Mary Pilecki at Forrester just published Turning Pricing Optimization Into Relationship-Based Pricing (subscription required) about using pricing optimization as part of a financial services institution's relationship strategy. The summary of the document says:
Consumers are beginning to look at their financial services institutions (FSIs) for a better deal — one based on the value and longevity of their relationship. Some FSIs are engaged in price optimization, but no one has successfully evolved that technology into the idea of dynamically pricing new products and services based on the customer's profitability. FSIs will need to implement this pricing technique in order to remain competitive, and those that do it first could gain significant market share. The steps to getting there include an enterprise focus on customer centricity, a collaborative technology partner, and development of a pricing center of excellence.
Mary notes that people not being rewarded for their relationships with their FSIs and complain that banks don't know them well (indeed bank customers want to be sold too more by their banks and FSIs). As a result customers negotiate and shop around and from the banks point of view this is risking the customer relationship.Not only does this mean, as she notes, that pricing should be managed to maximize the value of the relationship it also means you must make those decisions at the point of interaction so that customers don't go off and check out competitors while they wait for your price to come back. She also notes that there is little or no cross-Line of business consideration and that only credit pricing is really considered by many - there is not much consideration of variable pricing for other products (something about which I have blogged before - pricing in banking). To resolve this she recommends, as do I, that everything should feed through a pricing engine. To do this I recommend an approach like this:
- Focus on the pricing decision made for a product and a customer as a specific operational decision
As distinct from saying the decision is a strategic one as to how to price a product line.
- Build analytic models for various aspects of the customer
- Propensity to buy
- Price sensitivity
- Lifetime value
- Credit Risk
- Retention Risk
- Build some kind of decision model to show how these aspects interact and are constrained
- Offline, optimize the decisions based on this model to come up with the best rules for pricing for each customer segment
Do some what-if analysis and flex your constraints to see what the impact is of these changes. Come up with the best set of rules for pricing based on this modeling.
- Deploy these pricing rules into all the systems that need them, ideally using centralized decision management and decision services
This matches closely her notes on the use of business logic (rules), real-time analytics (or at least real time execution of analytics) and predictive modeling. She does not talk about adaptive control - the use of testing or multiple approaches for comparison - and I believe this is key as you won't know for sure what impact changes in your pricing will have so you will need to track and manage different pricing approaches so you can compare them.
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