Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical Analysis Accounting for Self-Selecting Members

One of the pressing issues in marketing is whether loyalty programs really enhance behavioral loyalty. Loyalty program members may have a much higher share-of-wallet at the firm with the loyalty program than non-members have, but this does not necessarily imply that loyalty programs are effective. Loyal customers may select themselves to become members in order to benefit from the program. Since this implies that program membership is endogenous, we estimate models for both the membership decision (using instrumental variables) and for the effect of membership on share-of-wallet, our measure of behavioral loyalty. We use panel data from a representative sample of Dutch households who report their loyalty program memberships for all seven loyalty programs in grocery retailing as well as their expenditures at each of the 20 major supermarket chains. We find a small positive yet significant effect of loyalty program membership on share-of-wallet. This effect is seven times smaller than is suggested by a naïve model that ignores the endogeneity of program membership. The predictive validity of the proposed model is much better than for the naïve model. Our results show that creating loyalty program membership is a crucial step to enhance share-of-wallet, and we provide guidelines how to achieve this.Attraction models;Endogeneity;Grocery retailing;Loyalty programs;Tobit-II model

Do Loyalty Programs Enhance Behavioral Loyalty: An Empirical Analysis Accounting for Program Design and Competitive Effects

This paper studies the effects of loyalty programs on share-of-wallet using market-wide household panel data on supermarket purchases.We find that loyalty programs relate positively to share-of-wallet, but the programs differ in effectiveness and some are ineffective.Both a saving component and a multi-vendor structure enhance the effectiveness of a loyalty program, but high discounts do not lead to higher share-of-wallets.Further, if households have multiple loyalty cards, the effectiveness of a specific loyalty program is much smaller.The positive loyalty program effects on share-of-wallet entail substantial additional customer revenues.However, given the high number of loyalty programs already available in the market, our model predicts that a new loyalty program introduction will only lead to small effects on share-of-wallet.loyalty;marketing;retailing

Is 3/4 of the Sales Promotion Bump Due to Brand Switching? No it is 1/3

Several researchers have decomposed sales promotion elasticities based on household scanner panel data.A key result is that the majority of the sales promotion elasticity, about 74 percent on average, is attributed to secondary demand effects (brand switching) and the remainder to primary demand effects (timing acceleration and quantity increases).We demonstrate that this result does not imply that if a brand gains 100 units in sales during a promotion the other brands in the category lose 74 units (74 percent).We offer a complementary decomposition measure based on unit sales.This measure shows the ratio of the current cross-brand unit sales loss to the current own-brand unit sales gain during promotion, and we report empirical results for this measure.We also derive analytical expressions that transform the elasticity decomposition into a decomposition of unit sales effects.These expressions show the nature of the difference between the two decompositions.To gain insight into the magnitude of the difference, we apply these expressions to previously reported elasticity decomposition results.We find that on average about one third of the unit sales increase is attributable to losses incurred by other brands in the same category (i.e., they lose 33 units).Thus, secondary demand effects account for a far smaller percent of the unit sales promotion effect than has been inferred from elasticity decomposition results.We find that the difference is due to the manner in which the two decomposition measures deal with the category expansion that occurs during a promotion.One interpretation is that the elasticity decomposition yields a gross measure of brand switching, in the sense that category sales are held constant.The unit sales decomposition yields a net measure of brand switching: it accommodates the category expansion effect that applies to both promoted and nonpromoted brands in the models.

How well does consumer-based brand equity align with sales-based brand equity and marketing mix response?

Brand equity is the differential preference and response to marketing effort that a product obtains because of its brand identification. Brand equity can be measured based on either consumer perceptions or on sales. Consumer-based brand equity (CBBE) measures what consumers think and feel about the brand, whereas sales-based brand equity (SBBE) is the brand intercept in a choice or market share model. This paper studies the extent to which CBBE manifests itself in SBBE and marketing mix response using ten years of IRI scanner and Brand Asset Valuator (BAV) data for 290 brands spanning 25 packaged good categories. It uncovers a fairly strong positive association of SBBE with three dimensions of CBBE – Relevance, Esteem, and Knowledge – but a slight negative correspondence with the fourth dimension, Energized Differentiation. It also reveals new insights on the category characteristics that moderate the CBBE-SBBE relationship, and documents a more nuanced association of the CBBE dimensions with response to the major marketing mix variables than heretofore assumed. Implications are discussed for academic researchers who predict and test the impact of brand equity, for market researchers who measure it, and for marketers who want to translate their brand equity into marketplace success

Consideration sets, intentions and the inclusion of "Don't know" in a two-stage model for voter choice

We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stageinvolves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit model for the actual choice. In both stages we use asexplanatory variables data on voter choice at the previous election, as well as socio-demographic respondent characteristics. Importantly, our modelexplicitly accounts for the three types of "missing data" encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the "do not know" response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set. Third, the "do not want to say" response is modelled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be non-ignorable and build a model based on utility maximization to describe the voting intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election,and what their voting intention is. A unique feature of the data set is that information is available on actual individual voting behavior, measured at the day of election. We find that the inclusion of the consideration set stage in the model enables the user to make more precise inferences on the competitive structure in the political domain and to get better out-of-sample forecasts.Bayesian method;Choice model;Election data;Polling;Probit model

Managing Product-Harm Crises

Product-harm crises are among a firm’s worst nightmares. Since marketing investments may be instrumental to convince consumers to purchase the firm's products again, it is important to provide an adequate measurement of the effectiveness of these investments, especially after the crisis. We provide a methodology through which firms can assess the impact of product crises in a quantitative way. Based on the model estimates, firms can estimate the required level of investment to recoup from the crisis. A key finding of this paper is that it is not only important to assess the extent to which business is lost as a result of the crisis, but also to find the new, postcrisis response parameters to marketing activities. The study of an Australian product-harm crisis for peanut butter reveals that a product crisis may represent a quadruple jeopardy for a firm: (i) loss of baseline sales, (ii) a reduced own effectiveness for its marketing instruments, (iii) increased vulnerability, and (iv) decreased clout. We arrive at this conclusion by using a time-varying error-correction model that allows for (i) shortand long-term marketing mix effects, (ii) intercepts and response parameters that change over time as a result of the crisis, and (iii) missing observations, which result from the absence of the impacted brands during the product-recall period. The time-varying error-correction model is applicable to other marketing-research areas in which these three requirements (or any subset thereof) apply

Do Loyalty Programs Enhance Behavioral Loyalty:An Empirical Analysis Accounting for Program Design and Competitive Effects

This paper studies the effects of loyalty programs on share-of-wallet using market-wide household panel data on supermarket purchases.We find that loyalty programs relate positively to share-of-wallet, but the programs differ in effectiveness and some are ineffective.Both a saving component and a multi-vendor structure enhance the effectiveness of a loyalty program, but high discounts do not lead to higher share-of-wallets.Further, if households have multiple loyalty cards, the effectiveness of a specific loyalty program is much smaller.The positive loyalty program effects on share-of-wallet entail substantial additional customer revenues.However, given the high number of loyalty programs already available in the market, our model predicts that a new loyalty program introduction will only lead to small effects on share-of-wallet.

Marketing Models and the Lucas Critique

The Lucas critique has been largely ignored in the marketing literature. We present a number of conditions under which the critique is most likely to (also) apply in marketing settings. Next, we provide some perspectives on how to diagnose and accommodate the Lucas critique, and identify various avenues for future research

Consideration sets, intentions and the inclusion of "Don't know" in a two-stage model for voter choice

We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stage involves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit model for the actual choice. In both stages we use as explanatory variables data on voter choice at the previous election, as well as socio-demographic respondent characteristics. Importantly, our model explicitly accounts for the three types of "missing data" encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the "do not know" response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set. Third, the "do not want to say" response is modelled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be non-ignorable and build a model based on utility maximization to describe the voting intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election, and what their voting intention is. A unique feature of the data set is that in