Making loyalty actionable

Taking action is widely mentioned as the top challenge in a customer listening initiative or voice-of-the-customer strategy. One method to making customer loyalty more actionable is to begin with a good framework.

The Loyalty Matrix is a very practical framework that segments customers into four groups based on their responses to a small battery of questions. The two axes in the matrix represent the two key aspects of loyalty – behavior (what a customer plans to do) and attitude (how they feel about working with your company). This forms the following four quadrants:
Loyalty Matrix
TRULY LOYAL – These customers have every intention of continuing to do business with you and they have a positive attitude towards your company. They like working with you and are more likely to increase their spending and recommend your company to others.

ACCESSIBLE – These customers have a good attitude about working with you but do not plan to continue their relationship. Since this is a rather odd combination, it’s not surprising that it is often a very small percentage of customers. It typically means something has changed in their business and they do not need your product or services any longer.

TRAPPED – These customers show every indication of continuing business with you, but they’re not very happy about it. They feel trapped in the relationship. This is common among organizations that are locked into a long-term contract, lack a suitable substitute, or find it too hard to switch. Eventually, trapped customers will find a better option.

HIGH RISK – As the name implies, these customers do not intend to return and don’t really like working with you anyway. Typically, they’re halfway out the door and not only will they no longer be a customer, but will also talk poorly about your company in the marketplace.

Many organizations use this framework and find it to be more versatile, more practical, and much more actionable than satisfaction scores, NPS, or other approaches. Here is a link to a short paper on the Loyalty Matrix if you would like to learn more. 

Patrick Gibbons

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