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This article by Leslie Ament in DM Review caught my eye today - Success Strategies in Leveraging Customer Intelligence. I got hold of the report (you can buy it from Aberdeen here) and there is a lot of good stuff but one thing in particular struck me.

Automate decisions with rules-based interactions. In contrast to average performers, leaders have developed decision-tree workflows and/or rules-based “best-fit” selections for interacting with customers in real-time across multiple channels. Use of predictive analytics and customer behavior modeling have enabled organizations to build matrixes of most probable outcomes (churn risk, propensity to buy, best offer) based upon unique sets of customer behaviors and prior transactions. As a result of these consistencies, companies can recommend the best possible, customer-centric action to take in each interaction – including cross-selling, special offers, or service initiatives.

This is a great justification for taking an EDM approach to customer interaction management. By automating interactions using business rules you can deliver consistent and agile treatment across multiple channels. By using analytics to refine those rules and allow for new ones (by adding predictions that can be used to better tailor interactions) you can improve the precision of this treatment. For instance:

1. Define rules for the various cross-sell offers you have and what the minimum criteria are for offering them
2. Define rules for when to make cross-sell offers e.g. not during complaints
3. Use analytics to mine your data on past behavior to develop a customer segmentation that divides up your customers based on fact not belief
4. Turn this segmentation into rules - a decision tree is often perfect
5. Define rules for pricing, perhaps using a decision table to look up pricing based on segment, product and other risk or benefit factors
6. Think about predictions you could make - can you predict risk of churn or likelihood to make future orders? If you could predict these things, would you change or add to your rules?
7. If they would make a difference, go back to your data and do more analysis to see how you can build models to predict these things
8. Add those models into the decision logic and add or change the rules that use them - perhaps drop the price for those at risk of churn or offer a different cross-sell for someone likely to make a subsequent purchase
9. and so on...

Now if you are doing all of this in a business rules management system then you can get immediate results by deploying the rule service as soon as you have any rules. This will help call center staff (by making more targeted offers) as well as automated systems (by giving them some way to target offers instead of just offering a standard one). As you enhance the rules a good business rules management system will let you re-deploy the rules without having to re-compile or re-start the systems. It will also let you update the offer logic without changing the processes that have "make cross-sell" as an activity within them. If the business rules management system makes it easy to add analytic models then you can cut the time from data to improvement in decisioning too for best results. All this is particularly key for those of you in e-commerce and for those whose strategy calls for real personalization especially when you have many many customers.

For those of you into this kind of Precision Marketing, I would recommend my colleague Jeff Zabin's blog Pareto Rules

Posted by jtaylor in Business Intelligence • Business Rules • Decision Technologies • Predictive Analytics |Digg This|Add to del.icio.us

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» A White Paper on Customer Decision Engines from Enterprise Decision Management - a Weblog
Ian pointed me to this white paper today - The Customer Decision Engine(free registration required). Unica published this about a year ago but I just saw it today. It's an interesting paper and prompted me to blog about this (in [Read More]

Tracked on April 30, 2007 11:53 AM