Sounds simple right? Most marketers would say that they use some notion of value when they target customers. I think there's more to this topic. First, most marketers use likelihood of response as a predictor far more often than value. A recent conversation with Tim Suther of Acxiom got me thinking about targeting techniques. Here's my take: Generally most marketers have three approaches to onsite targeting:1. Deterministic targeting. Also known as rules based targeting, this is the most commonly used approach and takes the form of - "If visitor performs the following actions, then offer x, y, or z. Or even simpler "If visitor belongs to segment A then offer product x.
2. Non-deterministic targeting. Also known as "self learning", this is when a decision system applies advanced analytics to train a model and apply it to making decisions based on visitor behavior.
3. Predictive targeting. This takes the second approach further and applies business rules, constraints, pre-existing segmentation, and analytical techniques like decision trees and neural networks to drive the targeting process. Not surprisingly this approach also requires the most complete profile of the customer.
Now what's not often discussed is the cost associated with each approach. What marketers must recognize is that the act of targeting isn't "free" and has a cost associated with it. This cost is made up of system costs, data costs, analytical costs, and experience costs. Of these costs, the hardest to assess is the experience costs i.e. the cost of presenting an offer to a customer and the subsequent reaction and impact on future value. This is exactly where customer value should fit into the equation. Before marketers decide to target a visitor online they must understand the overall value of the visitor to current and future business and use that value to help guide the targeting approach.
For instance, if I am running a financial services web site that see hundreds of prospects each day, I should be able to assign a "value" to each visitor based on what I know about them and people like them. This doesn't have to be a detailed metric like customer lifetime value but can be a simpler proxy. Once I can assign value I should use it to guide my approach - a simple (and cheap) one like rules based targeting or a complex (and expensive) one with a higher likelihood of success like predictive targeting. Or better still; the value or a value proxy should help me understand that some customers aren't worth targeting at all.
So the question for you is: How many of you use the concept of customer value to support online visitor targeting?
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Suresh Vittal is a Principal Analyst at Forrester Research where he serves Customer Intelligence professionals and contributes to the Forrester blog for that role. Syndicated from Forrester Research. Reprinted with permission.