Customer satisfaction and revenue weighting analysis

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Most of the studies on customer experience and customer satisfaction surveys that we do at OpenMet typically include an analysis weighting the results by revenues. This post will briefly explain this type of analysis of results and its main features.

What is revenue weighting?

Briefly, revenue weighting is a way to analyze customer results by magnifying the results of the customers giving the greatest revenues and minimizing the results of those with the least.

The main reason for using revenue weighting is that companies typically look to target the needs of their most profitable customers with their action plans and strategies, thereby obtaining greater profits and turnover. When we use weighting, the indicators of customer satisfaction or metrics on customer experience will change (sometimes very significantly), so our conclusions will be much more representative of the real importance of each customer for the company.

Let’s look at an example using the classic Pareto principle: if a company obtains 80% of its turnover from 20% of its customers, the opinions of this more lucrative 20% of its customers will logically have a greater weight in the indicators of satisfaction or loyalty. If we use the usual method (e.g. arithmetic means) to analyze customer responses, the responses of this 20% will be diluted by the 80% of the other customers.

The upshot is that we will have a customer satisfaction indicator in our scorecard that doesn’t really fit in with the company’s priorities.

How does revenue weighting work?

The most common solution is to change all calculations of results to use weighted averages. To choose the weighting to apply to each customer taking part in the survey, OpenMet basically uses two strategies:

1.Strict weighting: the revenue from each customer is used as the weighting. This system works well when there are small differences in revenues from each customer. By contrast, when the revenue of some customers is huge compared to others (e.g. several customers invoice millions of Euros, while lots of others invoice just a few thousand), the system poses problems because the opinions of smaller customers are virtually eliminated from the average due to their low relative weight.

2.ABC weighting: weighting is done by associating a relative weight to groups of customers, for example, based on the typical ABC grouping (or similar). The weight assigned to each customer depends on the weight assigned to its group. The weight of each group of customers is decided externally by taking into account the priorities of the company (e.g. A customers weighting = 10, B customers weighting = 3, C customers weighting = 1).

This strategy gives good results in some cases, such as when strategy 1 is not suitable. However, it can be difficult to assign weights because the weight of the group is somewhat arbitrary.


The weighting of results by revenue is very important for a good analysis of customer satisfaction or experience. It gives us indicators and metrics that offer a truer picture of the company and allows us to reach better conclusions and define action plans more precisely.

More information on Customer Satisfaction.