Situation-aware DSS framework for Interactive Marketing

ISBN: 9788890824227 Proceedings of the Business Systems Laboratory 3rd International Symposium “Advances in Business Management. Towards Systemic Approach”

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Situation-aware DSS framework for Interactive Marketing Tindara Abbate Reseracher, Department of Economics, Business, Environmental and Quantitative Methods University of Messina, Italy e-mail: [email protected] Giuseppe D’Aniello Research Fellow, Department of Information Engineering, Electrical Engineering and Applied Mathematics University of Salerno, Italy e-mail: [email protected] Matteo Gaeta Associate Professor, Department of Information Engineering, Electrical Engineering and Applied Mathematics University of Salerno, Italy e-mail: [email protected] Francesco Orciuoli Researcher, Department of Information Engineering, Electrical Engineering and Applied Mathematics University of Salerno e-mail: [email protected] Mirko Perano PhD graduate, Department of Information Engineering, Electrical Engineering and Applied Mathematics University of Salerno e-mail: [email protected], Corresponding author. In an increasingly dynamic context, firms are considering the relevance to interact directly with different actors, for creating and sustaining competitive advantage (Sawhney, et al., 2005). More specifically, firms are more oriented to engage and interact with customers in the exchange of ideas and knowledge (Sawhney, et al., 2005). In this perspective, customers change their role from passive recipient of information flow concerning products and services, developed principally by companies, to suitable actors that help to firms for defining new ideas and developing new products/services. Therefore, in this changing context, the Interactive Marketing aims at engaging customer in an intensive and persistent two-way communication over long period of time (Mulhern, 2010) to

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Business Systems Laboratory- Proceedings, ISBN: 9788890824227 3rd Int. Symposium - Perugia 2015

explore new ideas and new products, since this interaction leads to continuous feedback on specific problems and answers or anticipates customers’ needs and preferences. The main advantages of Interactive Marketing consist to precisely communicate to customers (Mulhern, 2010), with respect to the traditional marketing mechanisms, and, mainly, in providing the great opportunity to gather a huge amount of data, from consumers’ feedback, that can be exploited for generating, for instance, personalized offerings, recommendations on products and services which better fit the specific customers’ needs and so on (Ding, et al., 2012). In the last years, Interactive Marketing activities have undergone a remarkable growth thanks to the evolution of new information and communication technologies. In particular, Internet and the Web (especially with the advent of Web 2.0) represent two of the main assets for companies and customers in order to communicate and share information and opinions about products and services. On the other hand, the evolution of the Internet is leading to a novel paradigm which foresees the pervasive presence around us of a variety of things or objects – such as sensors, actuators, mobile phones, etc. – and their virtual representations, which are able to interact with each other and cooperate with their neighbours to reach common goals: the Internet of Things (IoT) (Atzori, et al., 2010). Such objects may link to information about them, or may transmit real-time sensor data about their state or other useful properties associated with the object. In the Internet of Things vision, individual objects of daily life, such as cars, roadways, pacemakers, refrigerators and so on, can be equipped, for instance, with sensors, which can track useful information about these objects. Since these objects can sense the environment and communicate, they have become tools for understanding complexity, and may often enable autonomic responses to challenging scenarios without humans’ intervention (Aggarwal, et al., 2013). It is expected that by 2025 Internet nodes may reside in everyday things, leading to a widespread diffusion of the Internet of Things (IoT) that could contribute invaluably to economic development ( National Intelligence Council, April 2008) (Atzori, et al., 2010). Being based on standard Internet protocols, IoT represents a suitable solution to gather data from heterogeneous sensors in order to enable fusion and provision of relevant and contextualised information in order to also support complex decision-making processes in several and heterogeneous application domains like, for instance, Commerce, Emergency Management, Security, e-Healthcare (De Maio, et al., 2011). Thus, suitable models and methodologies to exploit data coming from IoT to sustain modern Decision Support Systems (DSSs) are needed. In this scenario, Situation Awareness represents a powerful paradigm enabling the aforementioned capabilities. Situation Awareness has been defined by Endsley as “the perception of the elements in an environment within a volume of time and space, the comprehension of their meaning, and a projection of their status in the near future" (Endsley, 1995). One of the main tasks in Situation Awareness is the automatic identification of the occurring situation that is accomplished by means of several approaches which make use of heterogeneous Semantic and Computational Intelligence techniques (Benincasa, et al., 2015). In the context of Interactive Marketing, the synergy of Internet of Things and Situation Awareness could represent a fundamental asset to propose, for instance, real-time tailored advertisements, special offerings for individuals or specific group of users which are generated after decision processes (automatic, semi-automatic or non-automatic), taking care also of complex situations inferred by considering lower level information like customers’ interests ad behaviours, stock availabilities, existing marketing strategies and so on. The analysis of this information could be performed by means of techniques like, for instance, Fuzzy Cognitive Maps

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(Jones, et al., 2011) or Dempster-Shafer Theory of Evidence (McKeever, et al., 2009). Different approaches based, for instance, on Ontologies (e.g. SAW) are used to recognize occurring situations by means of inference operations accomplished with both ontology-based and rulebased techniques. Let us consider a shopping mall powered by several sensors, like NFC Tags or proximity sensors, which are attached to specific products and shelves into the stores of the shopping mall. Situation Awareness approaches can be used to analyse the data gathered by the aforementioned sensors and, thus, to understand a measure of interest of the customers with respect to specific products or categories of products in fixed time slices. These high-level information can be exploited to define (or adjust) personalised interactive marketing campaigns (e.g. executed by sending coupons or offerings to individuals or groups of customers). Moreover, information coming from a subset of all considered time slices can be used to support the definition of future marketing campaigns. The main objective of this work is to define a DSS (Cioca M., Cioca L.I., 2010) framework for Interactive Marketing in the context of blended commerce that is based on Internet of Things and Situation Awareness. In particular, identifying and taking care of the occurring situation in a commerce environment enables more suitable forms of adaptation of the marketing strategies. The proposed framework supports three types of decisions with respect to different marketing scenarios: 1. Short-term Interactive Marketing that provides immediate feedback to individual customers when they interact with the enhanced environment. 2. Medium-term Interactive Marketing that provides end-slice feedback to individual customers or group of customers after considering events and actions within a specific time slice. 3. Long-term marketing that provides the population of a suitable dashboard for the decision makers who are sustained by information collected in several time slices (typically days, weeks or months) in order to adapt existing marketing strategies (for the whole mall, brands, categories of products or products) or define new ones. Keyword: Internet of Things, Situation Awareness, Decision Support System, Interactive Marketing. REFERENCES National Intelligence Council, (2008). Disruptive Civil Technologies – Six Technologies with Potential Impacts on US Interests Out to 2025. Conference Report. Aggarwal, C.C., Ashish, N., Sheth, A., (2013). The Internet of Things: A Survey from the DataCentric Perspective. In: C.C. Aggarwal (Ed.). Managing and Mining Sensor Data. New York, NY: Springer. Atzori, L., Iera, A., Morabito, G., (2010). The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization. Computer Networks. 56(6):3594 - 3608. Benincasa, G., D’Aniello, G., Gaeta, M., Loia, V., Francesco Orciuoli, F. (2015). Resilient Semantic Sensor Middleware. In: Studies in Computational Intelligence, Vol. 570: 453-463. Switzerland, CH: Springer International Publishing.

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Cioca, M., Cioca, L.I. (2010). Decision Support Systems used in Disaster Management. Croatia, HR: InTech Publisher. De Maio, C., Fenza, G., Gaeta, M., Loia, V., Orciuoli, F. (2011). A knowledge-based framework for emergency DSS. Knowledge-Based Systems, 24(8):1372-1379. Ding, Q., Zhou, Z., Huang, B., (2012). Case Study of Application of Interactive Marketing in Ecommerce. In: proceeding IEEE Symposium on Robotics and Applications (ISRA) :347-350. Endsley, M., (1995). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1):32-64. Jones, R.E., Connors, E.S., Mossey, M.E., Hyatt, J.R., Hansen, N.J., Endsley, M.R., (2011). Using fuzzy cognitive mapping techniques to model situation awareness for army infantry platoon leaders. Computational and Mathematical Organization Theory, 17(3):272-295. McKeever, S., Ye, J., Coyle, L., Dobson, S., (2009). Using dempster-shafer theory of evidence for situation inference. In: Barnaghi, P.M., Moessner K., Presser, M., Meissner, S. (Ed.). Lecture Notes in Computer Science (pp. 149–162), vol. 5741. Berlin/Heidelberg, G: Springer. Mulhern, F.J., (2010). Direct and Interactive Marketing. Wiley International Encyclopedia of Marketing, 1:67-69. Sawhney, M., Verona, G., Prandelli, E., (2005). Collaborating to create: The Internet as a platform for customer engagement in product innovation. Journal of Interactive Marketing, 19(4):4–17.

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