Information Technology Reference
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RC
(1,0,0,0,0,0,0)
CC
(.8118,.1412,.0047, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
JT
(.1176,.1765,.1294,.1882,.2706,.0353, 0,.0471,.0235,.0118)
BC
(.8235,.1765) .
Additionally, the distributions for the sub-element scores within the first Region Code
define the sub-element covariate vectors as follows:
q79=(.0118,0,.0235,.0471,.3176,.2824,.3176)
q82a=(.0118,0,.0118,.0471,.2471,.4000,.2824)
q82b=(.0118,.0118,.0235,.1176,.2235,.3529,.2588)
q82d=(.0118,.0118,.0118,.0588,.2353,.3765,.2941)
q82f=(.0118,0,.0235,.0235,.1882,.4471,.3059)
4
Combining these covariates into x , using the estimates of
given in Table 2,
and evaluating the equations in (2) gives the probability distribution for Y for this
population profile as:
{ i
and 
1
1
2
3
4
5
Y
~
.00070
.00503 .13452
.47771
.38204
from which it follows that customers with this profile x will have an (expected) NPS of
70.1%.
3.4.3 Targeted Pathways for Improving NPS
A purely empirical way to compute NPS is to use the observed distribution (based on all
5,056 survey responses) of Y for p
5
in the formula
NPS
wp
, and this yields 61.7%.
ii
i
1
Consider now filling out the covariate vector x with the sample frequencies for the
observed demographic covariates and with the observed sample distributions for the sub-
element covariates. Using this x with the model yields a predicted NPS of 65.7%. The close
agreement between the data-based and model-based NPS scores is additional evidence that
the model fits the data well, and it also instills confidence in using the model to explore
“What If?” scenarios as outlined in Figure 3. Figure 3 defines sixteen “What If?” scenarios,
labels them with brief descriptions, and then shows expected NPS score if the scenario is
implemented. Table 6 contains a longer description of how each scenario was implemented.
Each scenario can be evaluated on the basis of how much boost it gives to the expected NPS
as well as the feasibility of establishing a company program that could make the
hypothetical scenario real.
We illustrated potential pathways to improve the overall NPS score, but this can also be
done with specific sub-populations in mind. For example, if the first region was under
study, then one could simply adjust the demographic covariates as illustrated in section
3.4.2 before implementing scenarios adjustments.
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