Information Technology Reference
In-Depth Information
customers include medical and dental doctors and staff and healthcare IT professionals in
small offices and clinics to large hospitals and regional and national healthcare programs. A
major company initiative is to create a sustainable competitive advantage by delivering the
absolute best customer experience in the industry. Customer recommendations are key to
growth in the digital medical space and no one has been able to do it consistently well. The
foundation for taking advantage of this opportunity is to understand what's important to
customers, measure their satisfaction and likelihood to recommend based on their
experiences, and drive improvement.
While descriptive statistics such as trend charts, bar charts, averages and listings of
customer verbatim comments are helpful in identifying improvement opportunities to
improve the Net Promoter Score (NPS), they are limited in their power. First, they lack
quantitative measurements of correlation between elements of event satisfaction and NPS.
As a consequence, it is not clear what impact a given process improvement will have on a
customer's likelihood to recommend. Second, they lack the ability to view multi-
dimensional relationships - they are limited to single factor inferences which may not
sufficiently describe the complex relationships between elements of a customer's experience
and their likelihood to recommend.
This section summarizes the use of multinomial logistic regression analyses that were
applied to 5056 independent customer experience surveys from Jan 2009 - Jan 2010. Each
survey included a question that measured (on a 5-point Likert scale) how likely it would be
for the customer to recommend colleagues to purchase imaging solutions from CSH. Five
other questions measured the satisfaction level (on a 7-point Likert scale) of the customer
with CSH services obtained in response to an equipment or software problem. Key NPS
drivers are revealed through the multinomial logistic regression analyses, and improvement
scenarios for specific geographic and business combinations are mapped out. The ability to
develop a quantitative model to measure the impact on NPS of potential process
improvements significantly enhances the value of the survey data.
3.1 CSH Customer survey data
The 5-point Likert response to the question about willingness to recommend is summarized
in Table 1 below. CSH calculates a unique net promoter score from responses on this
variable using the formula
5
ˆ
is a
vector of weights and where
ˆ
p
is the estimated proportion of customers whose
recommendation score is
i
. Two interesting characteristics of the weight vector are, first, the
penalty for a 1 (2) exceeds the benefit of a 5 (4), and second, the negative weight for a neutral
score is meant to drive policies toward delighting customers.
NPS
wp
, where
w
( 1.25,
0.875,
0.25, 0.75, 1.0)
i
1
ii
Recommendation Interpretation
1 Without being asked, I will advise others NOT to purchase from you
2 Only if asked, I will advise others NOT to purchase from you
3 I am neutral
4 Only if asked, I will recommend others TO purchase from you
5 Without being asked, I will recommend others TO purchase from you
Table 1. Meaning of Each Level of Recommendation Score
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