In 1996, Fred Reichheld published a best-selling—book called The Loyalty Effect, in which he proposed a model for business success with customers. The book explicitly prioritized maintaining the loyalty of existing customers rather than over focusing on attracting new customers. He used a compelling metaphor: the leaky bucket. If your bucket has a leak, you have two ways to keep it full: add more water or patch the leak. Reichheld’s insight was, in essence, that most companies choose to add water: they spend time, energy, and resources on attracting new customers, including initiatives such as reduced prices and/or special promotions for new customers that existing customers can’t access. This, he argued, had the effect of making the leaks worse, because the largely ignored existing customers became more likely to defect, which made it all the harder for companies to keep their existing-customer numbers up, let alone add to them. Far better, Reichheld believed, to patch the leaks first. That is to say, if a company had $1,000$ existing customers at the beginning of a given year, but during that year lost $100$ of them (that is, those customers leaked out of the bucket), the first $11$ percent of growth from the remaining base of $900$ customers would only get the company back to where it was at the beginning of the year—with $1,000$ customers (that is, the company would merely have refilled the leaky bucket).
If the company’s goal had been to grow, say, $8$ percent during the year in order to get to $1,080$ by the end of the year, the company would now have to gain $180$ new customers. Hitting the year-end goal would have been much easier had the company kept its existing customer base and needed only to acquire $80$ new customers. Hence, says the customer-loyalty model, companies should focus more attention and resources on maintaining the loyalty of existing customers so that they don’t have to win as many new customers to power growth.
How would you know whether you are engendering a level of loyalty that will result in existing customers staying rather than exiting? Here is where Net Promoter Score(NPS) comes in, a simple metric which helps answer the above question.
The key idea behind the NPS is not new and has been known as “word of mouth” for quite some time. It is based on the simple assumption that the most loyal and valuable customers of a firm intend to promote a company’s products and services actively to others. Following that logic, the concept is based on the perspective that every company’s customers can be divided into three segments: ”promoters”, ”passives”, and ”detractors”.
By asking this single question “How likely is it that you would recommend [company name] to a friend or colleague?”, the company can track customer response on a $0-to-10$ points rating scale and categorize customers as follows:
• The share of customers with scores of $9-10$, called “promoters”
• The share of customers with scores of $7-8$, called “passives”
• The share of customers with scores of $0-6$, called “detractors”
Promoters are therefore customers who actively intend to recommend a product, passives are customers who still intend to recommend a product but show a lower likelihood to do so, and detractors are those customers showing the lowest likelihood and probably no significant motivation to promote.
The final NPS measure is the share of promoters subtracted by the share of detractors. Thus, the NPS metric spans from $-100$ percent (zero percent promoters, $100$ percent detractors) to $100$ percent ($100$ percent promoters, zero percent detractors) with typical bandwidths of $25$ percent to $75$ percent in practice.
Notably, this classification is based on three major assumptions:
• First, it does not matter whether customers do indeed recommend the company to others – all that is captured by the measure is whether these customers estimate themselves to have a (high) likelihood of doing so. Whether these intentions really translate into behavior is of no importance for the concept. Furthermore, the concept is assumed to be applicable for any kind of company. However, it is likely that for some (mostly utilitarian) brands or products, customers simply do not intend to talk about to others – no matter whether they are satisfied or not.
• Second, low or lower scores for likelihood of recommendation are either interpreted as passive customer behavior (scores $7-8$) or even as if customers had expressed negative word of mouth and critique to others (scores $0-6$). Both assumptions are strong and can be very misleading.
• Third, it is assumed that data aggregation does not bias the results. A simple example shows that this assumption might be misleading. Imagine Company A showing five percent promoters, $90$ percent passively satisfied, and five percent detractors, resulting in a NPS of zero (five percent minus five percent). Company B shows $50$ percent promoters, $0$ percent passively satisfied, and $50$ percent detractors, leading again to a NPS of zero. Very likely, the same final NPS value measures two completely different phenomena that are very likely to require different managerial actions.
As we have seen above, it is fairly straightforward to calculate the NPS and that is one of the reasons why it is so popular.Customers are far more willing to respond to a single question instead of answering a long battery of detailed customer satisfaction questions.
The cost of field market research is substantially reduced and a single measure, such as NPS, is easier to use and communicate both inside the company and to external stakeholders than a wide range of different and probably more complex customer satisfaction measures.
NPS is a good leading indicator customer feedback metric i.e. a metric that is strongly correlated with future company performance measures which makes it a potentially useful non-financial performance measure for management control.
NPS may not be equally effective as a predictive metric in all industries.
The NPS concept makes strong and implicit assumptions for customer behavior so it should not be a replacement for all other customer measures (e.g., current customer profitability, relationship length with the customer, customer’s industry affiliation, customer’s country).
NPS does not give any deeper insights into the reasons why customers respond the way they do. However, without knowing why customers are not willing to recommend, there are few management actions that companies can take to improve the situation.
Even if customer motives were well understood, one has to be very careful about simply maximizing NPS values. It is easy to imagine a scenario where customers are quickly pleased and become promoters as a result of low product prices, notable loyalty bonuses, additional services, or valuable quality promises. Although these “underpriced and over-served” customers are likely to be loyal over time, they will not necessarily contribute to future performance measures such as sales growth or profits.
Increasing NPS values can also get very difficult and unprofitable if companies already have high market shares, and convincing additional customers to respond positively to a company’s efforts is very costly.