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Intelligence about the spatio-temporal behaviour of individuals is valuable in many settings. Generating tracking data is a necessity for this analysis and requires an appropriate methodology. In this study, the applicability of Bluetooth tracking in an indoor setting is investigated. A wide variety of applications can benefit from indoor Bluetooth tracking. This paper examines the value of the method in a marketing application. A Belgian shopping mall served as a real-life test setting for the methodology. A total of 56 Bluetooth scanners registered 18.943 unique MAC addresses during a 19-day period. The results indicate that Bluetooth tracking is a sound approach for capturing tracking data, which can be used to map and analyse the spatio-temporal behaviour of individuals. The methodology also provides a more efficient and more accurate way of obtaining a variety of relevant metrics in the field of consumer behaviour research. Bluetooth tracking can be implemented as a new and cost effective practice for marketing research, that provides fast and accurate results and insights. We conclude that Bluetooth tracking is a viable approach, but that certain technological and practical aspects need to be considered when applying Bluetooth tracking in new cases.

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.

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Purpose The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.