The use of Net Promoter Score (NPS) to predict sales growth: insights from an empirical investigation

Reichheld introduced NPS in the Harvard Business Review in 2003 as a survey-based metric that excelled in predicting future sales growth. As NPS is based on customer responses to one question— “How likely is it that you would recommend [company X] to a friend or colleague?” (Reichheld, 2003, p. 50)—it is simple to capture and calculate. Reichheld (2003, p. 48) considered NPS to be a measure of “intense loyalty” as customers put their reputation on the line when they provide recommendations to friends. Consequently, Reichheld (2003) argued that NPS is more powerful than measuring retention rates or repurchase intent as these metrics are not always tied to loyalty, but can be based on convenience or the lack of alternative products. The answers to the NPS question are given on a scale from 0 to 10 and are grouped into three categories: “Promoter” (customers answering with the highest scores 9 or 10), “Passive” (responses of 7 or 8), and “Detractor” (responses below 7). NPS is then calculated simply by taking the difference between “Promoters” and “Detractors” and dividing it by the overall sample size—hence the name “Net Promoter.”

In the original study, Reichheld (2003) correlated aggregated NPS values and other survey- based metrics (measured between 2001 and 2002) with a three-year sales growth figure (measured between 1999 to 2002) for multiple firms across twelve industries. As NPS showed the strongest correlations with sales growth, Reichheld (2003) concluded that firms can achieve future sales growth by increasing their NPS, and therefore he recommended that managers focus on improving NPS in order to grow sales.

The premise that NPS can predict sales growth seems to be intuitive: If customers spread positive word-of-mouth among their friends, some of their friends will try the brand and become customers, which will ultimately increase the brand’s sales. The positive relationship between other customer mindset metrics, e.g., customer satisfaction, and firm performance is well-established in marketing literature (see the meta-analysis by Otto et al., 2020), and NPS has also been linked successfully to word-of-mouth behavior (Keiningham, Cooil, Aksoy, Andreassen, & Weiner, 2007; Raasens & Haans, 2017), increased customer spending (Mecredy et al., 2018), retention intent (Leisen Pollack & Alexandrov, 2013), and actual retention (de Haan et al., 2015), all of which are important links in the NPS–sales growth chain.

However, while several studies have shown the positive relationship between NPS and customers’ intentions and behavior, research on the impact of NPS on sales growth is scarce and has yielded mixed evidence: Apart from Reichheld’s (2003) original study, there are only four academic studies which directly investigate the relationship between NPS and sales growth (see Table 1 for a summary), and none of them confirmed Reichheld’s (2003) claim that NPS is a superior predictor of sales growth. The studies by Morgan and Rego (2006) and Keiningham, Cooil, Andreassen, & Aksoy, (2007) found that NPS had no impact on sales growth, whereas Pingitore et al. (2007) and van Doorn et al. (2013) concluded that NPS is a predictor of sales growth but is not superior to other customer mindset metrics.

Academic uncertainty regarding Reichheld’s (2003) study, and its claim, was further bolstered by a number of articles which point out methodological issues in the calculation of the NPS metric, and/or in Reichheld’s (2003) original research design. Concerns regarding the NPS calculation focus on the reduction of response variability through the exclusion of parts of the sample, and the adoption of arbitrary cut-off points (e.g., Grisaffe, 2007; Kristensen & Eskildsen, 2014).² Methodological concerns around Reichheld’s (2003) original research design focus on the use of (1) cross-sectional data rather than longitudinal data (Keiningham, Cooil, Andreassen, & Aksoy, 2007), (2) correlations, which as the sole method of analysis cannot provide strong evidence of causation (Grisaffe, 2007), and (3) historic sales growth data, which is questionable as a proxy for future sales growth (Grisaffe, 2007; Shaw, 2008).

In evaluating the findings of the existing academic literature on NPS, consideration needs to be given to the fact that a number of these methodological issues are also present in other studies investigating NPS as a predictor of sales growth (see Table 1). Pingitore et al. (2007) replicated the original study using correlations, cross-sectional datasets, and historic sales growth, whereas other researchers adapted the original study: Keiningham, Cooil, Andreassen, and Aksoy (2007) used (partially) longitudinal data, but correlated NPS and sales growth measured for the same period. Furthermore, their study stretches across five industries but is based on a limited number of observations, analyzing a maximum of four firms in each industry and only using longitudinal data (for a maximum of four years) in two industries.³ While Keiningham, Cooil, Andreassen, & Aksoy, (2007) concluded that NPS is not a predictor of future sales growth, it needs to be noted that their results for two industries showed medium to strong correlations, which were statistically insignificant. This could be because the sample sizes (4 cross-sectional observations in the security systems industry and 32 longitudinal observations in the banking industry) were too small to give adequate power to detect statistically significant effects (Bonett & Wright, 2000). With this in mind, it is also worth noting that Keiningham, Cooil, Andreassen, & Aksoy, (2007) were unable to detect significant effects of any of the other measures of customer mindset on sales growth in their study (e.g., customer satisfaction or repurchase intent), which have been associated with sales growth in past studies (see Otto et al., 2020). Studies by van Doorn et al. (2013) and Morgan and Rego (2006) analyzed regressions and operationalized future sales growth. While van Doorn et al. (2013) used cross-sectional NPS data, Morgan and Rego (2006) used longitudinal data. However, it is uncertain if Morgan and Rego (2006) actually analyzed NPS, as their version of NPS is based on recommendation behavior (instead of intention; see Keiningham et al., 2008).

This diversity in research design and operationalization of NPS—using correlations versus regressions, cross-sectional versus longitudinal data, intention to recommend versus recommendation behavior—combined with often limited numbers of observations, make meaningful comparisons between studies difficult, and also make it difficult to draw strong conclusions regarding the validity of NPS. Additional methodological considerations include time lags, static versus dynamic NPS, and using NPS as a customer loyalty versus a brand health measure.

Our selection of a suitable research context is driven by the notion that the utility of NPS is almost certainly subject to industry differences (e.g., de Haan et al., 2015; Keiningham, Cooil, Andreassen, & Aksoy, 2007; Reichheld, 2003) and it is therefore unlikely that NPS (or other customer mindset metrics) could be the “one number you need to grow” (Reichheld, 2003, p. 54) in all industries. NPS works best in industries where customers are more likely to give recommendations, such as those with high emotional involvement in the purchase decision (Shaw, 2008). Gruca and Rego (2005) also note that customer mindset metrics work particularly well in consumer goods industries where customers have short interpurchase cycles, such as the apparel, athletic shoes, or beer industry. Consequently, we chose to conduct our study in the U.S. sportswear industry,⁴ focusing on seven of the biggest brands, which have a combined market share of two-thirds of the branded sportswear market. The dataset tracks the longitudinal development of the brands over five years and was provided by one of the brands.

To analyze the relationship between NPS and future sales growth we built a model that uses sales growth as the dependent variable, and NPS and the control variables as independent variables. To capture if NPS predicts future sales growth, NPS variables need to be lagged. Studies suggest that it is more likely that NPS predicts short term sales growth (e.g., Morgan & Rego, 2006; van Doorn et al., 2013; Williams & Naumann, 2011) therefore we adopted the shortest time lag possible, lagging NPS by one quarter; similarly, we also lag all control variables (Morgan & Rego, 2006). Two different models are used in our analysis: Model 1 (Eq. 1), similar to prior NPS research, uses static NPS levels while Model 2 (Eq. 2) uses NPS changes and also changes in (most of) the control variables (similar to the approach by Rego et al., 2013). This specification enables us to explore if high absolute levels of NPS, or improvements in NPS, are associated with sales growth. The models are specified as follows: where i stands for brand and t for time (quarter), β denotes the regression coefficients, υ _i represents brand-specific non-observable, time-invariant characteristics, and ε _i(t) is the error term which can vary across both time and brands. In order to address whether NPS performs better as a customer loyalty or brand health metric, we ran both models twice, once with the customer loyalty metric (current customer sample), and once with the brand health metric (all potential customer sample) of NPS.

Our balanced panel dataset tracks the same seven brands over time. Because this nested panel data structure violates key assumptions of OLS regressions (e.g., Habel & Klarmann, 2015), we estimated our models using a random effects model—also known as an error components model (Wallace & Hussain, 1969) or variance components model (Amemiya, 1971). Random effects regressions are designed to analyze panel datasets by considering heterogeneity across the different brands and their longitudinal development (Baltagi, 2013). Bell et al. (2019) point out that commonly used random effects models assume that the within (time-varying) effect and the between (firm specific or also time-invariant) effect are equal, which is usually not the case. Therefore, we explicitly included in our model separate within effects (represented by ε _i(t)), such as changing spending on advertising, and between effects (operationalized as υ _i), such as distribution strategy, which is not likely to change in the observed time frame (Baltagi, 2013).

The analysis commenced with a model free approach (see Table 2). In analyzing descriptive statistics of our data, we find that the mean level is 43.26 for the customer loyalty metric of NPS, while it is −3.22 for the brand health metric. Taking a closer look into the differences of the NPS levels, we also find that the standard deviation for customer loyalty NPS (20.61) is lower than for brand health NPS (34.44), which can be explained due to the fact that the maximum (79.7) and minimum customer loyalty NPS (.8) differ less than for brand health NPS (71.2 vs. -45.3). The differences across the metrics and samples, support East et al.’s (2011) findings, that non- or ex-customers of a brand are the largest source of negative attitude towards brands, suggesting that metrics which are only based on responses from current customers differ less and are biased upward. However, current customers change their mind about brands more than potential customers, illustrated by the higher means of NPS changes for the current customer sample (.56) than for the potential customer sample (.17).

The correlation analysis (see also Table 2) demonstrates that NPS measures are not correlated with either current or future sales growth. Hence, our results differ from the findings by Reichheld (2003) and Pingitore et al. (2007), who found high correlations between static levels of NPS and historic sales growth. This could be explained due to the fact that our correlation analysis is not directly comparable with their approaches, as their sample sizes are significantly smaller and observe longer, but prior, growth periods. Instead, our findings support Keiningham, Cooil, Andreassen, and Aksoy (2007), who used a similar correlation estimation approach to ours, finding no correlation between static levels of NPS and current sales growth.

Given that the correlation analysis did not detect a significant relationship between any measure of NPS and sales growth, we performed another model free analysis. We compared the average sales growth for all quarters where NPS levels/NPS changes are above and below their respective averages. The results (see Fig. 1) show that future sales growth is on average higher in quarters where NPS changes are high (customer loyalty NPS 10.4%, brand health NPS 10.2%), while it is comparatively low in quarters where NPS changes are below average (customer loyalty NPS 3.1%, brand health NPS 2.8%). In contrast, we find that average sales growth is similar when NPS levels are above (customer loyalty NPS 5.7%, brand health NPS 4.6%) or below (customer loyalty NPS 6.9%, brand health NPS 7.5%) average NPS levels, with a tendency for lower sales growth when NPS levels are higher.

In order to analyze if these differences are also statistically significant, we performed a one-sided t-test and calculated Cohen’s d (Cohen, 1988), controlling for different standard deviations by using a pooled standard deviation (Rosnow & Rosenthal, 1996). While the t-tests provide marginally significant findings for NPS changes across both NPS metrics (p ~ .1, see Fig. 1), the obtained values for Cohen’s d suggest that there are small differences across the averages for NPS changes while the differences for NPS levels are more likely to be only “noise” (Cohen, 1988, p. 25). Overall, this analysis provides initial indications that brands can grow their sales by increasing their NPS, however this does not necessarily imply that high NPS levels lead to sales growth.

Overall, the results of our random effects model show that NPS can be a significant, positive predictor of future sales growth (see Table 3). However, at the same time, the results suggest that the methodological criticisms of Reichheld’s (2003) study, (e.g., Grisaffe, 2007; Shaw, 2008), and the way NPS is used in practice (e.g., Fitzgerald, 2017; Markey, 2014), are of concern when considering how to obtain meaningful results from NPS data analysis.

Specifically, we only find the brand health use of NPS in Model 2 to be significant when it is operationalized as changes (β =1.458, p < .01), and also find the highest increase in model fit here (adj. R² = .350) versus the other three estimated models, but also compared to a base model that did not contain any of the NPS metrics at all (adj. R² = .322, not reported in Table 3). This demonstrates that it is important to distinguish between static NPS levels and dynamic NPS changes, suggesting that brands who improve their NPS tend to grow their sales in the future. As NPS levels are not significant in Model 1, it also implies that brands with high levels of NPS will not automatically be able to grow their sales if they do not improve their NPS. Our findings also suggest that in growing sales, NPS is best considered to be a measure of brand health and measured for all potential customers, implying that brands must not only influence the mindset of current customers, they need to improve the mindset of all potential customers, including non-customers.

Two of the control variables are significant across all models and samples, and the direction of their effects is in line with previous research. Market growth has a significant impact on future sales growth which implies, as the β-coefficients are above 1 (1.226 to 1.822), that the seven brands in our sample are able to outgrow the market consistently. Furthermore, we find a significant negative carry-over impact of current sales growth on future sales growth (−.525 to −.558) which is similar to the findings by comparable studies (e.g., Ittner & Larcker, 1998) implying that brands face difficulties in maintaining sales growth over time. Additionally, we find a significant effect of changes in categories on future sales growth in Model 2 (brand health NPS) suggesting that brands can grow their sales by expanding into new sports categories.

We also applied statistical tests to Model 2 (brand health NPS) to confirm the appropriateness of the model and the estimation techniques utilized. We performed the Breusch-Godfrey test for (first order) serial-correlation, and the Breusch-Pagan test to establish that there is no heteroscedasticity (see Baltagi, 2013). The tests showed that there is no serial-correlation (Breusch-Godfrey test: p = .752) and that there is no issue with heteroscedasticity (Breusch-Pagan test: p = .437). Furthermore, we explored (as proposed by Bell et al., 2019) the difference between within and between effects for all models using the Hausman-test. The Hausman test is commonly used as a general test to determine whether a random effects model or a fixed effects model should be used, but more specifically it tests for the equality of the within and between effects (Bell et al., 2019). The results of the Hausman-test confirm for Model 2 (p < .05 for both NPS measures) that the within and between effects are unequal and need to be modeled separately. In contrast, the Hausman-test for Model 1 demonstrated no difference in the within and between effects (p > .8 for both NPS measures). While these results do not necessarily require the use of a fixed-effect model or a more simplistic random effects model (which assumes both effects to be equal—see Bell et al., 2019), we re-estimated Model 1 for both NPS metrics by using these two alternative estimation techniques. However, the results of these additional analyses are not fundamentally different from our initial results and confirmed that NPS levels are not predictors of future sales growth.

To explore if changes in NPS can explain sales growth, we need to address whether our model explains differences in sales growth within a brand or between brands. Therefore, we focus on the within variance of our model which describes how much the variables utilized in our model, including NPS, explain sales growth of each brand individually over time. We follow a similar approach to that used by Hanchane and Mostafa (2012) by analyzing the share of the within effect in the overall variation. In Model 2 (using the brand health metric of NPS) 90.7% of the variation can be explained by variations within the brand over time (within effect 336.28, between effect 34.34), hence, the model explains within brand variation appropriately.

Although there are exceptions (e.g., Pingitore et al., 2007; van Doorn et al., 2013), there has been little empirical support for Reichheld’s (2003) claim that NPS predicts future sales growth, and others have found that NPS is not associated with sales growth at all (Keiningham, Cooil, Andreassen, & Aksoy, 2007; Morgan & Rego, 2006). Furthermore, Reichheld’s (2003) research methodology has been strongly critiqued within academia (e.g., Grisaffe, 2007; Keiningham, Cooil, Andreassen, & Aksoy, 2007; Sharp, 2008; Shaw, 2008). However, the research methodologies adopted in all other NPS studies to date are arguably subject to at least some of the same critiques as Reichheld’s (2003) original work. The work presented here is to the best of our knowledge the first to have addressed all of these methodological issues. Furthermore, our results suggest that these methodological issues could be a potential reason why past research has come to contradictory conclusions regarding NPS.

More specifically, our study identified no bivariate correlations between NPS and sales growth. However, when we analyze longitudinal data using a random effects model, we find that changes in NPS are a significant predictor of future sales growth. Hence, the actual effect of NPS on sales growth could not be detected by using what has to date have been the most common NPS research analysis approach (e.g., Keiningham, Cooil, Andreassen, & Aksoy, 2007; Pingitore et al., 2007; Reichheld, 2003). Of course, we do not claim that moving beyond the correlational analysis of Reichheld’s (2003) work to the panel regression models used allows full-strength claims of causality to be made. Nevertheless, we are able to supplement the simple concomitant variation of correlational analysis to add evidence of temporal precedence, which strengthens the causal interpretations of our findings in the spirit of the Granger causality tradition of precedence, or temporal relations (Granger, 2004; Granger & Newbold, 1986).

Furthermore, we find that the optimal time lag between NPS and future sales growth is one quarter. However, it may be that the optimal time period may be industry related. In the case of the sportswear industry, a short repurchase cycle (Gruca & Rego, 2005) is a likely explanation for the short time lag, particularly within the core market segment of 16–30 year olds. These characteristics are typical of many related consumer goods industries, and thus we consider our findings in this area to be quite likely generalizable to other comparable contexts.

Our analysis also illustrates that the operationalization of NPS matters. Reichheld (2003) and other researchers used static levels of NPS but interpreted the results as referring to dynamic changes in NPS (Grisaffe, 2007). Using both levels and changes, we demonstrate that only changes in NPS are positively related to sales growth, but that static absolute levels of NPS are associated with future point levels of sales. This supports research by Rego et al. (2013) who found that changes in customer satisfaction can explain sales growth, but that levels of customer satisfaction predict future levels of sales and market share. Therefore, if researchers using NPS seek a predictor of future sales growth, they should analyze changes in NPS; but if they are interested in predicting levels of sales, they should focus on NPS levels.

Equally important are the findings on sample selection. Initially, NPS was conceived and used as a transaction-based customer loyalty measure, and was therefore only captured for current customers. However, practitioners (e.g., Fitzgerald, 2017; Markey, 2014) have since proposed that firms should implement NPS not only as a transaction-based tool, but should also track NPS for all potential customers, which makes NPS more akin to a measure of overall brand health. Indeed, this usage of NPS is considered by practitioners to be closely related to future sales growth (Fitzgerald, 2017). This is confirmed in our study: Our results show that only measuring NPS for all potential customers provides reliable predictions of sales growth. This is a crucial extension of the current academic knowledge on NPS, as to the best of our knowledge, academic studies to date have considered NPS to be a measure of loyalty and thus measured it for current customers only. According to our results though, the recommendation intent of non- and ex-customers carries valuable information about sales growth. Notably, this finding is not unique to NPS, as we also found that other customer mindset metrics such as brand awareness, brand consideration, and purchase intent are predictors of future sales growth when captured for all potential customers. Hence, researchers need to carefully consider their choice of assessing customer mindset metrics (e.g., NPS) for current or for all potential customers (similar to the suggestions by Katsikeas et al., 2016), in particular, when investigating the relationship between consumer mindset and sales growth, as we find that non-customers are an important source of sales growth, which has been suggested before in research (e.g., Ittner & Larcker, 1998; Zeithaml et al., 2006).

Our study demonstrates that under the right conditions, NPS predicts future sales growth, supporting existing managerial behavior. In doing so, our results provide a possible explanation for the continued popularity of NPS in managerial practice for more than 15 years, despite strong academic calls for its abandonment (e.g., Sharp, 2008). However, as evidenced in our study, managers need to be careful with how they utilize NPS, and they should pay particular attention to the following issues. First, given that NPS is most effective in predicting short-term sales growth, it is best considered as a measure that can validate whether recent marketing actions have had the desired effect on consumers. Nevertheless, in growing long-term sales, managers need to consider other factors which require more time to change, for example, their product positioning, distribution strategy and product range.

Second, our findings show that it is only changes in NPS that predict sales growth. Firms should therefore incentivize and communicate changes in NPS, rather than absolute NPS scores. Managers need to focus on improving NPS, and tracking this improvement, regardless of the NPS level itself.

Third, we show that firms should use NPS as a forward-looking overall brand health metric, and track NPS for all potential customers. Our findings imply that brands cannot grow solely through the benefits associated with customer loyalty, such as retention (e.g., de Haan et al., 2015; Kristensen & Eskildsen, 2014) or word-of-mouth (e.g., Leisen Pollack & Alexandrov, 2013; Raasens & Haans, 2017); they also need to attract additional new customers to nurture brand growth. In this sense, NPS can be seen as a general brand health indicator. However, it cannot be considered as a diagnostic tool to identify specific underlying problems, such as whether or not the brand is currently struggling with brand awareness, brand consideration or the satisfaction of current customers. NPS therefore can be considered by managers as akin to “taking the temperature” of their brand (Grisaffe, 2007); a simple, easy-to-administer and understandable diagnostic, which indicates the need for further investigation. Consequently, firms need to ensure that they are not only tracking NPS, but are also able to follow up with more specific diagnostics based on the NPS status identified (as a physician may follow up a high temperature reading with more specific tests), including exploring if the respondent is currently a customer, a former customer or has never purchased the brand. This also helps managers to make NPS more actionable which will guide them in increasing their NPS and ultimately future sales.

Nevertheless, NPS is certainly not the “one number you need to grow” (Reichheld, 2003, p. 46) and managers should be cautious regarding this claim for three reasons. First, as noted previously, NPS can only help to predict short-term sales growth. Second, like any other metric, NPS can explain only a fraction of future sales growth by itself: the model fit for Model 2 for brand health NPS increased only slightly (by .028) when compared with a model without NPS. Despite this, the impact of NPS is considerable in economic terms. An increase of one NPS point leads to sales growth of 1.458 pp. in the following quarter. Given that the average sales volume of the seven sportswear brands in the U.S. is $3 billion per year, an increase of one NPS point can be translated into an increase of $44 million in annual sales, or $11 million per quarter. Third, extant literature suggests that NPS is most appropriately used in industries/segments with reasonably short interpurchase cycles (Gruca & Rego, 2005) and where customers have a high emotional involvement in the purchase decision (Shaw, 2008). Therefore, managers must explore the significance of NPS in their own industry and organization. Our proposed research methodology will enable them to operationalize this effectively.

NPS is one of the highest profile and most commonly used marketing metrics in practice (e.g., Kaplan, 2016; Safdar & Pacheco, 2019), but it has received comparatively little academic validation, and the prevailing scholarly opinion towards NPS has been generally negative (Bendle et al., 2019). Our study demonstrates the potential utility of NPS in theory and practice and is therefore a first important step in re-opening research on NPS. Managers will almost certainly continue to use NPS, and we believe that NPS should thus remain a part of the academic research agenda, in order that marketing scholarship may engage in meaningful conversations with managers on this key metric (Bendle et al., 2019). By considering our results and their limitations, we propose a future research agenda addressing five key directions (summarized in Table 8) including the generalizability of findings, predictors of future performance, antecedents of NPS, the role of non-customers, and the managerial usage of NPS.

First, while focusing on a single industry allowed us to develop a rich dataset and perform specific analyses facilitating new insights into NPS and its relationship with future sales growth, industry differences are common in relationships between customer mindset and firm performance (e.g., Gruca & Rego, 2005; van Doorn et al., 2013). Future research scenarios should include studying NPS and its relationship with future sales growth in other industries which are either quite similar to the sportswear industry (to confirm our results) or which are very different, to explore Reichheld’s (2003) claim that NPS has broad applicability. This research stream could eventually reach a zenith in a cross-industry study controlling for industry differences by utilizing not only industry-specific but also sector-specific (e.g., services versus durable goods, versus consumer goods) control variables in the analysis. At the same time, it would be interesting to explore if NPS can predict long-term sales growth and if the optimal time lag varies by industries and if this is related to industry specific interpurchase cycles. Furthermore, as our results are based on U.S. data, extending the study to other countries would help to improve our understanding of the relationship between customer mindset and firm performance, and differences in measurement across borders (e.g., Kristensen & Eskildsen, 2014; Zeithaml, 2000; van Doorn et al. (2013).

Second, while it is questionable that the validity of Reichheld’s (2003) claim that NPS is the best predictor of sales growth can ever be definitively addressed, future research should extend our comparisons of NPS with other customer mindset metrics and sales growth. The use of other customer mindset metrics as brand health metrics could also be explored, by capturing metrics for all potential customers, and identifying which metrics should be collected for current customers. Further work could also address alternative methods of calculating NPS based on the likelihood to recommend question, to overcome the uncertainty associated with the original NPS calculation (Grisaffe, 2007; Kristensen & Eskildsen, 2014). In addition, Reichheld (2003) also claimed that NPS would lead to profitable growth, suggesting that researchers need to test (1) the individual relationships between NPS and both sales growth and profitability, and (2) the relationship between increased NPS acquired by discounting sales prices (Bendle et al., 2019). Moreover, researchers could investigate the relationship between NPS and other firm performance metrics, such as cash flow and shareholder value, as managers also tend to assume a positive relationship between NPS and these metrics (Ramshaw, 2019).

Third, given the current lack of literature on the antecedents of NPS, future studies could explore both the precursors to NPS, and how they are either similar or different to the well-explored antecedents of customer satisfaction (e.g., Anderson & Sullivan, 1993) or loyalty (e.g., Dick & Basu, 1994). Another angle on this idea could be to explore how these antecedents differ between current customers and non-customers.

Fourth, research on customer mindset metrics needs to address the importance of non-customers, especially as practitioners are reporting declining levels of loyalty (e.g., Hyken, 2019), highlighting the importance of customer acquisition in brands growth. This research should distinguish between never-users and former users of a brand, as these groups are at different stages of the customer journey. Emerging research questions in this field should address which customer mindset metrics should be employed to understand these different groups and if firms should prioritize one of the groups to foster their growth.

Finally, a growing consultancy industry (Bendle et al., 2019) helped to introduce NPS into many companies without a clear understanding as to how NPS should be used, or if it helps firm growth. Future research could address this gap by investigating (1) the relationship between different applications of NPS and firm performance and (2) if companies that utilize NPS outgrow their competition.