The Core Decision Support Tool that Undergirds Our Approach to Customer Satisfaction and Service Quality.
Conventional CSM Has Increasingly Come Under Fire Because of Poor Correlation to Real-World Behaviors
 
Highly Satisfied Customers Are Defecting in Droves…
The CSM Loyalty Relation Must Be More Explicit…

Why Satisfied Customers Defect. Thomas O. Jones ; W. Earl Sasser Jr HBR 11/1/95
 
CS Can No Longer Get Away with Just Measurement – It Must Link to Profit and Other Metrics
The authors discuss the “service-profit chain” which establishes quantitative relationships between profitability, customer loyalty, and employee satisfaction, loyalty, and productivity. (Older manual approaches included the balanced scorecard) Putting the Service-Profit Chain to Work. James L. Heskett ; Thomas O. Jones ; Gary Loveman ; W. Earl Sasser Jr.; Leonard A. Schlesinger HBR:7/1/00
 
CSM Is Migrating to CRM Which Measures the Entire Customer Relationship Including Needs and Values
A Crash Course in Customer Relationship Management. HBR 3/1/00
 
 
The true measure of a satisfied customer is when there is no perceived gap between expectation and performance on the most important attributes of the product or service in question. Therefore, the research method must accurately measure importance, expectation, and performance. Traditional approaches often do not measure all three metrics, or they measure them using older, less precise measurement approaches. Additionally, three central limitations prevent traditional customer satisfaction designs from producing the more powerful new generation outcomes. We will look at each limitation in turn:
 
Limitation #1: Measuring performance on a scale. When performance is measured on a scale, it makes it very difficult to make the translation between the scale and what that change means in the real world (e.g., What exactly does it mean to improve from a 5.6 to a 6.2 on a 1 to 7 scale?). Solution: Measure the performance in terms of real-world experience. Let's use hold time on the telephone as an example--customers express how they think the institution is performing on hold time in terms of their actual experience in seconds (e.g., 30 seconds, 1 minute, 1 1/2 minutes, etc.).
 
Limitation #2: Inferring importance from performance. Most service quality or customer satisfaction approaches don't actually measure importance at all. They either correlate or regress performance scores of a key driver against some measure of overall satisfaction. These approaches then call that correlation or regression weight an "importance score." From the standpoint of experimental method, this is simply invalid. Correlations and regression coefficients are measures of association, not importance. Solution: Obtain importance for each attribute by using techniques expressly designed to measure importance (conjoint analysis, discrete choice analysis, etc.).
 
Limitation #3: Not linking customer satisfaction to real world outcomes. Customer satisfaction measurement typically calculates an overall index (called a CSI). When this index is tracked over time we can see whether the organization is improving or not. The more advanced traditional CS protocols even build simulation models that allow a strategist to change the performance of an attribute (e.g., from a performance of 5.6 to 6.2, per our previous example). The strategist then observes the impact on overall Customer Satisfaction Index (CSI) by plugging that change into a regression equation. Despite the usefulness of this approach, it has some severe limitations. Not only is it unclear how this change can be actually implemented (see Limitation #1), but if it is, how can it be known if the benefits of making the change will outweigh the costs? Solution: Build a CS simulation model capable of predicting real-world outcomes (sales, switching, revenue, etc.) by changing the key drivers in real-world ways (e.g., decrease hold time on phone from 63 seconds to 35 seconds).
 
 
 
Our New Approach overcomes the problems of the old method.

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