15h30 - 15h55
Modeling and Prediction of Keyword Behavior in Search Engine Marketing Campaigns
The objective of our research is to develop algorithms that can be used to increase return on investment in search engine marketing campaigns. More specifically, our aim is to create automated optimization and prediction methods in order to provide campaign managers with assistance in their decision making process on a daily basis.
15h55 - 16h20
Market Share Estimation in the Presence of a Dominant Brand and a Private Label
The private label growth is one of the most striking phenomena of the food industry sector development in the last decade. This work analyzes a scanner database through a market share attraction model, in order to understand the competitive interaction between private label and national brands categories and assess marketing strategies implemented by manufacturers and retailers in this specific context of competition.
16h20 - 16h45
Modelling and Analysis of Consumer Decision Making using A Stochastic Framework
The aim of this research is to design new stochastic models for predicting consumer behaviour with respect to an exhaustive set of choices that consumers face in a typical purchasing experience. Models are developed for both homogeneous and heterogeneous consumer groups. The effectiveness of our models is empirically tested using real life panel scanner data on the US market with respect to a variety of consumer products.
16h45 - 17h10
Is Umbrella Branding Strategy always Profitable for Private Labels?
Umbrella branding strategy means using the same name to market different products. The paper investigates when such strategy is profitable for a retailer offering national brands and its store brands. The analysis includes the strategic interactions between channel members and the positive spillover between private labels’ sales in different categories.