Clear Marketing Objectives Drive Good Analytics

Finding the measurement tool that best does the job is not simple, but it's a whole lot more doable if you've carefully and thoroughly described, prior to the tool-selection process, the problems to be solved. So Janet gives some examples.

Last week, I looked at data collection from the perspective of an e-commerce customer and at which behavior data, when appropriately tracked and interpreted, can be most useful in improving customer experience (and through that, increasing sales).

Site traffic, though a necessary data point for determining capital requirements and bandwidth constraints, is not very helpful to the marketer whose aim is larger average order size, more frequent visits from prior customers, lower incidence of product returns, or whatever other criteria have been identified as leading to increased revenue and profitability.

Note that I assume a level of marketing sophistication here in the way the marketing goals are stated. Great marketing organizations don’t focus on generic goals such as “getting the word out”; they look several levels deeper to the actual business issues that will make their business objectives reality. The more specific, measurable, and action oriented the marketing goals, the easier it is to apply meaningful metrics to the data we want to analyze and to ultimately know whether the objectives have been met.

Many businesses focus their Web analytics efforts on the tools that capture data. However, my experience shows that great results are an unmistakable outcome of having first invested the marketing muscle to figure out what the specific criteria for success look like. Then, when that work is done, find the tools that best measure against those goals.

There are many great tools on the market, and each specializes in approaching the problem in its own unique way. While selecting the right tool is not simple, it’s a whole lot more doable if the problems to be solved have been carefully and thoroughly described prior to the tool-selection process.

So let’s look at a few more examples of how clear marketing objectives drive good analytics.

Most marketers agree that repeat customers cost less to sell to than do new ones (with a few notable exceptions, such as infrequently purchased products or products in categories where post-purchase satisfaction is extremely low). Customer acquisition is, for many businesses, the most expensive part of the sales cycle, so common sense tells us that any increase in repeat-customer buying should translate straight into a bottom-line increase in profits.

If these statements are true in your business, wouldn’t you want to measure how prior buyers — rather than the larger set of total visitors or even total purchasers — are using your site and responding to your emails? Not that you wouldn’t also study the patterns and behaviors of the larger groups, but if repeat shoppers are your sweet spot, have you set up your analytics to call out that group so you can look at it independently?

Look even closer: Are there apparent subsets within that repeat-shopper segment? Are you reacting and reaching out differently to those who return weekly, as opposed to those who come back annually? Have you tracked the items purchased (or content read or ads viewed for noncommerce sites) by those best customers to figure out how to give them more of what they are looking for?

A small incremental purchase from every return customer can, for many businesses, be far more profitable than finding and winning over a first timer. And any change to the site that supports that goal is likely to pay off much more quickly than any customer acquisition effort.

I’m not against customer acquisition; it is essential to sustainable growth. But we are missing a key marketing principle if we put all our Web attention there — as so many sites have in the era of counting eyeballs. I will look next week at how analytics can apply to improving results in the new-customer arena as well.

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