Analyzing the Return on CRM - Customer Relationship Management

principle #1 of CRM: Value and values are given, and in return value and values are received.—Paul Greenberg

One thing is certain about CRM. It’s pretty much useless unless there is some sort of return for both your business and the customer. This is a science that encourages symbiotic relationships. That means that you have to define what value is to your business, and you also have to discover what value is to your customer. Then you have to figure out what are the things that you as a businessperson have to do to provide the value to your customer, which, as we saw in previuous Chapter, are quite different from what businesses want. That’s where analytics comes in. Then you have to figure out who’s going to do those things and make them accountable for doing them. That’s key performance indicators and benchmarks. Then you have to decide what you want from those customers. That’s return on investment (ROI) or more appropriately, as Peppers and Rogers calls it, return on customer (ROC).

Data, of course, is not the subject matter here. We took care of that a few chapters ago. This chapter is about what we do to data. Step by step, inch by inch, we’ll analyze, benchmark, and evaluate. Then we’ll judge, which is what we of the human species do.

Analytics:Figuring Out Whassup
Note: I have to admit, I liked this section of the third edition so much, I’m reproducing a small amount of it here, though most of this chapter is new. Really.

Here it is, back by popular acclaim.
From true premises, it is not possible to draw a false conclusion;but a true conclusion may be drawn from false premises—true however only in respect to the fact, not to the reason. —Prior Analytics, Aristotle

Since what is known without qualification cannot be otherwise, what is known by demonstrative knowledge will be necessary. —Posterior Analytics, Aristotle

So, class, what conclusion can you draw from these two statements? No, it doesn’t mean that posterior.

Aristotle is saying that from premise to knowledge takes demonstration. You may make a certain assumption, hoping it is true. You then have to create the proof of its truth. But it also can be that in the course of examining this premise, which conceivably might not be true, you still come up with a true conclusion because you uncovered facts that are incontrovertible. That is demonstrative knowledge. In order to develop a strategy or impact a group or make a point, you have to prove it.

For example, say your premise is that if you implement reduced prices during the recession to sell an item to a specific customer demographic, they will increase their purchases by some percentage. In the course of looking into that, you realize that this is a highly affluent group who isn’t concerned about price, but they are concerned about style. So you improve the styling of the product and the purchases increase by some percentage. That’s how this could work.

While those quotes (translated from Greek to English) may still be Greek to many of you reading this, the fundamental principles here are the governing principles of analytics. Use information that is examined in multiple ways and interpret it to come to a conclusion on how to make that information benefit, in this case, your business. In knowledge, there is power. But not just in information or data. Neither data nor analytics stands on its own.