Loyalty Metrics Research Articles

The Influence of AI Chatbots in Fintech Services on Customer Loyalty within the Banking Industry

The convergence of Artificial Intelligence (AI) and FinTech services has brought about a transformative impact on customer engagement in institutions. AI-powered chatbots have emerged as indispensable tools for enhancing customer experiences within the banking sector. In this era of digital transformation, financial institutions have recognized the vital importance of exceptional service quality and the utilization of chatbots in attracting and retaining customers. Consequently, there is a growing interest in comprehending the role of chatbot service quality in shaping customer loyalty. This study adopts an empirical and descriptive approach, employing a quantitative research method. Data were obtained from 729 respondents through an online survey employing judgemental nonprobability sampling. The gathered data were analysed using Smart PLS- Structural Equation Model (SEM). The study delineates the attributes of AI chatbot service quality and identifies their significant influence on customer loyalty, taking into account the mediating role of customer satisfaction. The findings of this study contribute to the understanding of the relationship between chatbots, customer satisfaction, and loyalty. The study validates the significance of customer satisfaction, enhances the understanding of chatbot effectiveness, explores the unique characteristics of chatbot interactions, and demonstrates how technology-driven customer service can be integrated into loyalty models. Moreover, this study offers practical implications that can guide businesses in optimizing their chatbot strategies. By prioritizing the enhancement of chatbot performance, personalizing interactions, integrating across channels, and monitoring customer satisfaction and loyalty metrics, organizations can elevate customer satisfaction, cultivate loyalty, and achieve positive business outcomes.

A STUDY ON OUTBOUND CUSTOMER COMPLAINT DUE TO INVENTORY VARIANCES AT APOLLO TIRES NDC1

This study defines into outbound customer complaints arising from inventory variances at Apollo Tyres NDC1, with the primary objective of understanding and addressing the underlying issues. Through a secondary objective, it aims to identify root causes, assess customer impact, apply the DMAIC methodology, and evaluate improvement measures. Using a structured approach, the study examines factors contributing to inventory variances and their implications on customer satisfaction and loyalty metrics. By implementing the DMAIC framework, inventory management processes are systematically analysed to identify areas for improvement. Improvement measures, including macro implementation, wall-to-wall audits, and the proposed Outbound Integrated Optimization and Navigation System (OIONS), show promising results in reducing inventory variances and mitigating customer complaint.

Who Is Health Care For? Achieving Equity for All Patients

Summary This issue of NEJM Catalyst Innovations in Care Delivery includes articles, case studies, and research reports on diverse workforces, equitable access to acute care, value-based care implementation, loyalty metrics, and mental and behavioral health.

Loyalty to Loyalty Metrics: Evaluating the Use of “Likelihood to Recommend” in Health Care Experience

Summary Loyalty in a customer relationship may be simply defined as a positive feeling about a brand that drives behavior to repurchase services or products and creates a preference for that brand in the future. The original articulation of the Net Promoter Score (NPS) was anchored in repurchasing behaviors in multiple industries, which notably did not include health care. As health care becomes increasingly consumer driven and fragmented by new and nontraditional models of care, efforts to measure patient and/or employee loyalty — often characterized as a likelihood to recommend or NPS, is growing. However, the value framework in health care is ultimately about better health, higher quality/safety, and exceptional experience at lower cost — not loyalty programs. In fact, most organizations do not track retention of patients longitudinally nor the growth associated with word of mouth. When strategically deployed, survey questions that ask about likelihood to recommend can help organizations establish trending data that can be a valuable indicator for health care organizations looking to drive improvement and to understand what is working and what is not. However, such data must be supplemented with robust analytics to assess operational and financial impact and methods of uncovering the drivers behind the likelihood-to-recommend scores. The authors offer a perspective of what they know to date and considerations for a meaningful approach.

Determinants of farmers’ loyalty to dairy processors in Minas Gerais, Brazil

ABSTRACT: The current study explores variables associated with the loyalty of dairy farmers to dairy processors in the Brazilian context. A multivariate discrete choice (Logit) model and alternative formulations assess the associations between loyalty metrics and farm and processor characteristics for a sample of 32 dairy farmers in 16 municipalities at the Zona da Mata in Minas Gerais. Twenty-two dairy processors were identified as milk buyers in the area studied, but each farmer indicated that they could sell to an average of five alternative buyers of milk. Farmers’ attributes such as production scale or the technological level are not statistically significantly associated with loyalty in this sample. The current milk price paid to farmers in our sample is not associated with increased loyalty (sales to a single processor for 6 or more years) in all estimated models; although, further research on this impact is merited to inform buyer-pricing policy. Variables associated with increased loyalty include payment of premiums for quality, farmer years of experience and cooperation among farmers in the purchase of inputs. Delayed payment is associated with reduced loyalty. We could not determine the effect of participation in technical assistance programs offered by processors on loyalty, because in our sample all farmers received free university-provided technical assistance. The payment of a premium based on milk volume was also unassociated with loyalty determination. The small size of our sample limits the ability to generalize our results but provides exploratory results that facilitate future investigation.

Marketing is scrambled: All evidence-based theorists are invited to breakfast

Shaw and Nowicki (2018) mistakenly suggests that the growing adoption of empirical science by marketing academics and practitioners is a return to an earlier time of treating all customers the same. Fundamental patterns discovered in buying and brand performance data accurately describe differences between buyers (e.g. in category buying rates and in personal brand loyalties), as well as predictable differences in loyalty metrics between rival brands. Theory has been proposed that fits these empirical generalisations; that rival brands largely differ and compete in terms of their mental and physical availability to category buyers (Sharp 2010). An implication is that marketers need to understand the differences between buyers in their category so that they can extend their brand's reach. This contrasts the practice of offering a single marketing mix to the whole market, and it is opposite to the practice of targeting a brand to a single homogeneous segment of the market and again offering a single marketing mix to that segment.

Modeling brand market share change in emerging markets

PurposeThe purpose of this paper is to examine what happens to key brand performance metrics as brands change in market share, in the context of packaged goods. The metrics are: penetration—the number of buyers a brand has; and loyalty—measured as purchase frequency (PF) and share of category requirements (SCR).Design/methodology/approachThe study utilizes 24 data sets in 17 packaged goods categories in three emerging markets: China, Malaysia and Indonesia. The authors examine changes in penetration, loyalty and SCR in the context of volume and value market share change. In addition, the authors examine whether initial price point and price movements influence the results.FindingsThe primary finding is that market share change is accompanied by a greater change in penetration than in any other metric. This finding is very consistent across categories and countries. The relative importance of the two loyalty metrics varies by country. SCR was a stronger factor in Indonesia, while PF was stronger in Malaysia. Analysis indicated that pricing strategy (initial price and promotional depth) did not alter the main pattern of results, suggesting the results hold for brands with different price levels and tactics.Practical implicationsIrrespective of circumstance, to grow in value or volume market share, brands should aim to grow in penetration, while the importance of changes in specific loyalty measures depends on market conditions.Originality/valueThis research extends past research on brand growth to the very different economic, geographic and cultural conditions of three crucially important emerging markets. Its main value lies in recommendations on how much to invest in building the size of the customer base vs consumer retention.

Investigating the Natural Monopoly Effect in Brand Purchasing: Do Big Brands Really Appeal More to Lighter Category Buyers?

The Natural Monopoly [NM] effect is that big brands appeal to somewhat ‘lighter’ or less frequent buyers of the product category, whereas small brands appeal to heavier category buyers. The NM effect has an important managerial implication. It implies that for a brand to grow from small to big, it must heighten its appeal particularly to lighter buyers of the product category. NM is potentially informative as to customer base analysis, providing empirical norms for a brand’s light and heavy category buyers. Moreover, NM could help managers understand loyalty metrics, because it pertains to systematic variation in the category purchase rate, which is an input into common loyalty measures such as SCR (share of category requirements). However, little is known as to the extent to which NM occurs and the extent to which it covaries with variables such as brand size, price and type (store brand, manufacturer brand). This study examines the NM effect in packaged goods markets using data on 24 FMCG categories in Holland. The analysis compares observed category purchase rates among buyers of each brand with theoretical ‘norms’ derived from the well-known NBD-Dirichlet model. It also examines the simple correlation between brand size and category purchase rate as a direct test of the NM effect. Analysis confirms the NM effect is quite pervasive, being present in most categories. The average correlation between brand size (i.e., penetration) and category purchase frequency is -0.36. This figure shows that buyers of large brands do buy the category less often on average. Second, the analysis finds the NBD-Dirichlet model produces accurate predictions of the average rate at which buyers of each brand buy the product category. However, there is nearly twice as much variation in the category purchase rates than predicted by the NBD-Dirichlet. As well as finding that bigger brands tend to have lighter category buyers, the study also finds lower-priced brands and store brands appeal more to heavy category buyers. The study also explains how bigger brands manage to be bought more frequently by their buyers, whilst simultaneously appealing to lighter category buyers. The answer is they obtain a higher share of requirements among light, medium and heavy category buyers: particularly heavy buyers. These findings are useful general knowledge for brand marketers and retailers. The NM effect will be subject to further research to test the extent that it is an empirical generalization.

The Divergent “Loyalty” Behaviors of a Captive Consumer

ABSTRACTThis research investigates situations where consumers display divergent loyalty behaviors. Study I qualitatively identifies and classifies word-of-mouth spreading behaviors of captive loyalists. The results reveal that, despite repurchase behaviors, captive loyalists spread mostly negative word-of-mouth. Study II quantitatively investigates four separate loyalty behaviors of captive loyalists. The results confirm that these consumers are likely to stay when dissatisfied, and allocate a significant share of their wallet to the provider whilst spreading negative word-of-mouth. The simultaneous existence of opposing loyalty behaviors has significant implications for erecting switching barriers and for applying customer loyalty metrics.

Do Store Brands Aid Store Loyalty?

Do store brands aid store loyalty by enhancing store differentiation or merely draw price-sensitive customers with little or no store loyalty? This paper seeks to answer this question by empirically investigating the relationship between store brand loyalty and store loyalty. First, we find a robust, monotonic, positive relationship between store brand loyalty and store loyalty by using multiple loyalty metrics and data from multiple retailers and by controlling for alternative factors that can influence store loyalty. Second, we take advantage of a natural experiment involving a store closure and find that the attrition in chain loyalty is lower for households with greater store brand loyalty prior to store closure. Together, our results are consistent with evidence for the store differentiation role of store brands. This paper was accepted by J. Miguel Villas-Boas, marketing.

Satisfaction as a predictor of future performance: A replication

Since the introduction of the Net Promoter concept there has been a vivid and ongoing debate among academics and practitioners about the performance of the Net Promoter Score (NPS) in comparison to other customer metrics, such as customer satisfaction, to predict company growth rates. We report results from a study using data from customers and firms in the Netherlands on the relationship between different satisfaction and loyalty metrics as well as the NPS with sales revenue growth, gross margins and net operating cash flows. We find that all metrics perform equally well in predicting current gross margins and current sales revenue growth and equally poor for predicting future sales growth and gross margins as well as current and future net cash flows. The NPS is neither superior nor inferior to other metrics. Taken together, our study suggests that the predictive capability of customer metrics, such as NPS, for future company growth rates is limited.

Impacts of perceived brand relationship orientation on attitudinal loyalty

PurposeThis paper aims to contribute to the empirical validation of the relationship model as it applies to the case of major national brands positioned in the frequently purchased packaged goods markets, and propose the perceived brand relationship orientation concept as an additional antecedent of trust, affective commitment and attitudinal loyalty. Yet, for a relationship to exist, the parties need to be mutually considered as potential relational partners. This paper seeks to introduce the concept of perceived brand relationship orientation, which is considered, along with perceived quality, as a significant direct antecedent of relationship quality (trust and affective commitment) and, indirectly, attitudinal loyalty.Design/methodology/approachThe empirical application involves major national brands positioned in frequently purchased packaged goods food categories (ice cream and frozen meals). The conceptualization and measurement of the perceived brand relationship orientation bears on a qualitative analysis of marketing experts and consumers. Refinement and validation of measures are applied to a convenience sample of 153 students and finally to a sample of 404 consumers, using exploratory and confirmatory factor analyses. Structural equation modeling is used to test the model and hypotheses.FindingsFirst, the authors validate the relationship marketing model in the case of strong national brands positioned in the frequently purchased packaged goods sector. The authors show that perceived quality impacts relationship quality (trust and affective commitment), which in turn influences attitudinal loyalty. Second, in addition to the effects of perceived quality, the authors show that perceived brand relationship orientation has direct positive impacts on trust and affective commitment and, in turn, has an indirect impact on attitudinal loyalty. However, this effect is limited to the case where consumers have a high (versus low) level of attitude toward the brand.Research limitations/implicationsThe application is limited to only two product categories and to strong national brands that enjoy high levels of perceived quality and attitude. Also, the model could be connected to behavioral loyalty metrics, in addition to attitudinal loyalty. The moderating impacts of relational disposition toward the brand should also be tested in future research.Practical implicationsTo develop consumers' attitudinal loyalty, brands must invest in programs converting efficiently perceived quality into trust and affective commitment as bases for differentiation and competitive advantage. Implications for brands' communication and distribution policies come along together with the necessity of enhancing the contacts and dialogue between the brand and the consumers. This confirms the potential outputs of brands' CRM strategies in the case of frequently purchased packaged good categories.Originality/valueThe conceptualization of BPRO in the case of frequently purchased packaged good categories is a new step in the consumer‐brand relationship understanding.

Impacts of in‐store manufacturer brand expression on perceived value, relationship quality and attitudinal loyalty

PurposeFor most frequently purchased packaged goods distributed in the super‐hypermarket channel, the store is where a manufacturer's brand can develop its image and its evaluation through an informal control on the purchase experience. Yet, for brands in this sector, better controlled in‐store purchase experiences can lead to enhance brand perceived value, relationship quality, and loyalty. To capture how these contacts take place, the authors develop the concept of in‐store manufacturer brand expression. This concept encompasses three dimensions, namely perceived quality of in‐store manufacturer brand presentation, in‐store manufacturer brand image expression, and perceived closeness of brand image with store image. The purpose of this paper is to examine the impacts of these components on brand evaluations. An empirical application on brands of the food and intimate apparel categories shows the differential impacts of these components on perceived value, relationship quality (trust‐credibility, trust‐benevolence, affective commitment), and attitudinal loyalty.Design/methodology/approachThe empirical application involves major brands positioned in frequently purchased packaged goods categories (ice cream, frozen meals and intimate apparel) and distributed in the super‐hypermarket channel over which they have no formal control. The conceptualization and measurement of perceived brand relationship orientation bears on a qualitative analysis of marketing experts and consumers. Refinement and validation of measures are applied to a convenience sample of 153 students and finally on a sample of 304 actual consumers. Structural equation modeling was used to test the model and hypotheses.FindingsPerceived quality of in‐store brand presentation has a direct positive effect on brand value and trust‐credibility whereas in‐store brand image expression has direct positive impact on trust‐benevolence and affective commitment. At the same time, perceived closeness of brand manufacturer image with store image has a direct negative impact on trust‐credibility. In addition, the authors observed that these effects have significant indirect positive and negative consequences on attitudinal loyalty, throughout the causal links which exist between value, trust and affective commitment. Also, the authors' results support the relationship marketing model in the case of strong national brands positioned in the frequently purchased packaged goods sector.Research limitations/implicationsThe application is limited to only three product categories and to strong national brands which enjoy high levels of awareness and market share. Also, the model could be connected to behavioural loyalty metrics, in addition to attitudinal loyalty.Originality/valueConceptualization of perceived brand relationship orientation in the case of frequently purchased packaged goods categories is a new step in the understanding and management of consumer‐brand relationship and mass market channel policies.

Invited Commentary—Net Promoter, Recommendations, and Business Performance: A Clarification on Morgan and Rego

One of the most controversial findings in Morgan and Rego (2006) was that two widely advocated loyalty metrics, “Net Promoter” and “Number of Recommendations,” have little or no value in predicting the financial outcomes of firms. We argue that neither measure was actually examined and that conclusions about the predictive value of these measures cannot be drawn from their analysis. A primary problem is that the measures used in Morgan and Rego (2006) do not adequately adjust for the presence of neutral word-of-mouth activity. Nevertheless, Morgan and Rego (2006) provide important information regarding other common customer metrics and firm financial outcomes. We are unaware of another longitudinal study that examines the predictive value of satisfaction and loyalty metrics in such a comprehensive way.

The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share‐of‐wallet

PurposeThe purpose of this research is to examine different customer satisfaction and loyalty metrics and test their relationship to customer retention, recommendation and share of wallet using micro (customer) level data.Design/methodology/approachThe data for this study come from a two‐year longitudinal Internet panel of over 8,000 US customers of firms in one of three industries (retail banking, mass‐merchant retail, and Internet service providers (ISPs)). Correlation analysis, CHAID, and three types of regression analyses (best‐subsets, ordinal logistic, and latent class ordinal logistic regression) were used to test the hypotheses.FindingsContrary to Reichheld's assertions, the results indicate that recommend intention alone will not suffice as a single predictor of customers' future loyalty behavior. Use of a multiple indicator instead of a single predictor model performs better in predicting customer recommendations and retention.Research limitations/implicationsThe limitation of the paper is that it uses data from only three industries.Practical implicationsThe presumption of managers when looking at recommend intention as the primary, even sole gauge of customer loyalty appears to be erroneous. The consequence is potential misallocations of resources due to myopic focus on customers' recommend intentions.Originality/valueThis is the first scientific study that examines recommend intentions and its impact on retention and recommendation on the micro (customer) level.

A Longitudinal Examination of Net Promoter and Firm Revenue Growth

Managers have widely embraced and adopted the Net Promoter metric, which noted loyalty consultant Frederick Reichheld advocates as the single most reliable indicator of firm growth compared with other loyalty metrics, such as customer satisfaction and retention. Recently, however, there has been considerable debate about whether this metric is truly superior. This article (1) employs longitudinal data from 21 firms and 15,500-plus interviews from the Norwegian Customer Satisfaction Barometer to replicate the analyses used in Net Promoter research and (2) compares Reichheld and colleagues' findings with the American Customer Satisfaction Index. Using industries Reichheld cites as exemplars of Net Promoter, the research fails to replicate his assertions regarding the “clear superiority” of Net Promoter compared with other measures in those industries.

A Longitudinal Examination of Net Promoter and Firm Revenue Growth

Abstract Managers have widely embraced and adopted the Net Promoter metric, which noted loyalty consultant Frederick Reichheld advocates as the single most reliable indicator of firm growth compared with other loyalty metrics, such as customer satisfaction and retention. Recently, however, there has been considerable debate about whether this metric is truly superior. This article (1) employs longitudinal data from 21 firms and 15,500-plus interviews from the Norwegian Customer Satisfaction Barometer to replicate the analyses used in Net Promoter research and (2) compares Reichheld and colleagues' findings with the American Customer Satisfaction Index. Using industries Reichheld cites as exemplars of Net Promoter, the research fails to replicate his assertions regarding the “clear superiority” of Net Promoter compared with other measures in those industries.

Regularities in buyer behaviour and brand performance: The case of Australian beer

In recent years, there have been calls for more emphasis on empirical generalisations in marketing. An empirical generalisation is a ‘pattern or regularity that repeats over different circumstances’. 1 Empirical generalisations are important for marketers. They provide a fact base from which to work, and are distinguished from mere opinions—even very learned or considered opinions. Three empirical generalisations to be discussed in this paper originated from the work of Ehrenberg and his colleagues. They are (1) repertoire buying, (2) the ‘double jeopardy’ phenomenon and (3) the duplication of purchase law. While many studies have demonstrated these generalisations, more evidence from other markets and conditions would be useful. Indeed, marketing textbooks typically do not discuss these findings, which limits the chance that practitioners become aware of them. This study examines whether these three established generalisations hold in quite a different market to those considered previously—the market for beer in Australia. Local market wisdom and other published research casts doubt over their applicability to this market. Therefore this study can either identify an exception to these generalisations, or find that they do apply where arguably they would not be expected to apply. The study finds that Australian beer drinkers do buy from repertoires of brands, that the brands do exhibit the classic double jeopardy pattern in loyalty, and that the duplication of purchase law also holds in this market. Therefore Ehrenberg's findings hold in another new context, one in which they were not necessarily expected to. The implications from these findings are that marketers should not expect many of their buyers to be very loyal, rather they should view them as people who occasionally buy their brand among a portfolio of other competing brands. Secondly, marketers should recognise that brand loyalty metrics are largely dictated by market share levels, and that somewhat lower levels of loyalty to smaller brands are to be expected. Thirdly, marketers should view their competition more widely and not focus too much on any one specific competitor brand. Rather, brands usually compete with all other brands in the market approximately in-line with the size of those other brands. This is to be expected unless there are marked functional differences between the competing brands.

Can a brand outperform competitors on cross‐category loyalty? An examination of cross‐selling metrics in two financial services markets

PurposeMany service organisations seek to grow by selling additional different products to their existing customers. Many managers are evaluated on the level of customer loyalty in terms of cross‐product holdings – for example, the average number of bank products or insurance policies held per customer. The purpose of this paper is to provide managers and researchers with some contextual knowledge and norms concerning “cross‐category” loyalty.Design/methodology/approachIn order to compare the levels of loyalty for competing brands, five relevant loyalty metrics were used in the analysis, with data sourced from two service industries, banking and insurance.FindingsThe results show little variation in loyalty scores between competing brands, and what variation there is can be explained by historic factors, without reference to CRM strategies. This suggests that investments into CRM and cross‐selling initiatives seem to have less effect on loyalty metrics than many marketing textbooks and CRM advocates have assumed.Practical implicationsMarketers should be very cautious of setting ambitious goals for increasing loyalty to their brand at a cross‐category level.Originality/valueVery few research papers have explored the issue of cross‐category loyalty. This is despite the value of the specific loyalty metrics as key performance indicators in service industries such as banking and insurance.

The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance

Managers commonly use customer feedback data to set goals and monitor performance on metrics such as “Top 2 Box” customer satisfaction scores and “intention-to-repurchase” loyalty scores. However, analysts have advocated a number of different customer feedback metrics including average customer satisfaction scores and the number of “net promoters” among a firm’s customers. We empirically examine which commonly used and widely advocated customer feedback metrics are most valuable in predicting future business performance. Using American Customer Satisfaction Index data, we assess the linkages between six different satisfaction and loyalty metrics and COMPUSTAT and CRSP data-based measures of different dimensions of firms’ business performance over the period 1994–2000. Our results indicate that average satisfaction scores have the greatest value in predicting future business performance and that Top 2 Box satisfaction scores also have good predictive value. We also find that while repurchase likelihood and proportion of customers complaining have some predictive value depending on the specific dimension of business performance, metrics based on recommendation intentions (net promoters) and behavior (average number of recommendations) have little or no predictive value. Our results clearly indicate that recent prescriptions to focus customer feedback systems and metrics solely on customers’ recommendation intentions and behaviors are misguided.