Using Data Enrichment to Identify High-Value Customers

Any business worth its salt understands the value of customer segmentation. Unsurprisingly, a top priority is often focusing on high-value customers – those likely to spend frequently and lavishly. 

Usually, gathering this data is a retrospective thing. Loyalty schemes are a good example. They’re a key tool in the armory and popular with both businesses and the customers they serve. The average consumer belongs to over 16 loyalty schemes. However, while data from such schemes is valuable, it only becomes available once a customer is already established.

The same applies to any method of tracking back on customers’ spending habits. It’s useful data, but it needs collecting first.

Here’s the thing: It’s possible to gather relevant data sooner than that. The technology already exists to identify high-value customers before they’ve spent a dime. Better yet, you may already have the solution in your existing tech stack.

The solution involves data enrichment. This article sets out how it works and explains why you may already have the technology to implement it at little or no cost. 

What Exactly is Data Enrichment?

Data enrichment is the process of gathering valuable (but freely available) information from simple data points such as an email address or phone number. For example, an email address can reveal things like:

• what websites, online services and social networks a person uses and – in some cases – how and how much they use them
• a person’s likely age (or age group)
• the type of email service they are using (corporate, free, paid, or anonymous.)
• whether they’ve been caught up in historical data breaches

This kind of information is used for fraud prevention purposes. For example, a quick check of an email address can ascertain whether an individual’s online presence seems legitimate.

Red flags can include addresses with no links to social networks and addresses that have never been involved in data breaches. (Since they are so common, it’s unusual for an established and legitimate email address to not appear on a breach database.) Combined with other factors, this information then informs someone’s risk score – the likelihood that they pose a risk to your company, that is.

The fact such data is often used for fraud prevention is key to why you may already have a means of accessing it. As explained here, social media lookup functionality can be used for anti-fraud purposes, as the digital footprint associated with every email address that signs up for an account can reveal reliable information on whether this person is likely to be a legitimate customer – or not. Plenty of hospitality businesses use such tools for fraud prevention.

As it makes use of data enrichment and other protocols to gather and collate hundreds of data points, there’s no reason why such a tool cannot be multi-purposed to also segment high-value customers – provided the vendor is offering such functionality. It’s similar to finding ways to extract extra value from CRM data.

What Data Could Point to a High-Value Customer?

Some of the ways to make use of enriched data are obvious, others less so. Consider the following:

Device IDs

Device IDs, as captured by device fingerprinting (a key feature of fraud prevention software), can tell you about the devices and operating systems people are using.

It’s not unreasonable to assume that somebody with the latest iPhone or Samsung Galaxy, or somebody using the latest MacOS, has a relatively high disposable income.

Subscription Information

The services people are signed up to can tell us plenty about their lifestyle. A social media lookup can reveal if an individual is signed up for services like Netflix and Disney+.

A single subscription doesn’t say a huge amount, but multiple subscriptions are more revealing. They point to a person that’s not had to make tough decisions about which subscriptions to maintain – someone with disposable income.

You can also take this further by employing a little lateral thinking about other sites and services. Somebody with a Github login may well be a programmer, for example. According to Indeed, that will give them an average salary of $73,271 in the US – far above average and possibly worth your marketing efforts.

Airbnb Trips

Beyond the above, the data Airbnb makes available can prove hugely revealing. In addition to whether an email address is registered for an account on the website, and when it did so, its API also tells us the number of trips people take.

There is also indication of whether this person’s identity has been verified – which is helpful in assessing the likelihood that this data is accurate.

People taking multiple trips per year are not sitting at home worrying about how to make ends meet. It’s probable that they are high-value customers.

Job Titles

LinkedIn reveals plenty about a person, and it’s easy to scrape the information with data enrichment techniques. A job title alone – especially something like CEO or Founder – can provide a fairly reliable indicator of financial status.

Glassdoor makes available a list of the highest-paid jobs by job title, at the top of which are CFO, Executive Director and CTO. Cross-reference the data against such a list, and it’s easy to filter down to a target market.

Locations and Billing Addresses

Locations and billing addresses allow you to drill down to a neighborhood – and it’s straightforward to analyze what kind of neighborhood it is.

An address in Beverley Hills or London’s Mayfair will likely indicate high net worth!

Card Types

Another form of potentially useful data comes from the BIN number (the first six digits of a card number) that the tool can access when people pay by card. Admittedly, this does require a customer to make an initial transaction. However, it’s revealing once that’s taken place.

An example, an American Express Platinum card has a $695 annual fee. That’s not a small amount of money, so it’s reasonable to assume that anyone with such a card can afford it. 

Obtaining the Data to Work With

Since all of this data can be gathered from just an email address, it’s easy to kick off the process. It’s far simpler to obtain a customer’s email address than it is to encourage them to make their first purchase or to fill in all this information themselves (assuming they are willing to share it in the first place).

The methods are no different to those you might use for any electronic marketing campaign. We’re talking simple ideas like building an email list by launching a new hotel or restaurant with a room or meal giveaway.

From the email addresses alone, it’s possible to create a segmented list of potentially high-value customers, based on any one (or a combination) of the factors described above.

Automating Data Collection

Putting these techniques into practice needn’t involve manual work. Most good fraud prevention solutions offer API access to their data. Extracting it in an actionable way is a relatively trivial task for any good developer.

For example, a list of frequent travelers with high-earning job titles and top-of-the-line hardware would be easy to produce.

How to Use the Data

With the data extracted and processed, there are plenty of options for making use of it: Special offers, content marketing drives, and regional promotions based on people’s locations are just three simple ideas.

Equally, you can use these techniques to weed out the “wrong” type of customer. First off, you can filter out fake addresses so that marketing budgets and email subscriber plans aren’t wasted on non-existent customers.

Perhaps more cynically, you may decide to send less promotional effort in the direction of customers who you profile as less willing or able to spend.

Forbes reports that in the US alone, there are over 11 million households with a net worth of between $1 million and $5 million. Furthermore, there are over 1.8 million “ultra-high net worth” households worth over $5 million.

Not only can you begin to identify the people in those households before they become a spending customer; you may also be able to do it while deploying sophisticated fraud prevention software.

If you have that data at your fingertips, or the mechanisms to gather it, it’s a no-brainer to make use of it.