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8 ways to improve captive finance with EDM

Dave Wright had a question for me about the role of EDM in captive finance organizations - that is a finance organization owned by and associated with a particular manufacturer. Dave works for such a company for a large agriculture/construction/landscape manufacturer. Like most captive finance organization their primary purpose is to drive sales of the equipment, though they do make some money on the financing itself. Dave pointed out to me that they already use a rules-based approach to deciding who is eligible for credit and so avoid the need to refer potential customers to a credit officer. With captive finance arms, losses from special programs typically go to the manufacturer although there is an attempt to make money on the financing overall. In this particular case the captive financing group offer customized payment schedules e.g. for farmers based on when they have income. So how can EDM help such an organization innovate?

  • Perhaps the most valuable step would be to include analytics in the decision-making process.
    This might include multiple types of analytics - descriptive analytics to segment customers to make more compelling offers (differentiating between those likely to get good financing elsewhere and those not likely to, between customers with a strong brand-preference and those without) and predictive analytics (predicting credit risk, of course, but also risk of purchasing a competitive product if no financing offered). Descriptive analytics would analytically refine the rules being used so that they were statistically significant and predictive analytics would enhance the data set available for decision making by adding predictions to the raw application data.
  • More sophisticated decision automation could allow the blended economics of the deal to be taken into account.
    For instance,  if an up-sell product is known to be more profitable then the financing for the up-sell can be made more competitive. Some products might have much higher rates of default or fraud than others and might therefore come with worse financing deals. Certain products might be entry products that often have subsequent purchases and so financing might be made more aggressive on those deals because of the long-term value of such customers. Critical to this process is going to be identifying that each decision (for a customer for a product on a given day) is a unique decision. If "lumpy" sets of decisions are considered instead it will be impossible to add some of this sophistication to the decision-making. Dave talked about repeat customers getting better financing, for instance. But not all repeat customers are equally valuable so the financing decision cannot simply lump all repeat buyers together.
  • Newer analytics, like Fair Isaac's Peacock product, do a good job of analyzing purchase patterns even where those patterns extend over time.
    For instance, identifying the subset of buyers of product X who return after a period and buy product Y. Marketing to existing customers can be made much more targeted if this kind of information is available and, again, if the decision to send an offer to a specific customer is considered a unique micro decision (rather than considering sending all existing customers an offer as a single decision).
  • Dealership advertising could be based on local demographics, similar to the way Best Buy has redesigned stores based on the dominant demographics for each store.
    Thus a dealership in an area dominated by larger corporate farms might offer different plans to one dominated by smaller farms. The products produced locally might also be relevant. Combining historical data with external data sources would be key.
  • Dave did not discuss any kind of loyalty program but this is another area where EDM can be applied.
    Using loyalty points to encourage the behavior desired and using rules-based processing of transactions to manage these points is a established use of decision management.
  • By considering the communication to a customer around each payment as a decision one could use regular payment communication to interact with customers.
    Instead of simply sending a standard statement/payment slip EDM could be applied to decide what to print for each customer. This would allow offers and information to be targeted to specific customers based on their purchase and payment history. Someone known to have an aging piece of equipment, for instance, might be made an offer for a new one.
  • Complete automation of the pricing decision could be used to let potential customers see what financing they might get on the website.
    This kind of transparency could be used to draw people into dealerships.
  • If these decisions are automated and modeled, a sophisticated business might use trade-off models that consider the relative value of spending money on advertising v lowering interest rates, or lower rates v cash discounts. This kind of decision models allow all the aspects of a deal (product, financiing, advertising, risk, profit) to be balanced to maximize profit given real-world constraints - macro control of micro decisions.

Interestingly one such organization working with Fair Isaac is Dell Financial Services - the captive finance arm of Dell Computers. They have applied EDM to develop what they refer to as a universal decision engine for continuous pricing and you can find out a little about this in their InterACT presentation

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