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How much did you spend on Amazon last week? If you had to provide receipts or proof of purchases, how accurate do you think your estimate would be? In an earlier article we reported on the first wave of findings for a UX longitudinal study. We found that attitudes toward the website user experience tended to predict future purchasing behavior. In general, customers of websites

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Watching how people interact with an interface tells you a lot about what works and what needs improvement. And while observing behavior is essential for understanding the user experience, it’s not enough. Just because a product does what it should, is priced right, and is reliable, doesn’t mean it provides a good user experience. Users can think the experience is too complicated or difficult. For

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UX attitudes can be associated with future website behaviors. We described how to create this linkage in an earlier article that described the results of an exploratory longitudinal study. In that article, we discussed the common challenges involved with linking attitudes to behaviors. Notably, attitudes about a general concept (user experience) can have difficulty predicting specific behaviors (future purchases). Often the lack of relationship between attitudes

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Buying and selling a car usually starts online. While few people purchase cars online without seeing them in person first, the vast majority conduct substantial research online before they set foot in a dealership. Sellers are also equipped with the fair market value of their cars and what they can expect in a trade-in to a dealer or when selling it to private parties. Much

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Users' attitudes about an experience affect their future behavior. People who think a website is less usable or less attractive will probably visit less, purchase less, and recommend the website less. Understanding users’ attitudes now (easier to measure) can help predict users’ behavior in the future (harder to measure). At least that’s the idea behind using standardized questionnaires such as the SUPR-Q. But there’s actually not

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You’ve probably taken a survey or two in your life, maybe even this week. Which means you’ve probably answered a few types of survey questions, including rating scale questions. Earlier I outlined 15 common rating scale questions with the linear numeric scale being one of the most used. Examples of linear numeric scales include the Single Ease Question (SEQ) and the Likelihood to Recommend item (LTR)

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Does the NPS predict growth? What data is there to support the predictive ability of the Net Promoter Score? In the original research reported by Fred Reichheld, he showed that the NPS strongly correlated with growth in 11 out of 14 industries. However, he used historical, not future, growth. While this established a sort of concurrent validity, it left open the question about the predictive

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Should you label all points on a scale? Should you include a neutral point? What about labeling neutral points? How does that affect how people respond? These are common questions when using rating scales and they’ve also been asked about the Net Promoter Score: What are the effects of having a neutral label on the 11-point Likelihood to Recommend (LTR) item used to compute the

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We talk a lot about measurement at MeasuringU (hence our name). But what’s the point in collecting UX metrics? What do you do with study metrics such as the SUS, NPS, or SUPR-Q? Or task-level metrics such as completion rates and time? To understand the purpose of UX measurement we need to understand fundamentally the purpose of measurement. But settling on a definition of measurement

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Have you recommended a product to a friend? Will you recommend that same product to a friend again? Which of these questions is a better indicator of whether you will actually recommend a product? If people were consistent, rational, and honest, it’s simple. The second question asks about future intent so that would be the logical choice. It may come as no surprise that people

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