Patrick Van Kenhove

The impact of task definition on store-attribute saliences and store choice

This study investigates the impact of task definition, a situational measure, on storeattribute saliences and store choice for do-it-yourself (DIY) products. Five types of task definitions experienced by shoppers of do-it-yourself products were identified and linked to store attributes. A survey of DIY shoppers revealed that task definitions were related to both store choice and store-attribute saliences. The study has important implications for retailer format strategies as well as for the interpretation of store evaluation processes, satisfaction, and loyalty.

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customers future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probstudy. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.

The influence of need for closure on consumer’s choice behaviour

Abstract Consumers select a decision strategy in a purchase situation that best meets their goals for that situation. One of those possible goals is to obtain closure. The dimension need for closure (NFC) (vs. avoidance) reflects the desire for clear, definite, or unambiguous knowledge that will guide perception and action, as opposed to the undesirable alternative of ambiguity and confusion. The purpose of this study is to identify the influence of NFC on choice behaviour in a low involvement purchase situation. Both high ( N =71) and low ( N =71) NFC participants participated in a shopping experiment. In a simulated environment, participants were asked to choose repetitively between different brands within two low involvement product categories. We found significant differences between high and low NFC participants with regard to the amount of information sought, the amount of information used, the use of decision rules and the level of confidence in their decisions made. Marketing implications, limitations and directions for future research are also provided.

Role of intrinsic search cues in the formation of consumer preferences and choice for pork chops

This study investigates the role of drip, colour, marbling and fat cover as intrinsic search cues in the formation of pork chop preferences and individual determinants. Data are collected from a sample of 443 pork consumers in Belgium through using repeated selection of chops from randomised photobooks and questionnaires including socio-demographic, attitudinal and behavioural variables. Data analysis includes mixture regression analysis, bivariate descriptive statistics and the estimation of multivariate probit models. Consumers sampled in this study prefer pork chops without fat cover. Preference for fat cover is stronger among male, 35+ aged consumers with lower levels of awareness of the relation between food and health and who like pork for other reasons than taste and nutritional value (all p<0.05). Preference for colour is equally consistent within an individual, though fifty-fifty light-dark, with dark chops being more preferred by 35+ aged consumers (p<0.05). Preferences for marbling and drip are not consistent and not determined by joint socio-demographic, attitudinal and behavioural factors. Preferences for cue levels are not correlated, except a weak relation between preference for dark chops without drip (r=0.116). Preferences are apparently formed by deductions with the use of single cues as key information, mainly based on fat cover or colour, and random choice on marbling and drip.

A pictorial attitude IAT as a measure of implicit motives

We tested the hypothesis that a pictorial attitude variant of the Implicit Association Test (PA–IAT) is a valid measure of implicit motives. The PA–IAT aims to capture attitudes towards pictures that are related to implicit motives. In the first two studies, we showed that the pictorial attitude Implicit Association Test (IAT) correlated more highly with non–IAT measures of implicit motives than other IAT variants. In the third study, we established the validity of the PA–IAT experimentally and showed that the pictorial attitude IAT correlated with non–declarative behavioural measures only if implicit motives were aroused. Copyright

Convergent, Discriminant, and Incremental Validity of the Pictorial Attitude Implicit Association Test and the Picture Story Exercise as Measures of the Implicit Power Motive

The Pictorial Attitude Implicit Association Test (PA–IAT) has recently been proposed as new measure of implicit motives. We report a study that provides the first evidence for the convergent validity of the PA–IAT by showing that the PA–IAT correlates significantly with a standard measure of implicit motives [i.e. the Picture Story Exercise (PSE)]. Discriminant validity of the PA–IAT was verified in the sense that the PA–IAT shared virtually no common variance with explicit motive measures. Our analyses revealed that the PA–IAT and PSE can best be conceived as related but distinct measures. We further showed that the PA–IAT had incremental validity in predicting performance on a memory recall task over and above the PSE. In general, our results confirm that the PA–IAT is a valid measure of implicit motives and can serve as valid alternative to the PSE. Copyright

Detecting sequential patterns for cross-selling fast moving consumer goods

In the marketing domain, sequential patterns have been usefully deployed for predicting various aspects of customer purchase behavior. However, to date, the applications of the technique have mainly focused on improving algorithms for detecting sequentially related events, whereas the implications of the sequences, and their, incorporation into a global structure of consecutive sequences have been treated to a lesser extent. In this paper, such a structure, that we will refer to as sequential architecture, will be empirically investigated for a specific case in a fast moving consumer goods setting.. Hence, the goal of this paper was to introduce a new concept that might prove to be a; relevant tool for marketing decision making rather than offering a sound solution within a clearly demarcated problem definition. As opposed to the traditional sequence-analysis approaches, in this study, an array of binary logit analyses was applied for detecting significant sequences among category purchases. We use the output of the logit analyses to define the category that is most significantly influenced per newly purchased category, and we select these links for constructing the applicable sequential architecture. Finally, we provide empirical evidence that the methodology suggested is able to double the performance of predicting purchases in categories that were not purchased previously by the consumer, compared to a random model. In summary, we have shown that (i) binary logit analysis provides a feasible alternative for detecting and selecting highly significant sequential relationships, (ii) a sequential architecture can be successfully compiled through the methodology offered in this paper, and (iii) the provided sequential architecture can be a useful tool in understanding and predicting customer behavior. Future applications possibly lie ahead in the field of inter-category management, shelf-space allocation, store-layout decisions, retailer promotions, customer profiling and individual customer predictions.

One Sail Fits All? A Psychographic Segmentation of Digital Pirates

This paper focuses on segmenting digital movie and TV series pirates and on investigating the effectiveness of piracy-combatting measures i.e., legal and educational strategies, in light of these segments. To address these research objectives, two online studies were conducted. First, 1277 valid responses were gathered with an online survey. Four pirate segments were found based on differing combinations of attitude toward piracy, ethical evaluation of piracy and feelings of guilt. The anti-pirate, conflicted pirate, cavalier pirate, and die-hard pirate can be placed on a continuum of increasing pirating frequency, subjective norm, pirating self-efficacy, habit, and decreasing in perceived harm, respectively. The segments also differ in deontological and teleological orientations. Second, in an experimental mixed design, we find that the educational strategy is more effective than the legal strategy in lowering pirating intentions for the conflicted and cavalier pirate. However, both strategies fail at lowering intentions of the die-hard pirate, although perceived harm and perceived impunity were significantly influenced. These findings offer a more profound understanding of pirate segments and how they react differently to piracy-combatting measures. As a result, better strategies can be developed to control digital piracy.

Belgian Retailing - Some Shopping Realities of a Surrealist Country

The aim of the paper is to identify and interpret shopping tendencies occurring or emerging on the Belgian retailing landscape. Belgium’s retailing history is looked at from a helicopter perspective first, before focussing next on the most relevant evolutions taking place within the current shopping environment. Amongst the multitude of diverse changes observed on the retailing landscape itself, the three most fundamental and far reaching shopping tendencies are identified and discussed: wide spreading multipurpose shopping under its diverse forms, the increasingly demanding nature of shopping in its main expressions, and finally the recent breakthrough of e-shopping.

Improving response prediction in direct marketing by optimizing for specific mailing depths

Response modeling is a very important application field of classification methods in direct marketing because the success of a direct-mail campaign is highly dependent on who is being targeted. To date, standard classification models are applied to predict future purchasing behaviour for the complete customer file. In practice, however, companies use mailing budgets, i.e. only a subset of customers will be sent mail. Just those customers with sufficiently high-expected response rates are mailed to. The percentage of the total population that will actually receive the mailing is referred to as mailing depth. Hence, the real classification problem is not to classify all potential recipients as well as possible, but rather to find those customers, within the budget limitation, with the highest probability of response. Therefore, we propose an innovative alternative route to improved overall performance by tailoring the classification method to fit the problem at hand. We adapt binary logistic regression by iteratively changing the true values of the dependent variable during the maximum-likelihood estimation procedure. Those customers who rank lower than the cutoff in terms of predicted purchase probability, imposed by the mailing-depth restriction, will not contribute to the total likelihood. We illustrate our procedure on a real-life direct-marketing dataset comparing traditional response models to our innovative. approach optimising for a specific mailing depth. The results show that for mailing depths up to 48% our method achieves significant and substantial profit increases.