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How optimisation can improve collections performance

Optimisation is a mathematical technique that maximises a goal, subject to constraints. The technique lends itself to a number of industries such as financial services, telecommunications, utilities and transport.

Similarly it can span disciplines, from risk to marketing as well as many operational ones. One thing remains consistent across these - performance can be improved by optimisation.

Collections is clearly a discipline that doesn't just span these industries, but also lends itself to optimisation. It's easy to see how, as the first constituent of an optimisation opportunity is the business goal. Whilst these may vary subtly between organisations, ultimately it will be one of a few such as:

	maximise debt collected
	minimise cost
	minimise net impairment
	maximise profitability

Next are constraints, and one doesn't need to go very far within a collections operation before one runs up against these. Operational resource is the obvious one:

	number of letters
	number of outbound calls
	number of inbound calls
	number of total calls
	minimum accounts to DCA
	minimum dialler volume

Similarly there may be business constraints present, requiring good performance on a number of KPIs such as:

	minimum debt collected
	maximum cost of collection
	maximum write off rate

Thus, all the ingredients for an optimisation problem are present, and therefore, so is the potential gain to be achieved through optimising the collections problems. The key question is "Where to optimise first?".

Collections analysis is complicated by multiple actions and outcomes that can occur, plus the sequencing and timing of these actions. With optimisation, it often pays dividends to start with simple problems, and grow to a more complete solution. There are a number of quick wins that optimisation lends itself to, including:

	Dialler optimisation – using response models by call centre and/or time of day to maximise both right party connect rates and debt collected across an operation, whilst maintaining existing resource constraints.
	Debt collection agency assignment – using response models by agency to maximise return whilst delivering the same volume of leads to each agency.
	Self cure strategy – targeting the right customers to leave alone to self cure for the right amount of time, to focus more valuable resource on higher priority cases.
	First contact – identifying the best first action for a customer, and choosing the best combination of actions that fits within the operational constraints of the business.

Optimising these decision points will typically generate more than a 10% KPI uplift, which will provide a good return on investment, and pave the way for the more complex path of collections path optimisation.

Adrian Carr
Senior Business Consultant
Decision Analytics
Experian

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How optimisation can improve collections performance