Is NPS Still The Chief Indicator Of Customer Sentiment?
By Shreesha Ramdas, Strikedeck
NPS And Its Relevance
Today’s retail world is beset with massive change, and standard sales principles are constantly under challenge. At the same time, the notion of customer loyalty has never been more important. According to the Wharton School’s Marketing Metrics, the probability of selling to an existing customer is 14X more than attracting a new one. Additionally, according to the SDL Global CX Wake Up Call report, 73% of consumers satisfied by the customer experience will recommend a brand to others. Knowing where a retailer stands with its customers has never been more important.
For years, the chief metric of customer sentiment, other than revenue and sales data, has been Net Promoter Score (NPS). It stands to reason — what better test of customer satisfaction is there than whether they recommend the retailer to another?
Where NPS Falters
While there is tremendous value in this metric, it suffers from two major problems.
The first is that it tends to be a trailing indicator. Changes in NPS happen after an experience occurs, and have likely set in for some time. This is also true for revenue and sales data. At this point, the damage is already done and may even be irreparable.
A second issue with NPS is that it is rather one-dimensional. Nothing about the metric shows what or why a customer feels a certain way or how something has changed. Uncovering what is really going on may be difficult and might take time, and trying to fix it may be impossible after a certain amount of time has elapsed.
A Better Solution? Real-Time Interaction!
Businesses today continue to be characterized by real-time interaction. Chatbots try to instantly engage online customers and potential customers the moment they hit a retailer’s web site. Apps provide the means to extend a retailer’s presence and services, while social media activity aims to build conversations and spread influence. Understanding customers in real time enables retailers to shape the customer experience as and when it happens. Retailers can make adjustments in the way they do business to improve experiences for other customers.
Real-time customer data must be multi-sourced and should be the total of all interactions and conversations with employees that are captured as notes. It should include email and other forms of input and communications, such as surveys and mini-surveys, research, follow-up reports, web site activity and more.
The next task is bringing this data together, analyzing it and turning it into actionable insights. The idea is to gain a 360° picture of customers with deeper levels of insight. What follows is the ability to interpret it and develop a roadmap. Oftentimes, this means finding meaningful patterns and trends. Of course, information without action is futile! Let real-time information direct decisions and prompt actions to address specific customers, as well as the way the retailer conducts business.
NPS is still meaningful, but it is far from real-time, and it lacks context and dimension. Retailers can improve customer loyalty and retention and expand revenue by gaining a realistic understanding of customers. Based on this, they can better cater to customers and provide improved experiences, while it is still possible to make a difference.
Shreesha Ramdas is the CEO and Co-Founder of Strikedeck. Previously, Ramdas was the GM of the Marketing Cloud at CallidusCloud, Co-Founder at LeadFormix (acquired by CallidusCloud) and OuterJoin, and General Manager at Yodlee. Prior to that, he led teams in sales and marketing at Catalytic Software, MW2 Consulting, and Tata. Ramdas advises several startups on marketing & growth hacking. You can find him on Twitter, @Shreesha.
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Strikedeck is the fastest and the most connectable Customer Success platform. Strikedeck uses machine learning and predictive analytics to bubble up insights and alerts about customer health, sentiment, and engagement.
Strikedeck is the fastest and the most connectable Customer Success platform. Strikedeck uses machine learning and predictive analytics to bubble up insights and alerts about customer health, sentiment, and engagement.
