vided by Kumar & Reinartz (2006) in Figure 9.

Consequently, relevant predictive variables need to be identified. In the first phase, this will include a procedure which Kumar & Reinartz called flattening the data. The basic idea is that the sourced data is transformed from its relational format into a customer-oriented one. In a flattened view, all data related to an individual will be contained in one observation (e.g. one row in the data table). From this data, descriptive statistics such as averages, medians, sums, etc. can be calculated. In our example one could, for instance, calculate the average monthly revenue generated in the mineral water category by each customer.

Following the flattening of the data, analytical variables might have to be created. This is the case when the variables generated in the previous data flattening step do not suffice. Possible examples would include interaction terms (e.g. variables resulting from the combination of values such as age and income), transformed variables (e.g. dates of each customer's transaction transformed into the number of days lying between them), or categorized variables (e.g. five defined income brackets from low to high). Finally, predictive variables need to be selected. As it is likely that the data set is now filled with a whole range of variables with different predictive capabilities, variables need to be sieved. For instance, salary might be a more important factor affecting the likelihood to purchase highpriced near-water brands than gender. To determine what variables show little correlation with the target value, Kumar & Reinartz suggested carrying out tests such as linear correlation analyses, pair-wise chi-square tests, or pair-wise simple linear regressions, and proposed supporting the variable exclusion process with histograms, scatter plots, box plots, and frequency tables. What can definitely be excluded are variables that take on only one value, such as name, customer ID, or home address. In addition to that, variables where the level of missing values exceeds a certain threshold might be excluded. In either case, however, it is important to keep in mind that variables deemed unnecessary for the analysis should not be erased from the table, but instead marked as unnecessary. Name, customer ID, and address, for example, are not going to be needed for the analytic process, but will certainly be required to carry out the campaign later on.

#### *4) Gain Customer Insight*

Following this step, it is time to actually gain the customer insight. In case of the mineral water example, it would be useful to build the predictive model on a predefined test set which contains customers that have already bought the flavored mineral water. In the course of model construction, one would then try to estimate the purchase likelihood for all the selected customers (who are not yet buying the new product) in the analytical model. To find the best way of doing so, one might try out different approaches in order to arrive at the model with the highest predictive power. For instance, one could apply differentstatistical tests such as linear or logistic regression, neural networks, factor analysis, or clustering. After doing so, the alternative models are compared by looking at the misclassification rates resulting from their application on the test set and eventually, the best one will be chosen. The final decision down to which level of purchase likelihood customers will be targeted by the flavored mineral water campaign, however, is another business decision to be made, Kumar & Reinartz pointed out.

#### *5) Act*

The ultimate step in the data mining process is an obvious one: to act on its results. The grocery retailer would proceed with his direct marketing campaign, mailing out vouchers for a specific brand of flavored mineral water to the selected customers. In addition to that, the company would monitor its campaign and learn from its evaluation. Furthermore, the outcome of the data mining process needs to be fed back into the company databases, with all results properly archived. As Kumar & Reinartz highlighted, possible information that requires documentation for future use or reference could include the following:

Raw data used

86

calculated. In our example one could, for instance, calculate the average monthly

Following the flattening of the data, analytical variables might have to be created. This is the case when the variables generated in the previous data flattening step do not suffice. Possible examples would include interaction terms (e.g. variables resulting from the combination of values such as age and income), transformed variables (e.g. dates of each customer's transaction transformed into the number of days lying between them), or categorized variables (e.g. five defined income brackets from low to high). Finally, predictive variables need to be selected. As it is likely that the data set is now filled with a whole range of variables with different predictive capabilities, variables need to be sieved. For instance, salary might be a more important factor affecting the likelihood to purchase highpriced near-water brands than gender. To determine what variables show little correlation with the target value, Kumar & Reinartz suggested carrying out tests such as linear correlation analyses, pair-wise chi-square tests, or pair-wise simple linear regressions, and proposed supporting the variable exclusion process with histograms, scatter plots, box plots, and frequency tables. What can definitely be excluded are variables that take on only one value, such as name, customer ID, or home address. In addition to that, variables where the level of missing values exceeds a certain threshold might be excluded. In either case, however, it is important to keep in mind that variables deemed unnecessary for the analysis should not be erased from the table, but instead marked as unnecessary. Name, customer ID, and address, for example, are not going to be needed for the analytic process, but will

revenue generated in the mineral water category by each customer.

certainly be required to carry out the campaign later on.

Following this step, it is time to actually gain the customer insight. In case of the mineral water example, it would be useful to build the predictive model on a predefined test set which contains customers that have already bought the flavored mineral water. In the course of model construction, one would then try to estimate the purchase likelihood for all the selected customers (who are not yet buying the new product) in the analytical model. To find the best way of doing so, one might try out different approaches in order to arrive at the model with the highest predictive power. For instance, one could apply differentstatistical tests such as linear or logistic regression, neural networks, factor analysis, or clustering. After doing so, the alternative models are compared by looking at the misclassification rates resulting from their application on the test set and eventually, the best one will be chosen. The final decision down to which level of purchase likelihood customers will be targeted by the flavored mineral water campaign, however, is

another business decision to be made, Kumar & Reinartz pointed out.

*4) Gain Customer Insight*

Needlessto say, Kumar & Reinartz are not the only authorsto write about data mining, but their work was chosen as one good example illustrating a general data mining process. In thisregard,Reutterer et al.(2007) provided a helpful, compact literature overview, despite their actual focus on basket analysis. Fayyad et al.'s (1996) or Berry & Linoff's (2004; originally published in 1997) contributions, for instance, are two established pieces of literature that should not go unmentioned. Alternatively, authors such as Tan et al. (2006), Chiu & Tavella (2008), Hastie et al. (2008), or Olson & Delen (2008) offered more recent publications.

Still, what needs to be added in relation to the data mining process described by Kumar & Reinartz (2006) is that it is often automated. Discussing the future of decision support systems in the marketing environment, Bucklin et al. (1998) made a staunch pledge for automation on the basis of increasing both efficiency and effectiveness. The two variables determining the possible degree of automation are the novelty of the product and the stability of the market, whereby existing products and stable markets would consequently allow for full automation. One only needs to picture all the information the grocery retailer from our example will have access to following interpretation of the loyalty program data. The decision about who to target in a single direct marketing campaign is certainly only one among many decisions the company needs to take, and most importantly, only one of numerous interesting possible analyses that also happen on a frequent basis. Indeed, setting up a formal project for every single analysis really does not seem like a very efficient approach. For that reason, it will definitely make sense to automate a good part of the analyses.

#### **3.4.2 A Look at Retailing and Market Basket Analysis**

Having established the use of data in general, the chapter on the value of data will now be concluded with a closer look at retailing, as this industry is the most prominent user of loyalty programs. Interestingly, findings show that retailing is also the industry which can draw the biggest benefit from its loyalty program data due to the large amount of information that is potentially available to these companies (Wood 2003). Wood reported findings from a telephone and email survey among marketing professionals of UK's top 1,000 companies, concluding that retailers were able to gain the biggest commercial value from their customer and prospect databases. Focused on local marketing initiatives, Byrom (2001) and Byrom et al. (2001) similarly praised the value of loyalty program data.

Among the different possible analyses that a retailer might undertake with the data of its loyalty program, a dominant part of that can be subsumed under the term basket analysis. While the company might also possess other information such as demographic or psychographic data perhaps, a good part of the value of the loyalty program lies in its ability to provide the company with transaction data for every single shopping incident of its members (i.e. information about their shopping basket). For that reason, a brief introduction to the research field of basket analysis will be given.

The basic idea is that the selection of products from different categories is based on related decisions. Understanding these can naturally be of great support to any marketing decision. Russell et al. (1999), for instance, highlighted the importance of including the influence from other products on the consumer's choice decision in the then possibly oversimplified consumer choice models. As far as basket analysis models are concerned, a whole range has been developed over time, trying to grasp the relationships among products and product categories. Referencing an early contribution by Agrawal et al. (1993) from the association rules category (see Table 4), Chen et al. (2005) exemplarily summarized a possible process as follows: between two individual product categories X and Y, an association rule such as X Y would indicate a pattern where Y is bought by the customer when X is purchased. The authors described "support" and "confidence" as the two selection measures for the association rule. In this context, support signifies how often both X and Y are recorded in the database, while confidence refers to the number of consumer shopping baskets comprising both X and Y, as compared to those with only product category X. In other words, confidence works as a measure of accuracy for the rule.

As mentioned before, several different models have been developed in order to better understand these patterns. Notable introductions to and overviews of these models include those by Russell et al. (1999), Seetharaman et al. (2005), and, in German-speaking literature, Boztuğ & Silberhorn (2006). As usual, the applied categorization method varies among these papers. Russell et al. (1999) can be considered as a sort of starting base. The authors began by defining "category" in the first place and went on to explore different types of choice dependence, which they used to develop a research agenda. Understandably, Russell et al. argued that it would be necessary to understand the goals driving consumers in order to be able to develop adequate models. While the authors discussed crosscategory consideration, cross-category learning (i.e. from earlier choices), and product bundling as the three types of cross-category choice dependence, Boztuğ & Silberhorn (2006) found the three factors of complementarity, heterogeneity, and coincidence to be driving cross-category choices.

*Table 3: Multi-Category Models of Consumer Purchasing*

88

seem like a very efficient approach. For that reason, it will definitely make sense

Having established the use of data in general, the chapter on the value of data will now be concluded with a closer look at retailing, as this industry is the most prominent user of loyalty programs. Interestingly, findings show that retailing is also the industry which can draw the biggest benefit from its loyalty program data due to the large amount of information that is potentially available to these companies (Wood 2003). Wood reported findings from a telephone and email survey among marketing professionals of UK's top 1,000 companies, concluding that retailers were able to gain the biggest commercial value from their customer and prospect databases. Focused on local marketing initiatives, Byrom (2001) and Byrom et al. (2001) similarly praised the value of loyalty program data.

Among the different possible analyses that a retailer might undertake with the data of its loyalty program, a dominant part of that can be subsumed under the term basket analysis. While the company might also possess other information such as demographic or psychographic data perhaps, a good part of the value of the loyalty program lies in its ability to provide the company with transaction data for every single shopping incident of its members (i.e. information about their shopping basket). For that reason, a brief introduction to the research field

The basic idea is that the selection of products from different categories is based on related decisions. Understanding these can naturally be of great support to any marketing decision. Russell et al. (1999), for instance, highlighted the importance of including the influence from other products on the consumer's choice decision in the then possibly oversimplified consumer choice models. As far as basket analysis models are concerned, a whole range has been developed over time, trying to grasp the relationships among products and product categories. Referencing an early contribution by Agrawal et al. (1993) from the association rules category (see Table 4), Chen et al. (2005) exemplarily summarized a possible process as follows: between two individual product categories X and Y, an association rule such as X Y would indicate a pattern where Y is bought by the customer when X is purchased. The authors described "support" and "confidence" as the two selection measures for the association rule. In this context, support signifies how often both X and Y are recorded in the database, while confidence refers to the number of consumer shopping baskets comprising both X and Y, as compared to those with only product category X. In other words,

to automate a good part of the analyses.

of basket analysis will be given.

confidence works as a measure of accuracy for the rule.

**3.4.2 A Look at Retailing and Market Basket Analysis**

*Source: Seetharaman et al. (2005); illustration adapted*

Probably inspired by Russell et al.'s (1999) suggestion to advance from singlecategorymodels to multi-category models, Seetharaman et al. (2005) undertook a literature review of these multi-category models in order to establish a statusquo. As depicted in Table 3, the authors distinguished the models by the number of outcomes that were modeled: either one of the three purchase decisions incidence, brand choice, or quantity, a combination of two of them (i.e. incidence and brand choice or incidence and quantity), or all three.

Based on the methodological approach employed rather than the modeled element of consumer purchasing, Boztuğ & Silberhorn (2006) adopted the categorization illustrated in Table 4. Asfor the exemples in literature as well as the explanations, the contributions by Mild & Reutterer (2003) and Reutterer et al. (2007) were used to complement the elaborations of Boztuğ & Silberhorn (2006).

*Table 4: Approaches to Basket Analysis*

*Source: Boztu & Silberhorn (2006), supplemented by Mild & Reutterer (2003) and Reutterer et al. (2007); illustration adapted*

Based on Mild & Reutterer (2003), Boztuğ & Silberhorn (2006) differentiated between explanatory and exploratory models in their detailed, though not exhaustive overview. As Mild & Reutterer (2003) explained, exploratory models are focused on exposing the relationship patterns that exist among multiple product categories. By contrast, explanatory models try to discover and measure inter-category choice effects caused by the company's marketing efforts (e.g. price or promotions), thereby considering external variables interfering with the customer's purchase decisions.

Comparing the contributions of Russell et al. (1999), Seetharaman et al. (2005), and Boztuğ & Silberhorn (2006), it seems fair to say that they almost perfectly complement each other. The first one gave a good introduction to the topic, outlining issues that needed to be resolved, while the second one gave a solid overview of the different approaches to solving these problems, categorizing them according to *what* the models try to capture. Eventually, the third contribution supplemented the previous works by looking at *how* the models worked, classifying them by their methodological approach.

# **3.5 Characteristics of Loyalty Schemes**

90

Exploratory

Explanatory

Pair-wise associations

Cluster analysis/ vector quantization

Autologistic model

Collaborative filtering

Multidimensional scaling

dence, brand choice, or quantity, a combination of two of them (i.e. incidence

Based on the methodological approach employed rather than the modeled element of consumer purchasing, Boztuğ & Silberhorn (2006) adopted the categorization illustrated in Table 4. Asfor the exemples in literature as well as the explanations, the contributions by Mild & Reutterer (2003) and Reutterer et al. (2007) were used to complement the elaborations of Boztuğ & Silberhorn (2006).

Use of a 2 x 2 matrix to determine pair-wise

Calculation of correlations between two or

Clustering of products or product groups based upon them being purchased together

Mapping of customers according to their

Prediction of a new customer's behavior based on the behavior of known customers

Visualization of meaningful underlying dimensions that enable an explanation of simi-

Modeling of inter-category choice decisions

Modeling of inter-category choice decisions according to utility theory, but in contrast to logit models with correlated disturbance

larities and dissimilarities in data

following utility theory

variables

*Source: Boztu & Silberhorn (2006), supplemented by Mild & Reutterer (2003) and Reutterer et al.*

Based on Mild & Reutterer (2003), Boztuğ & Silberhorn (2006) differentiated between explanatory and exploratory models in their detailed, though not exhaustive overview. As Mild & Reutterer (2003) explained, exploratory models are focused on exposing the relationship patterns that exist among multiple product categories. By contrast, explanatory models try to discover and measure inter-category choice effects caused by the company's marketing efforts (e.g.

measures of association

common preferences

more items

and brand choice or incidence and quantity), or all three.

 **Method Example References Explanation**

Association rules Agrawal & Srikant (1994), Chen et al. (2005)

Dickinson et al. (1992), Julander (1992)

Schnedlitz et al. (2001), Decker (2005)

Moon & Russell (2004)

Böcker (1978), Merkle (1981)

Russell & Petersen

(2003)

Logit models Hruschka et al. (1999),

Probit models Ainslie & Rossi

*Table 4: Approaches to Basket Analysis*

*(2007); illustration adapted*

(2000)

(1998), Manchanda et al.

(1999)

Breese et al. (1998), Mild & Reutterer

It is relatively clear why customers join and patronize a loyalty program. Predominantly, they join to receive different types of rewards, such as discounts, increased status, or increased service (see e.g. Smith & Sparks 2009). To make this decision, they weigh these benefits (more or less consciously) against the membership's disadvantages directly affecting them (e.g. usage of wallet space for the new loyalty card, privacy issues, etc.). The company does so in a similar, though (hopefully) more conscious and structured manner. Just like the consumer, the organization also tries to reach its respective goals by exploiting the characteristics (i.e. advantages) of customer loyalty programs. At the same time, the firm has to consider the disadvantages and dangers and make a trade-off with the expected positive effects. In this chapter, the goals the organization is commonly trying to achieve with its loyalty scheme will briefly be discussed by reviewing two established frameworks (found in the current section), upon which the commonly claimed positive effects (Chapter 3.5.1) as well as the negative effects and problems associated with loyalty schemes will be covered from the company perspective (Chapter 3.5.2).

As depicted in Figure 10, Butscher (2002) categorized the companies' goals into three hierarchical levels: core goals, primary goals, and secondary goals. Most importantly, the author argued, the company is interested in increasing its turnover, profit, or market share. This can be achieved in the medium to long run by realizing the goals from the lower hierarchical levels.

Butscher's rather self-explanatory model begs two comments: firstly, it is (as ever so often in these cases) unclear why these particular goals were chosen and arranged in this specific way. Why, for instance, is the increase of purchase frequency a secondary goal, while it is actually an *outcome* of customer loyalty (which is, however, placed at a higher hierarchical level)? At the same time, other secondary goals really are a target in their own right, as they are not linked to the fulfillment of a previous objective (e.g. the support of the company's public relations). Secondly, Butscher equalized the goal of customer loyalty with the aim of developing a relationship with the customer. It was established in Chapter 2, however, that the ability of customer loyalty schemes to establish true, possibly life-long relationships (as the author describes them) is rather unlikely. At best, an average retailing loyalty scheme can alter behavioral loyalty to a certain extent, manifesting itself in a change of purchase frequencies, basket sizes, share-of-wallet, etc.

*Figure 10: Loyalty Program Goals – Framework 1 Source: Butscher (2002); illustration adapted*

Another, slightly less straight-forward framework is that suggested by Diller (1997). As seen in Figure 11, the author has also applied some form of hierarchy to his version. Following Diller's line of thought, the customer loyalty program creates different types of effects via its rewards. Interestingly, three of the effects mentioned by the author (i.e. customer selection, knowledge, and interaction/integration) mean roughly the same thing. By signing up to the scheme, customer selection takes place in the sense that the members are now known by name and can be targeted individually. The company possesses information about areas such as personal data or shopping behavior, which it uses to improve interaction with the customer to betterintegrate himwith the organization. All these things, including improved image, then lead to the realization ofstrategic goalssuch as the identification of the customer with the company, increased commitment, satisfaction, and trust, as well as positive word-of-mouth. In the end, then, these effects impact turnover and protect the company from disloyal customers and competitive action (the security aspect), which in combination with the cost factor, results in improved profits and secures the organization overall.

*Figure 11: Loyalty Program Goals – Framework 2 Source: Diller (1997); illustration adapted*

It seems fair to say that, apart from the inclusion of costs, Diller's framework appears more homogeneous than Butscher's (2002). This is probably due to the fact that Diller (1997) refrained from including the actual drivers of turnover increase, such asthe aforementioned changesto purchase frequencies and the like. Moreover, it is appealing that the author made profitability the final stage in his model, anticipating the outcry of authors such as Reinartz & Kumar (2002) or Kumar & Shah (2004) who found the issue of profitability neglected in relation to loyalty schemes.

Certainly, different illustrations of this framework are possible, and despite some criticism, these two examples hopefully helped to provide an interesting introduction particularly to the following summary of positive effects, but also negative effects and issues commonly associated with loyalty programs.

#### **3.5.1 Benefits**

92

*Figure 10: Loyalty Program Goals – Framework 1 Source: Butscher (2002); illustration adapted*

ment of a previous objective (e.g. the support of the company's public relations). Secondly, Butscher equalized the goal of customer loyalty with the aim of developing a relationship with the customer. It was established in Chapter 2, however, that the ability of customer loyalty schemes to establish true, possibly life-long relationships (as the author describes them) is rather unlikely. At best, an average retailing loyalty scheme can alter behavioral loyalty to a certain extent, manifesting itself in a change of purchase frequencies, basket sizes, share-of-wallet, etc.

Another, slightly less straight-forward framework is that suggested by Diller (1997). As seen in Figure 11, the author has also applied some form of hierarchy to his version. Following Diller's line of thought, the customer loyalty program creates different types of effects via its rewards. Interestingly, three of the effects mentioned by the author (i.e. customer selection, knowledge, and interaction/integration) mean roughly the same thing. By signing up to the scheme, customer selection takes place in the sense that the members are now known by name and can be targeted individually. The company possesses information about areas such as personal data or shopping behavior, which it uses to improve interaction with the customer to betterintegrate himwith the organization. All these things, including improved image, then lead to the realization ofstrategic goalssuch as the identification of the customer with the company, increased commitment, satisfaction, and trust, as well as positive word-of-mouth. In the end, then, these effects impact turnover and protect the company from disloyal customers and

Several of the advantages have already been named, be it in relation to the value of data, the goals that companies try to achieve with loyalty schemes, or in other chapters of this paper. Those already cited benefits will be repeated and integrated into the following list. The aim of this section is to provide an extensive, though not necessarily exclusive laundry list of positive effects commonly attributed to loyalty programs, followed by a critical reflection, as some of these positive effects have been criticized and were shown not to apply in every setting (to name just one example, Frisou & Yildiz 2011 demonstrated how program effectiveness is dependent on consumer learning).

*Figure 12: Proclaimed Benefits of Loyalty Schemes*

*Source: Clayton-Smith 1996, Diller 1997, Hippner et al. 2001, Butscher 2002, Berman 2006, Kumar & Reinartz 2006, Humby et al. 2008, Mimouni-Chaabane & Volle 2010, Morrisson & Huppertz 2010*

As the relationships between these different points are not always clear, the list of generally claimed benefits in Figure 12 was presented in an unspecified order. The enumeration does, however, encompass a good part of the characteristics that are commonly claimed to be positive effects of loyalty schemes. Still, it should be noted that some of these effects have found themselves subject to criticism. In particular, the following associations with loyal customers have been increasingly called into question:

Going back to a series of publications by Frederick F. Reichheld (see e.g. Reichheld et al. 2000), director at the management consultancy Bain & Company, these claims have essentially been criticized for not being *universally* applicable. Building on a discussion set off by Dowling & Uncles (1997), today's best-known critics that need to be named in this regard are probably Reinartz & Kumar (2002). Studying company data of a US high-tech corporate service provider, a US mailorder company, a French food retailer, and a German direct brokerage house, the authors analyzed the shopping behavior, revenue streams, and profitability of over 16,000 individual and corporate customers over a period of four years.

Firstly, the authors found *no evidence for the claim that it costs less to serve loyal customers*. At best, they concluded, this link between loyalty and lower cost was industry-specific. The idea that gave rise to the claim for the existence of this link was that the initial customer acquisition cost could be distributed over a longer retention period in case of loyal customers. Furthermore, these customers were expected to incur a lower amount of service costs as they were more experienced with the product (and consequently with the trouble-shooting necessary). This, the idea went, would also cause customers to use cheaper communication channels, such as the internet instead of calls to the service center. Reinartz & Kumar found that in none of the four companies analyzed were longterm (i.e. loyal) customers cheaper to serve than short-term customers throughout the observed time period. For instance, experienced customers of the mailorder company who did actually use the internet instead of another more expensive channel to place their orders expected lower prices in that channel in return (thereby neutralizing any cost savings). In case of the high-tech corporate service provider, costs to serve loyal customers were even higher than those associated with short-term customers due to higher price pressure by the big and loyal corporate customers as well as increased costs for dedicated service teams.

Secondly, Reinartz & Kumar discovered that *loyal customers were not paying higher prices*. While it might sound reasonable that loyal customers are more likely to pay higher prices as they face higher switching costs or a switching barrier due to the associated uncertainty (e.g. as far as product or service quality is concerned), this was not found to be the case in practice. Loyal customers of the corporate service provider as well as the other three companies serving consumer markets were all demanding a tangible return for their loyalty or, in the best case, expected the same as short-term customers. In fact, long-term customers of the corporate service provider paid between 5-7% lower prices than short-

94

name just one example, Frisou & Yildiz 2011 demonstrated how program effec-

*Source: Clayton-Smith 1996, Diller 1997, Hippner et al. 2001, Butscher 2002, Berman 2006,*

As the relationships between these different points are not always clear, the list of generally claimed benefits in Figure 12 was presented in an unspecified order. The enumeration does, however, encompass a good part of the characteristics that are commonly claimed to be positive effects of loyalty schemes. Still, it should be noted that some of these effects have found themselves subject to criticism. In particular, the following associations with loyal customers have

*Kumar & Reinartz 2006, Humby et al. 2008, Mimouni-Chaabane & Volle 2010, Morris-*

tiveness is dependent on consumer learning).

*Figure 12: Proclaimed Benefits of Loyalty Schemes*

*son & Huppertz 2010*

been increasingly called into question:

term customers, with loyal customers of the mail-order company paying up to 9% less in a specific product category. As for the grocer and the brokerage house, the prices charged to each type of customer were equal. Possibly due to the loyal customers' better knowledge of the product and its value, the authors concluded that these consumers are actually more price-sensitive and not the other way around.

Thirdly, the authors highlighted that *measuring behavioral loyalty alone was not sufficient to judge the effect on positive word-of-mouth*. They did not necessarily prove the claim wrong that loyal customers speak more positively about the company, but pointed out that only behavioral loyalty combined with attitudinal loyalty is truly helpful in generating new customers. This conclusion was drawn from a separate study with a sample of customers from the French food retailer, where both the passive and active word-of-mouth behavior was tested with two questions. In this study, customers were first asked to recommend a particular food retailer (i.e. to test passive word-of-mouth) upon which they were asked whether they ever tell friends or family about positive experiences with the French grocer under review (i.e. to capture active word-of-mouth). In addition to that, their actual behavioral loyalty was looked up in the company database and finally, their attitudinal loyalty captured through a telephone survey. Interestingly, customers who exposed high levels of both attitudinal and behavioral loyalty were 54% more likely to engage in active and 33% more likely to participate in passive word-of-mouth behavior (for a more recent and detailed investigation of word-of-mouth activity among reward program participants see e.g. Ferguson & Hlavinka 2009).

As mentioned in the introduction to Reinartz & Kumar's (2002) elaborations, it is important to keep in mind that the truth of these claims is clearly dependent on the industry and the individual customer or customer segment. For instance, industries where loyal customers tend to pay more certainly do exist. An example would be a highly competitive market for mobile telephone services such as that in Austria. The different providers are constantly creating and advertising new, cheaper phone plans to attract new customers. Old customers, then, very soon find themselves paying for a more expensive phone plan than all the new customers have access to. Kumar & Reinartz (2006) later added that the case where customers become more profitable over time is when a contractual relationship is given, while it would not hold true in a non-contractual setting. In the case of the mobile phone services market, however, the contractual obligation is often not the only factor causing customers to stick to the more expensive phone plan. Next to being affected by the minimum period of contract duration (usually 18- 24 months in Austria), they often simply show too little involvement or fear uncertainties with regard to things like network coverage or service quality to switch to a new provider with better offers. Furthermore, providers in many countries often do not give customers the option to transfer their old phone number to a new provider (Austrian legislation has enabled this procedure since 2004, Germany since around two years earlier), which might pose an additional, significant exit barrier. Thus, even if they are not contractually bound, loyal customers in certain industries might very well find themselves paying more than new customers.

Apart from the industry, profitability also varies by the individual customer or customer segment. For instance, Reinartz & Kumar (2002) found that longterm customer segments who were only marginally profitable actually made up between 15% and 21% of the four analyzed companies' customers, while around the same percentage were highly profitable, though short-term customers. For that reason, the correlation between customer lifetime duration and profitability was only found to be weak to moderate.

It was already determined that it depends on the industry and the individual customer whether loyal patrons are cheaper to serve than short-term customers. Similarly, it is difficult to generalize in the case of price-sensitivity, although it is certainly less common that loyal customers can (and perhaps should) be exploited by the company in that regard. Eventually, the last point of criticism will be addressed. It was concluded in the end of Chapter 2 that despite an ultimate answer being outstanding, retailing loyalty programs, dependent on industry and program configuration, are generally not able to create or foster attitudinal loyalty. At least within a retail setting it seems safe to say, then, that the true power of positive word-of-mouth (which comes from a combination of both behavioral and attitudinal loyalty) cannot fully be capitalized on. In other words, the increase in positive word-of-mouth caused by loyalty schemes will be visible, but is not expected to be very big.

#### **3.5.2 Drawbacks**

96

other way around.

Hlavinka 2009).

term customers, with loyal customers of the mail-order company paying up to 9% less in a specific product category. As for the grocer and the brokerage house, the prices charged to each type of customer were equal. Possibly due to the loyal customers' better knowledge of the product and its value, the authors concluded that these consumers are actually more price-sensitive and not the

Thirdly, the authors highlighted that *measuring behavioral loyalty alone was not sufficient to judge the effect on positive word-of-mouth*. They did not necessarily prove the claim wrong that loyal customers speak more positively about the company, but pointed out that only behavioral loyalty combined with attitudinal loyalty is truly helpful in generating new customers. This conclusion was drawn from a separate study with a sample of customers from the French food retailer, where both the passive and active word-of-mouth behavior was tested with two questions. In this study, customers were first asked to recommend a particular food retailer (i.e. to test passive word-of-mouth) upon which they were asked whether they ever tell friends or family about positive experiences with the French grocer under review (i.e. to capture active word-of-mouth). In addition to that, their actual behavioral loyalty was looked up in the company database and finally, their attitudinal loyalty captured through a telephone survey. Interestingly, customers who exposed high levels of both attitudinal and behavioral loyalty were 54% more likely to engage in active and 33% more likely to participate in passive word-of-mouth behavior (for a more recent and detailed investigation of word-of-mouth activity among reward program participants see e.g. Ferguson &

As mentioned in the introduction to Reinartz & Kumar's (2002) elaborations, it is important to keep in mind that the truth of these claims is clearly dependent on the industry and the individual customer or customer segment. For instance, industries where loyal customers tend to pay more certainly do exist. An example would be a highly competitive market for mobile telephone services such as that in Austria. The different providers are constantly creating and advertising new, cheaper phone plans to attract new customers. Old customers, then, very soon find themselves paying for a more expensive phone plan than all the new customers have access to. Kumar & Reinartz (2006) later added that the case where customers become more profitable over time is when a contractual relationship is given, while it would not hold true in a non-contractual setting. In the case of the mobile phone services market, however, the contractual obligation is often not the only factor causing customers to stick to the more expensive phone plan. Next to being affected by the minimum period of contract duration (usually 18- 24 months in Austria), they often simply show too little involvement or fear uncertainties with regard to things like network coverage or service quality to

Next to the positive effects associated with loyalty schemes, there are, of course, also a range of alleged negative effects and general problems associated with such programs. The following enumeration in Figure 13, like that in the previous chapter, is not characterized by a specific order.

#### *Figure 13: Proclaimed Drawbacks of Loyalty Schemes*

*Source: Dowling & Uncles 1997, Passingham 1998, Wright & Sparks 1999, Parker & Worthington 2000, Dowling 2002, Berman 2006, Leenheer et al. 2007, Meyer-Waarden 2007, Humby et al. 2008, Lacey 2009, Cedrola & Memmo 2010*

These points will now be elaborated on one after another. Firstly, it was claimed by some authors that loyalty schemes merely *bribe customers*. For instance, Humby et al. (2008) reported that the chairman of the British grocery chain AS-DA was a renowned advocate of this view (the Wal-Mart subsidiary had abandoned the four-year pilot trial of its loyalty scheme around the turn of the century). The authors further highlighted that it was not uncommon to find a magazine comparing the rewards of different competing loyalty schemes almost like mobile phone plans, which contributes to the confirmation of this belief. Humby et al. did not go on to fundamentally refute this argument, but indicated that this does not mean that loyalty schemes were unable to generate loyalty in an emotional and not simply logical sense (such as the reaction towards bribery). It was determined previously that it is very difficult for loyalty schemes to generate attitudinal loyalty, implying that this point of criticism might essentially be correct. The question is, however, whether this is a bad thing. After all, any marketing action advertising special discounts or other types of promotion could be called bribery. The reward part, then, might well be considered another form of such bribery, without affecting the actual strengths of the loyalty scheme mentioned in the previous chapter.

Is it true that *customers prefer generally lower prices* over a loyalty scheme? This was the primary reason the aforementioned British grocer ASDA brought forward when abandoning its loyalty program pilots. Following an Every Day Low Pricing (EDLP) strategy, the company reasoned that the money would be better spent on a reduction of prices than on a loyalty scheme. Next to the overall strategy the company follows, other factors such as competitive actions or the characteristics of the industry will certainly affect this lower prices vs. loyalty program decision. Essentially, however, one factor will have the greatest influence: how cost-effective the loyalty program can be operated. Let us imagine a company facing two options: (1) to take 20 million EUR and invest the money in a loyalty scheme or (2) to decrease prices to an equal extent. Given that the loyalty scheme is profitable (i.e. it produces an increase in like-for-like sales which contribute to profit in excess of the costs of the program), the company has made a good investment. Similarly, if the decrease in prices leads to an increase in volume large enough to outbalance the turnover lost through this decrease, the company has made a good decision as well (other consequences of either move left aside). In the end, then, it will depend on the specific situation which one of these two options is the better one, or in other words, it cannot be generally stated that either one way is the right one (Tsao et al. 2010 take this one step further by discussing how a company should distribute its marketing budget between promotions and its loyalty program). Especially because while it might be true that all customers want low prices, only some ofthem shop exclusively on that basis(Humby et al. 2008). Furthermore, Humby et al. added, the loyalty scheme helped the company to minimize the cost of price cutting while maximizing competitive impact at the same time.

Parker & Worthington (2000) attested that the most common cause of failure is the ease with which *competitors can imitate the loyalty scheme* – something, that will inevitably happen to good programs in a competitive market. This would result in a return to the initial situation before the introduction of the first scheme, but with increased marketing costs for everyone (Meyer-Waarden 2007). Is the key, then, to have a differentiated program that cannot be easily copied? Would a sophisticated program characterized by high costs serve as an entry barrier for competition and conserve that program's competitive advantage? Let us consider Tesco's Clubcard, for instance. This loyalty scheme has helped to build up a considerable amount of know-how since its launch in 1995 (and the

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*Figure 13: Proclaimed Drawbacks of Loyalty Schemes*

*et al. 2008, Lacey 2009, Cedrola & Memmo 2010*

*Source: Dowling & Uncles 1997, Passingham 1998, Wright & Sparks 1999, Parker & Worthington*

These points will now be elaborated on one after another. Firstly, it was claimed by some authors that loyalty schemes merely *bribe customers*. For instance, Humby et al. (2008) reported that the chairman of the British grocery chain AS-DA was a renowned advocate of this view (the Wal-Mart subsidiary had abandoned the four-year pilot trial of its loyalty scheme around the turn of the century). The authors further highlighted that it was not uncommon to find a magazine comparing the rewards of different competing loyalty schemes almost like mobile phone plans, which contributes to the confirmation of this belief. Humby et al. did not go on to fundamentally refute this argument, but indicated that this does not mean that loyalty schemes were unable to generate loyalty in an emotional and not simply logical sense (such as the reaction towards bribery). It was determined previously that it is very difficult for loyalty schemes to generate attitudinal loyalty, implying that this point of criticism might essentially be cor-

*2000, Dowling 2002, Berman 2006, Leenheer et al. 2007, Meyer-Waarden 2007, Humby*

start of trials some time earlier), which undoubtedly makes it extremely difficult to copy. In fact, for precisely that reason the US grocery chain Kroger decideed to hire dunnhumby, the company that devised and still manages Tesco's loyalty scheme, when it attempted to set up its own program. This point of criticism consequently only applies to simple programs – the ones which are used as nothing more than a plain promotional tool. Similar to how any competitor can easily start sending out promotional leaflets or give 5% discount on all frozen pizzas to copy his competitor's move, it will be possible to imitate such a basic loyalty scheme. This is true, but no different with respect to any other promotional action.

The problem of handling and making sense of the *large amount of data* available to many companies, particularly in the retailing industry, has already been discussed in Chapter 3.4.1. This definitely is a challenging task, but one that many companies have successfully faced. In addition to that, the number of external service providers willing to assist companies in this endeavor is growing continuously.

"Food retailing loyalty schemes – and the Orwellian Millennium," Evans (1999) titled his article on the *privacy issue* associated with loyalty programs. Schemes that capture each customer's purchase history give the company access to detailed information about the shopping behavior of every person using his loyalty card. As many customers have a fear of this intrusion into their privacy, sometimes even coupled with an uncertainty about whether this data will be sold to a third party, this factor does play an important role for some customers in their decision to opt for a loyalty card. For example, using focus groups and semi-structured, qualitative interviews, Noble & Phillips (2004) discovered two different types of concern among the study participants: fraudulent use of the data (e.g. going as far as "identity theft" by someone inside or outside the company) or just simply the emergence of a "big brother is watching" feeling (see e.g. Sayre & Horne 2000 for further information on this topic). Apart from the extreme case of a misuse of data, the fact that companies are able to monitor behavior was in itself considered intrusive by some respondents. It needs to be kept in mind, however, that this matter is to a large extent strictly regulated by data protection laws, limiting the leeway given to companies. Still, organizations need to make sure that they communicate clearly to customers about this issue and adhere to their statements. Examples of problems include that of the US pharmacy chain CVS, where a lack of password protection enabled anyone with the membership number (which could, for example, be retrieved from old sales slips which the number was printed on), zip code and last name to have online access to that person's possibly sensitive purchase history. Another example was disclosed by Humby et al. (2008) in relation to Tesco's UK Clubcard. In this case, an upset woman complained about the company promoting condoms in the targeted mailing she had received, stating that this must be a mistake as neither she nor her husband had ever purchased such an item. In reality, the husband must have done so, of course, but the call center agent reportedly acted in a sensitive manner and blamed it on a defect of the computer system. In either case, at least at a theoretical level, the potential for intrusion and infringement of privacy or possibly even manipulation is considerable, Rowley (2004) highlighted. Even though some of the advice may be taking things a little too far, a set of guidelines for companies to consider can be found in a publication by the US Food Marketing Institute (FMI n.d.).

Another problem that companies are facing is that of *measuring the effectiveness* of their loyalty scheme. Stating that is was impossible following the sales bump caused by the launch of the program is not entirely correct. Given that the scheme captures transaction data, the company can at least track things like purchase frequency or turnover development on a customer level, and also, new promotional activities associated with the program can be analyzed without much difficulty. Ziliani (2005), for instance, offered an interesting way to measure the effectiveness of promotions with the help of a loyalty scheme. At the same time, it is naturally difficult to separate the contribution of the loyalty program to the development of an indicator of success (e.g. turnover) from that of other interfering variables. A possible solution would be a comparison with a control group of non-members, which is tracked from program launch onwards. As Butscher (2002) suggested, this comparison would ideally be based on both quantitative and qualitative indicators which were selected to best judge the achievement of one or a set of previously determined goals the company was aiming to achieve with the help of its loyalty program. At the same time, it is important not to forget to take other positive effects into account that are not captured by these indicators.

A voucher worth 2% on annual sales of 500 USD might not be deemed worth much in absolute terms, particularly not if compared with the 25% to 40% discounts which are not uncommon in retailing promotions, Cigliano et al. (2000b) noted. This might sound logical, but nevertheless, a large number of retailers successfully runs a loyalty scheme offering even less *reward value* than this 2%. Despite these low percentages, certain customers still seem to respond to them – particularly, but not only, if the industry competitors do not offer their own scheme. It needs to be added, however, that focusing exclusively on monetary rewards is not necessarily a good thing (see Chapter 3.3.8 for a discussion of reward types and the rate of reward). Berman (2006), for instance, called a focus on financial rewards a potential pitfall of loyalty schemes. This is even more important as customers might perceive non-financial rewardsto be of much greater value than they actually are expressed in monetary terms (i.e. stated as the under-

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continuously.

start of trials some time earlier), which undoubtedly makes it extremely difficult to copy. In fact, for precisely that reason the US grocery chain Kroger decideed to hire dunnhumby, the company that devised and still manages Tesco's loyalty scheme, when it attempted to set up its own program. This point of criticism consequently only applies to simple programs – the ones which are used as nothing more than a plain promotional tool. Similar to how any competitor can easily start sending out promotional leaflets or give 5% discount on all frozen pizzas to copy his competitor's move, it will be possible to imitate such a basic loyalty scheme.

This is true, but no different with respect to any other promotional action.

The problem of handling and making sense of the *large amount of data* available to many companies, particularly in the retailing industry, has already been discussed in Chapter 3.4.1. This definitely is a challenging task, but one that many companies have successfully faced. In addition to that, the number of external service providers willing to assist companies in this endeavor is growing

"Food retailing loyalty schemes – and the Orwellian Millennium," Evans (1999) titled his article on the *privacy issue* associated with loyalty programs. Schemes that capture each customer's purchase history give the company access to detailed information about the shopping behavior of every person using his loyalty card. As many customers have a fear of this intrusion into their privacy, sometimes even coupled with an uncertainty about whether this data will be sold to a third party, this factor does play an important role for some customers in their decision to opt for a loyalty card. For example, using focus groups and semi-structured, qualitative interviews, Noble & Phillips (2004) discovered two different types of concern among the study participants: fraudulent use of the data (e.g. going as far as "identity theft" by someone inside or outside the company) or just simply the emergence of a "big brother is watching" feeling (see e.g. Sayre & Horne 2000 for further information on this topic). Apart from the extreme case of a misuse of data, the fact that companies are able to monitor behavior was in itself considered intrusive by some respondents. It needs to be kept in mind, however, that this matter is to a large extent strictly regulated by data protection laws, limiting the leeway given to companies. Still, organizations need to make sure that they communicate clearly to customers about this issue and adhere to their statements. Examples of problems include that of the US pharmacy chain CVS, where a lack of password protection enabled anyone with the membership number (which could, for example, be retrieved from old sales slips which the number was printed on), zip code and last name to have online access to that person's possibly sensitive purchase history. Another example was disclosed by Humby et al. (2008) in relation to Tesco's UK Clubcard. In this case, an upset woman complained about the company promoting condoms in the lying cost to the company). All in all, it can be said that numerous loyalty programs even with such low reward values have in fact been successful in practice.

It is true that loyalty schemes are *not particularly easy to cancel* or cut back on, as customers seem to perceive the receipt of rewards almost as their given right a while after the program is launched. Thus, apart from the sunk cost associated with the scheme, the company has to cope with the reaction of the customers when abandoning their program. Still, many companies have "successfully shut down" their programs. Consequently, it is certainly a point that managers will need to consider before committing to a loyalty scheme, but at the same time it is not one that cannot be overcome if the program really needs to be shut down at some point in time.

Some critics argue that *it takes far too long for customers to achieve a truly desirable reward* in most loyalty schemes where a specific number of points or amount of turnover needs to be reached in order to redeem that reward. Companies naturally are limited in the degree of reward value they can hand out to each customer. It is obvious that even if, for instance, a consumer pools all his fuel expenses at a particular retailer (e.g. 1,500 EUR per year, arising from an average distance of 18,750 km [11,650 miles] traveled with a vehicle consuming 8 liters per 100 km [29.4 miles per gallon] and a theoretical fuel price of around 1 EUR per liter [3.8 EUR per gallon]), he will not be receiving a holiday weekend in Paris as a reward every year. To illustrate the time it takes to achieve a reward worth 50 EUR, the strategy consultancy Roland Berger (2003) compared four German reward programs (Lufthansa's Miles & More scheme, the coalition program Payback, the then still active stand-alone scheme of the fuel retailer Aral, and the loyalty program of the shoe retailer Görtz), based upon average usage frequencies and sales. Achieving that 50 EUR reward took Lufthansa frequent flyers 6.5 months, Payback users 11 months, Aral customers 12.5 months, and Görtz patrons a whopping 6.5 years. In a way, this discussion is linked to the choice of reward type and reward rate. As was previously established, companies have a wide range of options when it comes to rewards and should strive to optimize impact. Practice has shown that customers also find low-value rewards desirable and these do not always have to be monetary either. As far as rewards resembling a bigger value are concerned, multi-partner schemes can serve as a way out. In Roland Berger's study, for example, the two programs with the shortest time to reward redemption were coalition schemes (Lufthansa's Miles & More and the German Payback program), allowing customers to collect points at a range of partners, leading to a quicker accumulation of the reward currency.

"I have accumulated nine 'loyalty' cards from various stores and supermarkets. Does this make me more loyal, or less," asked a reader in a letter to the UK newspaper The Times cited by Wright & Sparks (1999, p. 429). Indeed, many *customers, even within the same industry, are members of multiple loyalty programs* (Dowling & Uncles 1997, Passingham 1998, Bellizzi & Bristol 2004, Liu & Yang 2009). To name just one example, Haeberle (2004) quoted a study by Forrester Research, according to which 54% of grocery shoppers were members of two competing loyalty schemes, 15% had joined three different programs, and 4% held membership cards of four or five food retailers. Altogether, that leaves only 27% of respondents who were members of only one program. Multiple card-ownership certainly does not have a positive influence on the effectiveness of the individualretailer'sloyalty program. Forinstance, Mägi (2003) discovered that having a competing loyalty card had a negative impact on share-of-wallet and Meyer-Waarden (2007) found that multiple memberships of geographically close retailers lead to a reduction of lifetime duration. This problem has already been touched in Chapter 2.3.3, with the naturally given presence of polygamous loyalty through variety seeking customers or the "just-in-case scenario" (i.e. customers possess a competing loyalty card to take advantage of that program just in case they are once in a while unable to patronize their preferred company) named as possible explanations. Nevertheless, it can never be excluded that certain customers are members of competing schemes only to engage in cherry picking. This is a disadvantage that just needs to be accepted, but one whose effects can at least be mitigated by differentiating the program from competitive offerings and applying a more effective reward structure. In addition to that, a first mover advantage such as that often quoted in relation to the introduction of new products or the entry of new geographical markets might exist as well. Despite the lack of empirical proof for this effect in relation to the introduction of loyalty schemes, this scenario is not unlikely if a reward structure creating a barrier of exit is present. Once the customer has advanced to a higher tier of the program or is half way into the collection of points for a desired reward, it is less likely that the customer significantly redistributes his share-of-wallet.

The claim that *programs are often similarly structured and consequently ineffective in a competitive setting* might apply to certain industries in particular countries, but cannot be considered generally true. Many companies, such as the ever so often named best practice case Tesco, have indeed managed to differentiate themselves from competition and created a competitive advantage. The following passage stems from a research report issued by the investment bank JP Morgan Cazenove on August 31, 2005 (quoted by Humby et al. 2008, p. 271): "contrary to popular belief, Tesco's most significant competitive advantage in the UK is not its scale. We believe Clubcard, which conveys an array of material benefits across virtually every discipline of its business, is Tesco's most potent weapon in the ongoing battle for market share." The wide range of options avail-

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of the reward currency.

some point in time.

lying cost to the company). All in all, it can be said that numerous loyalty programs even with such low reward values have in fact been successful in practice. It is true that loyalty schemes are *not particularly easy to cancel* or cut back on, as customers seem to perceive the receipt of rewards almost as their given right a while after the program is launched. Thus, apart from the sunk cost associated with the scheme, the company has to cope with the reaction of the customers when abandoning their program. Still, many companies have "successfully shut down" their programs. Consequently, it is certainly a point that managers will need to consider before committing to a loyalty scheme, but at the same time it is not one that cannot be overcome if the program really needs to be shut down at

Some critics argue that *it takes far too long for customers to achieve a truly desirable reward* in most loyalty schemes where a specific number of points or amount of turnover needs to be reached in order to redeem that reward. Companies naturally are limited in the degree of reward value they can hand out to each customer. It is obvious that even if, for instance, a consumer pools all his fuel expenses at a particular retailer (e.g. 1,500 EUR per year, arising from an average distance of 18,750 km [11,650 miles] traveled with a vehicle consuming 8 liters per 100 km [29.4 miles per gallon] and a theoretical fuel price of around 1 EUR per liter [3.8 EUR per gallon]), he will not be receiving a holiday weekend in Paris as a reward every year. To illustrate the time it takes to achieve a reward worth 50 EUR, the strategy consultancy Roland Berger (2003) compared four German reward programs (Lufthansa's Miles & More scheme, the coalition program Payback, the then still active stand-alone scheme of the fuel retailer Aral, and the loyalty program of the shoe retailer Görtz), based upon average usage frequencies and sales. Achieving that 50 EUR reward took Lufthansa frequent flyers 6.5 months, Payback users 11 months, Aral customers 12.5 months, and Görtz patrons a whopping 6.5 years. In a way, this discussion is linked to the choice of reward type and reward rate. As was previously established, companies have a wide range of options when it comes to rewards and should strive to optimize impact. Practice has shown that customers also find low-value rewards desirable and these do not always have to be monetary either. As far as rewards resembling a bigger value are concerned, multi-partner schemes can serve as a way out. In Roland Berger's study, for example, the two programs with the shortest time to reward redemption were coalition schemes (Lufthansa's Miles & More and the German Payback program), allowing customers to collect points at a range of partners, leading to a quicker accumulation

"I have accumulated nine 'loyalty' cards from various stores and supermarkets. Does this make me more loyal, or less," asked a reader in a letter to the UK able to structure a loyalty program enables every company to optimally match such to its own, the given industry's, and the customers' characteristics and demands. Particularly if the organization has a lead on its competitors in doing so, it will be less likely to end up ineffective in the competitive arena.

Another point of concern is that *customers' wallet space is limited*, while the number of loyalty cards available to stick into these wallets seems to be just the opposite. This is definitely an, interestingly often underestimated problem that loyalty programs face. There are, however, various ways around this issue. For instance, some companies optionally offer key fobs (e.g. UK's grocery retailer Tesco), others provide stickers with the bar code to be stuck on whatever the customer wants (e.g. the US textile retailer American Eagle Outfitters), while again others enable the loyalty program to be stored on the chip of any national bank card (e.g. the Austrian grocery chains Billa and Merkur). Alternatively, companies often allow for the customer to be looked up in the firm's database, making it unnecessary to bring the card along, but increasing the time (and thus the cost) of the payment or service process for the company. A final example is that where organizations have arranged for the customer service centers to issue a temporary card with a validity of, say, one day, in case the customer has forgotten the original (e.g. the Austrian department store Kastner & Öhler). All in all, it can be seen that this is certainly an issue that can be resolved with an according portion of creativity. Furthermore, as will be discussed in Chapter 4.2.1, coalition schemes can also serve as a mean to reduce the number of loyalty cards in customers' wallets.

As far as the problem of *rewarding already loyal customers* who would have continued to patronize the company even without the loyalty program is concerned, the critics seem to have a point. Interestingly, with very few exceptions such as Leenheer et al. (2007), authors who attempted to empirically measure the effectiveness of loyalty schemes have not taken the effect of such self-selecting members into account. Customers who are already heavy spending and loyal customers of the organizations, critics argue, are the ones who would derive the greatest benefit of a loyalty program membership. Consequently, these are the ones who are most likely to sign up for the scheme. In an analysis of seven loyalty programs of Dutch food retailers, Leenheer et al. discovered a small, yet positive influence of the loyalty scheme on share-of-wallet and added that this effect would have been seven times aslarge had they not accounted forthis endogeneity in program membership. Nevertheless, the overall impact of the program remained positive. In light of this problem, simple, promotion-like loyalty schemes do face the same difficulty as any other form of untargeted promotion would encounter. Sophisticated loyalty schemes, however, can compensate for this in that they allow the company to develop each customer individually – even the ones who were already considered loyal. Be it that they are successfully influenced by a crossselling incentive or that they improve the coupon redemption rate among loyal customers: the concern regarding self-selecting members is not always justified.

All in all, it can be said that most of these presumed problems or negative effects of loyalty schemes will not always be applicable. In fact, it will depend on the specific internal and external factors acting on the company whether these downsides have to be perceived as an issue in the first place. Particularly sophisticated programs can provide significant benefits to an organization and it will be up to its leaders to judge whether these outweigh expected costs and other disadvantages. Furthermore, if these problems really are encountered, there will be numerous ways to approach them available to the organization.

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in customers' wallets.

able to structure a loyalty program enables every company to optimally match such to its own, the given industry's, and the customers' characteristics and demands. Particularly if the organization has a lead on its competitors in doing so,

Another point of concern is that *customers' wallet space is limited*, while the number of loyalty cards available to stick into these wallets seems to be just the opposite. This is definitely an, interestingly often underestimated problem that loyalty programs face. There are, however, various ways around this issue. For instance, some companies optionally offer key fobs (e.g. UK's grocery retailer Tesco), others provide stickers with the bar code to be stuck on whatever the customer wants (e.g. the US textile retailer American Eagle Outfitters), while again others enable the loyalty program to be stored on the chip of any national bank card (e.g. the Austrian grocery chains Billa and Merkur). Alternatively, companies often allow for the customer to be looked up in the firm's database, making it unnecessary to bring the card along, but increasing the time (and thus the cost) of the payment or service process for the company. A final example is that where organizations have arranged for the customer service centers to issue a temporary card with a validity of, say, one day, in case the customer has forgotten the original (e.g. the Austrian department store Kastner & Öhler). All in all, it can be seen that this is certainly an issue that can be resolved with an according portion of creativity. Furthermore, as will be discussed in Chapter 4.2.1, coalition schemes can also serve as a mean to reduce the number of loyalty cards

As far as the problem of *rewarding already loyal customers* who would have continued to patronize the company even without the loyalty program is concerned, the critics seem to have a point. Interestingly, with very few exceptions such as Leenheer et al. (2007), authors who attempted to empirically measure the effectiveness of loyalty schemes have not taken the effect of such self-selecting members into account. Customers who are already heavy spending and loyal customers of the organizations, critics argue, are the ones who would derive the greatest benefit of a loyalty program membership. Consequently, these are the ones who are most likely to sign up for the scheme. In an analysis of seven loyalty programs of Dutch food retailers, Leenheer et al. discovered a small, yet positive influence of the loyalty scheme on share-of-wallet and added that this effect would have been seven times aslarge had they not accounted forthis endogeneity in program membership. Nevertheless, the overall impact of the program remained positive. In light of this problem, simple, promotion-like loyalty schemes do face the same difficulty as any other form of untargeted promotion would encounter. Sophisticated loyalty schemes, however, can compensate for this in that they allow the company to develop each customer individually – even the ones who were al-

it will be less likely to end up ineffective in the competitive arena.

# **4. Coalition Schemes**

"Coalitions represent both the natural evolution and the future of loyalty marketing programs, both within the USA and abroad" (Capizzi & Ferguson 2005, p. 76).

"The efficiencies inherent in coalition loyalty models […] make coalitions the natural endgame for loyalty evolution" (Ferguson & Hlavinka 2006, p. 297).

"According to Frequency Marketing Inc., there are 3 trends to watch for in card marketing in the new millennium: [...], and 3. coalition programs" (Barlow 1999, p. 1).

"The 22 major factors that will shape the future of customer loyalty: [...] 3. An explosion of loyalty coalitions and networks" (Clark 2006, p. 1 f.).

Weighing the advantages that characterize coalition schemes against their disadvantages, several practitioners and academics have proclaimed that multi-partner programs are an ongoing trend, and indeed, the next evolutionary step of loyalty schemes. Loyalty coalitions are, as compared to stand-alone programs, a relatively recent development. While the latter have existed at least since 1896 with the origination of the S&H Green Stamps (S&H 2009), it is possibly the founding of Air Miles in the United Kingdom in 1988 that marked the birth of coalition schemes operating on a grand scale (Air Miles 2009). Since then, several other programs have been introduced in different parts of the world, ranging from the United States and the United Kingdom to South Africa and South Korea. Still, several countries well penetrated with stand-alone programs and other regional or otherwise differentiated schemes (e.g. the US) have yet to witness the development of a strong nation-wide coalition.

This chapter will offer an overview of the differentscheme types(Chapter 4.1), advantages and disadvantages associated with coalitions (Chapter 4.2), notable success factors relating to these programs (Chapter 4.3), as well as studies on impact, spread and customer perceptions (Chapter 4.4). Finally, Chapter 4.5 will bridge the way to the empirical section of this paper, in which further questions concerning these schemes will be elaborated on.

### **4.1 Scheme Types**

As far as a possible classification of coalition schemes is concerned, different approaches can be taken. One option would be the categorization into sectorexclusive schemes on the one hand and non-sector-exclusive programs on the other hand. Another possibility would be to differentiate by geographical spread (e.g. international vs. national vs. regional), possibly supplemented by an indicator of size to capture the "true presence" of the scheme. Furthermore, one could classify schemes by reward type offered (e.g. UK's Air Miles focusing on flight rewards only vs. UK's Nectar offering a whole range of rewards), by the underlying goal of the scheme (e.g. Nectar offering benefits only to consumers, while the regional SmartTown Alliance in the USA is aimed at benefiting both the consumer and a non-profit organization of choice), or by whether the scheme is internet-based or a "regular" program. Just as well, however, existing programs could be grouped by industry (e.g. retailing, airlines, etc.).

Among the numerous forms of coalition schemes, the following two subchapters will be centered on the most common variations: multi-partner programs focused on retailing and airline alliances. Finally, a brief overview of other special types of coalitions will be given in the third sub-chapter.

#### **4.1.1 Retail-Oriented Coalitions**

For obvious reasons, the number of national coalition programs a market can accommodate is a lot smaller than that of stand-alone programs. Possibly because of the ability of retailing partners to generate the volume required for the customer to maintain interest in the scheme, retail-oriented, typically sectorexclusive coalitions represent a good proportion of the already limited number of coalitions in existence. At least for strong, national programs, it is a plain necessity to get one of the biggest retailers of each of the most important sub-sectors on board. In particular a big grocery retailer will be necessary to guarantee these short purchase frequencies that keep the customer involved with the program.

It needs to be added, however, that these coalitions do not rely exclusively on retail partners. In fact, next to being able to collect points at, say, a grocery, fuel, clothing, and book retailer, customers can often beef up their point balance by generating turnover at a bank, insurance company, travel agency, car rental company, or electricity provider – to name just a few examples. The partner mix is obviously different in every scheme, but as a general rule, it seems fair to say that despite including partners from other industries, these schemes remain retailoriented. Table 5 shows a sample list of a number of more or less well known programs falling into that group.

*Table 5: Retail-Oriented Coalition Schemes*

**4.1 Scheme Types**

As far as a possible classification of coalition schemes is concerned, different approaches can be taken. One option would be the categorization into sectorexclusive schemes on the one hand and non-sector-exclusive programs on the other hand. Another possibility would be to differentiate by geographical spread (e.g. international vs. national vs. regional), possibly supplemented by an indicator of size to capture the "true presence" of the scheme. Furthermore, one could classify schemes by reward type offered (e.g. UK's Air Miles focusing on flight rewards only vs. UK's Nectar offering a whole range of rewards), by the underlying goal of the scheme (e.g. Nectar offering benefits only to consumers, while the regional SmartTown Alliance in the USA is aimed at benefiting both the consumer and a non-profit organization of choice), or by whether the scheme is internet-based or a "regular" program. Just as well, however, existing programs

Among the numerous forms of coalition schemes, the following two subchapters will be centered on the most common variations: multi-partner programs focused on retailing and airline alliances. Finally, a brief overview of

For obvious reasons, the number of national coalition programs a market can accommodate is a lot smaller than that of stand-alone programs. Possibly because of the ability of retailing partners to generate the volume required for the customer to maintain interest in the scheme, retail-oriented, typically sectorexclusive coalitions represent a good proportion of the already limited number of coalitions in existence. At least for strong, national programs, it is a plain necessity to get one of the biggest retailers of each of the most important sub-sectors on board. In particular a big grocery retailer will be necessary to guarantee these short purchase frequencies that keep the customer involved with the program. It needs to be added, however, that these coalitions do not rely exclusively on retail partners. In fact, next to being able to collect points at, say, a grocery, fuel, clothing, and book retailer, customers can often beef up their point balance by generating turnover at a bank, insurance company, travel agency, car rental company, or electricity provider – to name just a few examples. The partner mix is obviously different in every scheme, but as a general rule, it seems fair to say that despite including partners from other industries, these schemes remain retailoriented. Table 5 shows a sample list of a number of more or less well known

could be grouped by industry (e.g. retailing, airlines, etc.).

**4.1.1 Retail-Oriented Coalitions**

programs falling into that group.

other special types of coalitions will be given in the third sub-chapter.

It needs to be kept in mind that this list is by no means exhaustive. In particular the group of regional schemes is, for obvious reasons, just a brief selection. Nevertheless, this enumeration hopefully provides a good pool of examples to examine in further detail if interest has been sparked. Some of them are more dominant in terms of size, while others were created on a smaller scale. Especially the United States is an example of a loyalty card market where no truly big, national scheme managed to strike root. S&H greenpoints and the Thank You program are mentioned as two examples of regional brands, which despite boasting a notable presence (in contrast to the Powercard in the US, for example), have not reached a significant nation-wide presence comparable to, say, the Nectar scheme in the UK.

Asfar asthe categorization in Table 5 is concerned,foursub-categories ofretailoriented coalitions emanated: (1) "regular" schemes, making up the majority of this coalition type, (2) programs with a focus on travel-related rewards (i.e. a slightly different form of "regular" schemes), (3) regional programs with limited geographical reach and/or only small program sponsors, and (4) internet schemes focused exclusively on points collection with online retailers. In particular the second group might require some further explanation. The idiosyncrasy of this kind of coalition is probably best explained with an example. Air Miles, a type of franchise present in various countries of the world, was wholly-owned ever since soon after its launch in the UK in 1988 by British Airways (British Airways 2008). As discussed in the fourth sub-section of Chapter 3.3.8, airlines have a clear cost advantage over regular retail-oriented schemes, as they can simply fill their otherwise empty seats with passengers travelling on redeemed miles. The additional variable cost incurred by such a passenger is likely to stand in no relation to the value of the reward as perceived by the customer. Naturally, this constellation only works if the airline owns the program, because otherwise the frequent flyer miles need to be purchased from the airlines – eating up at least part of the cost savings. On the downside, the target group of potential customers will be smaller with such travel-reward-oriented schemes, as not everyone is attracted by this type of reward. In addition to that, a wider choice of rewards is preferred by all customers – even by those who like to travel.

#### **4.1.2 Airline Coalitions**

Airline coalitions are, as opposed to most retail-oriented programs, not sector exclusive. While several small alliances exist as well, three coalitions dominate the airline industry (see Table 6). Of course, these alliances comprise much more than just loyalty programs which allow customers to collect and redeem miles with all the different member airlines. An extended network through code sharing agreements or cost advantages through common booking systems or sales offices are a few notable examples.

Still, the loyalty scheme is a significant part of these alliances, with the distinctive feature of these coalitions being that most of these alliance members operate their own, branded loyalty scheme. At the same time, the alliance is advertised to certain degree. This naturally makes for a few differences in terms of the advantages and disadvantages commonly associated with retail-oriented loyalty coalitions. The airlines Austrian and Lufthansa are two of the rare exceptions in the industry which use the same loyalty scheme brand (i.e. Miles & More). Since September 2009, Austrian has also been owned by the German Lufthansa, but the decision to adopt the same loyalty scheme in addition to being in the same alliance was implemented long before. It can thus be seen that different alliance governance structures are thinkable (see e.g. Gudmundsson et al. 2002 for further information on this topic).

1 As of February 2011

110

ing a notable presence (in contrast to the Powercard in the US, for example), have not reached a significant nation-wide presence comparable to, say, the Nec-

Asfar asthe categorization in Table 5 is concerned,foursub-categories ofretailoriented coalitions emanated: (1) "regular" schemes, making up the majority of this coalition type, (2) programs with a focus on travel-related rewards (i.e. a slightly different form of "regular" schemes), (3) regional programs with limited geographical reach and/or only small program sponsors, and (4) internet schemes focused exclusively on points collection with online retailers. In particular the second group might require some further explanation. The idiosyncrasy of this kind of coalition is probably best explained with an example. Air Miles, a type of franchise present in various countries of the world, was wholly-owned ever since soon after its launch in the UK in 1988 by British Airways (British Airways 2008). As discussed in the fourth sub-section of Chapter 3.3.8, airlines have a clear cost advantage over regular retail-oriented schemes, as they can simply fill their otherwise empty seats with passengers travelling on redeemed miles. The additional variable cost incurred by such a passenger is likely to stand in no relation to the value of the reward as perceived by the customer. Naturally, this constellation only works if the airline owns the program, because otherwise the frequent flyer miles need to be purchased from the airlines – eating up at least part of the cost savings. On the downside, the target group of potential customers will be smaller with such travel-reward-oriented schemes, as not everyone is attracted by this type of reward. In addition to that, a wider choice of rewards is preferred by all

Airline coalitions are, as opposed to most retail-oriented programs, not sector exclusive. While several small alliances exist as well, three coalitions dominate the airline industry (see Table 6). Of course, these alliances comprise much more than just loyalty programs which allow customers to collect and redeem miles with all the different member airlines. An extended network through code sharing agreements or cost advantages through common booking systems or sales offices

Still, the loyalty scheme is a significant part of these alliances, with the distinctive feature of these coalitions being that most of these alliance members operate their own, branded loyalty scheme. At the same time, the alliance is advertised to certain degree. This naturally makes for a few differences in terms of the advantages and disadvantages commonly associated with retail-oriented

tar scheme in the UK.

customers – even by those who like to travel.

**4.1.2 Airline Coalitions**

are a few notable examples.

*Table 6: Airline Coalitions Source: Company Websites*

### **4.1.3 Other Variations**

While no large retail-based scheme has gained a foothold in the US to date, other smaller, creative programs managed to do so. Generally, these schemes are characterized by a set up similar to that of their larger archetypes. Customers receive a discount on their turnover or collect some form of points at a range of retailers, restaurants, financial service providers, and other partner companies. What is special about these programs is, however, what these savings are used for. In case of college saving schemes, any discount or accumulated points will be used to pay for college tuition. BabyMint, for instance, offers members one dollar off tuition expenses for every reward dollar earned at around 175 colleges and universities across the US. Similar to BabyMint, Upromise also allows for a transfer of accumulated rewards to a college saving plan of the customer's choice (these so-called "529 college savings plans" are tax-free in the US and can be opened at a range of financial service providers).

Another interesting variation is that of schemes supporting non-profit organizations. In case of the SmartTown Alliance, for example, customers can choose a non-profit organization they want to support from a list provided. Any savings made with participating program sponsors are then split between the customer and the selected non-profit organization. Lastly, Stockback is an example of a program putting these rewards savings into an investment plan, while the Canadian Futura Rewards scheme represents a coalition allowing the customer to choose between several of these options. In this case, program members can use the rewards for education savings, retirement savings, charity, or cash payouts.

Somehow one is tempted to ask whether these programs are not simply regular retail-oriented schemes with a different range of reward options. In a way they are, but at the same time their different range of rewards goes hand in hand with such a fundamentally different positioning of the program, that it seems justified to set them apart in a special category.

*Table 7: Other Coalition Types*

# **4.2 Differences to Stand-Alone Programs**

#### **4.2.1 Advantages**

Coalition schemes offer several distinct advantages over their single-sponsor counterpart to both companies and consumers. Given the lack of literature on this topic, Clark & Clark (2009) have provided a useful basis, which has been restructured and further supplemented:

#### **Lower cost:**

112

College Saving Schemes

> Schemes Benefiting Non-profits

Variable Schemes and Others

*Table 7: Other Coalition Types*

special about these programs is, however, what these savings are used for. In case of college saving schemes, any discount or accumulated points will be used to pay for college tuition. BabyMint, for instance, offers members one dollar off tuition expenses for every reward dollar earned at around 175 colleges and universities across the US. Similar to BabyMint, Upromise also allows for a transfer of accumulated rewards to a college saving plan of the customer's choice (these so-called "529 college savings plans" are tax-free in the US and can be opened at

Another interesting variation is that of schemes supporting non-profit organizations. In case of the SmartTown Alliance, for example, customers can choose a non-profit organization they want to support from a list provided. Any savings made with participating program sponsors are then split between the customer and the selected non-profit organization. Lastly, Stockback is an example of a program putting these rewards savings into an investment plan, while the Canadian Futura Rewards scheme represents a coalition allowing the customer to choose between several of these options. In this case, program members can use the rewards for education savings, retirement savings, charity, or cash payouts. Somehow one is tempted to ask whether these programs are not simply regular retail-oriented schemes with a different range of reward options. In a way they are, but at the same time their different range of rewards goes hand in hand with such a fundamentally different positioning of the program, that it seems

> BabyMint USA www.babymint.com Upromise USA www.upromise.com

Rewards USA www.rainbowrewards.com

Alliance USA www.smarttownalliance.com

Futura Rewards Canada www.futurarewards.ca Stockback USA www.stockback.com

a range of financial service providers).

justified to set them apart in a special category.

Rainbow

SmartTown

**Category Program Geographical Presence Website**

### **Card more likely to be carried:**

#### **Better and faster rewards for the customer:**

#### **Improved pool of data:**

#### **Ability to increase the customer base of every program sponsor:**

### **Higher penetration rate:**

Coalitions can often boast a quick achievement of a high customer penetration rate through broad appeal, a generally higher media presence, as well as the other advantages benefiting customers. For example, Clark & Clark highlighted that UK's Nectar had signed up more than 11 million members within 12 months and 13 million within 18 months following its launch (which corresponds to a penetration rate of around 21%, given a *total* population of roughly 61 million people). By comparison, Germany's Payback (2001) reported 10 million active cards 16 months after launch (equating to a 12% penetration rate based on a *total* population of around 82 million). Apart from coalitions, such high numbers can really only be achieved by very large and long-standing stand-alone programs.

#### **Generation of appeal for a wider range of companies:**

 Particularly firms offering products characterized by a low purchase frequency might be more successful participating in a coalition program than operating a stand-alone scheme. While it might become a problem that customers will not maintain interest in a stand-alone scheme, this issue would likely be mitigated in the case of a coalition. In addition to that, Clark & Clark (2009) noted, customers used to collecting low point volumes often look forward to scoring a big chunk of points at once.

### **Firm-related benefits through industry exclusivity:**

Another point that makes coalitions appealing to organizations is the possibility of keeping the program sector-exclusive. This practice is employed by most retailing coalition schemes, giving the first firm to enter exclusivity within its sub-sector. Once the program reaches a high level of penetration, this could even act as a source of competitive advantage.

### **Greater simplicity for the customer:**

114

**Better and faster rewards for the customer:**

one big reward for the customer.

several partners of a coalition scheme are patronized.

protection to the firms from competitive moves.

stand-alone scheme, it is simply less likely to be the case.

**Improved pool of data:**

Due the fact that only one account needs to be tracked and taken care of, complexity for the customer is reduced to some extent.

### **4.2.2 Disadvantages**

Coalition schemes boast several advantages, but this does not mean that they are free from disadvantages. Naturally, there are downsides associated with these schemes as well. The following list aims to cover the most important ones (Humby et al. 2008, Clark & Clark 2009):

#### **Issues regarding ownership of data:**

Similar to consumers, partner companies of coalition schemes might also be concerned by privacy issues. In this case, the concern revolves around company-related data being handled by a third party. Adding up to this is the fact that each company's data is pooled together with that of other companies. Within every loyalty coalition, there will be contracts dealing with the topic of data ownership. Clark & Clark reported that for retailers, article-level basket data is typically both held and owned by the partner companies. Therefore, coalition operators would only hold customer information and data on the number of points collected by location and date. In practice, however, it differs from case to case whether coalitions really do not have access to detailed purchase histories, particularly to undertake valuable inter-industry customer segmentations based upon buying behavior. At any rate, the issue of data ownership is definitely a significant one. For instance, Nicolai (2003) reported that the negotiations between Shell Germany and the coalition scheme Payback failed because the fuel retailer did not want to leave control over customer data to a third party. It needs to be added, however, that Shell was at that time operating its own loyalty scheme and that negotiations only took place because Shell took over the German DEA chain, which was then part of the Payback coalition. Given this scenario, it might be argued that the outcome was predetermined to a certain extent. In addition to that, Nicolai wrote, Shell was unwilling to give up the flexibility that only its own scheme could provide.

#### **Target of customers' loyalty unclear:**

In case of coalition schemes, the question remains whether customers are actually becoming loyal to the partner company or to the program instead. In other words, it is unclear whether the link between customer and program sponsor is strengthened at all. An interesting report on this problem comes from Humby et al. (2008), who described the switch of the UK's grocery retailer Sainsbury's from the long-established Air Miles coalition to the then new Nectar coalition in 2002. Upon this change, Tesco decided to take Sainsbury's space in Air Miles and allowed for a conversion of its own Clubcard points to Air Miles. A week into the new partnership, Tesco claimed to have experienced a 450% increase in the use of its online store finder and a 300% increase in enquiries about home shopping. Furthermore, Tesco declared that one million new Clubcards were distributed. Ultimately, Humby et al. (2008) pointed out, Sainsbury's attributed a loss of 1% of sales volume to the decision to leave Air Miles – equal to what the authors calculated to be around 60,000 valuable customers lost to Tesco. On the one hand, this shows that coalition schemes really do work in creating lock-in with a range of customers, but on the other hand it also shows that for some consumers this lock-in relates to the program and not the company. What is particular about this situation is that the change was not from one existing to another existing coalition, but one from an existing to a new one. In such a constellation, there is no opportunity to immediately replace the lost customers with members from the new coalition. Still, since its launch, Nectar has long overtaken Air Miles in terms of the number of collectors and it can very well be argued, that Sainsbury's has meanwhile had a chance to compensate for this loss (Johnson 2002 reported that Air Miles had 6.5 million members in 2002; by contrast, Clark & Clark 2009 stated that Nectar had signed up 11 million members within a year from its launch). In fact, Loyalty Management UK, the company administrating the Nectar scheme, reported that it overtook Air Miles in terms of active cardholders within four weeks from its launch (Voyle 2002).

#### **Loss of control for program sponsors:**

Naturally, every company joining a coalition scheme as a partner will have to give up some control. Humby et al. (2008) highlighted that this will reduce the chance to quickly introduce tactical marketing initiatives and limit innovation, because all decisions have to be agreed on by the program sponsors. While this might be of particular concern for program-wide changes or marketing campaigns, this will be less significant for individual, companyspecific promotions. Still, as far as program-specific activities are concerned, the different sponsors will have to cooperate even when their agendas and priorities differ. In addition to that, involvement in the general program design will also be limited and the ability to "bind the program in to the business," which Humby et al. considered to be such an important feature for Tesco, constrained.

#### **Imbalance concerning partner contribution:**

116

**4.2.2 Disadvantages**

et al. 2008, Clark & Clark 2009):

**Issues regarding ownership of data:**

**Target of customers' loyalty unclear:**

Coalition schemes boast several advantages, but this does not mean that they are free from disadvantages. Naturally, there are downsides associated with these schemes as well. The following list aims to cover the most important ones (Humby

Similar to consumers, partner companies of coalition schemes might also be concerned by privacy issues. In this case, the concern revolves around company-related data being handled by a third party. Adding up to this is the fact that each company's data is pooled together with that of other companies. Within every loyalty coalition, there will be contracts dealing with the topic of data ownership. Clark & Clark reported that for retailers, article-level basket data is typically both held and owned by the partner companies. Therefore, coalition operators would only hold customer information and data on the number of points collected by location and date. In practice, however, it differs from case to case whether coalitions really do not have access to detailed purchase histories, particularly to undertake valuable inter-industry customer segmentations based upon buying behavior. At any rate, the issue of data ownership is definitely a significant one. For instance, Nicolai (2003) reported that the negotiations between Shell Germany and the coalition scheme Payback failed because the fuel retailer did not want to leave control over customer data to a third party. It needs to be added, however, that Shell was at that time operating its own loyalty scheme and that negotiations only took place because Shell took over the German DEA chain, which was then part of the Payback coalition. Given this scenario, it might be argued that the outcome was predetermined to a certain extent. In addition to that, Nicolai wrote, Shell was unwilling to give up the flexibility that only its own scheme could provide.

In case of coalition schemes, the question remains whether customers are actually becoming loyal to the partner company or to the program instead. In other words, it is unclear whether the link between customer and program sponsor is strengthened at all. An interesting report on this problem comes from Humby et al. (2008), who described the switch of the UK's grocery retailer Sainsbury's from the long-established Air Miles coalition to the then new Nectar coalition in 2002. Upon this change, Tesco decided to take Sainsbury's space in Air Miles and allowed for a conversion of its own Clubcard points to Air Miles. A week into the new partnership, Tesco claimed to have experienced a 450% increase in the use of its online store finder and a 300% increase in enquiries about home shopping. Furthermore, Tesco de-

One problem that might arise is that some of the program partners will become net contributors to the program, while others become net beneficiaries. In other words, some companies might distribute more point value than customers spend at their outlets, while the opposite situation might be given for other program sponsors. While this is a fairly natural situation, it can be expected that coalition operators have measures in place to adjust for this imbalance.

### **Coalitions virtually impossible to pilot:**

Due to specificities inherent to program structure, coalitions are pretty much impossible to pilot on a smallscale.In orderto work properly,multi-partnerschemes require a certain size and, in case of retailing programs, sector coverage.

 **Bad experiences with program attributed to sponsors:** When a customer encounters a problem with the coalition scheme that is located within the scope of the operator (e.g. points were not accredited, long waiting time in the customer service hotline, etc.), these negative experiences might be attributed to the program sponsors.

#### **Expanding grocers limit space for new partners:**

A final point brought forward by Clark & Clark (2009) is that grocers in many countries have started to expand their activities not just into the retailing of other non-food items, but also into other non-retailing sectors such as financial services. Since, however, a food retailer is essential to any retailing coalition as it brings the necessary purchase frequencies with it, this might (together with the usually present exclusivity agreement) restrict the coalition in terms of the other partners it can select.

# **4.3 Success Factors**

As with all other aspects of coalition schemes, research is scarce on this subject. Despite lacking empirical proof, Clark & Clark's (2009) list of four success factors associated particularly with coalition schemes shall nevertheless be reproduced here as it still represents a valuable contribution (see also Furinto et al. 2009 for a different approach to the subject). Certainly, there are other variables contributing to success as well, but the ones mentioned here might serve as a basic guideline.

Firstly, operators of a coalition scheme need to make sure that a high rate of penetration is reached fairly quickly. Most of the big programs have adhered to this principle and made sure that a major player from each key sector in retailing and possibly also financialservices was on board to build up turnover rightfrom the beginning. Furthermore, details were usually keptsecret during the planning phase, possibly with a few bits of information leaking out towards launch date to build momentum. The launch itself, then, was accompanied by a massive media campaign to create awareness and immediately sign up a large number of new members.

Secondly, particularly in the case of coalition schemes, being first is a significant advantage. Not only will the second player in the market have lost his chance to sign up the most suitable partners (i.e. generally each sector's market leader), but in addition to that, customers will already have started to patronize one scheme and might have become locked in or be unwilling to join a second scheme. For instance, Payback was launched as Germany's first coalition in March 2000. The second biggest player on the market today is HappyPoints, which was introduced as the loyalty program of the telecom provider Deutsche Telekom in October 2001 and expanded into a comparable, classic retail-oriented coalition in the years thereafter. Possibly the lack of first mover advantage combined with the lack of a big launch with many partners right from the beginning were what has kept the program in second place ever since. According to a study by GfK (2007) covering 8,000 households, in September 2007 60.8% of German households held a Payback card, while only 41.8% possessed a HappyPoints card.

Thirdly, coalitions need to make sure that they offer aspirational rewards that can be earned in a reasonable amount of time. As was mentioned before, this factor is one of the big advantages of multi-partner programs and its operators should make sure not to give that edge away.

Finally, coalitions need to emphasize the right choice and a suitable standard of communication channels. Pooling all program-related communication at the operator creates cost advantages, but at the same time, any negative experiences at the operator's customer touch points might fall back on the partner companies.

### **4.4 Impact, Spread, and Customer Perception**

118

**Coalitions virtually impossible to pilot:**

**Bad experiences with program attributed to sponsors:**

might be attributed to the program sponsors. **Expanding grocers limit space for new partners:**

in terms of the other partners it can select.

**4.3 Success Factors**

basic guideline.

Due to specificities inherent to program structure, coalitions are pretty much impossible to pilot on a smallscale.In orderto work properly,multi-partnerschemes require a certain size and, in case of retailing programs, sector coverage.

When a customer encounters a problem with the coalition scheme that is located within the scope of the operator (e.g. points were not accredited, long waiting time in the customer service hotline, etc.), these negative experiences

A final point brought forward by Clark & Clark (2009) is that grocers in many countries have started to expand their activities not just into the retailing of other non-food items, but also into other non-retailing sectors such as financial services. Since, however, a food retailer is essential to any retailing coalition as it brings the necessary purchase frequencies with it, this might (together with the usually present exclusivity agreement) restrict the coalition

As with all other aspects of coalition schemes, research is scarce on this subject. Despite lacking empirical proof, Clark & Clark's (2009) list of four success factors associated particularly with coalition schemes shall nevertheless be reproduced here as it still represents a valuable contribution (see also Furinto et al. 2009 for a different approach to the subject). Certainly, there are other variables contributing to success as well, but the ones mentioned here might serve as a

Firstly, operators of a coalition scheme need to make sure that a high rate of penetration is reached fairly quickly. Most of the big programs have adhered to this principle and made sure that a major player from each key sector in retailing and possibly also financialservices was on board to build up turnover rightfrom the beginning. Furthermore, details were usually keptsecret during the planning phase, possibly with a few bits of information leaking out towards launch date to build momentum. The launch itself, then, was accompanied by a massive media campaign to create awareness and immediately sign up a large number of new members. Secondly, particularly in the case of coalition schemes, being first is a significant advantage. Not only will the second player in the market have lost his chance to sign up the most suitable partners (i.e. generally each sector's market leader), but in addition to that, customers will already have started to patronize one The number of academic papers on this subject is fairly limited. Out of the 23 studies on the success impact of loyalty schemes that were reviewed in Chapter 2.3, only one dealt with a coalition scheme. In their evaluation of the Australian FlyBuys coalition, Sharp & Sharp (1997) observed a trend towards a weak level of excess loyalty, although the expected deviation was not consistently observed for all program sponsors under review. In fact, only two of the six participant brands showed substantial excess loyalty deviations and even this variance in repeat-purchase loyalty was observed for both members and non-members of the coalition. The authors explained these findings at least partly as the result of other loyalty efforts. In the end, then, Sharp & Sharp's study can be categorized as one supporting those who claim that it is very difficult for loyalty schemes to alter customers' purchase patterns. The overall majority of evidence points in the opposite direction, however. As was previously discussed, it is clearly possible for loyalty programs to alter purchase behavior, at least to a small degree. Ultimately, it probably boils down to the same problem observed with other studies on the effectiveness of loyalty schemes: the definition of success. Sharp & Sharp themselves noted that "… the results are mixed" and that "the markets […] remain *close to* 'normal' repeat purchase markets after the introduction of the loyalty program" (p. 483). Things did not look the same before and after program launch, but were observed to be "close to" normal. Without changing the content of this statement, one could easily rephrase this to resemble the conclusion drawn in Chapter 2.3: the loyalty programs caused change, but on average only to a small extent.

Moore & Sekhon (2005) conducted a more recent study of coalition schemes by administering a survey of 153 members of the UK's Nectar program. The authors posed a range of general questions relating to a wide range of topics such as customers' scheme-related knowledge or use of the card, and attempted to draw conclusions on the program's success in influencing behavior. Due to the similarity of this study's goals at least to the general questionnaire part of the present work, findings will be reviewed in greater detail. The following points were discovered by the authors:

Apart from a few exceptions such as that of poor communication, use out of habit, or a lack of knowledge of program sponsors, it seems fair to say that the outcome is generally what can be expected from coalition schemes. A good part of this study's abstract nevertheless leaves the reader with a rather grim feeling about coalitions. At the same time, however, the authors titled their article "Multi-Brand Loyalty Cards: A Good Idea" and closed their summary stating that "… there is evidence to suggest consumers perceive real benefits in coalition schemes and that there is a willingness to alter their behavior if the motivation is sufficient" (p. 625).

The importance of rewards as a motivator has already been discussed in previous sections of this paper, with its last point of mention in Chapter 4.3 on success factors associated with coalition schemes. Lara & De Madariaga (2007) dealt with this topic in a study focused exclusively on multi-sponsor programs. In their telephone survey of 521 members and 540 non-members of Spanish coalition schemes, the authors attempted to shed some new light on the research subject of loyalty rewards. Among a range of different elements associated with coalition schemes (e.g. number of participating companies, effort, exclusiveness, etc.), rewards were singled out as the most important factor by both members and non-members. As far as the type of reward is concerned, an association of satisfaction with intangible amusement rewards (e.g. games, raffles, etc.) and services was noticed for non-members and one with discounts and intangible amusement rewards for members. Particularly ecology and charitable rewards were found to be a very attractive element of the reward structure, which has long been disregarded in practice. Furthermore, the authors highlighted the importance of tailoring reward types to consumers and the goal the company tries to achieve. For instance, Lara & De Madariaga found that intangible amusement rewards would be particularly useful to acquire new participants, while rewards relating to benefits such as exclusiveness or special preference could help to strengthen the present member base.

Next to academic papers,studies by marketresearch specialistsserve as another source of information on coalition schemes. A notable contribution in this category comes from TNS Emnid (2006), which posed a range of general questions to a sample of 1,000 consumers. Despite having been commissioned by the German Payback coalition and therefore calling for a healthy bit of caution (particularly

120

only to a small extent.

were discovered by the authors:

at a quicker rate.

be that of the lead retailer.

by customers.

sponsors.

tention.

loyalty program" (p. 483). Things did not look the same before and after program launch, but were observed to be "close to" normal. Without changing the content of this statement, one could easily rephrase this to resemble the conclusion drawn in Chapter 2.3: the loyalty programs caused change, but on average

Moore & Sekhon (2005) conducted a more recent study of coalition schemes by administering a survey of 153 members of the UK's Nectar program. The authors posed a range of general questions relating to a wide range of topics such as customers' scheme-related knowledge or use of the card, and attempted to draw conclusions on the program's success in influencing behavior. Due to the similarity of this study's goals at least to the general questionnaire part of the present work, findings will be reviewed in greater detail. The following points

 Customers were generally satisfied, found the scheme easy to understand, convenient and easy to use, reliable, and trustworthy (although the level of

 Respondents preferred financial rewards when asked directly, though particularly aspirational rewards created high levels of involvement, resulting in customerstaking on what the authors described as "a role of planners," saving 2-3

Program members valued the fact that they were able to collect more points

 Contact between the program and its members was found to be limited, with communication being confusing, complicated, and thereby discouraging at-

Despite a high level of targeted communication, respondents still felt that

No special treatment as a result of being a program member was experienced

 The card was used with the majority of transactions with the two biggest retailers, while usage figures were clearly lower with the other coalition

 Customers found no differentiation between Nectar and its competitors. Except for the major retailer (i.e. most likely the grocery chain Sainsbury's), consumers exhibited a clear lack of awareness as far as other program sponsors are concerned. This resulted in many customers perceiving the scheme to

trust remained at a basic level), and were happy with the service.

times more points than people with low involvement.

their expectations were not met in that regard.

97% have never had problems with the program.

in relation to what questions were asked and how the outcome is presented), the findings might nevertheless be useful:

complemented by 12% stating that none of the programs would give them personal benefit and 18% saying that they do not know.

Within the range of findings presented by TNS Emnid, Payback scored a conspicuously high number of first and second places. Given that the study was commissioned by Payback's operator Loyalty Partner, it seems fair to challenge the survey's outcome in a few regards. A very obvious point is the question asking people to judge the importance of a range of loyalty scheme features. Coming even before items such as the program's ability to provide savings and benefits, the possibility to use the card at different shops took a surprising first place (and this characteristic is, after all, a great advantage of Payback). Leaving aside the issue of whether respondents might have been confused as to whether the question really meant different companies or simply different stores from the same chain, this ranking is most likely explained by the fact that both the answer category "very important" and "rather important" were combined in generating it. In other words, it is not unlikely that, as opposed to the ability to use the card at differentshops, a much bigger percentage of respondentsjudged the rebate function to be very important. In addition to that, it remains unclear why this specific range of programs was chosen for the list to be presented to respondents and whether the findings presented were not simply a flattering selection. Nevertheless, given an appropriate sense of caution, these findings still provide a few interesting insights, for instance, supporting the general view that multi-partner schemes are able to reach a much higher penetration rate than stand-alone programs.

This fact is also supported by further research on behalf of the Payback operator Loyalty Partner. GfK (2007) investigated the possession and use of loyalty cards in Germany by surveying 8,000 households. As mentioned in Chapter 4.3, in September 2007 60.8% of Germany's 34.3 million households held a Payback card. By comparison, 41.8% possessed a HappyPoints card, 11.7% a Shell Clubsmart card, and 8.7% a Lufthansa Miles & More card. As far as the use of these loyalty programs is concerned, 56.5% of respondents stated that they always use their Payback card, while 32.4% did so with their HappyPoints, 44.7% with their Clubsmart, and 30.9% with their Miles & More card. In addition to that, another 38.5% used the Payback card occasionally, 54.7% did so with their HappyPoints card, 38.7% with their Clubsmart, and 49.7% with their Lufthansa Miles & More card. What remains unclear is whether these usage rates refer to category spending or simply use with the program operators or, in case of coalitions, their partners. After all, a rate of only around 31% showing their Miles & More card on every flight taken with Lufthansa seems very low. By contrast, it seems more likely that Miles & More members were using their card on virtually every flight with Lufthansa, but that they selected this airline for only 31% of

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in relation to what questions were asked and how the outcome is presented), the

 When respondents were asked about the type of cards carried in their wallet at all times, the bank card finished first with a penetration rate of 82%, followed by the health insurance card with 77%, and the Payback card together with a credit card tied at third place with 32%. The card of the second largest German coalition HappyPoints was carried by 15% of people, with cards of

 Consumers were further confronted with a list of loyalty program names and asked which ones they know by name. 80% answered that they had heard of Payback, 50% were familiar with HappyPoints, 41% with the Family card of the Swedish furniture retailer Ikea, and 39% with the Lufthansa Miles & More frequent flyer program. With the exception of Ikea's Family card, no

 Requested to judge the importance of a range of loyalty scheme features, 76% found the ability to use the card at different shops to be either very or rather important. That it offers services, special offers, or advantages the customer does not have access to without the card followed with 63% and that it offers coupons which provide savings and advantages upon purchase as well as access to a wide range of reward types followed with a tied 51%. Having access to a reward within a short amount of time was judged very or rather important by 46% of survey respondents, the card being usable for online shopping by 43%, that rewards exclusive to the program are available by 38%, that the card offers a payment function by 22%, and that it offers a cre-

 Asked directly whether their purchase behavior has changed since they became a member of the respective loyalty scheme, 34% said they buy/book more flights and 38% that they are more likely to buy/book flights since joining Miles & More. The values for Payback in these two categories ("buy/book more" and "buy/book more likely") came in at 26% and 32%, these for the program of the perfumery chain Douglas at 25% and 35%, for HappyPoints at 20% and 30%, for the Haushaltskarte of the grocery retailer

REWE at 20% and 26%, and for the Ikea Family card at 9% and 18%. Finally, respondents were questioned as to which one of the nine programs presented in a list gave them the biggest personal benefit. Payback came in a clear first with 34%, followed by HappyPoints with 7% and the Ikea Family card, Miles & More, as well as bahn.comfort (a point-based frequent traveler scheme of the German railway company Deutsche Bahn) with 6% each. Four other stand-alone programs attracted the favor of between 1% and 5% of respondents,

stand-alone programs failing to surpass the 10% mark.

stand-alone program managed to surpass the 35% mark.

findings might nevertheless be useful:

dit card function by 12%.

their flights. Further background information on this issue is not provided, but one thing is in any case certain: among the programs surveyed, customers of Germany's largest coalition Payback showed the highest rates of both possession and usage.

# **4.5 The Next Evolutionary Step?**

As was mentioned in the introduction to this Chapter 4, several academics and practitioners consider coalition schemes to be some sort of evolutionary step. Naturally, this program type has drawbacks as well, but overall, advantages were often found to overweigh.

What is so striking, then, is the blatant deficit of literature on this topic. Some ground is covered by studies from market research organizations (usually commissioned by coalition operators, however; e.g. TNS Emnid 2006 or GfK 2007) and in fact, some of the academic literature has also been written by practitioners from research organizations (e.g. Capizzi & Ferguson 2005 or Ferguson & Hlavinka 2006). Next to a few current or former practitioners (e.g. Humby et al. 2008 or Clark & Clark 2009), the truly academic view on coalition schemes remains extremely limited (and includes only a handful of publications such as those by Sharp & Sharp 1997 or Lara & De Madariaga 2007). At best, authors from an academic background have mentioned examples of coalition schemes in the course of a study on a different aspect of loyalty schemes (e.g. Stone et al. 2004 referring to UK's Nectar coalition in a general evaluation of loyalty schemes or Rowley 2005 in a case study of Tesco's Clubcard).

Scientific information on this topic is consequently still scarce, with unbiased, empirical evidence of the purported superiority of coalition schemes still outstanding. For that reason, this paper will attempt to contribute another, muchneeded piece to this puzzle with the following empirical section.

# **5. Empirical Study Design**

Within Chapter 5, an overview of the study configuration will first be given (Chapter 5.1), followed by a description of the preparatory work necessary to conduct this research endeavor. The first steps in developing a conceptual framework will be discussed (Chapter 5.2) together with an elaboration on potential theoretical reference points for hypotheses formulation (Chapter 5.3). Lastly, the finalization of the conceptual framework (Chapter 5.4) and the process of construct operationalization will be examined (Chapter 5.5).

### **5.1 Study Configuration**

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and usage.

**4.5 The Next Evolutionary Step?**

often found to overweigh.

their flights. Further background information on this issue is not provided, but one thing is in any case certain: among the programs surveyed, customers of Germany's largest coalition Payback showed the highest rates of both possession

As was mentioned in the introduction to this Chapter 4, several academics and practitioners consider coalition schemes to be some sort of evolutionary step. Naturally, this program type has drawbacks as well, but overall, advantages were

What is so striking, then, is the blatant deficit of literature on this topic. Some ground is covered by studies from market research organizations (usually commissioned by coalition operators, however; e.g. TNS Emnid 2006 or GfK 2007) and in fact, some of the academic literature has also been written by practitioners from research organizations (e.g. Capizzi & Ferguson 2005 or Ferguson & Hlavinka 2006). Next to a few current or former practitioners (e.g. Humby et al. 2008 or Clark & Clark 2009), the truly academic view on coalition schemes remains extremely limited (and includes only a handful of publications such as those by Sharp & Sharp 1997 or Lara & De Madariaga 2007). At best, authors from an academic background have mentioned examples of coalition schemes in the course of a study on a different aspect of loyalty schemes (e.g. Stone et al. 2004 referring to UK's Nectar coalition in a general evaluation of loyalty

Scientific information on this topic is consequently still scarce, with unbiased, empirical evidence of the purported superiority of coalition schemes still outstanding. For that reason, this paper will attempt to contribute another, much-

schemes or Rowley 2005 in a case study of Tesco's Clubcard).

needed piece to this puzzle with the following empirical section.

To answer the research questions outlined in the introductory chapter to this paper, this study on customer loyalty schemes in retailing relied on both an empirical qualitative as well as an empirical quantitative component (see Figure 14). As was discussed in detail in Chapter 1.2, Germany was selected as the place to conduct this research, with the focus being put on the fuel retailing market. To be precise, Aral (as a partner company of *Payback* – Germany's biggest multipartner program) and Shell (with the industry's major stand-alone scheme *Clubsmart*) were selected as two subjects of study that ensure good comparability due to their similarities in terms of size and strength.