Customer Loyalty Improves Retention, Part 2

My last column started to explore the notion of loyalty. What do we mean by loyalty? Is loyalty about the way we behave or the way we think? And if even we can get a definition of what it is, how easily can we track, measure, and manage it?

The answers to these questions depend on what industry you happen to be in. The notion of loyalty is different if you sell biscuits (or cookies) than if you sell cars. The decision processes and their frequency are very different. Some commentators believe loyalty is essentially a behavioral phenomenon. Certainly in terms of managing retention, behavioral drivers are primarily used to trigger marketing events, such as promotions or e-mail. But that’s not necessarily because the altitudinal components to loyalty aren’t important, it’s just that they’re harder to work with.

The notion of customer loyalty is often nebulous, difficult to define, and hard to measure. But we shouldn’t let that put us off. In the work we’ve done for clients, we often see the disproportionate value of repeat customers to the overall business.

So, how do we define and measure loyalty?

In an ideal world, I wouldn’t have a loyalty measure, I’d have a loyalty dashboard. It isn’t really possible to measure and manage customer loyalty using a single metric. You need a number of different indicators providing different perspectives on how visitors and customers think about their relationship with your brand. It’s not just about how they behave, but also about what they think. The emphasis between the two will depend on the type of business you’re in.

In online, we’re pretty good at tracking behaviors, so it doesn’t come as a big surprise that behavioral data is often used to describe customer loyalty. In a quick survey of various Web analytics tools, I found most tools with a visitor loyalty metric base it on the visit frequency or the number of conversion events. What they generally don’t do, however, is take into account what visitors do when they get to the site. Someone who visits a site three times and spends five minutes there each time is considered more loyal than someone who visits once and spends half an hour on the site. A frequency metric may be interesting, but it’s not necessarily be very useful when it comes to thinking about loyalty.

Then there’s the issue of recency. Does recency have anything to do with loyalty? Does the fact that someone visited my Web site yesterday make her more loyal than someone who last visited a month ago? Probably not. But if they’ve visited more frequently in the past and visited more recently, they’re displaying characteristics of loyal behavior. Recency and frequency analysis in conjunction are better than looking at each individually, but we’re probably still not getting the full picture.

Customer loyalty also requires context. We live in a competitive world. We’re fighting for our share of wallet, budget, or just someone’s attention. We want our visitors and customers to spend more time or money with us than with the other guys. To be able to measure this context, I need some other data. I’m not going to get it from a Web analytics system.

Other data sources I can add to my loyalty dashboard to provide this context include third-party sources, such as audience panels, and my own surveys. Not everyone has access to panel data such as Nielsen//NetRatings or comScore, but if you do have that data, you can use it to add context to your Web analytics data. On a simple level, you can measure the duplication or overlap between your audience and that of your closest competitor. Or you can drill into deeper and look at the amount of time visitors spend on your site compared your competitor’s site.

If you don’t have access to these types of services, you can get at some competitive context by asking your visitors via surveys. You can ask which other sites they visit and, if relevant, how much time or money they tend to spend on these others sites. The data can then be analyzed to produce loyalty metrics that can be tracked over time or across different visitor segments.

Surveys can also be a wealth of powerful attitudinal information for a loyalty dashboard. Metrics such as propensity to return and propensity to recommend have been demonstrated to be strong predictors of loyalty and customer lifetime value. Satisfaction can also be used as a leading indictor for changes in loyalty. The benefit of these types of measures is they can give you an opportunity to act before it’s too late. Often, customers become attitudinally disloyal before they actually change their behavior.

There isn’t a one-size-fits-all approach to measuring customer loyalty. Think about measuring customer loyalty using a composite approach of different metrics drawn from different data sources. Create your own customer loyalty dashboard.

From insight must come action. Next, I’ll take a look at how we can utilize data-driven insights in our retention marketing activities.

Till then…

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