3 minutes to read. By author Michaela Mora on June 9, 2014 Topics: Analysis Techniques, Business Strategy, Customer Experience
It is often difficult to link customer loyalty to profits. Companies use attitudinal metrics such as overall satisfaction, likelihood to recommend and to use or buy their products again to measure satisfaction in general. Therefore, they interpret the high scores in these metrics as indicators of loyalty. Following the same train of thought, loyalty is used as a profit indicator.
In many cases, companies don’t have data about customers’ actual purchase behavior. In other cases, companies have a lot of customer behavior and transactional data but don’t analyze it in connection with these attitudinal metrics.
Unfortunately, you walk on loyalty shaky grounds without data on customers’ actions.
Intuitively, we expect satisfied customers to keep buying. However, we can’t always find a clear alignment between the reasons for buying a product and for satisfaction with it.
Often, first-time buyers make a purchase because of factors such as:
First-time purchases are filled with expectations. The product’s ability to meet them will have an impact on satisfaction, but not always on repeat purchases.
Dissatisfied customer may still continue buying the product or service because of:
Satisfied customers may still change to a competing alternative or stop buying altogether if customers find:
Satisfied customers driven by expectations of deals and discounts are very fickle customers. They are not likely loyal customers, even if they make repeat purchases.
This calls for caution when using satisfaction to gauge the potential for profits. You may have many “loyal,” but unprofitable customers. They may stay while the prices are low, but leave as soon as the prices go up or no deals are offered.
Many companies now use the likelihood to recommend as the key metric to measure loyalty (Net Promoter Score or NPS). After all, it makes sense that you would recommend products you buy for yourself.
However, in many customer experience studies, I have done over the years, I found many respondents giving both high recommendation and low satisfaction scores, and vice-versa. So, why would they recommend a product they were not entirely satisfied with? By the same token, why would they not recommend a product they were satisfied with?
Online product reviews can shed light on this paradox. Nuanced product reviews often include pros and cons. These are, in essence, trade-off analyses.
For example, reviewers may find a lot of benefits in a product. However, they would not recommend it for its high price, if they think a friend or relative can’t afford it. They may recommend a less satisfactory but more affordable alternative that does the job. The same can happen in the other direction.
In short, different factors may drive satisfaction and recommendations.
Given the trade-off analyses that can hide behind recommendations, this metric may not always be a good predictor of customer behavior. A new product can change the trade-off against or in favor of a recommendation and actual purchase behavior.
To identify loyal customers, companies need to triangulate the result from attitudinal metrics, its drivers, and their actual purchase behavior.
To make loyalty an actionable concept and link it to profits, companies should take into account customers who make repeat purchases and also score high on attitudinal metrics.
Repeat customers driven by deals and discounts are unlikely to be profitable. They are also far from being loyal.
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