How to maximise Customer Lifetime Value with Credit Bureau Data
Enhancing your account management data with credit bureau data
One of the use cases where scores and data can improve your customer lifetime value is in account management activities. Whether you are trying to assess the current credit limit or determine the collections action to follow when there is a payment delay, using the dynamic data available in its raw or aggregated form will improve overall results and ensure a better customer experience.
Many organizations we work with have successfully applied scores to application decisioning. When it comes to ongoing management of customers and accounts, they shy away from doing so. It is often seen as ‘too big a mountain to climb’ given the state of both legacy systems and siloed departments.
As credit bureaux across the globe promote new use cases for the data they hold, some organisations see it as a more straightforward first step to drive account management strategies. This is definitely a good step forward, but ignores the data the organisation already has.
The power and depth of the data held by each organisation for a particular customer should always be mined first. Other sources add to that power, whether in specific segments or overall, but they don’t replace the information that already exists.
This article is not about how existing account data can be used to drive strategies. It’s a given this is where most of the immediate power resides. Instead, we will focus on how the credit bureau may add to what you already have.
A credit bureau is an independent body that collates all the information related to credit products owned by customers. The information may contain payment profiles such as balances or payments or delinquency status including bankruptcy, insolvencies and charge-offs.
It provides organisations with customer behaviour across all their accounts, including yours, which can be very predictive of their future behaviour in your product. Its benefit will largely depend on how many customers do possess other accounts for your market.
Credit bureau data is powerful and can be used in a series of steps throughout the account management process: policy rules, to verify and confirm the information provided by the customer, as additional variables for segments where little data exists for a customer, or to store any additional contact details the bureau may provide to ensure contactability in the collections process.
The range of products offered differs by country, but credit bureaux also offer income estimators, affordability calculations and, most importantly, scores, which can then be combined with existing scores to optimise strategies.
Most advanced bureaux provide a few scores in account management. Traditional account management scores focus on the ongoing short to medium-term performance of existing account holders. Collections scores predict the probability of that customer rolling forward. Each decision area may use a different score or a combination of scores.
Scores are built and scaled on the overall population, so you cannot assume a specific points to double the odds (PDO) or score to odds on your own portfolio unless you align the scores. In some cases, and where the organisation is big enough, credit bureaux may build custom scores.
At this point, I would also like to mention that, in order to use this data effectively, you need to understand how up-to-date the data is. In most countries, organisations report the data to the bureau once a month (towards the end of the month). This means that there is a lag between what occurs in your accounts daily and how long it will take for the credit bureau to get this behaviour and for their scores to react to it. In credit limit or marketing management activities, the effect is not as significant, but when it comes to collections, having the latest data for the day does matter.
In countries where more than one bureau exists, it is recommended that an analysis is done to determine which one fits best. It is possible that one would be better in one area and not in another. How representative the bureau score is of your profile usually determines the overall benefit, but, in the end, the difference between bureaux will depend mainly on how long ago the score was developed and the segmentation applied, so it is worth keeping this in mind. Also, costs are important in deciding the ROI, so testing is key.
Bureau scores are about an early warning signal. How much time before the delinquency occurs in your own account will depend on where your product sits in the overall payment hierarchy of a customer.
If there is a credit bureau score, some organisations may just use that score and base their decisions on this alone. In use cases like collections, this lacks the latest information and can become suboptimal.
The best method is to combine both the custom behaviour scores and bureau scores. In some specific segments, like FPDs, bureau scores may be critical. In others, just an addition to the behaviour score.
So, what is a payment hierarchy?
This concept simply means that consumers have an order in the way they pay. At the top of the pyramid, customers will tend to pay with more commitment. This will include housing payments. As you go down the pyramid, customers will tend to miss more payments. The order of the pyramid may change by country or loyalty. An example is shown for illustration purposes.
If you sit towards the high end of the payment hierarchy, then the bureau score will certainly bring additional benefits to your decision-making as an early warning of the customer starting to miss payments. If you are on the lower end, perhaps the benefit may not warrant the cost unless your customers have other accounts at the same level.
So, in summary, credit bureau data and scores play a key role in all your strategies. The key is to understand how they differ from what your organisation already has and what value they can add to your champion strategy. Whilst they do have a role to play, behaviour scores have an advantage in that they are specific, relevant and, most importantly, in some areas like collections, they are timely and use the latest data available.
Until next time…