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Eric Benjamin Seufert, in Freemium Economics The foundation of the metric is the belief that customers are either promotersextremely satisfied users who will serve as enthusiastic brand Looking for loyal ltr as well as sources of key repeat Looking for loyal ltr, or detractorsextremely dissatisfied users who will undermine brand growth by spreading unflattering testimony of a product experience. The net promoter score is a quantitative interpretation of qualitative data points.
Neutral responses are thrown out, and the percentage of the detractors is subtracted from the percentage of the promoters to produce the net promoter score, which spans a scale ranging from to Any net promoter score above zero is considered good, with scores above 50 considered exceptional.
A negative score implies a user base with stronger negative sentiment than positive and which thus spre more negative than positive testimony to potential clients. One of the greatest problems in conducting freemium data analysis is the incomparability of user segments due to the vast differences in their behavioral profiles. This incomparability is an asset when examining quantifiable, auditable records such as revenue, since analysis of many freemium metrics, which are highly stratified, benefits from excluding large swaths of the user base via some bimodal characteristic usually payer or non-payer.
But incomparability is a liability when attempting to draw broad, qualitative conclusions about the general appeal of the product. And because a net promoter score focuses attention on the extremes of satisfaction, it serves as a capable al of how well the long tail of the monetization curve is developing. If the net promoter score indicates that NPUs are serving as enthusiastic promoters for the product, then an evaluation of the benefits of advertising can be undertaken with more awareness of how it might negatively affect the product. The net promoter score is not without its critics; arguments can be made that it is overly simplistic, and any qualitative, questionnaire-based data point is vulnerable to dishonesty and respondent bias i.
But in a freemium environment, with massive scale, these considerations are at least somewhat ameliorated by the availability of a large volume of data, which should help correct for biases. Given that most quantitative metrics are singularly focused on granular, quantitative behavioral patterns, the net promoter score, when taken within the context of the entire portfolio of minimum viable metrics, accommodates a level of balance between observed behavior and opinion on the part of the user.
In other words, the net promoter score contrasts what users with what they do, painting a more detailed picture of product engagement. A qualitative indicator of user engagement can serve as a useful waypoint in achieving the delicate balance that must be struck between frequency of use and session lengths; engagement is an outgrowth of satisfaction, which is largely subjective, and a more complete assessment of it can be made through a metric grounded in subjective opinion. Jeff Sauro, James R. Lewis, in Quantifying the User Experience Another common example of converting continuous rating scale data into discrete topbox scoring is the popular Net Promoter Score NPS; www.
Promoters are those who rate a 9 or 10 topboxdetractors are those who rate 0 to 6, and passive responders are those who rate a 7 or 8. In fact, usability explains a lot of the variability in the NPS Sauro, For example, 15 users attempted to make travel arrangements on the website expedia. At the end of the usability test they were asked the NPS question. Here are their responses:. The appeal of top-box scoring approaches like the Net Promoter Score is that they appear easier to interpret than a mean.
Many executives are comfortable working with percentages. So knowing there is a higher percentage of customers likely to recommend your product than dissuade others from using it may be more helpful than just knowing the mean response is a 7. A leading competitor, the industry average, and historical data for the same product are all helpful—but all usually difficult to obtain.
It's not surprising that many software companies now use the NPS as a key corporate metric. I commissioned a study in March to survey the sentiments of customers of 17 consumer and productivity software products. For more details on the study see www. The average and high NPSs for your industry can be used as valid benchmarks if the comparisons are meaningful for your product. Introduced in by Fred Reichheld, the NPS has become a popular metric of customer loyalty in industry Reichheld,—see www. The developers of the NPS hold that this metric is easy for managers to understand and to use to track improvements over time, and improvements in NPS have a strong relationship to company growth.
Since its introduction, the NPS has generated controversy. For Looking for loyal ltr, Keiningham et al. In general, top-box and top-box-minus-bottom-box metrics lose information during the process of collapsing measurements from a multipoint scale to percentages of a smaller of Sauro, dand thus lose sensitivity although increasing sample sizes can make up for lack of sensitivity in a metric.
Also, there is no well-defined method for computing confidence intervals around the NPS. The mean of this converted data will be the NPS expressed as a proportion, and you can compute a confidence interval using the methods presented in Chapter 3 for rating scales. As far as we know, there has been no systematic research on the accuracy of this approach, but at least it provides some indication of the plausible range of a given NPS. Even practitioners and researchers who promote the use of the NPS point out that the metric, by itself, is of limited value.
You also need to understand why respondents provide the rating they do. If you can make changes that will increase loyalty, then increased revenue should follow. So, do improvements in usability increase customer loyalty? In total, we examined LTR data from users from over 80 products such as rental car companies, financial applications, and websites like Amazon.
The data came from both lab-based usability tests and surveys of recent product purchases where the same users answered both the SUS and the LTR questions. As shown in Fig. Figure 8. Mean and One self-reported metric that has gained rapidly in popularity, especially among senior executives, is the Net Promoter Score NPS.
The respondents are then divided into three :. Note that the categorization into Detractors, Passives, and Promoters is nowhere near symmetrical. To calculate the NPS, you subtract the percentage of Detractors ratings of 0—6 from the percentage of Promoters ratings of 9 or Passives are ignored in the calculation.
The NPS is not without its own detractors. One criticism is that the reduction of scores from an point scale to just three Detractors, Passives, Promoters in a loss of statistical power and precision. The confidence interval associated with the difference between Looking for loyal ltr two percentages is essentially the combination of the two individual confidence intervals. You would typically need a sample size two to four times larger to get an NPS margin of error equivalent to the margin of error for a traditional TopBox score. Case Study He analyzed data from users asked to complete both the SUS questions and the NPS question for a variety of products, including websites and financial applications.
Another common example of converting continuous rating scale data into discrete topbox scoring is the popular Net Promoter Score netpromoter. Promoters are those who rate a 9 or 10 topboxdetractors are those who rate 0 to 6 and passive responders are those who respond 7 or 8. In fact, usability explains a lot of the variability in the NPS Sauro, —for more details, see Chapter 8.
For example, 15 users attempted to make travel arrangements the website expedia. At the end of the usability test they were asked the Net Promoter question. The appeal of top box scoring approaches like the Net Promoter Score is that they appear easier to interpret than a mean. So knowing there is a higher percentage of customers likely to recommend your product than dissuade others from using it may be more helpful than just knowing the mean responses is a 7.
A leading competitor, the industry average, or historical data for the same product are all helpful—but are usually difficult to obtain. The average and high Net Promoter scores for your industry can be used as valid benchmarks if the comparisons are meaningful for your product. Tom Tullis, Chapter 10 presents five case studies showing how other UX researchers and practitioners have used metrics in their work.
These case studies highlight the amazing breadth of products and UX metrics. Erin Bradner from Autodesk looks at how the Net Promoter Score can be used to build this model of user satisfaction and quantify the value of a good user experience. The second case study by Mary Theofanos, Yee-Yin Choong, and Brian Stanton from the National Institute of Standards and Technology focuses on how to use various UX metrics to evaluate a system that provides real-time feedback to fingerprint users at U.
Tanya Payne, Grant Baldwin, and Tony Haverd from OpenText show how to use Single Ease Question and task completion rate as part of an iterative de process for an enterprise software product deed to create, edit, and manage websites. Viki Stirling and Caroline Jarrett from Open University integrate larger scale quantitative techniques with what has been learned from small-scale, qualitative techniques to improve a university prospectus.
Amanda Davis, Elizabeth Rosenzweig, and Fiona Tranquada from the De and Usability Center at Bentley University tested the feasibility of integrating biometric measures with qualitative user feedback when comparing a digital textbook with a printed textbook. The large amounts of empirical data that are the result of quantitative studies can be used to validate des. The risks are that they can sometimes be too narrow and not holistic enough, so they leave out important factors that are part of the bigger picture. This can be mitigated by using quantitative data in a strategic way, keeping the overall user and business goals in mind while still analyzing the details in the data.
In the many successful cases, quantitative data can be triangulated with qualitative data to deepen the understanding of the user and the system. This combination can provide Looking for loyal ltr data that can identify the big picture issues, patterns, and more detailed findings for specific issues. The following case study is an example of the power of triangulating data. This case study considers many user touch points. Users interact with products across Web-based and desktop applications, mobile apps and through person-to-person contact e. Deers must consider the complexity of the user interactions across these interfaces and strive to unify the experiences across visual de, content, and interaction de.
By understanding when and what emotional responses users have, deers can construct the user experience appropriate to the context of the interaction, resulting in a more unified whole. Our client for the project was a leading sunscreen manufacturer, rated 1 by Consumer Reports Magazine. We had two main goals for the project. The second goal was to make recommendations for the branding and Web site de of their products based on participant feedback.
The study was conducted in a formal usability lab setting at the De and Usability Center at Bentley University. This project provides a case study for researching emotional responses in participants to inform de recommendations for an enhanced and consistent user experience. Our study addressed the multiple contact points of consumer product purchases, spanning the physical product packaging, printed and digital advertising, and online Web platforms. The study focused on mothers with young children under the age of 18 living at home, who shopped at Whole Foods grocery store and who had purchased sunscreen and insect repellent in the last year.
We wanted to understand how current consumers perceived the product in relation to other natural skincare products before transitioning into a more competitive market that included non-natural products. Emotions are often instantaneous and may be unconscious, so researchers should not depend solely on self-reported responses Gonyea, With no single physiological measurement directly assessing emotions, researchers must measure affective responses with a variety of tools. The combination of these data and self-reported qualitative data provides richer insight into emotions.
We coordinated five tools—electrodermal activity EDAeye tracking, Microsoft Product Reaction Cards, net promoter scores NPSsand qualitative feedback—to understand the emotional impact of product packaging, digitaland Web site de. The use of biometrics provides information about affective responses as they occur. EDA data were the most appropriate choice for this study, since the measurement device was non-invasive and mobile. The Affectiva Q Sensor is a wearable, wireless biosensor that measures emotional arousal via skin conductance. The unit of measure is EDA that increases when the user is in a state of excitement, attention or anxiety, and reduces when the user experiences boredom or relaxation Figure 7.
Figure 7. Eyetracking glasses and EDA gloves. EDA, also known as skin conductance or galvanic skin response GSRis a method of measuring the electrical conductance of the skin, which varies with its moisture level. Sweat glands are controlled by the sympathetic nervous system Martini and Bartholomew, ; therefore, skin conductance is used as an indication of psychological or physiological arousal. In addition to biometrics, eye tracking can be used to pinpoint emotionally charged experiences.
Observers look earlier and longer at emotionally charged images than neutral images, perhaps to prepare for rapid defensive responses Calvo and Lang, Looking for loyal ltr The main advantage of this technique is that it does not rely on a questionnaire or rating scales, and users do not have to generate words themselves.
The NPSs were used to understand the appeal of the interface. This surveying tool asks participants about their willingness to promote a company or product, indicating their loyalty and future growth Reichheld, During each min, moderated Looking for loyal ltr the participants wore the Q sensor, which monitored their EDA. Depending on the task, the participants either wore eye-tracking glasses or worked on a computer monitor with an eye-tracking system, both of which monitored their eye movements.
Participants also performed the think-aloud protocol. They performed the following tasks:. Analyze product packaging.
Participants were asked to view a shelf of four sunscreen products and then decide which product sif any they would purchase. They picked up and examined the product packaging while completing this task.Looking for loyal ltr
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