of what's out there, and we focused on a single dimension — geography. Other open data sources cover transport, education, health, and more.

A few things to keep in mind: this data is aggregated at the area level, not the individual level, so it works best as a complement to your own customer data rather than a replacement. Some datasets are updated more frequently than others, so freshness matters. And the real value comes from combining multiple sources — any single dataset tells a partial story.

The challenge for most businesses isn't access to the data. It's the work of linking, cleaning, refreshing, and structuring it into something useful. That's where Cogstrata comes in — we do the heavy lifting so you can focus on using the insight.