Towards Solving the Social Science Challenges with Computing Methods

Preface

Computational Social Science is a highly interdisciplinary field in which social science questions are investigated with modern computational tools. This book provides insight into different social problems, which call for the new practices offered by computational social science. It comprises methods for efficient management of complex engineering design, prediction of malicious threads of information systems, and cyber security. It includes topics in awareness of privacy in social networks, prosodic modelling for speech synthesis, and investigates the structure of co-occurrence networks and provides insights into the loosely coupled cloud applications. Another aspect of the field of computing in Social Sciences is envisaged by proposing an extension of synergy models in IMC environment to n-communication channels, and identification and evaluation of knowledge management through information system analysis. ← 9 | 10 → ← 10 | 11 →

Jana Suklan¹, Vesna Žabkar², Nadja Damij³

(1) School of Advanced Social Studies in Nova Gorica Gregorčičeva 19, 5000 Nova Gorica, Slovenia
jana.suklan@fuds.si

(2) Faculty of Economics, University of Ljubljana Kardeljeva ploščad 17, 1000 Ljubljana, Slovenia
vesna.zabkar@ef.uni-lj.si

(3) Faculty of Information Studies in Novo mesto Sevno 13, 8000 Novo mesto, Slovenia
nadja.damij@fis.unm.si

Extending Synergy Model in the IMC Environment to n-Communication Channels

Abstract: The presence of synergy represents a key feature in planning marketing activities using integrating marketing communication approach. By combining different multimedia activities, companies are able to benefit from synergy between integrated communication channels. In the presence of synergy, optimal media budget allocation, optimal media mix and advertising carry-over effects differ. The extension of a marketing-mix model provides an insight into how different components of the model interact and elucidate the causes of main and side effects resulting in instant sales and brand awareness over time. In order to understand relationships between different components of the integrated marketing communications model, indicators for four communication channels were integrated into the model. Along with four communication channels an empirical tool for evaluating marketing effectiveness in decision-making processes was developed. Such a tool is very important, since the multichannel marketing environment presents a challenge for marketers and a major source of financial input for companies.

Keywords: Synergy, Carry-over effect, Media Planning, Marketing Integration Model.

1.Introduction

Using market data we calibrated the proposed model to establish the presence of synergy between television and online advertising. Recognizing interaction effects between on-line and off-line activities, we advised managers to consider inter-activity trade-offs in optimally planning marketing-mix ← 11 | 12 → strategies (Naik et al. 2005, pp. 2–4). Our focus remained on the extension of the already defined models in the literature that incorporate synergy between media (Naik and Raman 2003, pp. 376) in a manner that meets a specific data collection technique. An article written by Naik and Raman (2003, pp. 383–387) laid the groundwork for the n-media generalisation with differential carryover effect and asymmetric synergy as a future research option. Throughout the analysis, based on detailed collection of reliable proprietary data for each media separately, we intend to contribute to the understanding of consumer response to cross-media strategies.

Marketer’s intuitive knowledge must be supported analytically through the development of analytical models to capture the essence of a context and limit the complexity of the model at the same time (Coughlan et al 2010, pp. 3–6). Analysing the data and discussing it with the marketer’s increases the understanding of patterns in consumer reactions towards advertising. Throughout the analysis we aimed to understand the relationships and effects of the important indicators in the model.

As Keller (2010, pp. 58) puts it, the challenge for marketers is to choose the right communication options among different for a campaign or business model in order to maximize “push” and “pull” effects in the multimedia environment. Our aim was to include a more complete picture of marketing integration strategy in the previously specified advertising model. We include both direct and indirect channels, and personal and mass communication channels. In terms of channels of distribution, we included on-line as a direct and interactive channel, then resellers (supermarket, catalogue showroom) as an indirect channel. In terms of marketing communications, personal communications were employed through personal selling (company stores) and mass communications through advertising on television.