It is well established that the Net Promoter Score (NPS) is directly linked to revenues of service providers. And it totally makes sense as it is a customer advocacy metric that measures the potential willingness of subscribers to recommend a product or service to others – directly driving new sales on your behalf.
Here are some examples of various research that have come to this same conclusion over the years:
- 84% of consumers indicated that they would be happy to recommend their service provider to family and friends if it served them better (Coleman Parkes, 2017)
- Annual revenue increase per customer is around 140% between the customer giving a 1-3 versus 9-10 score (Medallia, 2014)
- 49% of consumers would switch to a service provider if recommended by friends and relatives (Coleman Parkes, 2017)
Where it gets really interesting is that even after all this research many business leaders are not willing to put their money where their mouth is and to invest in actual improvement of the Net Promoter Scores the company receives. Too often when the time comes to say “yes, go ahead” for business cases that will drive actions to improve the Net Promoter Score, these cases get deprioritised. And who can blame them (beyond lack of vision) if there is nothing else but generic research available to show that those particular actions will drive revenue? It is too tough decision to make for many quarterly results driven leaders.
Would really help them is at least some level of understanding over the revenue impact specific efforts could potentially drive. We at Openet call this NPS Business Case Simulation. It takes into account the current NPS and business operations data from the service provider and uses it to simulate the impact of various actions on the revenue. The reliability of such approach will depend on the robustness and availability of the data the organisation has. But in most cases the estimation will be significantly more accurate than generic research from other companies. And that will take most of the guess work out from the impact of NPS on the company revenue.
Such an approach is possible when the NPS data is available on a customer level and it can be joined with other data (such as CRM and Billing). It will enable simulating average revenue a customer produces for the business based on the behaviours and spending of their peers. In best cases this can lead to over 90% accuracy in predicting revenue impacts of specific NPS improvement actions. But even much less accurate simulations are beneficial as they help to prioritise and understand the options at hand.
To move from generic NPS and revenue linkage research to company specific information, service providers will need to link their NPS data to CRM and Billing on customer level. Then they need sophisticated statistical analytics combined with prediction models to extrapolate mass-scale predictions for NPS improvement efforts. That enables businesses to move from generic assumptions into business decisions that are packed up by their own, relevant customer insights.