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Self Service and EDM

I downloaded and reviewed an excellent little white paper by Allen Bonde today. You can find it on CRMguru - Transition to Transformation: 5 Steps for Optimizing Your Customer Experience. Allen does his usual excellent job of outlining some of the challenges and steps to bring true self service to your customers (his website has more). Anyway, this got me thinking about the value of EDM, business rules and predictive analytics to self service.

Allen notes a couple of key requirements / first steps that seem pertinent to a discussion of the value of EDM.

  • True multi-channel support
  • Embedded analytics
  • Understanding user preferences

Now EDM helps with all these aspects of moving up the self-service food chain.

True multi-channel support means delivering the same decisions and interactions with your customers regardless of their choice of channel. This means that Gold customers get better interactions than regular customers, even if they chose to use the web or email. How many companies really manage that? Not many - the channel choice normally overwhelms the customer type in determining the quality of interaction. Using decision automation technologies, especially business rules, to automate customer treatment decisions and to allow unattended systems to make customer service decisions means taking control of these interactions away from the channel delivery mechanism. Centralized rules management and enterprise deployment makes this possible.

Embedded analytics is a key "idea" in EDM. The intent with an EDM infrastructure is to move beyond anlaytics as reporting or analysis tools and into the business of changing behavior. By embedding predictive analytic models into business rules, you can automate more complex decisions and drive them more accurately from your data. Thus you can embed predictions of wilingness to pay for service, likelihood to attrite, likelihood of correct self-diagnosis etc in your segmentation and other rules to make better decisions about how to treat your customers as they self serve.

Lastly both analytics and business rules have a role in understanding your customers. The use of analytics to describe aspects of your customers not explicit in their data - but implicit in the patterns in your data -  and the use of business rules to use this data and customer data to segment customers effectively helps meet Alan's definition of understanding your user's preferences. Centralized business rules management also allows you to use information or preferences collected in one channel to drive more targeted behavior in another - my choices on the website cause the ATM to cross-sell different things.

Anyway, food for thought.

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