Measuring Net Promotor Score (NPS) involves asking a single question to a customer. Often this question is posed at the end of a customer care / sales / marketing call, or via an automated form.
The NPS question usually is, “On a score of 1 to 10 how likely is it that you’d recommend (brand name) to a friend or colleague? “
You can see the potential for problems here. Responses could vary depending on the mood of customer, their immediate experience with their service provider, or they could try and be helpful and provide different scores for different services. So responses to a customer care rep asking this question could be:
“Eh – you’ve just kept me waiting 10 minutes to talk to a customer care person who couldn’t fix my problem, so how does minus 1 sound?”
Or
“Definitely a 10 – it’s Friday afternoon and I’m heading off for the weekend.”
Or
“I’d give your video service a 5, your phone service a 2 and your customer care service 7, but your marketing is beyond annoying so you’re only getting 1 there”.
Digital service providers need to go beyond NPS and continuously measure the experience of their customers – at all touch points. This includes from network to care and all points in-between. Service providers are increasingly collecting and preparing data, in real-time, and feeding that into analytics and operational systems. So why not use this data to feed a central customer experience index (CEI). The diagram below is from a white paper that Analysys Mason developed for Openet on using big data to develop a customer centric organisation.
As you can see source data is fed into departmental / function KPIs. These are then weighted and fed up to a CEI. By having his data service providers can have a corporate KPI based on their overall CEI score. They can also drill down and have CEI scores for segments and individual customers. However, what would provide additional intelligence to CEI is enrichment of the real-time experience data as it is collected. This could involve looking up subscriber profile repositories or data warehouses to get information such as churn propensity scores, life time values, etc, etc. This way service providers can see the CEI of customers with a high churn propensity score or high value and so on. This then can help to drive operational systems such as real-time offers, CEM, network optimisation and care with automated actions. Add machine learning into the equation and service providers can anticipate behaviour based on a variance of the CEI score and set up automated corrective actions, marketing communications and offers.
The potential for CEI goes way beyond having a measure to tell you if you’re customers would recommend your service to a friend. By using real-time data, enriching it, feeding into function KPIs and then upstream to a CEI service providers can use this data to improve the customer experience by turning real-time intelligence into real-time action.
Download the Analysys Mason Openet white paper: Harnessing Big Data to Cost Effectively Create a Customer Centric Enterprise
Register for the Openet Webinar on September 21st – Right Data, Right Time: Building a Customer Experience Index and Improving CEM to Deliver Smarter Engagement
