Michalis Michael, CEO of DMR, explains whether the Net Promoter Score should be replaced by more relevant metrics

More innovative ways to measure customer experience may be needed in today’s increasingly digital world.

How can businesses measure the success of their marketing efforts? How does their current and future performance compare to their competitors? How to know, for example, the levels of satisfaction and loyalty felt by their customers? The rise of social media over the past decade has simultaneously made these questions easier and, in many ways, harder to answer.

On the one hand, the internet is replete with all the data needed to determine the performance of a given business, as customers willingly – if not enthusiastically – share thoughts and opinions that provide insight into issues as vital as customer satisfaction. clients. On the other hand, the sheer volume of data available can make it difficult to separate the essential from the non-essential.

With the amount of potential key performance indicators (KPIs) provided in a social media world, all businesses, large or small, need to consider that some metrics are more key than others.

On top of that, some companies – as McKinsey partners Frédéric Gascon, Raffaele Carpi and John Douglas noted – “measure and manage performance using lagging metrics, such as meeting monthly production targets. or quality. By the time the results are known, it is too late to influence the consequences. “

It is therefore clear that the stage is set for new ways of measuring performance: state-of-the-art methods capable of harnessing AI and machine learning technology to sift through swathes of data, and able to articulate actionable KPIs in a simple and accessible format.

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Go out with the old one

This is by no means a new idea. In 2003, the concept of the NPS – Net Promoter Score – was born, and its simplicity reflects a real and permanent desire to condense KPIs into a simple and actionable score.

A company’s NPS can be calculated by asking customers a single question, typically asked via a survey: “On a scale of 0 to 10, how likely are you to recommend Brand X to your friends and colleagues?” “

Respondents can then be divided into different categories. Those who provide a score of 0 to 6 are considered detractors, 7’s and 8’s are passive, while 9’s and 10’s can be understood as active promoters. The NPS score itself can then be calculated as follows: NPS = (promoters-detractors / all respondents) x 100, with scores ranging from -100 to +100. The result is a single number that Fortune says is used by 60% of Fortune 1000s to predict customer behavior and, by extension, the outlook for a given business.

As appealing as a single number is, however, the NPS has its flaws – in fact, according to a TQM Journal study, the NPS was found to “be a very poor predictor of customer loyalty and satisfaction.” .

Perhaps this is not surprising – after all, not all survey responses are true, and they don’t always accurately reflect what really happened. And, in a digital world teeming with customer opinions, as brands’ Twitter accounts deal with Retweet’s complaints and praise, it’s likely that better results are waiting to be found ‘in the wild’, where no one is. asking questions.

The goal, therefore, should be to combine the admirably concise report and sometimes the predictive power of a single score-based figure with all the nuances provided by the vast amounts of information available today.

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In the new

Through the use of machine learning models, it is quite possible to collect detailed and unsolicited customer reviews through analysis of social media and other online publications. Social media listening processes allow us not only to measure “buzz” – or the volume of online posts about a brand – but to determine the sentiment expressed towards that brand, whether positive, negative or neutral.

Semantic machine learning models can go even further, in fact, by recognizing purchase intent and recommendations; actual behavior is captured by engagement ratios for likes, comments and shares of a brand’s social media posts, and even the reach of a brand’s PR initiatives.

It goes without saying that this type of data is much more reliable and multifaceted than the responses to the surveys on which the NPS depends. The good news is that the information described above can indeed be consolidated into a single KPI.

Our data scientists, for example, were able to choose from all of the available social intelligence metrics – buzz, purchase intention, net sentiment scoreTM (which is, itself, our branded composite commercial metric) – and condense them. into a number between 0 and 1 that we call the Social Presence Score (SPS).
The process is a bit more complex than the formula for calculating the NPS – it involves annotating the data and weighting those metrics with our own internal method – but there are several business advantages to exploring alternatives to NPS.

By using systems such as SPS, companies will have the opportunity to compare their brand and its overall performance in the market to that of their competitors (and, indeed, to their own performance in previous months or years), d ” identify specific metrics that require improvement and predict future performance. – including sales.

Not only brands, but also individuals are marketing in a world too complex to be measured with surveys alone, and therefore it is more important than ever to adopt KPIs based on sources such as social intelligence – to alongside exciting technology capable of transforming these KPIs. into actionable information.

Written by Michalis Michael, CEO of DMR