Using Lifestyle and Location Data in Treatment Decisions

Why situational lifestyle and location data need to play a role in your treatment decisions

We are often focused, understandably, on trying to understand the financial aspect of an individual’s life in order to determine their ability to repay an outstanding debt. However, trying to understand the broader aspects of individual’s circumstances is more difficult, yet often rewarding.

Using bureau data is utterly critical in understanding your customer and their ability to afford credit and repay a debt, however, it is only one part of the jigsaw puzzle. An example to bring the challenge to life is that from a purely financial point of view, it is entirely feasible that a single parent with children who is socially renting and making a decision between paying a debt owed or putting food on the table could look exactly the same as a young professional who is renting privately in a city centre location making a decision between paying a debt owed or a night out.

Another known issue with basing decisions purely on information from a credit bureau is the concept of having a ‘thin file’. There are a number reasons why a consumer may have a ‘thin file’

  • The consumer may have never had credit
  • The consumer doesn’t have any lines of credit listed on their credit reports, like a credit card, mortgage, car loan, student loan or any other kind of loan. A credit bureau may not be able to generate a credit score if this is the case.
  • The consumer is new to credit or they are re-establishing credit.

If a consumer has recently opened their first credit card or loan, don’t expect them have a credit score right away. It takes time to build a credit history and develop a credit score. The same applies if they have recently started re-establishing credit after having closed many of their credit lines. Being ‘thin’ is not desirable from a credit record perspective but all it is really reflecting is a lack of knowledge about the individual which in turn means that if they owe money, you are unable to get a good sense of their financial well-being which could lead to the incorrect treatment path being selected.

The journey to know more

The pursuit of increased understanding of individuals began in the UK in the early ’60s, but hampered by limited computational power, analysis was limited to administrative geographies. Over time with the advent of more powerful technology, increased availability of data at varying levels), creators of segmentation tools have moved to provide solutions that no longer just describe areas, they now describe individuals, households and postcodes*. This progression has allowed users to significantly increase their understanding of the individual; the household they are part of, the home in which they reside and the area within which they live, which is far more useful when it comes to debt recovery in particular.

As a creditor, ideally you would combine your internal and external data to create something specific for your customer segments, however, if this is not feasible there are a number of products available which can shortcut your way to an increased and more rounded understanding of the situational, lifestyle and demographics of individuals.

The future of insight

On 25 May 2018, General Data Protection Regulation (GDPR) came in to force, providing greater transparency around how permission is given for a consumer’s data to be used and processed. GDPR is likely to have removed a large swathe of the underlying data used to build certain products or solutions, particularly that which relates to individuals. It is therefore expected that rather than these products disappearing, there will actually be a resurgence in the use of non-personal data as providers of demographic segmentations continue to understand the impact of GDPR on their product set.

TDX Group is in a unique position to combine geographic segmentation tools, credit bureau data and our own performance insight to understand the best course of action for any given consumer. Processing millions of consumer accounts every year, for clients across multiple sectors, we are able to draw on this wealth of knowledge to build a debt map of the United Kingdom. We use this to understand who is making the choice between putting food on the table and a night out – something we can’t determine from financial data alone. For example, looking close to our Nottingham base, Nottinghamshire is less likely than the rest of the UK to make a payment against an outstanding debt. However, within the county this can range from as low as half the national average to almost three times the national average. It is of little surprise that the areas known to make payment against outstanding debts are, as some data providers would term them, ‘Family Fortunes’ or ‘Rural Vogue’ and those unable to are known as ‘Strained City’ or ‘Budget Generations’. The correlation between situational data and resulting penetration is stark and obvious – yet may not be demonstrated by financial data only.

Combining financial, situational, lifestyle and location data to obtain a more complete understanding of the consumer is key to understanding the consumer situation, the right treatment path and ultimately how they will respond throughout the debt recovery process.

*the exception is the Open Area Classification from the Office of National Statistics which remains committed to describing areas at administrative geographies.