If rising or uncomfortable high Days Sales Outstanding is the measurable presenting symptom in a credit control department, Value at Risk is the tool in triaging collections patients and prioritising them for treatment.
Value at Risk and Value in Dispute is the measurable and rational basis for customer segmentation. All the way along, and in each ageing bucket, from 30 to 60 to 90 days, identifying and ordering customers by Value at Risk or in Dispute can steer your credit operations tactically.
This is logical, but it is also a critical factor that helps Credit manager stay sane and retain control in our current climate. Here is why.
The ongoing issues caused by remote working or hybrid remote working are causing Collections teams to witness a diminishing RPC rate. Customers are harder to locate by phone, and so Right Person Contacted first time is difficult to achieve. When around 80-90% of your Collections customers are not available on usual contact numbers the first time you call, how do you prioritise those on who? You will spend the extra time in skip tracing?
The first segmentation factor is not valuable, but this is paired with analysis of past cases to predict the probability of self-healing- i.e., identifying those customers that typically respond to letters and pay eventually, if late, without the more direct intervention of telephone calls.
Skiptracing in itself can involve reaching out to colleagues of the primary contact who may alert that person of your intention to reach them. It does not necessarily, though, do anything positive for customer relations.
Advice from McKinsey and Co.
McKinsey recommends using a simple segmentation grid: plotting the likelihood of self-healing on a scale from low to high on the x-axis, against the Value at Risk on the y-axis. These with a high likelihood or track record of self-healing and high value at risk are the obvious place to start.
This is key because you will know better what this axis alone cannot tell you – whether the lower value but not yet so high likelihood customers are also frequent orderers, with a high lifetime value. One customer with a high-value overdue invoice for one-off or infrequent orders may not be as vital to your corporate turnover as your loyal, long-standing customer that makes frequent orders. These may be your bread and butter customers.
So Value at risk is not a means of segmentation on its own, without further consideration of :
A) likelihood to self heal or pay by responding to reminders letters; and
B) customer lifetime value or amount spent in the past year.
AI or Experience?
While it may be possible to use an algorithm to present this information or to use Artificial Intelligence (the application of algorithms to data), a skilled and qualified Credit Management professional will be able to discern this themselves. We can all benefit from logical methodologies, but taking away the skill, art and customer understanding built up over time by Collections managers would devalue the role.
Intelligent machines in the hands of unskilled Credit Managers
I completely endorse the Mckinsey approach: simple, logical and a springboard for prioritised action. I don’t believe that AI should be used to replace the kill of a professional, enabling companies to farm out collections to third party suppliers offshore or to unskilled workers in-house who are expected to follow the rules without question.
We work closely with the Chartered Institute of Credit Management in the UK, and I often hear with young recruits who blossom from CICM training. Our software is designed to support credit management professionals but not to replace them.
Empowered to Succeed
Seasoned fellows of the CICM and newly qualifying professionals alike see our software’s immense value in the Collections process. This is mainly by virtue of the ample supply of data it bestows and the searchable, sortable, downloadable nature of that data as it is presented in the UI (on-screen in the user interface).
We here at Data Interconnect often hear from our users how much satisfaction and success they derived from applying the data in the system intelligently. It is not so much that it makes them better at their jobs: it makes them;
- More efficient, with easier access to more granular data;
- More available to model that data, due to time saved in gathering that data and preparing letters;
- More confident in the way they approach their work, how they prioritise, how they can visually represent their logic; and
- More satisfied that they have done a good job, made the right decisions, and achieved good outcomes in consequence.
Back to the frontline
Segmentation of creditors by Value at Risk is a powerful tool for cutting through the daily frustration of difficulty obtaining the right person to contact and reassuring colleagues that contacts for skiptracing have been prioritised carefully.
While hybrid home working and office working models persist, I feel great empathy with the Credit Collections managers out there struggling to get past the IVR machine or virtual receptionist that guards employee telephone numbers like Beefeaters guard the Crown Jewels.