The Net Promoter Score is ubiquitous, with many large organizations using it as a key metric. But despite its widespread adoption, there are vocal critics. It’s been called snake oil, deceptive, fake science, and harmful. In our webinar series and on our website, we’ve addressed several aspects of the NPS, including the enmity toward it.
Drugstores seem to be on every city corner. They are a blend of convenience store, retailer, and pharmacy. Drugstores are a type of mass merchant enterprise, but they fit in a smaller box than their big-box counterparts. According to Kentley Insights, the total 2020 drugstore revenue in the United States was $312.1 billion. Additionally, e-commerce
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).
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,
To make better decisions, you need data. That’s become a truism. But can the process of using the data lead to bad outcomes? It seems like a hypothetical question, but it doesn’t take long to find a few key metrics that, when tracked, can lead to unwanted outcomes: On-time departures: On-time flight departures for airlines
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
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
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,
The food delivery market has been growing significantly over the past five years. That growth exploded in the United States from $17 billion in 2018 to $26 billion in 2020 (partly due to COVID-19). This market is highly competitive and has ultra-thin profit margins. Technologies such as route optimization enable faster and cheaper delivery, but
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