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In the world of business there are few metrics as popular as the net promoter score (NPS). Originally created by the consulting firm Bain & Company, the NPS measures customer satisfaction by asking them on a scale of 0 to 10, with 10 being most positive, how likely they would recommend a product or service to their friends or colleagues. An effective indicator for customer retention and profitability, this question helps organizations understand which customers are promoters (scored 9 or 10) or detractors (scored 0 to 6) to generate the NPS, which is calculated by finding the difference in the percentage of both. Although the NPS is effective in helping organizations pivot and improve their processes, its inherent limitation is that, by the time a subset of customers respond poorly, the damage would have already been done. To reactivate customers who are already detractors would require additional incentives that could have been prevented in the case where measures were taken earlier to improve the relationship. In many cases, organizations also have limited information on the state of many customers as they do not respond to NPS surveys.
From airlines to insurers, organizations across industries use AI to predict the net promoter scores of their customers. This allows them to proactively improve their relationship with customers who are predicted to become detractors, leading to an overall reduction in customer churn. Organizations that take proper advantage of the NPS also know that the key to unlocking its value is to supplement it with additional open-ended questions that can provide insight to why customers respond positively or negatively. While this would be the case in a real-time evaluation, AI models that predict NPS ahead of time also offer marketers with statistically driven explanations to each one of its predictions. This means that your marketers can take a personalized approach to understanding how to improve your relationships with each of your customers.
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