Can alternative metrics provide new insights from Net-Promoter data? : a thesis presented in partial fulfilment of the requirements for the degree of Master of Business Studies in Marketing at Massey University, Palmerston North, New Zealand
Marketers regularly use loyalty measures to better understand consumers’ purchase
behaviour. In commercial market research the loyalty metric, Net Promoter Score
(NPS), is commonly used due to its simplicity, and because there are claims that
increases in NPS relate to increases in company revenue. However, the connection
between NPS and revenue growth rates is widely criticised by scholars, casting doubt
on the wisdom of implementing strategies that focus on increasing the numbers of
highly loyal customers.
This research considers whether alternative metrics, derived from Net-Promoter
data, can provide new insights into customer loyalty. It examines whether the NPS,
likelihood mean, and Polarization Index measure different aspects of loyalty in the
real estate (n=1,818) and agricultural (n=2,785) sectors. It then evaluates the ability
of the three measures to predict changes in same customer spend and company
revenue using data from the agricultural sector.
The findings show that the NPS and likelihood mean measure similar aspects of
loyalty and that the Polarization Index measures a different aspect of loyalty when
applied to 11-point Net-Promoter data. Longitudinal comparisons suggests that the
NPS and likelihood mean are poor predictors of the current (t) and future (t+1)
spend by the same customers, compared with the Polarization Index which provides
a more accurate prediction. In contrast, the NPS and likelihood mean are found to
have a strong relationship with current (t) and future (t+1) company revenue, while
negative relationships were observed for the Polarization Index.
These findings suggest that loyal customers increase their spending less than disloyal
customers, as they have likely reached saturation point with the company’s
products. However, loyal customers still contribute to company revenue growth by
attracting new customers, presumably through Word-of-mouth (WOM). Therefore
growth comes through penetration and increasing the amount spent by the least
loyal customers, rather than through increasing spend by loyal customers.