Globally Distributed R&D Work in a Marketing Management Support Systems (MMSS) Environment

Globalisation, liberalization and rapid technological developments have been changing business environments drastically in the recent decades. These trends are increasingly exposing businesses to market competition and thus intensifying competition. In such an environment, the role of marketing management support systems (MMSS) becomes exceedingly important for the long-term growth of an organisations marketing expertise and success. In this paper, we discuss the evolution of a globally distributed R&D project spanning three continents in developing an MMSS for the motion picture industry. We first provide the conceptual background of the MMSS and knowledge management systems relevant for our work. We then provide a detailed case study of our MMSS implementation. We specifically focus on the following elements of our work: globally distributed R&D efforts, knowledge elements, and fit between demand and supply sides of MMSS. We conclude with a discussion of implications for future research in this area

A Viral Branching Model for Predicting the Spread of Electronic Word-of-Mouth

In a viral marketing campaign an organization develops a marketing message, and stimulates customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach, and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed Viral Branching Model allows customers to participate in a viral marketing campaign by 1) opening a seeding email from the organization, 2) opening a viral email from a friend, and 3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities already in the early stages of viral marketing campaigns. The Viral Branching Model is app

Demand-Driven Scheduling of Movies in a Multiplex

This paper describes a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies within each day of the week, on which screen(s) different movies will be played, and at which time(s). The model consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time; and (ii) an optimization procedure that quickly finds an almost optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule we formulate the so-called movie scheduling problem as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We have applied this combined demand forecasting /schedule optimization procedure to a multiplex in Amsterdam where we supported the scheduling of fourteen movie weeks. The proposed model not 2 only makes movie scheduling easier and less time consuming, but also generates schedules that would attract more visitors than the current ‘intuition-based’ schedules

A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth

In a viral marketing campaign, an organization develops a marketing message and encourages customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed viral branching model allows customers to participate in a viral marketing campaign by (1) opening a seeding e-mail from the organization, (2) opening a viral e-mail from a friend, and (3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities in the early stages of viral marketing campaigns. The viral branching model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.branching processes, forecasting, Markov processes, online marketing, viral marketing, word of mouth