Every CEO loves numbers. So when you receive the results from your company’s latest customer satisfaction survey showing most of the performance ratings are at the top of the scale between seven and 10, you might start patting yourself on the back. What you don’t realise is that the glossy chart-riddled report on your desk has lured you into a false sense of security.
Customer satisfaction is now the major differentiator between competitors, so it is not surprising that improving customer experience is a top priority for businesses looking for a competitive advantage. Even better is when your company tops the chart in sector-wide independent research, such as the annual Which? consumer survey. However, the results do not actually reflect the true situation.
This is because most of the survey tools being used to measure customer happiness are simplistic numerical ratings that not only do not provide particularly valuable insights. They are also masking unhappy customers.
In fact, the surveys most commonly used by companies around the world, where customers are asked to rate a firm’s performance on a scale between zero and 10, are frequently mislabelling customers as ‘satisfied’ or ‘loyal’. Our research at the University of Cambridge has found that up to 42% of the supposedly satisfied customers who have given a rating between seven and 10 are unhappy with one or more aspects of the company’s products or services, and may already be considering changing their allegiance to the competition.
Net promoter score does not correspond to customer behavior or even correlate with changes in company revenue.
There are a number of single-question customer satisfaction metrics being used, such as the net promoter score (NPS), customer satisfaction (CSAT) and customer effort score (CES). NPS is one of the most popular and claims to measure a customer’s overall satisfaction with a company’s product or service and the customer’s loyalty to that brand. It simply asks customers a single question: on a scale of zero to 10, how likely are you to recommend this company’s product or service to a friend or a colleague?
But customer loyalty is multidimensional and it is not enough to rely on a single measurement alone to predict whether your customers are going to stick by you. Even worse, we have found NPS does not correspond to actual customer behaviour or even significantly correlate with relative changes in company revenue. The simple fact is that companies are losing customers despite having high NPS scores.
I am not saying that we should completely dismiss single-question metric tools like the NPS, but companies should also consider using other data sources.
Focus on customers’ comments
So where do you start? Taking a deep-dive into your customer accounts is a good first step. Your customers’ transaction history, such as spending value and frequency, holds valuable insights. Any change in behaviour may indicate a change in loyalty status.
And back to that customer satisfaction report sitting on your desk, how do you identify the 42% of ‘false satisfied’ customers that are actually dissatisfied and find out what their problem is? The answer can be found in customers’ verbatim comments within the open-ended answers of surveys.
While comments in the open response section provide more in-depth feedback, the information they contain is often not utilised very well by businesses. The sheer variety of comments can be daunting and difficult to analyse. In contrast, numerical scores are easy to compare and turn into impressive looking graphs, but they give only limited insight into underlying concerns or suggestions for improvements.
The standard process to recover service failure may not be the right way to make all your customers happy again.
Manually analysing open-ended answers and comments in surveys is time-consuming and it’s easy to miss things. This is where big data analysis can help. If you have a fully automated model that can capture emotion and the complaint status (whether it is an initial complaint or an ongoing issue), then you have a valuable tool that can tell you if customers are starting to become dissatisfied.
Also, you have to consider and understand your customers’ culture and language differences. People from different backgrounds have different ways of expressing complaints and compliments about a service. Thus, the standard process to recover service failure may not be the right way to make all your customers happy again.
Combining these insights from direct customer comments with analysis of customer transactions and other sources can provide companies with a bespoke 360-degree view of the loyalty status of their customers.
The goal is to use those insights to diagnose the underlying factors causing customer dissatisfaction and then write a prescription to cure it. I have been working with a number of UK B2B companies through research undertaken at the Cambridge Service Alliance to generate this kind of prescription.
So, next time your customer survey spits out a number suggesting everyone’s happy – raise a quizzical eyebrow – and reach for some big data. Your customers will thank you for it.
Mohamed Zaki is deputy director of the Cambridge Service Alliance and senior research associate with the Institute for Manufacturing at the University of Cambridge.