Case Study: Estimating NPS Before It Happens

Overview

Developed in 2003, Net Promoter Score (NPS) has become a leading measurement of consumer loyalty, having been adopted by more than two thirds of Fortune 1,000 companies today.  There are many programs available to measure and improve a company’s NPS, post interaction, yet predicting NPS before the consumer interaction is more difficult and may provide businesses with much higher levels of actionable insight.  In this study, Everywhere’s proprietary NPS equivalency system, NPSe (NPS estimated), is compared to a major automotive brand’s actual NPS rating.

Methodology

NPSe was gathered during the course of normal consumer support events for a San Jose, CA automotive dealer.  During each event, consumers were asked to rate their product and their feedback was noted. This verbal feedback was converted into numerical feedback by mapping each component of a large set of adjectives to a specific position on a numerical spectrum.  Each position on the spectrum was assigned eDetractor, ePassive, or ePromoter status (the “e” indicating estimated).  Lastly, the following calculation was performed to produce NPSe:

Sample Pool

The evaluation was conducted in the San Francisco Bay Area between January 1st, 2019 and February 16th, 2019 and included 148 responses.  Of those responses, 88 (59.5%) were formulaically assigned an NPSe score, with the remainder categorized as indeterminate and removed from the calculation.

Results

According to Indexnps.com and Bain.com, this automotive manufacturer’s NPS has landed between 76 and 77 from 2017 and 2018, while Everywhere’s calculation for the same brand indicated an NPSe of 82.7 over the course of the study.

While not identical to Indexnps’ and Bain’s numbers, SupporTrend’s data is certainly close enough to identify similar trends, and, crucially, the information is made available before the consumer interacts with the brand face-to-face.  This early insight provides Everywhere customers with the ability to deploy the appropriate resources to effectively improve NPS.

Additional insights

Beyond simply predicting the NPS of future consumer interactions, Everywhere data is tagged with rich detail including location, gender, date, whether the service event resulted in a revenue-generating appointment, and many more.  This fidelity allows our customers to take NPS analysis much further than incumbent solutions do. From the study above, for example, we can easily identify historical NPSe-related outcomes such as NPSe over time, NPSe by gender, and the correlation between NPSe and revenue-generating consumer visits for this particular Everywhere customer.

Conclusion

The Everywhere NPSe system produces comparable numbers to traditional NPS, and in some ways provides an analogous measurement system. Furthermore, it yields detailed information beyond the anticipated NPS number, ahead of the consumer’s transaction with the brand. This high level of detail and early warning can provide managers with an indispensable tool to solve NPS issues before they occur.

Find out how to gain early insight on your products today!