Customer-Focused Strategy

Traditional retailers focus on common metrics, such as click-thrus, conversion, and cart abandonment. Often, however, too many retailers focus their efforts around only these metrics, defining elaborate plans to get more traffic, more people into the funnel, and hopefully more converters. So, rather than stepping back and asking some basic questions — such as “Who is my customer?” and “Why does my site matter to him or her?” — they blindly move forward with a strategy focused around metrics rather than customers.

I’ve done a great deal of work with media and entertainment companies, which tend to develop customer- or audience-focused strategies. If your revenue stream is selling advertising, getting more people to your site, getting them to engage more for longer periods of time, and getting them to repeat in a given month, then an audience-focused strategy is key. Although certain metrics such as page views, unique visitors, and video starts still make up a key part of the online marketing plan, growing audience segments that are valuable based on behavior, repeat visits, and time spent on the site are crucial to executing on any effective marketing plan.

So, where to begin?
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Attribute Selection**
Start with your most engaged visitors and discover which attributes differentiate them from other visitors.

Building a customer-focused strategy means understanding what makes valuable customers valuable. For instance, perhaps an engagement with a particular offer or product category will show value across a customer segment, but that engagement has a sharp correlation with customers under 50; or perhaps different behavioral attributes — such as social likes and tweets — correlate with a strong affinity with a particular product line. Developing customer segments with a focus on the behaviors and metrics that define those segments will help you create customer personas that you can target through specific campaigns, marketing channels, or product offers.

As an analogy, think of a salesperson in a store. A good salesperson can spot a customer and size him or her up pretty quickly. The salesperson can determine whether a person is likely to buy, can make a suggestion about particular product that might be of interest, and can spot people who are just browsing or killing time. We are trying to use digital data to size up the same thing in our online world by picking up on behavioral cues and creating a virtual “gut instinct” based on known correlations.

Correlation and Causation
Don’t assume correlation equals causation.

Segments are segments. They are helpful for understanding a customer set and what might motivate those customers, but in the end, they are just assumptions. You would never assume in a face-to-face sales scenario that you know what a customer wants; you might make an educated guess and ask questions accordingly, but in the end, you are making assumptions and expect the interaction to distill more data.

Perhaps in your data you identify a particular product category that seems to excite a set of customers, so you develop a program to target that product category across that customer segment … but it shows no meaningful lift. Does that mean that the segmentation strategy was wrong? Not necessarily. All that the segmentation shows is commonalities and differentiators. It doesn’t show true customer motivations. Your targeting program made some assumptions about what the data showed, and your assumptions were wrong — not the data.

So, the first thing to do is to implement the segmentation strategy across the entire targeting population and see which segments respond to which offers. This will give you a better understanding of motivators on a segment-by-segment basis.

Target the Past — and the Future
Don’t target based only on past behaviors, but also on the next page.

If you knew what customers wanted when they arrived at your site, you would be golden. I want a watch. I land on Zappos, and presto: all its watches appear, already sorted to my interest. But, unfortunately, this intent-based mind reading is not possible, so we have to focus on what we do know. We know from the data what customers have looked at previously. We know how their interests are similar to and different from what others have looked at and converted. So, what is the “tipping point behavior”?

If you are clear about the tipping point behaviors that would put me in the watch-buyer segment, you can identify the behavior and automatically assign me to that segment when I exhibit interest. For watches, perhaps it’s as easy as clicking through from a paid Google search; you see that my referrer includes watches or popular watch brands, and immediately you promote me to a member of the watch-buying segment. Or perhaps I get there by viewing more than three watch brands across two visits. Whatever the determinate behavior, leverage what you know now based on where I am in my visit and target me immediately.

Better understanding your customers, their interests, and their motivations should have a direct impact on your bottom line. Driving conversion, after all, is key, but it may take time to have a meaningful and stabilized impact.

In Consulting, we’ve developed a number of tools and techniques for easily identifying what makes customers valuable and which activities draw them in. Analyzing those valuable customer segments and how they change over time can help you devise a strategy that will supply a wealth of effective, tactical targeting efforts that will ultimately help you achieve that goal.