2. Power Micro Segmentation
Traditional segmentation places debtors into broad buckets that determine treatment strategies. With automated processes and predictive analytics, collections teams are able to split customers into smaller micro-segments, which can then be used to route accounts through optimal next steps. To power micro segmentation, collections and risk teams need to use both internal and external data to give a full picture of a customer’s financial position. A variety of predictive models can be used to power segmentation that take into account factors including willingness to pay, ability to pay, presence on do-not-call or similar lists, and engagement among others.
3. Maximize Resource Effectiveness
Collections efficiency is also a key consideration when segmenting customers and optimizing treatment strategies. Predictive models can be used to allocate resources where they can be most effective. To do this, collections teams need to deploy models that answer the question: What’s the best path forward based on not just the customer but also on resource availability and cost? The optimal output from collections analytics models will be segmentation that empowers maximum resource use and money recouped at the lowest possible cost. Many operational data points can be used to assist segmentation including resource availability, resource effectiveness, even which agents are most likely to convert certain types of accounts from default to good-standing.
4. Optimize Payment/Settlement Offers
Empowering your agents to make the right offer at the right time is an essential part of every collections strategy. To do this agents need to be able to see and analyze all of the data, not just from one specific account but all accounts that have defaulted across the business, to understand when the time is right to make an offer and exactly how much that offer should be.
Expecting agents to be able to access and compute that volume of data to make an educated settlement offer is obviously unrealistic, unless they possess analytics superpowers. Which means that many offers might be ower than they could be, reducing the amount repaid. 3
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