Net Promoter Score (NPS) can be simply defined as the likelihood of your customers recommending your service to their network of people. You would have received a call from your bank or telco to asking you to rate their services. This is the simplest method of gathering an NPS survey score. Often one also gets an SMS immediately post a transaction, such as a call center enquiry etc… These are also NPS measuring techniques at an atomic level or touch-point NPS.
The Net Promoter Score framework is widely used to measure the loyalty of a firm’s customer relationships. On an 11 point scale of 0 to 10, NPS classifies all the customers who review your services in 3 main categories namely Promoters (9s and 10s), Passives (7s and 8s) and Detractors (0 to 6). The score can be as low as -100 which signifies that everyone is a Detractor or +100 which signifies that everyone is a Promoter. This score is gained by subtracting the percentage of Detractors – the people who are least likely to recommend your services from the percentage of Promoters – the people who are most likely.
Over the years NPS has proved to be a useful tool to gauge customer satisfaction. However off late it has been criticized for not being very accurate since it takes into consideration only a small segment of the customers base which may not have been ideally sampled. Also, the traditional methods of capturing customer feedback will not work as new mediums and channels of customer communication open up. These new sources of data could be structured on unstructured; big or small..
The advent of advanced data analytics has transformed the way organizations address customer feedback. Advanced data analytics can help in overcoming the challenges of low predictive coverage and accuracy since it uses a census approach to data collection. It allows you to capture data about nearly any customer interaction with the brand and from any source and data of any format – structured or unstructured. Moreover, though the traditional NPS process does its job, there is a substantial time gap between reporting and decision making, which directly impacts the quality of the decision. Advanced data analytics supports the development of develop actionable NPS to immediately act upon available insights. This is a critical need because current processes do not guide with ‘what next?’
Measuring NPS is now more important than ever. With limited or no differentiation between services or product, listening to the customer and acting on it will decide, who survives and who perishes.
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