Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables

Companies employ international diffusion models to assess the local market potential and local diffusion speed to support their decision making on market entry. After their entry into a country, they use the model forecasts for their performance controlling. To this end, empirical applications of international diffusion models aim to link differential diffusion patterns across countries to various exogenous drivers. In the literature, macro- and socioeconomic variables like population characteristics, culture, economic development, etc. have been linked to differential penetration developments across countries. But as companies cannot influence these drivers, their marketing decisions that shape national diffusion patterns are ignored. Is this reasonable? What then, is the role of marketing instruments in an international diffusion context? We address this issue and compare the influence of these prominent exogenous drivers of international diffusion with that of industry and marketing-mix variables. To account for all of these factors and simultaneously accommodate the influence of varying cross-country interactions, we develop a more flexible yet parsimonious model of international diffusion. Finally, to avoid technical issues in implementing spatially dependent error terms we introduce the test concept of Moran's I to international diffusion model. We demonstrate that the lead-lag effect in conjunction with spatial neighborhood effects controls most of the spatial autocorrelation. Using this combined approach we find that --- for cellulars --- industry and marketing-mix variables explain international diffusion patterns better than macro- and socioeconomic drivers. --

Data-Driven Pricing for a New Product

Decisions regarding new products are often difficult to make, and mistakes can have grave consequences for a firm’s bottom line. Often, firms lack important information about a new product, such as its potential market size and the speed of its adoption by consumers. One of the most popular frameworks that has been used for modeling new product adoption is the Bass model. Although the Bass model and its many variants are used to study dynamic pricing of new products, the vast majority of these models require a priori knowledge of parameters that can only be estimated from historical data or guessed using institutional knowledge. In this paper, we study the interplay between pricing and learning for a monopolist whose objective is to maximize the expected revenue of a new product over a finite selling horizon. We extend the generalized Bass model to a stochastic setting by modeling adoption through a continuous-time Markov chain with which the adoption rate depends on the selling price and on the number of past sales. We study a pricing problem in which the parameters of this demand model are unknown, but the seller can utilize real-time demand data for learning the parameters. We propose two simple and computationally tractable pricing policies with O(ln m) regret, where m is the market size

Applications of Chance Constrained Optimization in Operations Management

In this thesis we explore three applications of chance constrained optimization in operations management. We first investigate the effect of consumer demand estimation error on new product production planning. An inventory model is proposed, whereby demand is influenced by price and advertising. The effect of parameter misspecification of the demand model is empirically examined in relation to profit and service level feasibility, and conservative approaches to estimating their effect on consumer demand is determined. We next consider optimization in Internet advertising by introducing a chance constrained model for the fulfillment of guaranteed display Internet advertising campaigns. Lower and upper bounds using Monte Carlo sampling and convex approximations are presented, as well as a branching heuristic for sample approximation lower bounds and an iterative algorithm for improved convex approximation upper bounds. The final application is in risk management for parimutuel horse racing wagering. We develop a methodology to limit potential losing streaks with high probability to the given time horizon of a gambler. A proof of concept was conducted using one season of historical race data, where losing streaks were effectively contained within different time periods for superfecta betting