NPS Is More Than Just A Number

Finding the context behind your Net Promoter Score with Qualitative Data

Qualitative data gets a bad rap. 😤 Overshadowed by the favorite sibling, quantitative data, qualitative data is often too quickly dismissed as less scientific and more subjective. In some ways, this stereotype is understandable. There are billions of ways for your customers to tell you the same thing, which makes it difficult to get to the good stuff. However, it’s important to realize that “quantitative” metrics, like Net Promoter Scores (NPS) and other rating mechanisms, are equally subjective. On a scale of 1–10, what one person considers to be a 5 might be a 3 to another.

Let’s say a movie critic goes to see the latest Marvel film. They find it enjoyable, so they give it a 6, the highest they will ever rate a cookie-cutter superhero movie. Meanwhile, the biggest Marvel fan in the world might go see that same movie and find Professor Hulk cringe-worthy, so they also give it a 6, the lowest rating they’ve ever given to the franchise. Same score, two very different perceptions of Earth’s mightiest heroes. 🦸 In these cases, this is where qualitative data can jump in to provide context to numerical data, normalize it relative to biases, and even deliver stronger insights than the numbers alone.

NPS might sometimes work on its own, but finding the context behind the number with qualitative data makes for a better story.

To give a more technical example outside the world of superpowers and spandex-wearing vigilantes, during the COVID-19 pandemic, economists were struggling to forecast consumer responses to supply chain woes and rapidly evolving market conditions. A team of researchers at The Futurist Group developed a methodology that anchored on qualitative research, stating that “the missing ingredient is an enhanced understanding of consumers’ changing needs, attitudes, and behaviors” (Harvard Business Review, 2020). By putting numerical transaction data in the context of consumer sentiment, this team’s model yielded a far more accurate projection compared to traditional economic analysis. In May, the U.S. Commerce Department reported a 17.7% improvement in Retail sales, and while the industry only anticipated a 9% advance, their team predicted a 13% improvement. That put this team’s prediction within 5% of the actual outcome. But maybe you’re still not convinced. 🤔

Let’s dig into Net Promoter Scores. NPS boils down to a single question: how likely would you be to recommend this product/service? Customers are asked to answer this question on a scale from 1–10, where a score of 9–10 are your “promoters”, a score of 7–8 are “passives” and anything 6 or lower are “detractors.” The scores are then tallied up and the percentage of customers that are promoters, net of the percentage of detractors, is your NPS.

Since its introduction in the early 2000s and popularization by Bain & Company, NPS has been a business standard for evaluating a brand’s strength, particularly when it comes to customer loyalty. And it’s easy to understand why it’s so popular — an egalitarian, non-denominational (indeed it is used across industries), and digestible metric that is the clean answer to life, the universe, and everything. 👍 At face value, it certainly sounds like a magic number!

42 is actually a pretty good NPS.

But just as the mice learned in Douglas Adams’ classic, the number is meaningless without context. A common mistake while measuring NPS is stopping short of understanding why a customer is or isn’t likely to recommend that product or service. Research shows that “the two most widely used measures, customer satisfaction (CSAT) and Net Promoter Scores (NPS), fail to tell companies what customers really think and feel, and can even mask serious problems” (Harvard Business Review, 2021). NPS benchmarks also tend to vary greatly by industry, where the lowest scores are typically found in — yep, you guessed it — TV providers. 🤢

Everyone is a promoter if you operate in a monopoly.

NPS, though simple in definition, is more complicated in practice. When implemented correctly, the NPS framework requires that respondents supplement their 1–10 score with an open-ended response. These responses should be aggregated and distilled into the key attributes that affect the consumer’s preferences, also known as the NPS drivers. But even if you appreciate the merits of qualitative data analysis and want to do NPS the right way, it can be a seriously tedious process to comb through thousands of tweets, online reviews, and surveys…until now. 😉

You knew it was coming, this plug for our product, but I promise I’ll keep it short and sweet. 😊 Digging through documents and data sucks — and it’s not a good use of your time or brain power. Resultid’s simple-but-mighty AI tools make analyzing qualitative data fast and easy. For example, simply run your NPS data through our platform to quickly distill those open-ended responses into Themes that are ranked by relative significance. Use our built-in Sentiment Analysis feature to normalize your raw scores for individual biases. Finally, use our in-product Tags to identify the NPS drivers that affect consumer decisions. After all, if you took away anything from this blog, it’s that NPS is just a number until it is put into context. You just let us do the boring stuff 🥱so you can focus on the important part — communicating those insights and actioning next steps for your team. 🤩

Reading thousands of lines of text data is a drag, quickly isolate key Themes within positive and negative responses with Resultid.

Based on our customer Council and feedback from our 1500+ users, we are working on even more features that can demystify your qualitative data, including the ability to track trends over time. Please check it out and let us know your thoughts at bertie@resultid.com! ✌️