Key Points
- Open Data enriches first-party data to provide customer insight without excessive data collection
- There are a wide range of Open Data sources available to use, each with their own value
- Insights generated from Open Data provide analytical capabilities not possible with just first-party data
- Choosing the right data sources and continually maintaining them can be resource intensive (which is where The Data Refinery can help)
How much do you know about your customers? A lot? A little? Nothing? The reality is you only know as much as your data tells you.
Yes, gut feel is a real thing, but in the modern ultra-competitive marketplace, the experience of an individual (or even a team of) marketers isn’t enough. You have to have reliable, credible data to give you insight into what your customer's behaviours are and what triggers them to make the decisions they do.
Most B2C organisations will collect mountains of first-party data about their customers with every engagement such as PII (name, email, address), interactions (ad clicks, email, website activity) and transactions (purchase, refunds). That’s all well and good, but it leaves a gaping hole as to the context of who that customer truly is, beyond the fact they decided to buy a new set of golf clubs on a Saturday at 5 o’clock after seeing a Facebook Ad.
The typical way of filling this void would be to collect even more first-party data from the customer during the engagement. This means longer forms, innocent-looking data collection (“free products on your birthday” promotions, I’m looking at you), and of course, cookie tracking.
With the introduction of GDPR 2018 (DPA 2018) and the shift away from invasive tracking by major players like Google and Apple, many of these methods are no longer viable – or even possible.
This is where Open Data swoops in to save the day.
Open Data to the rescue
First of all, what is Open Data?
Open Data is publicly available data that can be freely used, re-used and redistributed by anyone, limited only by the need to provide appropriate attribution. There are a plethora of Open Data sources out there like the Office for National Statistics, Land Registry, Met Office, and Ofcom. Open Data is a way to add insight relating to customers that is not always derivable via typical interaction and transactional datasets. Combining this data will help to dismiss or validate any segmentation or assumptions made about a customer.
In most cases, the data is aggregated based on multiple points of data – for example, the demographics of a postcode are generated by taking the inputs from the Census data. This means that on its own, Open Data doesn’t provide much insight.
The magic happens when Open Data is combined with existing first-party data. By taking as little as a customer’s postcode, it’s possible to:
- Provide an enhanced view of who a customer is, their demographics, income, living situation, connectivity, and age without painful upfront data collection
- Link customer purchasing activities with recent events (i.e. buying new flooring after a house sale, buying suncream when the weather is hot)
- Build relevant, highly-targeted customer segmentations ready to be used across multiple marketing channels
Unlocking customer insight
To illustrate the power of Open Data, take a look at this fictional example with a small selection of Open Data sources to demonstrate how layering multiple data sets can provide deep, meaningful insight into different customer groups.
First-party data
To make the Open Data useful, there needs to be some link to your existing customer data. At The Data Refinery, we use a postcode to make the association to Open Data, so that’s what this example will be based on.
Not a whole lot of useful information can be determined about these customers, other than where they live, and even then, that’s not particularly helpful.
Office for National Statistics
The Open Data offered by the ONS provides an insight into the demographics of a customer. This can be examined at an individual customer level, or aggregated to see the overall trends of the types of customers being attracted to a brand or product.
| First Party Data |  |
|||
| # | Postcode | Socio-demographic supergroup | Socio-demographic group | Socio-demographic subgroup |
|---|---|---|---|---|
| 1 | LS14 1BG | Constrained city dwellers | Ageing city dwellers | Retired independent city dwellers |
| 2 | HG2 8HJ | Suburbanites | Suburban achievers | Comfortable suburbia |
| 3 | LN5 9AH | Suburbanites | Semi-detached suburbia | Older workers and retirement |
On its own, the ONS data can generate some useful segments, but it’s possible to extrapolate other attributes such as area type (for marketing outdoor vs indoor products) or industry type indicating where a group or subgroup could represent different working professions.
Land Registry
Using the average house price in the customer's area, we can project a typical income level, where more expensive homes typically house families with higher income and thus spending capability.
| First Party Data |  |
|||||
| # | Postcode | Min Price | Avg Price | Max Price | Type | Build |
|---|---|---|---|---|---|---|
| 1 | LS14 1BG | £60,000 | £95,000 | £125,000 | Semi-detached | Old House |
| 2 | HG2 8HJ | £600,000 | £1,597,731 | £2,725,000 | Detached | Old House |
| 3 | LN5 9AH | £132,000 | £151,000 | £167,500 | Semi-detached | Old House |
With house price data appended, we can now start to segment customers relating to potential spending power. Customers in higher income brackets will potentially be receptive to spending more at a more regular interval, making a certain product more applicable to sell to them.
Department for Levelling Up, Housing & Communities
This data complements the Land Registry data and provides an additional dimension regarding income and home ownership. This information helps to confirm that an area contains larger homes and not a smaller, expensive properties that you might find in dense city areas. Insight can be derived here by thinking about floor space and room count as opportunities to market comparable products, where household items could be considered.
| First Party Data |  |
||
| # | Postcode | Square Meters | Room Count |
|---|---|---|---|
| 1 | LS14 1BG | 67.76 | 3 |
| 2 | HG2 8HJ | 396 | 12 |
| 3 | LN5 9AH | 76 | 5 |
A view of the typical household makeup for a customer with that given postcode can provide the insights required to successfully cross-sell within a household (Family member vs Family). It’s possible to identify which customers are likely part of a family with a dependent, or potentially living with a partner or alone.
Ofcom
Typical uses for this data sit primarily in the marketing and communications space, where customers that are restricted by connectivity would be reached more effectively or appropriately via certain channels.
| First Party Data |  |
|
| # | Postcode | Broadband Speed |
|---|---|---|
| 1 | LS14 1BG | 6.9 Mbps |
| 2 | HG2 8HJ | 38.1 Mbps |
| 3 | LN5 9AH | 1.5 Mbps |
This type of restriction will often influence how a customer would interact with a brand and also the services they might use. Poor connectivity means such customers would be less likely to use media, streaming or gaming services due to the reliance on bandwidth. Having this intel helps prevent wastage, as there is little point in marketing to customers who are restricted in the ability to consume certain content types.
Met Office
The weather plays a massive role in consumer behaviour (us Brits love anything to do with the weather) – so bringing in live weather forecasts can start to surface patterns in behaviour and even trigger communications based on expected weather. Examples of the weather impacting sales might be a footwear brand selling more boots when it’s raining. This creates a link between a real-world event and a sale.
| First Party Data |  |
||
| # | Postcode | Avg 5 day temperature | Avg 5 day rain probability |
|---|---|---|---|
| 1 | LS14 1BG | 21˚C | 7.8% |
| 2 | HG2 8HJ | 20.6˚C | 7.4% |
| 3 | LN5 9AH | 23.2˚C | 26.4% |
Having weather forecast data can lead to dynamic segmentations based on the expected future weather conditions – it’s going to be sunny in the next 5 days, are you stocked up on suncream?
Open Data compliments first-party data
Only one Open Data source was added to a customer profile at a time in this example, but to maximise the value of the data, multiple Open Data sources and first-party datasets should be layered to provide an enriched view of real-world customer activities.
The addition of first-party data is an essential part of this. As Open Data is mostly based on aggregate data, an individual may not fit the segmentations described by Open Data without the context provided by first-party data.
For example, it may be suggested that a customer is retired and in an older age bracket, but in reality, the occupants could be a young family living in an area typically populated by the older generation. This is difficult to spot with just Open Data but could be easily identified through a purchase or behavioural traits from first-party data.
Having these enriched customer profiles enables “gap analysis”. This is looking at what a customer is currently spending with you vs what the data is saying they could potentially afford. A customer with a low average order value but whose income is high indicates there is a lot of potential to increase sales with that customer, and therefore they should be the focus of marketing efforts. While a customer who is already spending a high amount relative to their income levels may not yield the same results.
Utilising Open Data makes it possible to understand who your customers are, opening up a wide range of opportunities for creative segmentations without any invasive tracking or excessive data collection.
Choosing the right sources
The availability of Open Data sources can make it a minefield when it comes to finding reliable datasets. With region-specific data and varying licences, it’s important to choose datasets which are relevant to your use case while ensuring you’re allowed to use the data commercially.
Maintaining several datasets can become time-consuming and complex. Datasets are updated at varying frequencies and must be kept up to date to provide accurate information. Met Office Data needs to be updated daily (or even hourly!) to be accurate, while Office for National Statistics based on Census data is every 10 years.
How The Data Refinery uses Open Data
At the Data Refinery, we have been piecing together several UK-based Open Data sources to provide our clients with a complete view of customers using the most basic of starting points – without costing a fortune.
From ONS data uncovering demographic characteristics to house price information as a wealth indicator, our built-in data enrichment functionality allows our customers to build highly targeted audiences to send the right retention messages at the right time, or build look-a-like audiences in acquisition tools.
Combined with our predictive analytics powered by our advanced machine learning engine, The Data Refinery provides a 360˚ view of your customers with insights into who they are without excessive data collection or invasion of their privacy.
Drop us a message or request a demo, we’d love to show you how it works!
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