Multistage performance modelling in digital marketing management

Abstract

IntroductionInteractive media, apart from one-to-one communication, allow for detailed tracking of the performance of online campaigns and improving it with the use of dedicated technologies (Hoffman and Novak, 1995). Several interdisciplinary research directions can be observed with the main goal of increasing outcomes from online advertising and identification of new challenges (Guha et al., 2010). Campaign execution is usually based on media plans, which are prepared at strategical level with conventional planning methods (Cannon, 2001) or dedicated models dealing with a multi-objective approach in the online environment (Hengbo and Yanfeng, 2012; Du and Xu, 2012). While campaign planning takes place periodically, the online advertising systems require tuning and optimization in real time. In the online environment, methods based on multivariate testing or stochastic models or contextual selection (Tang et al., 2013) are used. Apart from optimization, improvement is performed with the use of behavioral targeting systems (Yan et al., 2009), retargeting customers with revealed interest in specific products (Lambrecht and Tucker, 2013) or real time bidding systems (Yuan et al., 2013). Intensive online advertising can affect websites and makes information seeking more difficult and causes an increased cognitive load, frustration and other negative emotions (Brajnik and Gabrielli, 2010). Due to factors related to cognitive avoidance, the dropping performance of online advertising is observed over time (Kelly et al., 2010). With connected characteristics related to audience, changes of the performance over the time, campaign planning requires the use of past data with the ability of taking into an account uncertain information. Earlier approaches based on stochastic or fuzzy models use same importance for data from past periods and from recent results.Due to searching for a more realistic approach, the presented research is based on the use of past data with a forgetting function and weighted importance of data from past periods. The work is presented as follows: after the literature review and the problem statement, the conceptual framework of a multistage performance modelling is presented. In the next stage, the proposed framework is used in an empirical study for dynamic evaluation of data from experimental advertising campaign. Finally, conclusions are presented.1. Literature ReviewInteractive media created the ability to measure different results from online marketing campaigns and use them in the decision process related to planning at both strategy and tactical levels. New metrics are used in this field for media planning for the evaluation of online campaigns and are based on the direct response and interactions or longer term influence on brand awareness (Novak and Hoffman, 1996; Pavlou and Stewart, 2000) with an essential quantitative approach to media planning (Hoffman and Novak, 2000). New approaches dedicated to online media use its specifics, but conventional media planning based on earlier approaches is still applicable in that area (Cannon, 2001). The basis for online campaign execution within the portal selling advertising space is the scheduling at different slots the use of different creations. Estimations of potential audiences and the ability to serve advertisements use the analysis based on the planned behaviors and site pre-visit intentions (Wu, 2007). At the operational level other areas deal with the real time campaign optimization and searching for the best method of resource exploitation with the use of stochastic models (Chakrabarti et al., 2009). Even when a banner selection is performed at the operational level and the selection is used with the different parameter scheduling, the execution is based on plans from a strategical level (Amiri and Menon, 2003). Scheduling is used within advertising server applications to select specific content as an answer to a request coming from a web browser. …