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December 1, 2012

Helping Retailers Minimize Bad Debt

Bad debt is an amount owed to a business or individual that is written off by the creditor as a loss (and classified as an expense), because the debt cannot be collected and all reasonable efforts to collect it have been exhausted.

If you offer credit to customers, you run a risk of not being able to collect it. Then why do companies give credit?  Because by offering credit there is a greater opportunity to acquire more sales. The increase in profits from incremental sales should more than cover bad debts.

According to research done in the United Kingdom, SMEs (Small and Medium enterprises) in particular write off an average of £14,000 in bad debt per annum, which means that at a 5% profit margin they would have to make additional sales of £280,000 to make up for the loss. In any case, this shows that bad debt does not simply affect a company’s cash flow and bottom line performance, but sales and marketing efforts as well - a crucial issue for start-up's, or growing companies such as SMEs. Thus, effective management of credit is crucial for the success of any company.

Identifying Bad Debt is important
Minimizing bad debt adds value to both Business and Customers:
- Value to Consumers: reduced consumer prices and greater purchasing power, since consumers would likely be faced with higher prices if businesses were unable to recoup losses resulting from bad debt.
- Value to Businesses: It helps them keep costs down and reduce their risk of financial insolvency and bankruptcy that may be triggered by unrecovered bad debt.

Predictive Analytics as a tool to manage bad debt
Predictive Modeling can be used to build customer-level models that capture the likelihood that a customer will take a particular action. These models exploit patterns from customer attributes, and other historical and transactional data to identify risks and opportunities. In the case of bad debt, the objective would be to predict if a customer will pay back the debt. Probability of the customer to pay back his debt indicates the risk level associated with it. This probability is the output of the model which is given in the form of a risk score from 0 to 10 assigned to each customer. Inputs for these models can come from various sources like CRM and sales.

These predictive models are built using statistical methods to examine all variables relevant to default using regression techniques. They identify a select set of key variables and then weight the variables based on their importance to the outcome.  These scores are very valuable in quantifying the forward-looking risk for a customer/prospect before offering credit.

Once Prospects with a high potential for bad-debt are identified, the retailer can take targeted actions - it can offer them pre-payment options only, or it can price differentially.  And if Customers do default on payments, predictive analytics can help retailers maximize recoveries also.

For further reading, please refer some of our recent work that helped a B2B retailer minimize its bad debt.