With all due respect to Shakespeare, names do matter. And though a rose by another name may, in fact, smell as sweet, a technology without a clear and commonly understood name is difficult to talk about. Clear definitions enable understanding, which leads the way to the adoption of new technologies. So if we are serious that optimizing the value of customer data is critically important, we had better invest some energy into understanding what we are all talking about.
As in any nascent marketplace, we see a lot of businesses seeking differentiation by coining new terms or revising and reusing older terms to describe their initiatives. To engage in any sort of useful conversation about customer data analysis, we first have to establish a common lexicon. Therefore, in an effort to provide some clarity, I am going to suggest some general meanings that will allow me to make distinctions among the many sorts of services arising on the scene.
As always, I welcome reader input, whether in agreement or opposition. I want to come up with definitions that can be agreed upon, at least to a degree that allows for easier communication among industry practitioners. So I’m happy to hear from others who have different ideas of how to distinguish and define. I’ll weigh your comments and refine my definitions in accordance with any consensus that develops.
From my point of view, “Web analytics” are designed to measure and understand e-customers, and each company has its own way of approaching the problem. Some focus narrowly on particular markets or applications (tracking only marketing campaigns, for example, or profiling specifically based on clickstream behavior).
The more macro-oriented firms identify Web analytics as a key part of an overall approach to understanding current and prospective customers. “Business intelligence” is a popular term for this more general approach. The solutions in this category include those with a holistic approach that attempt to combine legacy systems and disparate data sources to profile the total customer experience; they also include the many solutions that grew out of more established customer relationship management (CRM) systems.
Combining data warehousing of transaction and CRM information with Web analytics is critical, especially for click-and-mortar companies that have customer data from many sources — not just Web logs. But whether it is essential that this capability be built into the analytics solution is for you to choose. There are good arguments for building some of this capability in-house, and equally compelling reasons for outsourcing the whole thing to a pure-play dedicated to dealing with data issues. In a coming article I’ll look at building it yourself versus outsourcing to an expert.
Though there are many other terms, this handful represents the jargon I hear most often and those that seem to cause our clients and readers the most confusion. Are there others you wonder about? Or would you like to offer another take on defining these core functions? Let me know. I’ll keep working toward clarity with the help of all of you, the ClickZ readers.
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