How to Statistically Analyze Net Promoter Scores

In a famous Harvard Business Review article published in 2003, Fred Reichheld introduced the Net Promoter Score (NPS). The NPS uses a single likelihood-to-recommend (LTR) question (“How likely is it that you would recommend our company to a friend or colleague?”) with 11 scale steps from 0 (Not at all likely) to 10 (Extremely likely).

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UX and Net Promoter Benchmarks of Mass Merchant Websites

With the COVID-19 pandemic’s effect on consumer shopping behavior (e.g., increased online shopping for delivery or contactless pickup), mass merchant revenues rose dramatically in 2020 and the first part of 2021. For example, Target reported a $15B sales growth in 2020, higher than its total sales growth over the past 11 years. For another example,

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Sample Sizes Needed to Exceed NPS Benchmarks

So, you’re planning to collect data and you want to know whether your Net Promoter Score (NPS) is significantly above 50%. Established benchmarks can help research teams know if they’ve reached acceptable thresholds, such as a high Net Promoter Score (e.g., more than 50%). A high NPS is associated with successful product launches. But an NPS

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How to Compare a Net Promoter Score with a Benchmark

We recently described how to compare two Net Promoter Scores (NPS) statistically using a new method based on adjusted-Wald proportions. In addition to comparing two NPS, researchers sometimes need to compare one NPS with a benchmark. For example, suppose you have data that the average NPS in your industry is 17.5%, and you want to

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Sample Sizes for Comparing Net Promoter Scores

Sample size estimation is a critical step in research planning, including when you’re trying to detect differences in measures like Net Promoter Scores. Too small of a sample and you risk not being able to differentiate real differences from sampling error. Too large of a sample and you risk wasting resources—researchers’ and respondents’ time and,

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Quant or Qual Research? 27 Words to Help You Decide

When approaching a UX research project, one of the first things to consider is the method. And UX research has many methods. Methods can be categorized as quantitatively focused (e.g., A/B tests) or qualitatively focused (e.g., interviews). Most UX research methods can collect both qualitative and quantitative data. For example, surveys often collect both closed-ended

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Seven Reasons Survey Questions Are Answered Incorrectly

Surveys are an essential method for collecting data. But like all research methods, surveys have their limitations. Unless the survey is administered by a facilitator, a respondent has only the survey’s instructions, questions, and response options for guidance. Earlier we wrote about ways to improve the clarity of your questions and certain words to watch

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Exploring Another Alternate Form for the UMUX-Lite Usefulness Item

When thinking about user experiences with websites or software, what is the difference between capabilities and functions? Is there any difference at all? In software engineering, a function is code that takes inputs, processes them, and produces outputs (such as a math function). The word capability doesn’t have a formal definition, but it most often

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Sample Size Estimation for NPS Confidence Intervals

Sample size estimation is a critical step in research planning. It can also seem like a mysterious and at times controversial process. But sample size estimation, when done correctly, is based mostly on math, not magic. The challenge is that the math can get complex, so it becomes easier to defer to simple rules or

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Evaluating NPS Significance Tests with Real-World Data

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

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