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

Abstract

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.