Customer Analytics > Optimizing Collection Efforts, Part2


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In Customer Analytics > Optimizing Your Collection Efforts, Part1 I focused on how companies are enhancing their collection efforts by setting up risk treatments at the point of acquisition.  Now that the customers are on board, and a portion of the customers have entered into collections, what can be done to optimize collection efforts and thus minimize the amount of outstanding debt?  All environments vary but the following steps set a direction in optimizing collections efforts.

1. Collections Database: Develop a collections database which should include-

  • Customers currently in collections with their previous transactional data back to when they activated
  • Customers previously in collections with their transactional data and their collections payment history
  • A collections customer contact history (both inbound and outbound) with the contact date, the offer / treatment and the resulting action (payment, refusal of payment,..)

2. Collections Analytics: Develop collection models, which predict the likelihood of a customer paying their outstanding debt.  At the very least 2 models should be build which 1) predict the likelihood of payment and 2) predict the payment amount (or amount percentage) assuming payment(s) will occur.  Additional analytics opportunities include:

  • Create separate models based on the product, time in collections,..etc. 
  • Create collection customer segmentations to better customize the collections treatments.  For example, segment based on outstanding balance or time in collections (receivable age).
  • Create predictive analytics around what day, time and channel to best contact the customer in order to make the contact process more efficient and less costly. 

In my next post I will focus on how to use the data and analytics within actionable strategies and the required tracking and optimization required.

Republished with author's permission from original post.

Roman Lenzen
Roman Lenzen, Partner and Chief Data Scientist at Optumine, has delivered value added analytical processes to several industries for 20+ years. His significant analytical, technical, and business process experience provides a unique perspective on improving process efficiency and customer profitability. Roman was previously VP of Analytics at Quaero and Director of Analytics at Merkle. Roman's education includes a Bachelor of Science degree in Mathematics from Marquette University and Masters of Science in Statistics from DePaul University.


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