Mike Kresse | Thursday, March 17, 2016
Any company knows – and accepts – that some customers have a higher risk of non-payment than others. From a credit and collections perspective, a key challenge is to manage this risk by identifying the customers which are most at risk of going past due.
If a company needs to follow up on 50,000 customers and only has 25 collectors, it is important to deploy those collectors as effectively as possible. This means focusing on the accounts where the most difference can be made. In reality, many customers will pay on time or shortly thereafter without any input from the collections team. Chasing such payments is not an efficient use of anyone’s time – the objective is to target accounts which are likely to become significantly overdue.
In order to decide where best to focus their efforts, credit managers need to know more about an account than how much money the customer owes and when the payment is due. Some credit managers will achieve this by gathering information about a customer’s payment history using reports or spreadsheets. However, no two collectors will approach this task in exactly the same way – and in reality, decisions about which customer to call next are often based on the monetary value of a particular account.
A more efficient approach is to use predictive analytics technology to gain a greater understanding of individual customers’ behavior patterns. By drawing upon descriptive, diagnostic and predictive information, predictive analytics can identify the customers who are most likely to go past due. But identifying high-risk customers is only part of the exercise. Credit managers also need to know which course of action they should take in order to convert high risk customers into paying customers.
Predictive analytics can help here too by advising the credit manager what action should be taken for individual accounts. In some cases, this might mean calling the customer and having a certain sort of conversation. In other cases, such as a high value account which is at risk of deteriorating, an onsite visit might be necessary.
In short, predictive analytics ensure that your collections risk mitigation strategy is harmonized; at the same time enabling your team to spend more time contacting customers in a highly personal way.
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