The analytical community is getting increasingly interested the concept of attribution. And while much of this is focused on digital marketing attribution, I want to take a step back to describe the wider application of attribution and the traditional techniques that can be used to solve a range of attribution challenges.
I’ll use as an example a UK bank that I worked with that was collecting debt on customers that had defaulted on loans. For many businesses, there is a clear result in customer engagements (i.e., the customer responded, purchased, etc.) In this instance, while it’s known which customers made a payment, it might be unclear which action (or combination of actions) triggered payment.
Collections teams often have paths of activities that are analogous to customer journeys (how’s that for lateral thinking?). It may be that a high-risk customer will receive a text message five days after they have defaulted, a letter at 10 days, and a telephone call at 15 days. Collections teams will often have different paths for high-risk and low-risk customer. A low-risk customer may receive the same contact escalation path (text/letter/phone call) but on a longer cycle (e.g., at 10/15/20 days).
Multivariate testing helps reveal hidden relationships
My banking client had wisely created a test of the two different paths using two similar sets of customers. The more aggressive path collected more debt, but the costs were higher. However, there were options to improve upon this, and we chose to build two customer-level predictive models – one for each path – to predict the likelihood of payment.
This led to the creation of four segments that enabled much more effective decisions:
- Model 1: High-probability to pay (for both paths)
- Action: Apply low-risk path to save cost
- Model 2: Low probability to pay (for both paths)
- Action: Apply low-risk path to save cost (but also test an accelerated strategy)
- Model 3: High probability to pay for high-risk path (but low probability to pay on low-risk path)
- Action: Apply high risk path to maximise uplift
- Low probability to pay for high-risk path, (but high probability to pay for low-risk path)
- Action: Apply low-risk path
These models uncovered ways to improve collected debt by 2 percent with no increase in cost, which is a significant performance improvement. More importantly, it highlights the value of attributing outcomes to actions because many collections departments focus on perceived risk as they begin collection paths and do not attribute collected debt to specific activities.
Lessons for marketers
This example is a good first step towards a detailed attribution solution because by treating the path as one action, the halo and cannibalisation effects of the different actions are accounted for. Of course, there are opportunities to dive deeper and understand which of the underlying activities are driving the behaviour and multivariate testing can support this.
There are a few clear takeaways here for marketers:
- Good test data is essential to achieve good attribution results.
- Customer-level attribution can deliver results that are applicable across all channels.
- Traditional modelling techniques can support building conditional models or net-lift models using different paths.
- Simplifying the problem (as much as possible) will give you results that are more easily incorporated into your campaign strategies.
To learn more about how SAS Customer Intelligence 360 uses multivariate testing to create better customer experiences, read this blog post by Suneel Grover.
How is debt collection like attribution modelling? was published on Customer Intelligence Blog.