Collections Capacity Optimization - Part 1

The following article is the first in a series from credit cycle consultants PIC Solutions that looks at optimizing the capacity of collection operations in a bank or financial institution environment. PIC has written many articles on collector effectiveness at the creditor level.

Our previous industry tips focused on maximizing the productivity and effectiveness of collectors. This month we extend this discussion and embark on a new series of tips that focus on optimizing the capacity of collections call agents. The tip of the month articles will explore the typical management information that is required to plan capacity, some basic capacity models, and the implications of capacity optimization for opportunities to outsource.

Why Capacity Optimization

The business case and argument for collections capacity optimization is relatively straightforward. At its most basic, capacity optimization is about having the right quantity and quality of call agents to deal with both current and future business demands. Having too few agents in collections will lead to poorly worked queues and ultimately to an increase in bad debts. Having too many agents may be good for bad debt, but come at additional cost. Not understanding current seasonality and not planning for future collections events similarly may result in mistimed resourcing – overcompensated hiring and firing of agents, a particularly expensive exercise.

Whichever way you look at it, collections capacity optimization is fundamental to managing costs. Despite this incontrovertible argument, many in-house collections operations sadly either do no capacity planning, or at most, conduct ad hoc capacity planning.

Tip 1: Monitor the Right Metrics

All capacity optimization models are data dependent. Our first tip in the series addresses some of the key metrics to monitor in order to optimize capacity.

The most important metrics required in order to optimize collections capacity includes:

Volume of accounts: The total monthly volume of accounts which are 1 cycle or more in arrears.

Number of new collections accounts: The number of new accounts entering the collections calling queues per day.

Self cure rate: The percentage of customers who regularize their accounts without a call or action required from a collector.

RPC rate: The Right Party Connect rate refers to the percentage of calls that a collector makes where the collector gets to talk to the debtor directly. It is affected predominantly by data quality but can be influenced by dialer campaign techniques.

PTP made rate: The Promise to Pay made rate is the total number of customers who make a commitment to pay on their accounts within a time period. It is typically expressed as a percentage of RPC’s.

PTP kept rate: The Promise to Pay kept rate is the total number of customers who made a commitment to pay on their accounts and who subsequently effected payment within a time period over the total volume of accounts. It is typically expressed as a percentage of the PTP made rate.

Working hours in a day: The average total number of productive working hours per day for call agents. It is important that this is correctly measured and excludes non productive time like tea breaks, etc.

Overhead: The total time in a year that call agents will spend in non call related activities, including training and holidays.

Minutes per contact: The average number of minutes per customer call. It is best to measure this separately for Right Party Connect (RPC) and Non-Right Party Connect (nRPC) calls.

In the future, readers can look forward to tips on how to put these basic metrics into use through generic capacity modelling templates. In addition, they can anticipate tips on how to improve volume forecasting and conduct sensitivity analysis.

PIC Solutions provides consumer credit solutions to a wide range of blue-chip organizations. We are experts in the fields of credit, risk and software and have an established track record of success powered by solutions.