Last time, I looked at different ways you might think about and measure customer loyalty. It’s not realistic to consider and measure customer loyalty as a single entity. You need to create a loyalty measurement dashboard with a number of appropriate, relevant indicators. These indicators might be behavioral, attitudinal, or financial. To do this, you must look at several different data sources, such as Web analytics data, surveys and other customer feedback data, and any market or context data that may be available.
Following on the tricky issue of measuring customer loyalty comes the issue of what to do about it. If you can look at the different aspects of customer loyalty through different metrics, then the questions are: What do you do with this information? How do you act on it in a way that positively affects customer loyalty? How can you accelerate building loyalty when it ascends, and how can you manage it when it declines?
On my customer loyalty dashboard, I have a mixture of metrics. Some are more strategic, potentially even key performance indicators (a customer satisfaction index, for example), and some are more operational or tactical (such as recency or frequency measures). The strategic measures tell me how I’m doing over the longer haul, while the tactical measures tell me what I need to do in the shorter term. The tactical measures are more likely to be behavioral metrics as, generally speaking, it’s easier to observe, react to, and influence customer behavior than customer attitudes.
RFM (define) analysis is traditionally used to manage retention programs. Customers are segmented according to how recently they’ve transacted, how frequently they’ve transacted, and what their monetary value to the business is. These segments can form the basis of differentiated retention and communication programs depending on which segment the customer sits in. Customers who are in the top segment for recency, frequency, and monetary value display loyal behavior and are the ones you don’t want to lose. They probably deserve some special treatment.
A particular case of the RFM approach is new customers, the ones who just transacted for the first time. You probably haven’t made any money on them yet. You need to get them to transact again before you begin to recoup your marketing costs. They’re also at the steepest point on the “friction curve,” the amount of effort required to get them to transact again.
Retention is like momentum. Once you get them started, it’s easier to keep them going. In the case of new customers, if you can get them to transact again, they’re more likely to transact a third time, then a fourth, and so on. So customer retention, like conversion, isn’t one process but instead a series of mini-events designed to move a customer from one state to the next.
RFM’s key advantage is its simplicity. It’s easy to do the analysis, create the segments, and put together some specific customer communication. There are a couple issues with it. First, it’s assumes people who behave the same way on these dimensions will respond the same way to specific communications. On its own, RFM doesn’t help with crafting the retention marketing message. With a multicategory retailer, different types of people will buy different types of products. They may have similar shopping profiles but be interested in completely different things. As well as knowing when to intervene, then, the marketer must also know how to intervene. What will the trigger be?
The other issue is with recency. If you have a regular interaction with your customers in some way, by the time you notice they haven’t been around for a while, it may be too late. By the time they cancel the service, stop visiting the site, or whatever, they’ve stopped doing business with you. They may already be a lost cause. They might have stopped being attitudinally loyal some time earlier, but it’s taken time to get to the point of being behaviorally disloyal.
We must be able to anticipate changes in customer loyalty rather than just react to them. Often, customers give signals or clues that their loyalty is shifting for the worse. They may change their behavior patterns, they may start calling customer service more often, and they may stop returning your calls. These are all indicators change is happening.
In customer retention marketing, predictive analytics warns the marketer that something may be up with a customer. Predictive models seek to identify customers who may be at risk based on other data changes. No predictive model will ever be 100 percent accurate, but if it’s good enough it can at least reduce the risk of customers taking their business elsewhere. Data input for these models will, of course, be specific to the individual business and the available data.
As markets become more competitive and retention becomes more important to digital marketer’s job description, we must start thinking seriously about customer loyalty. What does loyalty mean in your business? Does it mean anything at all? If it does, how do you know if you’ve got it? What are the relevant metrics? How can you positively affect those measures?
Lots of questions, but not necessarily difficult ones. Think them through carefully, and build a customer loyalty dashboard accordingly. As the saying goes, “Be careful what you measure, because you’ll get what you measure.”
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