Case Studies - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 241

product. A new feature (we'll call L&T) was included in the product we were
studying. When we plotted users' satisfaction with the L&T feature against their
willingness to recommend the product containing the L&T feature, we found
that the L&T feature was lower on the y axis relative to other aspects of the
interface (as shown in Figure 10.1 ). Using the L&T feature (L&T Ease of Use)
and locating it (L&T Discoverability) scored lower in satisfaction than Product
Quality, Product Value, and Product Ease of Use, but they also scored lower in
Importance. Users place less importance on this new feature relative to quality,
value, and ease of use. Data show that users' satisfaction with the L&T feature is
not as strongly correlated as quality and ease of use to their likelihood to recom-
mend the product and is therefore not as important to driving growth of prod-
uct sales.

The labels on the quadrants in Figure 10.1 tell us exactly which aspects of the
user experience to improve next. Features that plot in the upper left quadrant,
labeled FIX, are the highest priority because they have the highest importance
and lowest satisfaction.

Data in Figure 10.1 indicate that if we were to redesign the L&T feature, we
should invest in L&T Relevance as it plotted higher on the Importance axis than
L&T Discoverability and Ease of Use. Discoverability and ease of use of the L&T
feature are in the HOLD quadrant, indicating that these should be prioritized
last.

10.1.3 Prioritizing Investments in Interface Design
So how much does the user interface of a software product contribute to users'
willingness to recommend the product? We had been told by our peers in the
business intelligence department that the strongest predictors to a user's willing-
ness to recommend a product are:

1. Helpful and responsive customer support (Support)

2. Useful functionality at a good price (Value).

We ran a multiple-regression on our survey data set ( Figure
10.2 ) and found that variables for the software user experience
contribute 36% to the likelihood to recommend ( n = 2170).
Product Value accounted for 13% and Support accounted for
another 9%. To verify the contribution of software user expe-
rience to willingness to recommend, we ran another multiple
regression on data from a second, similar survey ( n = 1061) and
found the contribution of user experience variables to be 40%.

We then ran a third survey 1 year later. Regression formu-
las from the first survey and the third survey are shown, where
LTR represents Likelihood to Recommend. In Year 1 we cal-
culated the improvement targets shown in Figure 10.3 (left).
We set a target of 5% increase in users' likelihood to recommend
our product and we knew how to achieve that increase from

Figure 10.2 Simulated analysis of aspects of the
customer experience contributing to customers'
likelihood to recommend a product. Note that
some graph data are simulated.