Case Studies - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 239

the Net Promoter Score (NPS) to simplify the characteristically long and cumber-
some surveys that typified customer satisfaction research at that time. His research
found a correlation between a company's revenue growth and their customers'
willingness to recommend them. The procedure used to calculate the NPS is
decidedly simple and is outlined here. In short, Reichheld argued that revenues
grow as the percentage of customers willing to recommend a product or company
increases actively relative to the percentage likely to recommend against it. (Note:
Net Promoter is a registered trademark of Satmetrix, Bain and Reichheld.)
At Autodesk we've been using the Net Promoter method to analyze user sat-
isfaction with our products for 2 years ( Bradner, 2010 ). We chose Net Promoter
as model for user satisfaction because we wanted more than an average satisfac-
tion score. We wanted to understand how the overall ease-of-use and feature set
of an established product factor into our customers' total product experience
( Sauro & Kindlund, 2005 ). Through multivariate analysis—used frequently in
conjunction with Net Promoter—we identified the experience attributes that
inspire customers to promote our product actively. These attributes include the
user experience of the software (ease of use), customer experience (phone calls
to product support), and purchase experience (value for the price).
This case study explains the specific steps we followed to build this model
of user satisfaction and outlines how we used it to quantify the value of a good
user experience.
10.1.1 Methods
In 2010, we launched a survey aimed at measuring user satisfaction with the
discoverability, ease of use, and relevance of a feature of our software we'll refer
to here as the L&T feature. We used an 11-point scale and asked users' satisfac-
tion with the feature, along with their likelihood to recommend the product.
The recommend question is the question that is the defining feature of the Net
Promoter model. To calculate the NPS, we:
1. Asked customers if they'd recommend our product using a scale from 0
to 10, where 10 means extremely likely and 0 means extremely unlikely .
2. Segmented the responses into three buckets:
Promoters : Responses from 9 to 10
Passives : Responses from 7 to 8
Detractors : Responses from 0 to 6
3. Calculated the percentage of promoters and percentage of detractors.
4. Subtracted the percentage of detractors from the percentage of pro-
moter responses to get the NPS.
This calculation gave us a NPS. Knowing that we had 40% more custom-
ers promoting than detracting our product does mean something. But it also
begged the question: is 40% a good score?
Industry benchmarks do exist for NPS. For example, the consumer software
industry ( Sauro, 2011 ) has an average NPS of 21%—meaning a 20% is about