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Measuring customer loyalty

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

A lot of the answers to these types of questions will depend on what industry or vertical you happen to in. The notion of loyalty is different if you are selling biscuits (or cookies) than if you are selling cars. The frequency of the decision and the purchase decision process itself are very different. Some commentators believe that loyalty is essentially a behavioural phenomenon. Certainly in terms of managing retention, it’s primarily the behavioural drivers that are used to trigger marketing events such as promotions or emails. But that’s not necessarily because the altitudinal components to loyalty are not important, it’s just that they are harder to work with.

My own view is that the notion of customer loyalty is often nebulous, difficult to define and hard to measure. But we shouldn’t let that put us off! Often in the work that we have done for clients we see the disproportionate value of repeat customers to the overall business. So, how do we define and measure loyalty?

In my ideal world I wouldn’t have a loyalty measure, I would have a loyalty dashboard. I don’t think it’s really possible to measure and manage customer loyalty using a single metric, I think you need a number of different indicators giving you different perspectives on how visitors and customers are thinking about their relationship with your brand. It’s not just about how they behave but it’s also about what they think and the emphasis between the two will be dependent on the kind of business that you are in.

In our online world we’re pretty good at tracking behaviours and so it doesn’t come as a big surprise that behavioural data is often used to describe customer loyalty. In a quick survey of various web analytics tools, most of those that have a “visitor loyalty” metric base it on the frequency of visits or perhaps the number of “conversion” events. What they generally don’t do, however, is take into account what the visitor does when they get to the site. So, someone who visits 3 times and spends 5 minutes on the site each time is considered to be more loyal than someone who visits one and spends half an hour on the site. So, a frequency metric may be interesting but may not necessarily be very useful when it comes to thinking about loyalty.

Then there is the issue of recency. Does recency have anything to do with loyalty? Does the fact that someone visited my website yesterday make them more “loyal” than someone who last visited last month? Probably not. But if they have visited more frequently in the past and have visited more recently, then they are displaying characteristics of “loyal” behaviour. Recency and frequency analysis in conjunction are better than looking at them individually but we’re probably still not getting the full picture.

I think that customer loyalty also needs context. We all generally live in a competitive world. We are fighting for our share of the wallet, the 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 that from a web analytics system.

Other data sources that I can add to my loyalty dashboard to give me this context include 3rd party sources such as audience panels or my own surveys. Not everyone is going to have access to panel data such as Nielsen NetRatings or Comscore but if you do have that data, you can use it to add to context to your web analytics data. As a simple level you can measure the duplication or overlap between your audience and that of your closest competitors or you can drill into more depth and look at the amount of time visitors spend on your site compared your competitors.

If you don’t have access to these types of services, you can get at some competitive context by asking your own visitors through the use of surveys. You can ask your own visitors 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 analysed to produce some loyalty metrics that can be tracked over time or across different visitor segments.

Surveys can also be the vehicle to give you a wealth of powerful attitudinal information for your loyalty dashboard. Measures such as “propensity to return” and “propensity to recommend” have been shown in the past to be strong predictors of loyalty and customer lifetime value. Satisfaction can also be used as a leading indictor for changes in loyalty and the benefit of these types of measures is that they can give you an opportunity to act before it’s too late. Often customers can become attitudinally disloyal before they actually change their behaviour.

There isn’t a “one size fits all” approach to measuring customer loyalty and I would encourage you to 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 needs to come action and next time I will have a look at how we can utilise data-driven insights in our retention marketing activities. Till then…

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