From Uncertainty to Action: How BI Helps You Navigate an Uncertain Economy
Part II: Improving Collections When Money is Tight
Last month we introduced the first in a series of articles about how BI-based analytics help a business better cope with an uncertain (or volatile) economy. In that article we discussed the analytics organizations can leverage to improve inventory management.
That article detailed how to balance the fulfillment of prospective sales with the need to optimize purchasing habits. The goal — a delicate balance that achieves a system of “just-in-time” inventory.
Part II of this article focuses on collections – including key statistics and tools that help an organization better anticipate potential bad debt.
Analyzing Collections in Aggregate
Standard A/R Aging — who’s past due, how many days, and the amount — lacks key insight because it doesn’t provide big-picture indicators. Group behavior – shown in aggregate calculations – are essential.
This includes comparisons of different kinds of debt. An increase in high-risk debt is not necessarily bad – if a corresponding increase in low-risk debt has also occurred. It’s only when high-risk debt increases disproportionately to low-risk debt that organizations should look further into underlying conditions.
It’s Not Just About Today
Gauging the impact of a volatile economy on corporate collections also relies upon the ability to compare analytics results over a protracted period of time. A single snapshot of A/R Aging, for example, gives no insight into trends and no indicators as to how a changing economy is affecting collections. It’s only in comparison – client debt today versus client debt 3, 6, and 12 months ago – that reveals a potential correlation of economic conditions with organizational debt.
A Picture is Worth a Thousand . . . Numbers
When it comes to collections analytics, graphics are a must.
Tools such as ‘heat charts’ (a format that uses color gradients to represent increasing severity) provide instant visibility into varying severities of debt (such as ‘normal’, ‘medium’, and ‘extreme’ risk). And, by representing this ‘heat’ across all (or select) clients, businesses have immediate insight into whether their overall aging is getting better or worse – and in what areas.
Look Beyond Your Clients
Clients aren’t the only people responsible for their own debt and their ability to pay it off.
An organization’s sales staff (or account managers) can significantly influence clients’ willingness to pay down their debt in a timely manner. Analyzing this ability not only reveals who is better (or worse) at collections, it also enables an organization to identify those skill-sets that are most effective when it comes to client collections – especially during times of economic uncertainty.
DSO – ASAP!
Of all collections statistics, no organization should be without DSO – “Days sales outstanding”. This measurement gauges how quickly a client pays for their purchases; most importantly, it informs an organization as to whether that number is increasing or decreasing, enabling a correlation between economic conditions and client payment habits.
Look for Anomalies
One of the most overlooked—but crucial—insights in collections reporting is the presence of anomalies. Going beyond ‘x’ days’ and ‘y’ dollars”, businesses need to watch for such anomalies as clients who have an abnormally high percentage of high-risk debt. Such abnormalities hint at a client’s risk of defaulting on their payments and putting you into the unenviable position of having to write off bad debt.
Economic Volatility Demands Increased Analytics
BI-based analytics go beyond standard A/R Aging to use the history of collections to provide additional insight for potential lean times ahead.
This insight – into aggregated receivables, comparative trends, or anomalous behavior — enable organizations to more effectively gauge the current economic impact and establish policies (special payment terms, dunning notices, etc.) that support a more reliable cash flow in the future.
Check out DataSelf’s BI-based “AR; High-Risk Debt Clients” dashboard to see this in action.
