CREDIT MANAGEMENT
What Is Debtor Segmentation In Collections ?
By Wasilwa Miriongi
With availability of technology solutions , the manipulation of customer data has gotten even easier and new emerging trends like segmentation commonly applied in marketing has taken root in collections as well .
In collection perspective , Customer segmentation refers to the strategy of dividing debtors into groups based on static and dynamic factors including credit score , type of account and payment method , to improve customer correspondence and compliance to credit policy .
The best debt collection and recoveries operations deploy risk-based segmentation to identify a hybrid of high , medium and low propensity to roll and pay distressed borrowers to ensure that appropriate , tilted treatments are applied across the delinquent life cycle .
By analyzing your debtors ’ previous payment behavior combined with credit bureau data , you will be able to identify which are high risk , medium risk , or low risk . This segmentation into the three basic categories gives you the opportunity to treat
78 MAL48 / 22 ISSUE each debtor appropriately based on risk .
It is important for collections departments to segment customers because they have complex differences . Each customer is in a different collections stage and a unique financial situation . Segmenting resolves obligations in a way that resonates with customers while improving collections rates .
It is also important for collections departments to segment customers to personalize customer correspondence and help collectors in upgrading their collection efforts . Invoice level segmentation is also one of the trending methods adopted by leading companies to prioritize invoices based on invoice value and due-date .
Traditionally , the collectors are known to call the customers for outstanding invoices and overdue payment commitments . This has been feasible because of limited number of assigned accounts and personal acquaintance of collectors with the buyer Accounts Payable teams .
As it is today it is not uncommon for collections analysts to get assigned to
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It is important for collections departments to segment customers because they have complex differences . Each customer is in a different collections stage and a unique financial situation . Segmenting resolves obligations in a way that resonates with customers while improving collections rates . hundreds or thousands of accounts . It has become important to bucket customers into segments with similar attributes so that the collections analysts can not only scale collections process with an increasing number of customers but also keep the communications personalized .
Several techniques can be used for debtor segmentation , from the simple k-means algorithm to the advanced model-based clustering to the continuously improving machine learning .
By using a method that includes machine learning , your models will continually update with new information provided and will teach itself if a debtor was identified as low-risk , but in fact , rolled on payment and how to determine such cases in the future .
Whichever approach you take , the most important thing would be to ensure you have enough data to segment these debtors . Combining your historical payment and operational data , with credit bureau data , will enable you to get a more accurate picture of a debtor ’ s propensity to pay or roll .
In the absence of a cohesive Credit Collection Strategy and dedicated analytical support to identify the reasons behind an increasing aged debt , along with a stagnated level of debt , the following issues need addressing : How can we identify why this is happening ? How can we support our customers to ensure minimal amount of time in debt ? How can we categorize customers based on their behaviors when in debt ? How can we use the information we have , to determine the best collection strategies to implement to recover this debt ? How can we use this information to proactively engage with our customers without using a “ one size fits all approach ”