Philippe Baecke

Data augmentation by predicting spending pleasure using commercially available external data

Since customer relationship management (CRM) plays an increasingly important role in a company’s marketing strategy, the database of the company can be considered as a valuable asset to compete with others. Consequently, companies constantly try to augment their database through data collection themselves, as well as through the acquisition of commercially available external data. Until now, little research has been done on the usefulness of these commercially available external databases for CRM. This study will present a methodology for such external data vendors based on random forests predictive modeling techniques to create commercial variables that solve the shortcomings of a classic transactional database. Eventually, we predicted spending pleasure variables, a composite measure of purchasing behavior and attitude, in 26 product categories for more than 3 million respondents. Enhancing a company’s transactional database with these variables can significantly improve the predictive performance of existing CRM models. This has been demonstrated in a case study with a magazine publisher for which prospects needed to be identified for new customer acquisition.

Improving purchasing behavior predictions by data augmentation with situational variables

Nowadays, an increasing number of information technology tools are implemented in order to support decision making about marketing strategies and improve customer relationship management (CRM). Consequently, an improvement in CRM can be obtained by enhancing the databases on which these information technology tools are based. This study shows that data augmentation with situational variables of the purchase occasion can significantly improve purchasing behavior predictions for a home vending company. Three dimensions of situational variables are examined: physical surroundings, temporal perspective and social surroundings respectively represented by weather, time, and salesperson variables. The smallest, but still significant, increase in predictive performance was measured by enhancing the model with time variables. Besides the moment of the day, this study shows that the incorporation of weather variables, and more specifically sunshine, can also improve the accuracy of a CRM model. Finally, the best improvement in purchasing behavior predictions was obtained by taking the salesperson effect into account using a multilevel model.

Identifying influencers in a social network

Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers. A Shapley value method for finding top influencers in a customer network is proposed.Using referral behaviour data improves the selection of top influencers.Simulation-based influence spread overestimates actual influence spread.Considering the influence of two-hop neighbours improves the influencer selection.

Including spatial interdependence in customer acquisition models: A cross-category comparison

Within analytical customer relationship management (CRM), customer acquisition models suffer the most from a lack of data quality because the information of potential customers is mostly limited to socio-demographic and lifestyle variables obtained from external data vendors. Particularly in this situation, taking advantage of the spatial correlation between customers can improve the predictive performance of these models. This study compares an autoregressive and hierarchical technique that both are able to incorporate spatial information in a model that can be applied on large datasets, which is typical for CRM. Predictive performances of these models are compared in an application that identifies potential new customers for 25 products and brands. The results show that when a discrete spatial variable is used to group customers into mutually exclusive neighborhoods, a multilevel model performs at least as well as, and for a large number of durable goods even significantly better than a frequently used autologistic model. Further, this application provides interesting insights for marketing decision makers. It indicates that especially for publicly consumed durable goods neighborhood effects can be identified. However, for more exclusive brands, incorporating spatial information will not always result in major predictive improvements. For these luxury products, the high spatial interdependence is mainly caused by homophily in which the spatial variable is a substitute for absent socio-demographic and lifestyle variables. As a result, these neighborhood variables lose a lot of predictive value on top of a traditional acquisition model that typically is based on such non-transactional variables.

Mind the gap – Assessing maturity of demand planning, a cornerstone of S&OP

The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations.,The authors developed a maturity assessment model for demand planning through iterations of theoretical and empirical work, combining insights from literature and practitioners. An online survey is developed to validate the model using data from different industries.,The authors identify six dimensions of demand planning maturity: data management, the use of forecasting methods, the forecasting system, performance management, the organisation and people management. The empirical study indicates that demand data are well managed and organisation readiness is high, yet improvements in the forecasting system and the management of forecast performance are needed. The results show a positive relationship between the size of an organisation and its demand planning maturity.,The contribution of this work is to propose an assessment model and survey instrument for demand planning maturity. This will help the practitioner to understand the current level of maturity of the demand planning process, reflect on the desired level and develop action plans to close the gap.,There is broad literature on process maturity assessment in general and on sales and operations planning (S&OP) maturity in particular. However, there is no comprehensive model for assessing the maturity of demand planning, which is a specific and critical process within the overall S&OP process. The authors fill this gap by offering a demand planning maturity model.

A Survey on Mobile Data Uses

Mobile devices leave an unprecedented volume and variety of digital traces of human beings. In this paper, the authors propose an overview of multiple uses of mobile data published in the scientific literature. The organization of the survey follows a typology built on two criteria: interaction level and focus of analysis. Crossing these two dimensions would suggest 8 research areas. Only 4 of them are actually covered by the collected pieces of work. They are discussed in turn showing off the main characteristics of them. Finally, the discussion of the 4 remaining areas highlights new research areas with a special focus on the possibility to use mobile data to influence individual users towards efficient collective behaviors. To conclude, current and future research avenues suggest that mobile devices and their underlying data are likely to be employed in many domains and may be used not only to observe human life but also to influence it.

Incorporating neighborhood effects in customer relationship management models

Traditional customer relationship management (CRM) models often ignore the correlation that could exist in the purchasing behavior of neighboring customers. Instead of treating this correlation as nuisance in the error term, a generalized linear autologistic regression can be used to take these neighborhood effects into account and improve the predictive performance of a customer identification model for a Japanese automobile brand. In addition, this study shows that the level on which neighborhoods are composed has an important influence on the extra value that results from the incorporation of spatial autocorrelation.