Multi -dimensional Customer Satisfaction - part 7

I closed the last blog with the calculation for the combined satisfaction scores from three measurements taken from three different people within the customer organization. This calculation yielded a single value to characterize customer satisfaction which, I believe, represents a research-based approach to the problem of reporting customer satisfaction when the interaction between the company and the customer has multiple interfaces. Since I have noted in previous blogs in this series the inadequacy of a single measurement of customer satisfaction for the complex customer (a customer that is characterized by two or more people interfacing with the company), I will omit repetition of the discussion in this blog. This blog will focus on the interpretation and use of the combined score.

The Longitudinal Perspective

The strategic value of the combined score lies in the longitudinal perspective. In other words, the view of the combined score over time provides the quantitative assessment of the direction of customer satisfaction from measurement period to measurement period. If the strategic direction of the company is to maintain customer satisfaction, the measurement over multiple time periods can be used to validate consistency. Alternatively, if the strategic direction of the company is to improve customer satisfaction, then the trend line of the measurement over multiple time periods should indicate a positive slope. Thus, the use of the combined score provides the numerical perspective for evaluation of strategy with respect to customer satisfaction.

The results from period-to-period measurement will not follow a smooth, straight line. Most likely, it will be erratic in its movement from period-to-period. I have discussed this effect in previous blogs and hence will not cover this erratic movement in detail at this time. However, the key is to realize that the results in any one period came from multiple samples, each of which contains sampling error. Thus, the likelihood of a smooth curve which contains no sampling errors is essentially zero!

The trend line constructed through the combined score for each period will provide a measure of the “fit” of the trend line to the scores. The term most frequently used to describe the “fit” of the trend line is”r-square.” This statistical term indicates the percentage of the information contained within the combined scores that is explained by the trend line. Thus, r-square may have a value between 0.0 and 1.0 or it may be noted by percent. Thus, a trend line with an r-square of 0.8 (80%) would be interpreted to mean that the trend line explained 80% of the information contained in the time series of combined scores. If every combined score fell exactly on the trend line the r-square would be 100%. On the other hand, if every point were randomly located about the line the r-square would be 0.0%. As long as the r-square is 60% or more, the trend line is considered to be a reasonable fit to the trend line of the combined scores data.

Thus, the use of trend lines created from the combined scores in each measurement period provides the quantitative assessment of the customer satisfaction strategy. Trend lines can be created for overall satisfaction with the company, product performance, service performance, etc.

Tactical Use of the Combined Scores

While trends are very important for strategy evaluation, they are of little help with operational tactics that take place in the market place every day. The people on the “front lines” are frequently required to respond to customers. The combined scores along with the customer contact model can be used to provide the background knowledge of the customers for these people.

For example, the customer contact model is an excellent tool for training the customer-contact personnel on where to focus their energy. The customer contact model uses time with the customer, information quantity and information quality as the three measures for valuing the customer contact. By directing sales, service and other customer-contact personnel (such as accounts receivable) to focus on these three measures, there is a reasonable chance that customer encounters will increase in value. (I must admit that this does pose an interesting dilemma for me, since I have been writing about the need for building relationships with your customers for the last several series (loyalty and retention series). The relationship with the customer still represents the best barrier to competition and the best long term strategy for customer satisfaction. The point here is to note that the three customer contact measures must be included in any customer contact even when relationship-building is the primary objective.)

Another tactical application of the multiple-dimensional measurement is to use the customer contact scores themselves as a way of assessing the effectiveness of individual customer-contact personnel. Average customer contact time should be relatively consistent for all sales contacts. This same logic should hold true for service contacts and contacts by accounts receivable personnel. Hence, a review of the customer contact measurement information can indicate customer-contact personnel who may be spending either excessive or insufficient time with customers. The manager or supervisor can use this information to coach the appropriate personnel who differ significantly from the “norm,” especially if other parameters of performance also indicate inadequate performance levels.

The same methodology described in the previous paragraph would apply to the area of quantity of information (the second parameter of the customer-contact model) for each of the customer-contact groups. The average scores for quantity of information should not vary greatly within a given group of customer-contact personnel. If the average score for sales contacts with engineering personnel is near 3.0, then sales personnel with much higher or lower scores may indicate potential problem areas. One could argue that a much higher score for quantity of information may be positive rather than negative. While a much higher score for quantity of information may indicate a strong relationship with the customer and hence more effective sales performance, it may also indicate a lack of understanding of the scoring system or a bias either intentional or unintentional.

Once again the methodology applied to the customer contact time can also be applied to the quality of information (the third parameter of the customer-contact model). The average scores for quality of information should also not vary greatly within a given group of customer-contact personnel. If the average score for sales contacts with engineering personnel is also near 3.0, then sales personnel with much higher or lower scores may also indicate potential problem areas. The same argument holds as noted in the previous paragraph, namely, a much higher score may be a very positive sign. Likewise, it may also indicate a lack of understanding of the measurement or a bias created by the customer-contact personnel. Quality of information, much like quantity of information represents very subjective areas and, as such, are very difficult to monitor for accuracy.
I will close this blog with a few thoughts about bias and error of the customer-contact parameters; especially the parameters of quality and quantity of information. Since these parameters are best assessed by the customer-contact personnel, it is imperative that the measures be used as management tools for improvement of customer relationships and satisfaction. Should these three parameters be used as a basis of performance reviews or incentives, a bias will undoubtedly appear. The two keys to successful use of the customer-contact information is to train your customer-contact personnel adequately on its use and value and then to be sure to use the information only for managing the customer process.

In my next blog I will cover in more detail the use of the combined scores. It is important that the underlying assumptions be clear so that misapplication of the calculations does not occur. When these assumptions are clearly understood, the use and value of the computations also become obvious.