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average for products such as Quicken, QuickBooks, Excel, Photoshop, and
iTunes. Common practice at Autodesk is to place less stock in benchmarks but
rather focus carefully on the aspects of the user experience that drive up the pro-
moters while reducing the detractors.
To isolate the “drivers” of a
good user experience, we also
included rating questions in our
survey that asked about the overall
product quality, product value, and
product ease of use. We asked these
questions on the same 11-point
scale used for the recommenda-
tion question. We then calculated
mean satisfaction scores for each
experience variable. Satisfaction is
plotted along the
x
a
x
is shown in
Figure 10.1
.
.50
Product Quality
.45
FIX
LEVERAGE
.40
Product Value
.35
.30
Product Ease of Use
.25
L&T Relavance
.20
HOLD
MAINTAIN
.15
L&T Discoverability
L&T Ease of Use
.10
8.4
8.6
8.8
9
9.2
9.4
9.6
9.8
10
Next we ran a multiple regres-
sion analysis with Net Promoter
as the dependent variable and the
attributes as independent vari-
ables. This analysis showed us which experience attributes were significant con-
tributors to users' likelihood to recommend the product. Because it uses the beta
coefficient, the analysis takes into account the correlation between each variable.
Those correlations are plotted against the
y
axis in
Figure 10.1
. The
y
axis repre-
sents the standardized beta coefficient. We call the
y
axis “Importance” because
correlation to the question “would you recommend this product?” is what tells
us how important each experience variable is to our users. Plotting satisfaction
against importance gives us insight into which experience attributes (interface,
quality, or price) are most important to our users.
Satisfaction
Figure 10.1 Anatomy of a Key Driver Analysis. Note that some graph data are
simulated.
10.1.2
Results
According to
Reichheld (2003)
, no one is going to recommend a product
without really liking it. When we recommend something, especially in a profes-
sional setting, we put our reputations on the line. Recommending a product is
admitting we are more than satisfied with the product. It signifies we are willing
to do a little marketing and promotion on behalf of this product.
This altruistic, highly credible, and free promotion from enthusiastic custom-
ers is what makes the recommend question meaningful to measure. Promoters
are going to actively encourage others to purchase our product and, according to
Reichheld's research, are more likely to repurchase.
We wanted to determine how a customer's likelihood to recommend a given
product was driven by specific features and by the overall ease of use of that
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