Defecting wine club members: an exploratory study

Sandra K. Newton (School of Business and Economics, Sonoma State University, Rohnert Park, California, USA)
Linda I. Nowak (College of Business Administration, California State University, Stanislaus, Turlock, California, USA)
Mayuresh Kelkar (Bertolon School of Business, Salem State University, Salem, Massachusetts, USA)

International Journal of Wine Business Research

ISSN: 1751-1062

Publication date: 20 August 2018

Abstract

Purpose

The purpose of this study is to investigate the range of explanations for why wine club members defect and move on.

Design/methodology/approach

This quantitative research study uses data from US wine consumers, gathered through an online survey of 399 former wine club members who had quit their membership in the recent past. Consistent with literature on customer churn rates in subscription markets, data are analyzed using descriptive statistics, factor analysis, hierarchical multiple regression and analysis of variance.

Findings

The results reported by respondents indicate that higher levels of perceived product quality, fair value in pricing, variety seeking and commitment to customer service at the beginning and at the end of a wine club membership lead to higher levels of customer satisfaction and a desire to recommend the club to others even after quitting. Though variety seeking is more commonplace among experienced wine drinkers, the good news for wineries is that consumers are more likely to recommend a wine club to others if at least a year has passed after they decided to quit.

Practical implications

The results provide implications for wine club managers seeking to improve wine club retention with suggested means for mitigating the rate of customer attrition.

Originality/value

This paper presents original research addressing a variety of reasons why wine club members quit. The extant research has found that factors such as product quality, fair pricing, service commitments and variety-seeking behavior affect members’ satisfaction with their wine club, as well as their desire to recommend it to others. The authors have attempted to combine all these factors into a single study to gain insight into wine club members’ switching behavior, and to find out what the wineries can do to improve customer loyalty.

Keywords

Citation

Newton, S., Nowak, L. and Kelkar, M. (2018), "Defecting wine club members: an exploratory study", International Journal of Wine Business Research, Vol. 30 No. 3, pp. 309-330. https://doi.org/10.1108/IJWBR-06-2017-0038

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

Wine clubs are an important contributor to profitability within the direct-to-consumer (DTC) distribution channel for US wineries (Teaff et al., 2005). The DTC distribution channel choice has also shown to positively affect gross profit margin and winery growth rates (Newton et al., 2015). Consequently, on entry to a winery’s tasting room, visitors are encouraged to sign up to the annual schedule of wine deliveries that constitute wine club subscriptions. Wine clubs are typically the most profitable sales channel for wineries in the USA and their high contribution to profitability is a key component of a winery’s long-term sustainability (Insel, 2008; Thach, 2009). For many US wineries, this is the main, and often only, sales channel contributing to profitability. The growing importance of this sales channel in recent years just contributed total DTC shipments of $2.33bn in 2016, with that volume of wine shipments exceeding 5 million cases (Williams and O’Donnell, 2017).

Wine clubs enable a winery to grow its customer base without using distributors and wholesalers and without the consequent need to deal with the complexities of competing for shelfspace in retail stores (Williamson et al., 2012). While wine clubs have some of the highest profit margins, often contributing upwards of 50 per cent higher than alternative sales channels, wine club memberships are also a great public relation tool, and a premium way that wineries can stay connected with members (Penn, 2003). With the number of wineries in the USA at the end of 2015 totaling approximately 8,702 with 4,054 of those located in California, it is no wonder that US wineries use wine clubs and regular club member events to keep customers returning to the winery (Franson, 2016).

Wine business research has explored a number of winery, tasting room and customer dissatisfaction issues. Teaff et al. (2005) identified a list of 30 wine club characteristics, whereas Charters et al. (2009) investigated the importance of wine visitors’ tasting room experiences. While investigating consumer purchase intentions, Barber and Taylor (2013) found that although not necessarily the primary target group, those visitors with lower purchase intentions offered key information as to product, pricing and actual purchase behavior. Researchers have also found that a wine customer’s satisfaction is greatly influenced by the service and tasting room experience, and leads to increased purchase intentions (Shapiro and Gómez Miguel, 2014). Fountain et al. (2008, p. 18) indicated that the “tasting room experience has the potential to be utilized more effectively in enhancing post-visit purchase and brand loyalty”. They encouraged wineries to develop loyalty programs and other post-visit connections. Wine clubs are a means to continuing these post-visit connections through purchase discounts, access to new releases, wine dinners and special events.

A major contribution of this research study is its investigation into reasons why wine club members leave – a direct impact to a winery’s customer retention and profitability. Therefore, this paper seeks to answer the following research questions:

RQ1.

What are the factors that cause wine club members to quit?

RQ2.

Do these factors differ among the demographics of wine club members?

Our paper is organized into six sections. Section 2 offers a review of relevant literature. Section 3 describes the survey design and statistical methodology. Section 4 presents findings and results. Section 5 is a discussion of the research and the implications for wine industry practitioners. Section 6 concludes with this study’s limitations and future research.

2. Literature review

Evidence on purchasing behavior in consumer goods categories indicates that consumers buy from a repertoire of acceptable brands from familiar sales outlets. In addition, a report from the Database Marketing Institute (Hughes, 2015) studying hotels, gas stations, drugstores and grocery stores found that the majority of these businesses’ profits comes from the 70-85 per cent of customers who are loyal to the business, regardless of price fluctuations. This repurchase loyalty metric is consistent across a number of industries, and countries where such research has been conducted on consumer goods purchasing behavior (Castéran et al., 2017; Habel and Lockshin, 2013). Specific examples include research on alcohol purchasing, which confirms this classification as a repertoire market (Banelis et al., 2013; Dawes, 2007). Accordingly, the role of wineries in trying to attract customers should be to then convince consumers of the appeal in adding a brand to a consumer’s repertoire of acceptable brands for purchase. The aim is to then encourage repeat purchase behavior for existing customers, because repeat purchase rates, often referred to as customer retention, are important to the bottom line and are major contributing factors to the net growth rate of a business.

2.1 Attrition and retention

Wineries in the USA have attempted to combat the regular substitution of wine brands in repertoire markets by applying a customer retention tool that is more consistent with subscription services, such as telecommunications and sports club members (McDonald et al., 2014; Wong, 2011). Successful management of customers in a subscription model can lead to lower churn rates, but still requires continuous acquisition in order for the business to grow (Castéran et al., 2017; Hassouna et al., 2015; Riebe et al., 2014). The aim for wineries is to thus identify likely indicators of attrition, like subscriber heterogeneity and/or seasonality, and act assiduously to reduce their effect (Schweidel et al., 2008).

Therefore, it makes sense to understand what makes wine club customers leave and go elsewhere. An Inc.com magazine article (James, 2012) stated that it takes ten times as much work to win new customers, as it does to keep old ones. And once you have a group of loyal customers, there can be numerous opportunities for cross-selling of products and services. According to Inc.com, over 75 per cent of the time, customers said they changed businesses because of an unsatisfactory service experience. Only a third of the time did customers switch because of quality issues and just a quarter of the customers left because of price increases.

Michael LeBoeuf’s (2000) research supports similar findings as to why customers defect. It states that 68 per cent of customers leave a business because of an attitude of indifference toward the customer by the owner, manager or an employee, which can be a result of a failure to appreciate the different expectations of a subscriber group to a wine club (Schweidel et al., 2008). LeBoeuf’s research found that only 14 per cent of the time is the defection related to dissatisfaction with the product. Interestingly, it is quite common for new visitors to a winery to have a great experience at the winery with the tasting room staff, while enjoying the wines and the atmosphere. Before leaving that day, a tasting room staff member usually invites a new visitor to join its wine club, alluding that the fun will continue with wine shipments directly to their home and invitations to exciting events throughout the year. New wine club members leave the winery believing that the unique feeling of belonging to something special will continue through their wine club participation. For many customers, this is true. In 2015, the average length for wine club membership increased from 26 months to about 28 months, yet many still discontinue their wine club membership after just one year (Franson, 2014a; Penn, 2016a). With the wine industry club attrition rate average of about 18 per cent, wineries must persistently sign up new members to sustain or grow (Penn, 2015).

Wine business trade articles continue to recommend ways to refine practices to find new members, keep members in the wine club and increase wine club memberships, as well as how to successfully sell its wine club to tasting room customers (Berglund, 2003; Fisher, 2007; Franson, 2014b; Penn, 2016b). McMillan (2014) wrote in his Blogspot of the importance of economic utility of a wine consumer, the consideration of “the amount of pleasure a consumer gets from a good or service”.

Understanding the motivations of wine club members is extremely complex, and may depend on personality type and whether or not the consumer is more involved with the product or with the purchase. According to White and Thompson (2009), product-involved wine consumers choose their wines very carefully, and are more interested in a wine club because of opportunities to deepen their understanding about food and wine. These consumers are also more interested in the potential benefits, such as access to rare wines and special blends. Purchase-involved wine consumers are most influenced by financial rewards, such as purchase discounts and free shipping. How does a winery keep these two different types of wine club members as loyal consumers, especially the financially motivated consumers, when almost all wine clubs offer discounts? Lockshin and Corsi’s (2012) review of wine consumer behavior indicates that actual behavior is rarely measured. In this paper, we seek to identify the reasons for the wine consumer’s actual behavior – leaving the wine club.

2.2 Variety seeking

The notion of an optimal level of stimulation has been used to explain variety-seeking tendencies in consumers and has been extensively researched in marketing since the 1960s. For example, Berlyne (1963, 1970) found that consumers might be satisfied with their current choices, but may be looking to try something new or different just for the fun of it, or even for the thrill of it. Menon and Kahn (1995) further theorized that an individual might engage in variety-seeking or novelty-seeking behaviors as a way to satisfy his or her need for further stimulation. Consumers may alternate among familiar items or switch to new items to satisfy a desire for novelty or complexity (Fiske and Maddi, 1961; Maddi, 1968). Chrysochou et al. (2012) found wine preference differences between generation cohorts. The study alludes that Generation Y’s wine purchasing behaviors are more marketing added value based, e.g. promotions or labeling.

Consumers may change brands for a number of reasons. Consumers may be curious about other retailers or product offerings, or may be motivated by a lower price (Raju, 1980; Van Tripj et al., 1996). According to a study conducted by Park et al. (1990), the frequency or intensity of consumption and the mode of consumption can also affect how quickly a consumer feels satiated, and thus feel a need to seek variety. In 2001, Berné et al. conjectured that food service retail managers should realize that different customer profiles might have different levels of variety-seeking intensity. In the case of apparel, variety seeking and product characteristics interact with each other, but do not conclusively explain why consumers switch brands (Michaelidou and Dibb, 2006). Olsen et al. (2015) investigated variety-seeking behavior among US wine consumers. Their research found that there were significant differences between high variety-seeking consumers compared to moderate variety-seeking consumers and variety avoiders. High variety seekers have a higher tolerance for risk, pay more for wine, purchase wine in more locations, prefer more varietals and consider themselves more knowledgeable about wine than the other two categories of wine consumers.

There is anecdotal evidence that wine consumers may switch wine clubs solely because they are seeking variety, either in the choices of wine available or perhaps in the member wine events themselves. Consumer decision-making processes are complex, and the reasons for switching brands, (or in this case, wine clubs), may not be easily apparent. Feinberg et al. (1992) revealed that the level of variety seeking in a specific market might be a basic feature of that particular market. With approximately 8,702 wineries in the USA alone, one could see how variety seeking and brand switching could easily take place. If a wine club member becomes unhappy with the service he or she is receiving, the quality of the product or even the pricing of the wine, then the switching costs are low. With two quick e-mails, the wine club member can dis-enroll from one wine club and enroll in another.

2.3 Commitment

How does a customer decide to join a wine club in the first place? Many times, it is because they have had a very positive experience in the winery’s tasting room. The tasting room staff has succeeded in making the customer feel special. Positive emotion (positive affect) can have a direct and significant effect on customer satisfaction (Oliver et al., 1997), which then leads to purchase intention, or in this case, wine club membership. However, a strong emotional connection to a winery cannot make up for poor wine quality, unsatisfactory service or prices that are perceived as being very high (Robinette et al., 2002).

The customer satisfaction drivers for wine club members take numerous forms. Perceptions of quality, competitive pricing and service quality are typically the key drivers to all loyalty programs. According to Dodd (1999), satisfied customers return to the winery, tell their friends and spend more on wine than typical first-time visitors do. In support of these findings, Yu and Dean (2001) also found that affective (emotional) component of customer satisfaction is a better predictor of continued loyalty than price and quality. Once the winery visit is over, and the new wine club member has returned home, how does the winery keep that emotional connection with the customer?

Joining a wine club can be the first step in building a strong emotional bond with the winery. And if a winery is able to cultivate this relationship and work to build a strong sense of belonging, then a feeling of commitment toward the winery and its products may be developed. Sharma and Patterson (2000) defined commitment as a consumer’s belief that an ongoing relationship is worth investing time, energy and money. Extremely positive, consumption-related experiences are likely to lead to high levels of customer commitment (Hirschmann and Holbrook, 1982).

Nowak et al. (2006) found that a critical element of millennial customers’ post purchase attitudes was their tasting room experience, and that commitment was the strongest predictor of brand equity. This research, however, focused on the millennial’s positive relationship with attitudes toward a wine brand, and it did not look beyond intentions.

2.4 Product quality

Mercedez-Benz, BMW and Porsche – What do all of these brands have in common? Why do consumers pay more for these automobiles? While marketers refer to this as brand equity, the incremental value added to a product because of its name (Farquhar, 1994), consumers might proclaim it as product quality. Wine consumers are willing to pay these price premiums for wines that are considered to be of high quality or prestigious. According to early researchers such as Aaker (1991, 1996), brand equity is a multidimensional concept that consists of brand loyalty, brand awareness, perceived quality, brand associations and other proprietary brand assets.

Product quality can be a significant predictor of customer satisfaction and can lead to repeat sales (Anderson et al., 1994). Research has also shown that among wine customers, the strongest predictor of brand equity is product quality (Nowak and Washburn, 2002). No matter how wine consumers perceive quality, whether they are novices or aficionados, past research shows that the higher the level of perceived quality, the higher the level of repurchase intention.

2.5 Fair pricing

According to Nowak and Washburn (2002), perceived fair pricing is highly predictive of brand equity in the wine industry. Most wineries price their wines in line with their competition, hoping to remove price as a decision factor by neither pricing high nor low relative to the competition. Some wineries deliberately price their wines high to give the perception of superior quality. Others use penetration pricing, where pricing the wine below its perceived value offers the idea that the wine is a “good value”. Gallo, Constellation, The Wine Group and Bronco, the 2016 top four wine producers in the USA by case sales (Penn, 2016b), have numerous brands that fall into this pricing mechanism. Regardless of the pricing strategy, the question is whether price becomes a decision factor for wine club members. Once they start receiving their wine shipments, does the overall cost become a factor in their continuance of the membership?

2.6 Customer satisfaction

For a winery, high levels of customer satisfaction may contribute to positive word-of-mouth, repeat business and increased profits. When researchers refer to high levels of customer satisfaction, they are referring to “very satisfied” customers. Research found that this factor is highly correlated with superior economic returns (Anderson et al., 1994). Schlossberg (1990) proposed that merely satisfying customers is not sufficient, and that a firm should “delight” a customer to build loyalty and loyalty-driven profits. Yeung et al. (2002) found that with increased levels of customer satisfaction comes increased profitability. Of course, there is a point at which the business cannot start giving product away or hand delivering wine to keep the customer “delighted”. There has to be a balance. However, pro-active companies work to ensure that their customers are “very satisfied” and they are following up regularly with customer surveys to monitor their performance as perceived by the customer. Delta Airlines sends out surveys to customers asking of its past trip performance with two main questions – “would you recommend Delta” and “would you travel again with Delta?”

Consumers and marketers recognize wine to be a very complex product (Orth et al., 2007). Whether this complexity is due to pricing, the expanse of grape varieties or the locations of origin, ranging from appellation, region and country, it can lead to much consumer confusion in the marketplace. This complexity can lead to ambiguity and risk on the part of wine business managers, as they develop their marketing and business strategies to sell their product. Following Orth et al.’s (2007) call for research, this study strives to improve our understanding and create new knowledge that will assist wine businesses in its marketing and wine club customer retention strategies.

2.7 Hypotheses

Based on the literature, we propose that positive perceptions will have a positive relationship with customer service attitudes toward the winery, even when the wine club member cancels their membership. Research has found predictive qualities with perceptions of product quality, variety seeking, fair pricing and feelings of commitment; therefore, we propose that:

H1.

Higher levels of perceptions of commitment at the beginning, fair pricing, product quality, variety seeking and commitment at end lead to higher levels of customer satisfaction, even at the end of the membership.

Would you recommend the winery to a friend? This recommendation question has been found to be a key indicator to a firm’s growth, even more so than evaluating customer satisfaction and loyalty (Reichheld, 2003). However, Keiningham et al. (2007) found alternate results where the single recommendation was not as predictive as multiple indicators. As our study is exploratory, we propose that:

H2.

Higher levels of perceptions of commitment at the beginning, fair pricing, product quality, variety seeking and commitment at end, lead to higher recommendations, even at the end of the membership.

Research has found that variety seeking may differ by customer demographics (Berné et al., 2001); therefore, we propose that:

H3.

The levels of perceptions of commitment at the beginning, fair pricing, product quality, commitment at end and customer satisfaction at the end, as well as age and gender, will vary by the consumer’s level of variety-seeking behavior.

3. Research methodology

3.1 Subjects and design

Our survey was launched using Qualtrics, an online survey software, and the services of Qualtrics.com, a provider of panel data. With panel data, respondents are self-select, and therefore, a convenience sample, which cannot be taken as a general population sample. Data for this survey research study were collected in 2017 from adult US wine consumers who had been a member of a wine club before, and had cancelled their wine club membership. Only those potential participants of the panel responding, “I will provide my best answers” to the question, “Do you commit to providing your thoughtful and honest answers to the question in this survey?” were permitted access to the survey. This qualifying statement was an attempt to mitigate respondent bias with panel data and survey research (Johnson, 2016; Pedhazur and Schmelkin, 1991). This resulted in 399 usable survey responses for the analyses. The survey was pre-tested with a convenience sample (N = 20) composed of individuals who had been a past member of a wine club, and minor revisions were made.

3.2 Measures

To stay consistent with prior research, all scales in the measurement instrument were adapted from existing instruments. For almost all scales, we used Likert scale end choice points of 1-5 with 1 being the highest rating of strongly agree and 5 being the lowest rating of strongly disagree.

Product quality was measured using items adapted from research by Nowak et al. (2006). Fair pricing was measured using items adapted from the research by Nowak and Washburn (2002). Variety seeking was measured using items adapted from the research by Van Tripj et al. (1996).

Commitment to the winery was measured using items adapted from research conducted by Meyer and Allen (1991); Bansal et al. (2005) and Nowak et al. (2006). Commitment to the winery was measured at two moments in time – at the time of wine club sign-up and at the time of wine club defection. The following statements were made: “This winery made me feel like part of the family” and “This winery made me feel a strong sense of belonging”.

Customer satisfaction was measured using two items adapted from Oliver and Swan (1989). Both questions, when you canceled your membership, rate your feelings of the winery (very unhappy with to very happy with) and when you canceled your membership, rate your feelings of the winery (very unfavorable to very favorable), used a 10-point semantic differential scale with 10 being the highest rating. Recommendation was measured using the single item adapted from Reichheld (2003). The question “How likely is it that you would recommend the winery to a friend or colleague?” (not at all likely to extremely likely) used a 10-point semantic differential scale with 10 being the highest rating.

Hollebeek et al. (2007) found that a wine consumer’s level of involvement affects purchase intentions. Wine involvement was measured using items adapted from the wine involvement scale first developed by Brown et al. (2007) and adapted by others (Pratt, 2010; Olsen et al., 2015).

In survey research, demographics of respondents may moderate variables of interest. Older wine consumers have been found to spend more money than younger consumers when visiting the tasting room (Charters and O’Neill, 2001; Shapiro and Gómez Miguel, 2014). Shapiro and Gómez Miguel (2014) found that female wine consumers, who are younger and more educated, are more likely to buy wine in the future than other wine consumers.

To answer the research questions and test the hypotheses, SPSS 24.0 was used to conduct a number of statistical analyses, e.g. descriptives, factor analysis, hierarchical multiple regression and analysis of variance (ANOVA). Assumptions were tested and data were found to be acceptable for further analyses, e.g. factor analysis, multiple regression and ANOVA.

4. Results

4.1 Descriptive statistics and factor analysis results

The US target sample was diverse originating from 44 states with the largest of 13 per cent from CA, 9 per cent from NY, 9 per cent from FL and 5 per cent from WA. The wine club origins were also diverse originating from 41 states with the largest of 43 per cent in CA, 15 per cent in NY and 5 per cent in WA. Over 64 per cent of the respondents were Millennials, aged 21-39, whereas 59 per cent were women. Over 85 per cent of respondents had some college degree and more. We adopted the Wine Market Council’s (Brager, 2014) metric for determining how often wine is consumed on a five-point scale, ranging from daily to a few times a month or less. A total of 71 per cent of the respondents indicated that they drank wine at least a few times a week, and thus, were considered high-frequency wine consumers for the purpose of this study; this percentage makes anecdotal sense due to the framing of the target sample. A summary of sample study descriptive statistics can be found in Table I.

Principal components factor analysis was conducted using Varimax with Kaiser Normalization rotation extracting five factors. The Kaiser–Meyer–Olkin measure of sampling adequacy equals 0.725 suggesting that the factor analysis is appropriate. The five factors had Eigenvalues greater than 1, ranging from 3.942 to 1.025; the five factors explained 72.6 per cent of the total variance, which is considered satisfactory (Hair et al., 1998). Items that cross-loaded or did not load above 0.40 were removed from the analysis. The resulting factor analysis in Table II includes factor loadings and items measuring each of the factors: commitment at time of sign-up, product quality, fair pricing, variety seeking and commitment at time of defection.

The items for each scale were then summed-and-averaged creating the variables used in the analysis. Internal consistency of the scales used in this study was determined by computing Cronbach’s alpha. The Pearson correlation matrix of the main study variables with their reliability scores (Cronbach alpha) at the diagonal are shown in Table III.

We used the Harman one-factor test (Podsakoff and Organ, 1986) to test for the presence of common method bias by entering all study variables into a principal components analysis. As there was no general factor in the unrotated factor structure, we concluded there was no common method bias in the data.

4.2 Hierarchical multiple regression results

Hierarchical multiple regression was used to test H1: higher levels of perceptions of commitment at the beginning, fair pricing, product quality, variety seeking and commitment at end lead to higher levels of customer satisfaction, even at the end of the membership.

The control variables, age and gender were entered into the equation first, using multi-step regression analyses, to assess the effect the variables had on the dependent variable of interest – customer satisfaction. Next, the variable (commitment at the beginning of the wine club experience) was entered into the equation as an independent variable. Finally, variables (product quality, variety seeking, fair pricing and commitment at the end of the wine club experience) were entered into the equation as independent variables.

Table IV details the hierarchical regression results and variable coefficients for each of the three steps. The initial equation at Step 1 reveals no relationship between the level of customer satisfaction and the control variables, age and gender, at alpha cutoff = 0.05. Results at Step 2 show that the relationship was significant with the level of customer satisfaction when the variable (commitment at the start of the wine club experience) was entered into the equation. Results at Step 3 show the significant relationship between the level of the customer’s satisfaction when all remaining study variables were entered into the equation. The final hierarchical regression results show the overall fit of the model, R2 = 0.668, and while adjusting for inflation, adjusted R2 = 0.436, an F = 44.890, Sig.= 0.000, offering strong support for H1.

Our results suggest that product quality, fair pricing and commitment at the end may be the primary drivers of their customer satisfaction or dissatisfaction when the respondents quit their wine club memberships. The estimated coefficients for these variables are significant at the 1 per cent level: −0.916 for product quality, −0.290 for fair pricing and −0.872 for commitment at the end. The variable, variety seeking, is also an important driver of their customer satisfaction or dissatisfaction when the respondents quit their wine club memberships. The estimated coefficient for variety seeking is significant at the 5 per cent level at 0.257. Our results indicate that at the end of their wine club membership, their level of customer satisfaction is predicated on decreasing levels of commitment, decreasing perceptions of product quality and fair pricing, and increasing perceptions of variety seeking. The respondents’ age, gender and level of commitment at the beginning were of no import in the final model.

Hierarchical multiple regression was used to test H2: higher levels of perceptions of commitment at the beginning, fair pricing, product quality, variety seeking and commitment at end lead to higher recommendations, even at the end of the membership.

Following the steps used in testing H1, the control variables, age and gender were entered into the equation first, using multi-step regression analyses, to assess the effect the variables had on the dependent variable of interest – customer recommendation. Next, the variable (commitment at the beginning of the wine club experience) was entered into the equation as an independent variable. Next, variables (product quality, variety seeking, fair pricing and commitment at the end of the wine club experience) were entered into the equation as independent variables. Finally, the variable (customer satisfaction) was entered into the equation as an independent variable. While customer satisfaction and the recommendation item were correlated at 0.823, Sig. = 0.000, we entered customer satisfaction into the equation to evaluate its strength and its change effect.

Table V outlines the hierarchical regression results and variable coefficients for each of the four steps. The initial equation at Step 1 reveals no relationship between the level of recommendation and the control variables, age and gender, at alpha cutoff = 0.05. Results at Step 2 show that the relationship was significant with the level of recommendation when the variable (commitment at the start of the wine club experience) was entered into the equation. Results at Step 3 show the significant relationship between the level of the recommendation when product quality, variety seeking, fair pricing and commitment at the end of the wine club experience were entered into the equation. Results at the final step show the significant relationship between the respondent’s level of the recommendation when customer satisfaction was entered into the equation. The final hierarchical regression results show the overall fit of the model, R2 = 0.742, and while adjusting for inflation, adjusted R2 = 0.737, an F = 140.368, Sig.= 0.000, offering strong support for H2.

Our results suggest that product quality, commitment at the beginning, commitment at the end and customer satisfaction may be the primary drivers of their willingness to make a positive recommendation even when the respondents quit their wine club memberships. The estimated coefficients for these variables are significant at the 1 per cent level: −0.424 for product quality, −0.237 for commitment at the beginning, −0.872 for commitment at the end and 0.800 for customer satisfaction. The variables, fair pricing and gender, are also an important driver of their willingness to make a positive recommendation even when the respondents quit their wine club memberships. The estimated coefficients for fair pricing and gender are significant at the 5 per cent level: −0.196 for pair pricing and 0.309 for gender. Our results indicate at the end of their wine club membership, their level of willingness to make a positive recommendation is predicated on decreasing levels of commitment, both at the beginning and end; decreasing perceptions of product quality and fair pricing, their gender, and corresponding level of customer satisfaction. The respondents’ age and level of variety seeking were of no import in this model. Interestingly, with customer satisfaction in the equation, age and commitment at the beginning of the relationship became significant predictors, whereas variety seeking was no longer a significant predictor of recommendation.

4.3 ANOVA results – study variables and consumer demographics

ANOVA tests were used to test H3: the levels of perceptions of commitment at the beginning, fair pricing, product quality, commitment at end, customer satisfaction at the end, as well as age and gender, will vary by the respondent’s level of variety-seeking behavior.

To evaluate the respondent’s variety-seeking behavior, the variable was transformed to a dummy variable to express low and high variety-seeking behavior, where variable means of 1-2.75 were deemed high variety seeking and 3.25-5 were deemed low variety seeking. We chose to omit from the analysis, those respondents deemed neither high nor low variety seekers, having a variety-seeking variable mean = 3. The dummy variable was entered as the factor to analyze the main study variables. Table VI summarizes the ANOVA results where perceptions of product quality, commitment at end, customer satisfaction at end and gender were each significant at alpha cutoff = 0.05, offering support for H3 for those variables. Perceptions of fair pricing was significant at alpha cutoff = 0.10. The variables commitment at the beginning and age were not significant.

Those respondents with a higher variety-seeking behavior expressed lower perceptions of product quality, fair pricing, commitment at the end and customer satisfaction at the end of their wine club membership, than those respondents who were not so inclined. While age was not significant, 75 per cent of the respondents were high variety seekers; as to gender, 71 per cent of the male respondents and 78 per cent of the female respondents were high variety seekers.

He et al. (2016) found that consumers’ attitude toward the brand changes over time after the purchase, therefore, we chose to explore possible differences with how long ago the respondent had quit the wine club and the main study variables. The main study variables were entered into ANOVA as the dependent variables with the length of time since leaving the wine club as the nominal independent variable using two groups: one year or less and greater than one year. Table VII outlines the ANOVA results where perceptions of customer satisfaction at end was significant at alpha cutoff = 0.01, recommendation was significant at alpha cutoff = 0.05, and perceptions of variety seeking was significant at alpha cutoff = 0.10. There were no significant differences with the study variables: commitment at the beginning, product quality, fair pricing and commitment at the end.

Those respondents who had been away from the wine club for over a year expressed lower perceptions of their variety-seeking behavior, but much higher perceptions of their customer satisfaction at the end of their wine club membership, than those respondents who had left their wine club no more than one year ago. Those respondents who had been away from the wine club for over a year were also still more likely to recommend the winery to another, than those respondents who had left their wine club no more than one year ago.

The baby boomers’ brand attitude after the purchase has been found to decline less than those consumers who were younger (He et al., 2016). To further clarify and better understand consumer differences as to reasons for leaving their wine club memberships, we chose to explore and analyze a number of other study questions not contained in the main study variables that refer to general service, wine shipments and member events. These questions were analyzed using ANOVA with age groups and gender entered as the factors. Table VIII summarizes the ANOVA results indicating mean scores for age groups and gender. Those significant statements at alpha cutoff = 0.01, 0.05 and 0.10 are so identified in the Table.

Males were found to be more satisfied with the winery’s overall services, shipping experience and member’s discounts than the females. Wine consumers in the 40-51 and 52-70 appeared to believe wine shipments were too frequent, and were less satisfied with the shipping experience than the Millennials. Millennials were also looking for different wine club member events than other age groups. While some responses to the questions were not significantly different by age group and gender, there did seem to be consistent perceptions among all respondents that shipping costs were too high, there were not enough wine club benefits, and that wine club member discounts were not significant.

5. Discussion and implications

There is little a winery can do if the wine consumer leaves a wine club because of the cost of the wine – that it is too expensive. Other possible reasons for defecting might be variety seeking (time for a change) or product quality (tastes change or product quality changes). This study found fair pricing and product quality to be significant predictors, in that both factors greatly influenced their customer satisfaction at the end of their wine club membership, as well as their willingness to recommend the winery to another. A wine consumer’s variety-seeking behavior influenced their customer satisfaction at the end of their wine club membership; however, it did not affect their willingness to recommend the winery to another when also considering their level of customer satisfaction.

Certain factors that can be sources to why wine club members defect can be mitigated by the winery. To retain loyal customers, Menon and Kahn (1995) suggested that marketers and retailers provide variety in product and service assortments; and Barclay (2005) offered tips to increase a winery’s wine club potential. The wine club manager or representative may ask why a wine club member is leaving, but unless learning takes place to mitigate another from leaving for the same reason, another wine club member will leave. Furthermore, as indicated by Schweidel et al. (2008), customer retention in subscription markets can often be explained by the heterogeneity of a customer database, as much as organic attrition. Riebe et al. (2014) reinforced the idea that there will always be a level of attrition, and marketers need to recognize the importance of continual acquisition required in order for a brand to grow.

Lifetime value has been offered as the key metric to wine club members, recognizing that it is not the conversion rate from visitor to wine club member, nor is it the first sale, but in fact the second and subsequent sales (McMillan, 2014; Castéran et al., 2017). It encompasses all the events and offerings by the winery and staff through the length of the membership, while adapting and changing through time. How does the staff maintain that positive emotional connection now that the customer is out of reach? What if the wine club member is too far away to attend special wine club member parties? This study investigated the consumer’s and at the end of the wine club relationship and found that when looking at their customer satisfaction, their level of commitment at the beginning was not as relevant as their commitment at the end of the relationship. Following Robinette et al. (2002), this study found lower levels of commitment at the end of the relationship and this significantly influenced their overall customer satisfaction and willingness to recommend the winery to another. So, what is the winery’s plan for keeping the customer emotionally connected to the winery? Perhaps each wine club member should have his/her own “personal concierge”, or a specific individual from the winery.

Teaff et al. (2005) recommended future research to understand better the wine club customer’s needs; this research identified customers’ needs that had been deemed important and thus perhaps were not being met. Recognizing the consequence of a successful wine club, this understanding might better prepare wine club managers to facilitate change in winery processes and wine club administration. In addition, applying Schweidel et al.’s (2008) framework could be useful in understanding how, and if, wine’s characteristics as a repertoire market could contribute to understanding the different nature of consumer buying behavior in general for this subscription service. McDonald et al. (2014) investigated a subscription service churn rate for an emotive service, which could apply to wine as a product of aesthetic consumption (Charters et al., 2009).

While Olsen et al. (2015) found that younger wine consumers tended to seek more variety and older wine consumers typically avoided variety, this study found that there were no age differences with variety-seeking behavior. However, this study found that 72 per cent of the men and 78 per cent of the women were higher variety seekers. Also, higher levels of variety-seeking behavior did negatively influence perceptions of product quality, fair pricing, commitment at the end and customer satisfaction at the end.

Berné et al. (2001) found that when looking to increase customer retention, service managers should look for ways to mitigate the impact of the customers’ variety seeking over focusing only on increasing customer satisfaction. This research found that while commitment was lower when the wine club member quit than when they joined, respondents’ variety seeking was a much more significant factor when looking at their customer satisfaction rather than their willingness to make recommendations.

Winery staff must also realize that variety seeking may negatively affect the length of their wine club membership, and may not be mitigated by tasting room staff efforts to improve customer service and customer satisfaction (Berné et al., 2001). However, higher levels of customer satisfaction and willingness to recommend the winery to another after being away for over a year may mean that, after a short hiatus from the club, past members could be convinced to return with the promise of new and improved member events, shipping deals and special access to award winning wines.

6. Limitations and future research

This research study is not without limitations. For this study, we used US panel data, where potential respondents were invited to participate in the study through the third-party panel data source, Qualtrics. With the respondents’ self-selection, caution should be taken, as the findings are not generalizable beyond the wine industry. However, other small- or medium-sized firms may evaluate the efficacy of our results and make their own management decisions. And, while generalizability to the population is not possible, the wine consumer diversity of geographic coverage across states may help mitigate some limitations within the convenience sample (Pedhazur and Schmelkin, 1991). We analyzed two segments of our respondent’s variety-seeking behavior, high and low, whereas other research has measured three segments (Olsen et al. (2015); future research might investigate three segments.

Van Tripj et al.'s (1996) results found understanding product characteristics helpful in mitigating brand switching by consumers. Here, marketing strategies might focus on attracting those consumers with a need for variety by enticing them with an alternate brand within the winery. This research did not ask why the wine club member joined; perhaps determination of this key point, management can better improve marketing and relationships with members. Future research might conduct longitudinal studies of current wine club members to realize when the decline of customer loyalty turns to departure, to identify factors that might mitigate departures and to learn what is being done right and what could be done better. Wine club managers might also consider surveying its current wine club members and its past wine club members to determine gaps between the two, as a way to identify specific areas of need for improvement and customer retention.

While 43 per cent of the respondents had been a member of a wine club located within California, not all countries and regions through the USA have embraced the concept of wine clubs as evidenced within California. Therefore, future research might investigate differences between wineries of states, as well as wine consumers.

If variety seeking is a key motivation of why wine club members discontinue their memberships, how does a winery proactively work to avoid this financial loss? Wineries that have a large number of varietals may be able to keep the variety seekers interested a little longer by allowing the club members to mix their own cases. Wineries that specialize in only one or two varietals will be more challenged, and may look at creative ways to retain the club member through special events, wine club dinners and active involvement in harvest and crush. In either case, it seems inevitable that some attrition cannot to be avoided. As the wine industry continues to grow more competitive, capturing and retaining loyal club members will continue to be a challenge.

Descriptive statistics

Descriptives N (%)
Gender
Male 163 40.9
Female 236 59.1
Total 399 100
Age
21-39 258 64.7
40-51 96 24.1
52-70 40 10
71 and higher 5 1.3
Total 399 100
How long ago did you leave this wine club?
0 month-1 year 266 73.5
>1 year 96 26.5
Subtotal 362 100
Missing 37 9.3
Total 399 100
How often do you consume wine?
A few times a month 44 11
About once a week 70 17.5
A few times a week 133 33.3
Several times a week 110 27.6
On a daily basis 42 10.5
Total 399 100
Your highest level of education
Some HS 11 2.8
HS grad 41 10.3
Some college 83 20.8
College grad 163 40.9
Some grad school 27 6.8
Completed grad school 74 18.5
Total 399 100
Your approximate average household income
$0-$50,000 91 22.8
$50,001- $100,000 196 49.1
$100,001-$200,000 88 22.1
> $200,001 24 6
Total 399 100

Factor analysis of main study variables

Study variables Factor loadings
FP PQ VS CB CQ
Commitment at time of sign-up (CB)
CB1_When I signed up for the wine club, this winery made me feel like part of the family 0.064 −0.031 −0.020 0.850 0.251
CB3_When I signed up for the wine club, this winery gave me a strong sense of belonging 0.062 −0.020 −0.021 0.851 0.265
Product quality (PQ)
PQ1_Overall, I considered the quality of the wine to be excellent 0.306 0.588 0.115 0.494 −0.064
PQ2_The wines met my standards for quality 0.387 0.653 −0.018 0.269 −0.051
PQ3R_I believe that the general quality of the wine was low −0.066 0.848 −0.176 −0.117 −0.005
PQ5R_I was disappointed with the quality of the wine −0.006 0.836 −0.139 −0.126 0.090
Fair pricing (FP)
FP3_Compared to similar product, these wines were priced right for me 0.847 0.048 −0.017 0.084 0.131
FP4_I perceived the wines to be fairly priced 0.860 0.011 −0.030 0.062 0.197
FP5_The wines appeared to be competitively priced 0.808 0.123 −0.073 0.031 0.144
Variety seeking (VS)
VS3_I was just ready for a change −0.034 0.060 0.808 −0.136 0.226
VS4_I was looking for something different 0.001 −0.037 0.821 −0.167 0.057
VS7_I found good alternatives online −0.018 −0.218 0.584 0.219 −0.240
VS8_I found good alternatives at other wineries −0.098 −0.214 0.660 0.193 −0.250
Commitment at time of defection (CQ)
CQ1_This winery made me feel like part of the family even at the end 0.278 −0.001 −0.024 0.296 0.823
CQ3_This winery gave me a strong sense of belonging even at the end 0.271 0.012 −0.025 0.318 0.822

Pearson correlation matrix with the reliability scores (Cronbach alpha) at the diagonal

Study variables CustSat3 ComBeg ProdQual FairPrice VarietySeek ComQuit Age
Customer satisfaction 0.919
Commitment at start −0.251** 0.880
Product quality −0.429** 0.075 0.758
Fair pricing −0.395** 0.214** 0.235** 0.837
Variety seeking 0.201** −0.006 −0.228** −0.110* 0.710
Commitment at end −0.533** 0.452** 0.111* 0.428** −0.077 0.918
Age −0.087 0.038 0.002 0.077 0.117* 0.061
Gender −0.066 0.141** −0.027 0.197** −0.053 0.207** 0.014
Notes:
**

Correlation is significant at the 0.01 level (two-tailed); *correlation is significant at the 0.05 level (two-tailed)

Hierarchical regression analysis summary for variables predicting customer satisfaction

Hierarchial regression step Predictor variable B Std. error Std. beta Significance R2 Adj. R2 Significance
Step 1 0.109 0.007 0.094
Age −0.266** 0.154 −0.086 0.084
Gender −0.295 0.227 −0.065 0.195
Step 2 0.265 0.063 0.000
Age −0.239 0.15 −0.078 0.111
Gender −0.139 0.223 −0.031 0.532
Commitment at beginning −0.596* 0.12 −0.244 0.000
Step 3 0.668 0.436 0.000
Age −0.188 0.117 −0.061 0.110
Gender 0.213 0.177 0.047 0.231
Commitment at beginning −0.011 0.104 −0.005 0.914
Product quality −0.916* 0.111 −0.329 0.000
Fair pricing −0.290* 0.102 −0.123 0.005
Variety seeking 0.257* 0.115 0.087 0.026
Commitment at end −0.872* 0.091 −0.442 0.000
Notes:
**

Coefficient is significant at the 0.01 level (two-tailed); *coefficient is significant at the 0.05 level (two-tailed)

Hierarchical regression analysis summary for variables predicting recommendation

Hierarchial regression step Predictor variable B Std. Error Std. Beta Significance R2 Adj. R2 Significance
Step 1 0.008 0.003 0.219
Age −0.266 0.185 −0.072 0.152
Gender −0.265 0.273 −0.049 0.332
Step 2 0.097* 0.090* 0.000
Age −0.226 0.177 −0.061 0.203
Gender −0.034 0.263 −0.006 0.898
Commitment at beginning −0.888* 0.142 −0.302 0.000
Step 3 0.481* 0.471* 0.000
Age −0.162 0.136 −0.044 0.237
Gender 0.403** 0.206 0.074 0.051
Commitment at beginning −0.179 0.120 −0.061 0.138
Product quality −1.088* 0.129 −0.326 0.000
Fair pricing −0.388* 0.118 −0.137 0.001
Variety seeking 0.314* 0.134 0.089 0.019
Commitment at end −1.045* 0.105 −0.441 0.000
Step 4 0.742* 0.737* 0.000
Age 0 0.097 0 0.998
Gender 0.309* 0.145 0.057 0.034
Commitment at beginning −0.237* 0.085 −0.081 0.005
Product quality −0.424* 0.097 −0.127 0.000
Fair pricing −0.196* 0.084 −0.069 0.020
Variety seeking 0.132 0.095 0.037 0.165
Commitment at end −0.417* 0.081 −0.176 0.000
Customer Satisfaction 0.800* 0.040 0.645 0.000
Notes:
**

Coefficient is significant at the 0.01 level (two-tailed); *coefficient is significant at the 0.05 level (two-tailed)

ANOVA Results with low and high variety-seeking behavior

Variety seeking High Low
N = 340 N = 37
Study variables M SD M SD MS F Significance
Commitment at beginning 2.19 0.91 2.15 0.97 0.048 0.058 0.810
Product quality 2.30 0.79 1.85 0.89 7836. 10.601 0.001***
Fair pricing 2.67 0.97 2.37 0.75 3.067 3.377 0.067*
Commitment at end 2.87 1.13 2.47 1.15 5.159 4.029 0.045**
Customer Satisfaction at end 5.85 2.11 7.03 2.41 45.883 9.963 0.002***
Age 1.47 0.70 1.49 0.87 0.012 0.023 0.879
Gender 1.62 0.49 1.41 0.50 1.503 6.318 0.012**
Notes:

*p > 0.10; **p > 0.05; ***p > 0.01; all mean score averages on questions, except customer satisfaction at end, age and gender, had five-point scale where 1 is highest; 1 = strongly agree and 5 = strongly disagree; customer satisfaction at end questions had 10-point scales, where 10 is highest

ANOVA Results time since quitting

How long since quitting Greater than one year One year or less
N = 96 N = 266
Study variables M SD M SD MS F Significance
Commitment at beginning 2.17 0.902 2.22 0.917 0.118 0.166 0.684
Product quality 2.18 0.724 2.27 0.826 0.486 0.759 0.384
Fair pricing 2.70 0.942 2.67 0.950 0.057 0.064 0.801
Variety seeking 2.26 0.837 2.10 0.719 1.760 3.110 0.079*
Commitment at end 2.86 1.099 2.86 1.151 0.001 0.001 0.980
Recommendation 6.25 2.509 5.52 2.736 37.716 5.258 0.022**
Customer satisfaction at end 6.50 2.029 5.80 2.202 35.381 7.601 0.006***
Notes:

*p > 0.10; **p > 0.05 and ***p > 0.01; all mean score averages on questions, except customer satisfaction at end, and recommendation, had five-point scale where 1 is highest; 1 = strongly agree and 5 = strongly disagree; customer satisfaction at end and recommendation questions had 10-point scales, where 10 is highest

ANOVA results – general wine club question items

Question items Age Groups in years Gender
21-39 40-51 52-70 71> ANOVA F Male Female
N = 258 N = 96 N = 40 N = 5 N = 163 N = 236 ANOVA F
Service
The wine club staff was not very responsive 3.16 3.15 3.28 3.6   3.16 3.18  
I was not treated special when I visited the winery 2.77 2.83 2.98 3.4   2.81 2.82  
Overall, I consider the winery’s services to have been excellent 2.41 2.56 2.7 2.4   2.29 2.6 7.797***
Shipments
The wine shipments were too frequent 3.14 2.54 2.68 3 6.223*** 2.92 2.96  
The wine shipments were too large 3.22 2.92 3 3   3.1 3.14  
The shipping costs were too high 2.38 2.27 2.2 2.8   2.43 2.28  
I was satisfied with the shipping experience 2.55 2.69 3 2 2.303* 2.38 2.79 12.465***
I did not like the selection of wines in the club shipments 2.87 2.97 2.78 3.6   2.95 2.85  
Member events
I was looking for different wine club member events 2.41 2.77 2.73 3 3.266** 2.55 2.53  
There was nothing special about the wine member events 2.38 2.53 2.4 3   2.54 2.35 3.108*
There were not enough wine club member benefits 2.31 2.49 2.23 3.2   2.47 2.27 2.996*
General
Wine club member discounts were not significant 2.22 2.31 2.15 3.4   2.45 2.11 8.893***
The winery was not easily accessible 2.85 2.94 2.7 2.4   2.83 2.86  
I had accumulated too much of this winery’s wine 2.88 2.83 2.48 2.4   2.85 2.8  
Notes:

Only the significant ANOVA F scores are indicated at *p > 0.10; **p > 0.05 and ***p > 0.01; all mean score averages on questions, except customer satisfaction at end, age and gender, had five-point scale where 1 is highest; 1 = strongly agree and 5 = strongly disagree; the age group 71 and older only had an N = 5; therefore, these mean scores should not be considered reliable representation of this age group

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Corresponding author

Sandra Newton can be contacted at: sandra.newton@sonoma.edu