Forsta clients will now be able to leverage predictive net promoter scores (pNPS) to foster a greater understanding of their full customer base
Forsta, a provider of market research, customer experience (CX) and employee experience (EX) technology, announced at Web Summit that it will expand its capabilities to include predictive net promoter scores (pNPS) created in cooperation with the CX data specialists at GemSeek. Through this solution built on the Forsta Human Experience (HX) Platform, Forsta clients will now have access to cutting-edge analytics-powered predictions of silent customers and those who don’t provide the direct feedback needed to determine NPS or other customer experience metrics. This offering will enable a deeper understanding of the experiences of every customer.
This announcement comes as Predictive NPS will be one of the key topics at a Web Summit Master Class in Lisbon, Portugal, by Forsta on storytelling, predictive analytics and the human experience of data.
Why Predictive NPS (pNPS)?
Customer experience (CX) measurement traditionally relies on direct feedback responses that only come from a portion of the customer base. In certain cases where response rates are low or survey frequency doesn’t correspond to the dynamics of the business model, organizations become vulnerable to unexpected churn, lower satisfaction rates and poorer performance in general. “Silent customers,” who don’t respond to surveys or don’t submit feedback, can account for as much as 80% to 95% of a company’s entire customer base, according to different estimations.
How does Predictive NPS (pNPS) work?
Predictive NPS uses existing operational, customer and financial data as a source for an advanced machine-learning model that assigns satisfaction scores to customers who don’t respond to surveys or give direct feedback about their experience. pNPS runs on a library of advanced data science models that assesses which of these additional data points have the highest potential to predict satisfaction, looks for similarities and patterns between respondents and non-respondents and as a result determines predicted scores for silent customers. Predictive NPS scores can be used to direct action towards customers at both a strategic and tactical level – to keep customers from churning, convert neutral customers to promoters, and upsell happy customers and prompt for referrals.
The model combines behavioral data (CRM and demographics, usage, and more) and customer satisfaction data (survey responses) from across a customer base and employs advanced data science methodologies to identify which factors in customer behavior have the highest impact on customer experience.
