Knowing a consumer’s credit information at a single point in time only tells part of the story. I often hear one of our Experian leaders share the example of two horses, running neck-in-neck, at the races.
Who will win?
Well, if you had multiple insights into those two horses – and could see the race in segments – you might notice one horse losing steam, and the other making great strides.
In the world of credit consumers, the same metaphor can ring true.
You might have two consumers with identical credit scores, but Consumer A has been making minimum payments for months and showing some payment stress, while Consumer B has been aggressively making larger pay-offs. Trended data adds that color to the story, and suddenly there is more intel on who to market to for future offers.
To understand the whole story, lenders need the ability to assess a consumer’s credit behavior over time.
Understanding how a consumer uses credit or pays back debt over time can help lenders:
- Offer the right products & terms to increase response rates
- Determine up sell and cross sell opportunities
- Prevent attrition
- Identify profitable customers
- Avoid consumers with payment stress
- Limit loss exposure
The challenge with trended data, however, is finding a way to sort through the payment patterns in the midst of huge datasets.
At the singular level, one consumer might have 10 trades. Trended data in turn reveals five historical payment fields and then you multiple all of this by 24 months and you suddenly have 1,200 data points.
But let’s be real … a lender is not going to look at just one consumer as they consider their marketing or retention campaigns. They may look at 100,000 consumers. And on that scale you are now looking at sorting through 120M data points.
So while a lender may think they need trended data – and there is definitely value in accessing it – they likely also need a solution to help them wade through it all, assessing and decisioning on those 120M data points.
Tapping into something like Credit3D, which bundles in propensity scores, profitability models and trended attributes, is the solution that truly unveils the value of trended data insights. By layering in these solutions, lenders can clearly answer questions like:
- Who is likely to respond to an offer?
- How does a consumer use credit?
- How can I identify revolvers, transactors and consolidators?
- Is there a better way to understand risk or to conduct swap set analysis?
- How can I acquire profitable consumers?
- How do I increase wallet share and usage?
Trended data sounds like a “no-brainer” and it definitely has the ability to shed light on that consumer credit horse race. Lenders, however, also need to have the appropriate analytics and systems to assess on the huge volume of data points.
