Forsta has launched a predictive Net Promoter Score (pNPS), created in collaboration with GemSeek.
Companies can access the pNPS solution within the Forsta Human Experience platform, which aims to break down the walls between customer experience, employee experience, and market research.
Users may unlock predictions about “silent customers” – i.e. people who don’t provide the feedback required to measure NPS and other customer experience metrics.
Offering Companies Intelligent Insights
Most CX measurements rely on direct feedback and responses only gleaned from a portion of the customer base. This means in cases where response rates are low, or surveys are delivered infrequently, companies often become vulnerable to unexpected churn and poor satisfaction rates.
Indeed, Forsta notes “silent customers” who don’t offer feedback can account for up to 95 percent of a company’s customer community, leading significant data gaps.
“To understand the full human experiences of their customers, companies need to look beyond customers’ direct interactions and verbatim feedback,” added Giles Whiting, COO and Managing Director of Forsta.
“Smart analytics models like Forsta’s Predictive NPS allow companies to not only understand but predict and act upon customer needs.”
These capabilities will provide Forsta clients with the opportunity to cater to customer requirements in a proactive, personalised manner.
Unlocking the Power of Predictive NPS
The Forsta Predictive NPS solution leverages existing customer, operational, and financial insights to enable a pioneering machine learning system.
It also runs on a selection of advanced data science models, assessing which additional data points have the best potential to predict levels of satisfaction.
The tools look for similarities and patterns between non-responding and responding customers, to determine the most accurate predicted scores for silent customers.
Forsta says predictive NPS scores helps direct positive action towards customers at both a tactical and strategic level, keeping clients from churning, and converting neutral consumers into potential promoters.
The model links behavioral data, from CRM insights, demographics, and uses, with customer satisfaction data to identify which factors in a CX strategy have the most significant impact on customer interactions.