What are omnichannel contact center solutions?
It's important for contact centers to meet customers where they are and on their preferred channels. Read this blog to learn more about omnichannel co...
The Team at CallMiner
August 09, 2018
Debt collection companies are now turning to speech analytics in order to help them reduce delinquencies and mitigate losses allowing businesses to maximize their accounts receivable recovery. Collection analytics aids to understand customer preferences and behavior patterns, which in turn helps in developing better collection strategies.
Collection strategies are primarily needed to improve productivity. It is not feasible to hire agents (which costs money) to keep making collection calls from a list of payments due. Collection strategies help to determine which accounts have a higher probability of losses, categorize the different types of customers, and prioritize and target customers.
Collection analytics gives valuable information about the customer which can help develop varied collection strategies in different stages of obtaining due payment. There are primarily three stages of collection, which can be broadly classified as the early stage, the mid-stage and the final stage of collection.
In the early stage of consumer default, there is a higher chance of self-cure (i.e., customers are likely to pay by themselves without the need to make collection calls). Analytics play a vital role in identifying which customers, based on their behavior pattern (such as payments made before or after due date) are likely to pay on their own.
The mid-stage deals with customers that the collection agencies need to focus their efforts on. Here again, analytics can help segment the customers as high, medium or low risk. A risk score is a metric indicating how likely a consumer is to make payments on time, while a collection score is a metric indicating the most probable amount a delinquent consumer is likely to pay.
Collection strategies can then be targeted to recover maximum money from high-risk customers and to determine follow up intervals. A possible change in loan terms for the medium and high-risk groups is also determined.
The final stage normally deals with considering the account as a write-off. However, collection analytics steps in to decide whether the payment default is due to mismanaged finances, bad economy or the financial situation of the customer. These parameters help in deciding a hardship plan and renegotiation terms to retain the customer.
Collection analytics help in developing different strategies for maximum efficiency. Some of which are:
Collection analytics is beneficial for organizations in developing and implementing an overall collection strategy. Key areas impacted by collection analytics include:
Collection analytics helps increase collection efficiency, reduces costs, increases recovered amounts, enhances customer service, increases customer retention, reduces debt write-offs and maximizes account receivables.
Furthermore, collection analytics gives insights into customer behavior and delinquency that helps prepare customer profile data and create customer segments. All of these analytics help in creating flexible collection strategies.
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