Back in my eBay days, when Net Promoter was just becoming a huge deal, there came a time when we were asked to include the measurement in our user research. Even then I was extremely uncomfortable with the simplistic nature of the measure, but I just didn’t have the experience to speak up about it. It seems that NPS is finally getting the wind taken out of its sails. The latest takedown I read is Matt LeMay’s excellent On Net Promoter and Data Golems:

This very ubiquity is a huge part of what makes Net Promoter so attractive. It’s a system with an official-sounding name that consistently produces a measurable quantitative output. The score it produces can be easily benchmarked against that of any other company. And this is why, no matter how many times it is critiqued and debunked, Net Promoter only seems to grow in power and pervasiveness. The primary value of Net Promoter is not how effectively it predicts customer loyalty, but rather how effectively it covers your ass.

The main problem with the NPS question—”How likely are you to recommend [product] to a friend or colleague”—is that it’s a data model that doesn’t fit the social model of recommending products.

But by oversimplifying the multifaceted and highly variable human context around recommendation, Net Promoter falls into one of the biggest pitfalls of the “data-driven” age: it puts forth a data model that does not accurately reflect the underlying social model. When’s the last time you thought to yourself “I am likely to recommend this product to my friends or colleagues” as opposed to something like, “I can’t wait to tell my friend Tricia about this new slow cooker because I know that she doesn’t like to cook things on the stove”?

Luckily, Matt provides some really good ways to improve the use of NPS. Is this is something you deal with in your company I highly recommend his article.