217 research outputs found
Do Loyalty Programs Really Enhance Behavioral Loyalty? An Empirical Analysis Accounting for Self-Selecting Members
Full text linkOne 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
Discrete-time discrete-state latent Markov modelling for assessing and predicting household acquisitions of financial products.
Full text link
Adaptive Multidimensional Scaling: The Spatial Representation of Brand Consideration and Dissimilarity Judgments
Full text linkWe propose Adaptive Multidimensional Scaling (AMDS) for simultaneously deriving a brand map and market segments using consumer data on cognitive decision sets and brand dissimilarities.In AMDS, the judgment task is adapted to the individual respondent: dissimilarity judgments are collected only for those brands within a consumers' awareness set.Thus, respondent fatigue and subjects' unfamiliarity with any subset of the brands are circumvented; thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived.Estimation of the AMDS model results in a spatial map in which the brands and derived segments of consumers are jointly represented as points.The closer a brand is positioned to a segment's ideal brand, the higher the probability that the brand is considered and chosen.An assumption underlying this model representation is that brands within a consumers' consideration set are relatively similar.In an experiment with 200 subjects and 4 product categories, this assumption is validated.We illustrate adaptive multidimensional scaling on commercial data for 20 midsize car brands evaluated by 212 members of a consumer panel.Potential applications of the method and future research opportunities are discussed.scaling;brands;market segmentation
A comparison of multidimensional scaling methods for perceptual mapping
No full textMultidimensional scaling has been applied to a wide range of marketing problems, in particular to perceptual mapping based on dissimilarity judgments. The introduction of methods based on the maximum likelihood principle is one of the most important developments. In this article, the authors compare the three available Maximum Likelihood Multidimensional Scaling (MLMDS) methods, namely, MULTISCALE, MAXSCAL, and PROSCAL, and the traditional multidimensional scaling (MDS) method KYST in a Monte Carlo study with 243 synthetic data sets. The MLMDS methods outperform KYST with respect to recovering the perceptual maps. MAXSCAL recovers the true distances between brands somewhat better than MULTISCALE, which is somewhat better than PROSCAL. With regard to distance recovery, the MLMDS methods are quite robust to violations of distributional assumptions. The decision criteria for selecting the number of dimensions are less robust to distributional violations. The results support the use of Consistent Akaike Information Criterion for the selection of the number of dimensions. The authors recommend that dissimilarity judgments be collected on interval scales or on ordinal scales with a substantial number of scale values. The authors discuss implications of the results for the design and analysis of perceptual mapping studies
- …
