NPS

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The Net Promoter Score (NPS) is widely used by organizations. It’s often used to make high-stakes decisions on whether a brand, product, or service has improved or declined. Net Promoter Scores are often tracked on dashboards, and any changes (for better or worse) can have significant consequences: adding or removing features, redirecting budgets, even impacting employee bonuses. Random sampling error, however, can often explain many

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When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed to the concept of margins of error—political polls include them.

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The Net Promoter Score (NPS) is a popular business metric used to track customer loyalty. It uses a single likelihood-to-recommend (LTR) question (“How likely is it that you will recommend our company to a friend or colleague?”) with 11 scale steps from 0 (Not at all likely) to 10 (Extremely likely). In NPS terminology, respondents who select 9 or 10 on the LTR question are

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Despite the ease with which you can create surveys using software like our MUIQ platform, selecting specific questions and response options can be a bit more involved. Most surveys contain a mix of closed-ended (often rating scales) and open-ended questions. We’ve previously discussed 15 types of common rating scales and have published numerous articles  in which we investigated their measurement properties. Now we turn our

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The Net Promoter Score (NPS) is a widely used metric, but it can be tricky to work with statistically. One of the first statistical steps we recommend that researchers take is to add confidence intervals around their metrics. Confidence intervals provide a good visualization of how precise estimates from samples are. They are particularly helpful in longitudinal research to help differentiate the inevitable fluctuations in

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We’ve all spent a lot of time at home this year. The pandemic has made already-popular video streaming services seem essential. The popularity makes sense given the relatively inexpensive subscription fees, the lack of long-term contracts, and the many channels of access (through websites, mobile apps, smart TVs), and there is a LOT of content (albeit distributed across different services). But no matter how good

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There are a lot of ways to display multipoint rating scales by varying the number of points (e.g., 5, 7, 11) and by labeling or not labeling those points. There’s variety not only in how rating scales are displayed but also in how you score the responses. Two typical scoring methods we discussed earlier are reporting the raw mean of responses and using top-box scoring. We’ve also shown

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Software dominates our professional and personal lives. A product's usability, in addition to its features and capabilities, is an important influence on how likely users are to adopt new technology and recommend the software. Between 30 and 60% of the variation in Net Promoter Scores is determined by a product's usability. Customers tend to recommend products they have positive experiences with. Similarly, bad experiences lead customers

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If you’ve had a meeting recently it was probably a virtual meeting. The use of online meeting software with integrated video and screen sharing capabilities, such as Zoom and Google Hangouts, has been growing for years. To say the usage has now skyrocketed is probably an understatement. Online meetings are ubiquitous in business and are now common in education and personal use. Children attend classes

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