Information Technology Reference
In-Depth Information
4. Discussion
Alternative measures to NPS of customer advocacy include customer satisfaction (CSAT)
and Customer Effort Score (CES) (Dixon et al., 2010). CES is measured on a 5-point scale and
is intended to capture the effort required by a customer to resolve an issue through a
contact-center or self-service channel. (Dixon et al., 2010) compared the predictive power of
CSAT, NPS and CES on service customers' intention to do repeat business, increase their
spending, and speak positively about the company. They concluded that CSAT was a
relatively poor predictor, while CES was the strongest. NPS ranked in the middle.
The choice of which customer advocacy measure to use depends on many factors such as
the type of company-to-customer relationship, the degree to which recommendations (for or
against a company) influence a purchase decision, and whether the measures will be
complemented by other customer feedback. To gain an in-depth understanding of
customers' experiences and how to improve them may require multiple indicators. In the
end, it is the action taken to drive improvements that customers value that is most critical.
Our case study validates the feasibility for using a multinomial logistic regression model as
a means to identify key drivers of NPS, though it is clear that the same methodology could
be employed with alternative measures of customer advocacy. Improvement teams at CSH
have used this model to prioritize projects relative to their expected impacts on NPS. A
novel aspect of our model development was the implementation of monotone constraints on
the slope parameters of the ordinal covariates. Our illustrative SAS code showing how to
impose the constraints on the maximum likelihood estimates should be of significant help to
practitioners interested in doing the same thing.
5. Appendix A
1.
data indata;
2.
infile 'C:\CarestreamHealth\indata.txt';
3.
input RC CC BC JT Y q79 q82a q82b q82d q82f;
4.
run;
1.
proc logistic data=indata;
2.
class RC CC BC JT
3.
q79 q82a q82b q82d q82f/param=glm;
4.
model Y = RC CC(RC) BC JT
5.
q70 q79 q82a q82b q82d q82f;
6.
run;
6. Appendix B
1.
data indata;
2.
set indata;
3.
array cc{23} cc1-cc23; do i=1 to 23; if CC=i then cc{i}=1; else cc{i}=0;end;
4.
if BC=1 then bc1=1;else bc1=0;
5.
array jt{9} jt1-jt9; do i=1 to 9; if JT=i then jt{i}=1; else jt{i}=0;end;
6.
array q{6} q1-q6; do i=1 to 6; if q79=i then q{i}=1; else q{i}=0;end;
7.
array a{6} a1-a6; do i=1 to 6; if q82a=i then a{i}=1; else a{i}=0;end;
Search WWH ::
Custom Search