Your meme has now firmly established itself in a human mind. It was noticed, understood, remembered, and recalled. It aligned well enough with our dispositions and life-stages that it actually helped to meet an existential need. We engaged with your innovation not once, but multiple times, even logging many hours with it, and you managed somehow to overcome our inhibitions, or distract us from them, well enough to earn our subscription fees or monetize our attention.

But one customer won’t keep your lights on or your investors happy. No, to expand your user base, there are still two unforgiving bottlenecks that your digital venture must survive, and both of them are social in nature: we will need to recommend your meme to others who themselves must be receptive to adopting it. Psychology has something to teach you about these last two trials as well.

What would possess us to recommend your meme to someone else? We didn’t invent it and we have no financial stake in its success. And we’re not overly inclined to charity, especially to tech giants and Silicon Valley. So why do we do it?

Proof is everywhere that recommendations are critical to business. Many apps on our smartphones send a notification after a few days of use, asking us if we would recommend it in the app store. More and more web sites, applications, and now even Windows 10 display a pop-up dialog box asking us if we are likely to recommend the experience. Many sellers on Amazon encourage us to recommend our purchases. And finally, the customer-satisfaction surveys fielded by world’s biggest enterprises have been using the Net Promoter Score (NPS), a trademarked question and scoring method that begins with, “How likely is it that you would recommend _____ to a friend or colleague?”

As you commit to aligning your digital designs with our psychology, you will want to know if your efforts are paying off, and sooner or later you will be fielding an NPS-like question in your own surveys. This has become the main customer-service metric around the world. If you follow the trademarked method, you will ask us to give a rating to this question on a scale from (0) “Not at all likely”, through (5) “Neutral”, up to (10) “Extremely likely”.

The founders of the NPS refer to those of us who choose 9 or 10 as promoters. We are the most likely to recommend your meme. Those who choose 0 through 6 are detractors. We won’t recommend it, and we are likely to speak negatively of it. And those who choose 7 and 8 are passives, that is, we are just giving a polite answer but harbor no strong feeling.

To calculate the NPS score (Figure 19-1), you subtract the percent of promoters from the percent of detractors, yielding the “net” percent of promoters, as described in the name. The score can range from -100% to +100%. So for example, in 2003, AOL had 32% promoters and 42% detractors, for a net of -10%. Around the same time, MSN had 41% promoters and 32% detractors, for a net of +9%. ⁱ

Figure 19-1. The calculation of the Net Promoter Score ©. ⁱⁱ

The NPS question is itself a meme, and a successful one, so it is interesting to explore its origins, especially to understand how recommendations came to such prominence. To do that, we go back to 2003 when a Harvard-trained market researcher by the name of Frederick Reichheld, one of the trademark-holders for the NPS , was searching for a better way to measure customer satisfaction. He was part of a mini-movement of scholars, including James Heskett, also from Harvard, and Ross Goodwin from Hewlett-Packard, who were seeking predictors of profitability other than mere customer happiness. Although everyone wanted satisfied customers, executives craved something a little closer in the causal chain to cold hard cash.

In Reichheld’s search, he became a Bain Fellow and began working closely with a firm in Silicon Valley called CustomerSat, later known as Satmetrix Systems. There he met the leaders of Satmetrix, André Schwager and Rosalie Buonauro, who had recently learned from staff analysts Doug Doyle and Henry Evans that a survey question was partially predicting key business-performance metrics that publicly-traded enterprises were reporting to the SEC. What question was it? It was our likelihood to recommend it to a friend or colleague. ⁱⁱⁱ With this insight, Schwager and Buonauro went on to help Reichheld publish an article titled “The One Number You Need to Grow .” ^iv In 2006, Reichheld published a book titled The Ultimate Question. ^v

As Reichheld put it, “It turned out that a single survey question can, in fact, serve as a useful predictor of growth. But that question isn’t about customer satisfaction or even loyalty—at least in so many words. Rather, it’s about customers’ willingness to recommend a product or service to someone else.” ^vi

What is the magic in such a question that led to its use across the globe? The answer is it’s social. It means that one of us who uses your meme has become so convinced of its value that we have decided to join you in spreading it to our most important and intimate connections. A recommendation is the next money event for your online venture; it is required for your meme to go viral. But why would we do such a thing?

Our motivation is the same as yours; it’s what unites inventors and users. We share the same fundamental objective in the age of information: we’re both trying to spread the meme of our personal identity. We all want others to know our name. And when we, as users, stumble on to the killer app that you made, or your mind-blowing video, or your new TV series, or even something as humble as a really elegant piece of open-source code that you wrote, we anchor our identity to your invention and recommend it to others, thereby increasing the memetic fitness of both. The reward for you is an invention that goes viral and a spike in stock prices. The reward for us is the growth in our social capital: the social credibility we gain among social relations that increases our access to network resources and expected returns. ^vii

Thus, many of us in the new millennium recommended playlists through iTunes, shared TED talks through Facebook, and described what we were binge-watching on Netflix as we conversed with friends, both online or face-to-face . Each time a friend to whom we recommended something returned to us, days or maybe months later, and thanked us for turning them on to a particle of culture in the global soup of memetic content, we drew satisfaction from the fact that they encoded our identity together with our recommendation indelibly in memory. We were influential. This was both a reward in itself and a favor we could call upon in the future.

This is why in the summer of 2015, Slack became one of the fastest spreading productivity apps to date. ^viii Middle-managers all across Silicon Valley recommended that their teams start using Slack, a group-messaging and file-sharing collaboration platform. As a result, over 150M of us were using Slack monthly, and its valuation exploded to over $2B less than two years after it was invented.

Those middle-managers wanted to do what social psychologist Robert Cialdini in 1976 termed basking in reflected glory. They wanted to bask in the reflected glory of Slack , to be known as the person who introduced this culture-changing tool to their companies, and to spread their own meme as they spread the Slack meme. As Cialdini put it, “[P]eople make known their non-instrumental connections with positive sources because they understand that observers to these connections tend to evaluate connected objects similarly… Being merely associated with someone else’s success and failure [has] much the same effect as personal success and failure.” ^ix

We not only gain the esteem of others when we recommend great things we have purchased, our self-esteem rises. America’s founding father of psychology, William James, was the first to observe this in 1890:

[A] man’s Self is the sum total of all that he can call his, not only his body and his psychic powers, but his clothes and his house, his wife and children, his ancestors and friends, his reputation and works, his lands and horses, and yacht and bank-account. All these things give him the same emotions. If they wax and prosper, he feels triumphant; if they dwindle and die away, he feels cast down.

So you should fully expect to feel great when you see promoters recommend your meme, and terrible when detractors revile it. For this reason, you should begin tracking your recommendations and execute deliberate plans to make them increase.

One of the things that the NPS got right was to stop throwing out the data from detractors. Prior to its appearance, many research studies in the business sector reported only the percent who were satisfied or very satisfied (the so-called Top-2 Box score , meaning the percent of people who chose the top two scale items in a satisfaction or recommendation rating). This effectively jettisoned the rest of us from further analysis, including those among us who were the most angry. (A simple arithmetic average does not have this problem—it reflects each and every response in the sample.)

Ignoring detractors was a mistake, because those among us who warn others against adopting a meme also stand to increase our social capital by helping prevent others from having the poor experiences that we endured. How often have you read negative reviews that implied somewhere that their reason for writing was to “make sure no one else experienced the same thing”?

This explains the outrage in social media that erupted in 2015 when it was revealed that Volkswagen had engineered millions of diesel engines around the world to cheat on emissions tests. It wasn’t enough to decouple our identities from the brand (by scraping off the Fahrvergnügen bumper stickers and throwing out our T-shirts). Instead we took to Twitter and Facebook to troll, flame, and hate on the company.

At this point, there has been more than a decade of research on the NPS, and as you can imagine, Reichheld’s early enthusiasm about it being the “one number” and the “ultimate question” has been tempered by empirical truth. All good memes eventually try to spread into inhospitable ecosystems, in this case, misuses of their original insights. Scholars now argue that it makes no sense to ask if we would recommend an entire company like Microsoft or Google, which has dozens of products. As Doug Doyle says, “When you ask me if I would recommend something, you have to be clear—recommend what?” Also, the NPS scoring method has never out-performed traditional averages in predictive power. Finally, when predicting overall company growth, good old customer satisfaction has never been shown to be a bad predictor itself; quite often it’s just as good as likelihood to recommend. ^x

But if you’re trying to predict whether we would ever purchase something again, or renew a subscription, or get referrals that could make your business go viral—recommendations are still a key metric. Also, regardless what your absolute NPS number is, or whether it predicts next quarter’s profits, if it spikes or dips quickly, your senior leadership should know about it.

Just as they should know about actual recommendations. Many analysts like Doyle are moving away from NPS, and instead measuring actual, active recommendations. They use a question to the effect of, “Have you recommended ______ to a friend or colleague in the past month?” It can be answered with a simple yes or no, thus making the beloved percentage the best summary statistic for it, and simple enough to take to C-level leadership. Doyle implemented this at Microsoft . In this way, his surveys didn’t ask our intention to recommend (which mainly reflects our underlying satisfaction), but instead our behavior of recommending.

In the next chapter, we examine closer how promoters and detractors actually behave on social media, and how we are sometimes even more extreme than we would have been alone, without group interaction.