Customer ‘Loyalty’ — It’s Never a Sure Thing

As “Data,” “CRM” and the “Customer Database” continue the march toward the center of the marketing organization in tens of thousands of businesses, not surprisingly another “classic” marketing concept is rather “new” again — customer loyalty.

In fact, the top three luxury brands have higher loyalty rates than all other luxury category brands. J.D. Power cites customers of the top three luxury brands cross-shop the other two far more than brands in the second group, with the possible exception of Audi.

In the example of Lexus, they increased customer loyalty by targeting higher-value customers or “loyalists” in the first place.

How to Target More Loyal Buyers in The First Place

Use the simple approach we’ve outlined with RFM, and layering in demographic data, or derived data — for example, inter-order purchase time. Granted all of this assumes you have a viable customer and marketing operations database that makes this easy enough to execute. You can then use statistical methods — regression, for starters — to identify what the most predictive variables are.

Without delving into more detail than the average reader of this column is interested in, regression is a statistical method used for predicting the unknown value of a variable from the known value of two or more variables — also called “the predictors.”

The variable whose value is to be predicted is known as the dependent variable and the ones whose known values are used for prediction are known as independent (explanatory) variables. The good news is, you can do all of this with software, and/or an analyst can do this for you.

In other words, given what we know about our customers (spending, geography, age, urbanicity, etc.) we can identify what the values of other variables are that we don’t know, i.e. what is predictive of a better and more loyal customer.

We then allow this type of intelligence to inform who we target for acquisition (the prospects who spend like your most valuable buyers), the offer, and how we target them. This, too, can be accomplished through customer intelligence and database marketing software. This is not a trivial endeavor and is the subject of many other articles we’ll share.

Most importantly, marketers would do well to inform their customer loyalty by benchmarking and improving the quality of the customer being acquired, and methodically improving it.

The Bottom Line ― Stack the Deck in Your Own Favor

Knowing your customer is the first part of the equation that solves for your loyalty success. Differentiating the customer experience based on common sense loyalty scores like we have touched on herein do help. All loyalty work, of course, comes at a cost — and so marketers who miss or under-perform on loyalty measures often begin with a common error. That error is not looking upstream and starting with acquiring customers with the “genome” to become a loyal customer in the first place.

The evidence is overwhelming — targeting higher-value customers or “loyalists” in the first place outpaces any shiny black card you can print a logo on, or the cleverest “club” you create for loyal customers.

So be good to your customers, yes. But be good to yourself as a marketer — and start with the right customer in the first place. You’ll change the game in your loyalty program.

Author: Mike Ferranti

Mike Ferranti is the founder and CEO at Endai Worldwide in New York City. In this blog, he plans to offer ideas and perspective that energize, stimulate and motivate performance through the lens of his nearly 20 years of data, technology and marketing experience. Mike draws upon the logical, cultural and subject matter expertise in digital and data-driven marketing—with an occasional parallel between business performance and athletic performance.

One thought on “Customer ‘Loyalty’ — It’s Never a Sure Thing”

  1. Great article Mike! Really good thoughts to consider for us right now as we set up our own Customer Loyalty program. Looking forward to future articles.

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