Lisa Bradner of Forrester (who is part of Forrester's marketing blog team) wrote Realizing The Life-Cycle Marketing Vision back in March but Ian pointed it out to me last week. The summary of the document says:
"In simple terms, life-cycle marketing is about profiling a customer's relationships with a company or brand over time in order for marketers to reach them more effectively. In reality, life-cycle marketing can be hard to pin down and harder still to implement. To begin successfully putting theory into action, marketers must capture the right data to identify their profitable customers and to understand their behavior over time."
While I agree with Lisa that capturing data is necessary, it is not sufficient. Not only does it require better decision-making, it requires connected decision-making which was, interestingly, one of the foci of Fair Isaac's CEO's keynote at InterACT last week. Now Forrester defines life-cycle marketing as:
"A customer intimacy strategy that uses customer data to model and predict behavior over time to guide retention marketing programs"
Lisa goes on to discuss how this requireds a focus on detailed information about the customer as well as a focus on their lifecycle (not the company's). I have blogged before about the value of customer information, especially detailed "cell-level" information. While it is useful it is not sufficient - you must be able to take action against it (see this post for instance on using cell-level information). You must be able to use the customer's previous activities to predict their future needs and likely future responses (predictive analytics) and combine this with rules (their preference rules, your strategy rules, legal requirements and restrictions and anaytically-derived rules). This let's you not just segment them very finely (as I and McKinsey would argue), it let's you personalize the way you interact with them. This is particularly key in this era of "the Long Tail" where both hits and niches matter.
Not only does this require good, unified customer data and effective segmentation, it also requires support for preferences and compliance (business rules) and an ability not just, as Lisa says, to measure results but to experiment and measure results to improve over time (what we call adaptive control). This is, of course, the basic proposition of enterprise decision management or EDM. What is interesting, however, is the value of connected decisions in this environment. If you want to make the best decisions at a given point in the customer lifecycle you must be connecting those treatment decisions with previous decisions made and likely future decisions. You must judge how to treat a customer who, say, is suddenly less profitable in the context of previous decisions, say marketing them less-profitable but very "sticky" products. It will do no good to make customers sticky if the next step will be to de-prioritize them thanks to lower profitability! You must connect these decisions and manage, monitor and improve those connections just as you do the individual decisions. Best Buy, who presented at InterACT last week, is a particularly good example of someone who is beginning to do just that.
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