November 13, 2006, 10:00, Room MA 12 (bātiment MA) (click here for the map)
A typical marketing department usually runs a portfolio of mass campaigns such as TV Spots, Radio, Inserts, Promotional offers, Co-Marketing, etc. Running such campaigns is usually a very costly process both in terms of budget and resources. Moreover, there usually is a lack of quantitative measurement of the effectiveness of these campaigns as well as the return on marketing investment in terms of marginal sales and market share. The goal of this study is to build a marketing measurement and optimization system which helps marketing managers to address two key issues in their decision processes: 1) Quantifying the marginal impact of incremental investment in a given type of mass campaign on sales and market share 2) Determine an optimal budget and resource allocation strategy across the portfolio of campaigns which maximizes the total profit and market share. To address these issues, we adopt a modeling process which consists of two steps. We firstly develop a set of machine learning models using historical data. Secondly, we combine those models into a nonlinear objective function which measures the overall Utility of the campaigns portfolio under budget and resource constraints. Optimal campaign diversification and budgeting is then achieved by maximizing such a utility function.