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| The
Core Decision Support Tool that Undergirds Our Approach to Customer
Satisfaction and Service Quality. |
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| Conventional
CSM Has Increasingly Come Under Fire Because of Poor Correlation
to Real-World Behaviors |
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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 |
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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 |
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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 |
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| 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: |
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| 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.). |
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| 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.). |
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| 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). |
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