An Integrated Framework for Competitive Multi-channel Marketing of Multi-featured Products

For any company, multiple channels are available for reaching a population in order to market its products. Some of the most well-known channels are (a) mass media advertisement, (b) recommendations using social advertisement, and (c) viral marketing using social networks. The company would want to maximize its reach while also accounting for simultaneous marketing of competing products, where the product marketings may not be independent. In this direction, we propose and analyze a multi-featured generalization of the classical linear threshold model. We hence develop a framework for integrating the considered marketing channels into the social network, and an approach for allocating budget among these channels

Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time

A strategic and operational view of competitiveness and cohesion in the European context

The persistence of spatial developmental disparities suggests that the strategic directions of any initiative targeting regional competitiveness should follow the lines of (1) maximizing its competitive impact and (2) matching the territorial specificity. According to this perspective, the paper discusses an original theoretical construct and points to graphical representations of operational forms that may configure a policy of territorial development along four co-existent levels: (1) urban fields, (2) clusters, (3) development areas, and (4) disadvantaged areas. The main implication for public policy initiatives resides in facilitating the progress towards building up such a potential for growth.agglomeration, competitiveness, development, spatial network, territorial planning

Incentives for Transmission Investment in the PJM Electricity Market: FTRs or Regulation (or Both?)

This paper presents an application of a mechanism that provides incentives to promote transmission network expansion in the area of the US electric system known as PJM. The applied mechanism combines the merchant and regulatory approaches to attract investment into transmission grids. It is based on rebalancing a two-part tariff in the framework of a wholesale electricity market with locational pricing. The expansion of the network is carried out through the sale of financial transmission rights for the congested lines. The mechanism is tested for 14-node and 17-node geographical coverage areas of PJM. Under Laspeyres weights, it is shown that prices converge to the marginal cost of generation, the congestion rent decreases, and the total social welfare increases. The mechanism is shown to adjust prices effectively given either non-peak or peak demand.Electricity transmission expansion, incentive regulation, PJM

When-To-Post on Social Networks

For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily and weekly behavior patterns and the visibility of the message. While previous work has focused on overall network dynamics and message flow cascades, the problem of recommending personalized posting times has remained an underexplored topic of research. In this study, we formulate a when-to-post problem, where the objective is to find the best times for a user to post on social networks in order to maximize the probability of audience responses. To understand the complexity of the problem, we examine user behavior in terms of post-to-reaction times, and compare cross-network and cross-city weekly reaction behavior for users in different cities, on both Twitter and Facebook. We perform this analysis on over a billion posted messages and observed reactions, and propose multiple approaches for generating personalized posting schedules. We empirically assess these schedules on a sampled user set of 0.5 million active users and more than 25 million messages observed over a 56 day period. We show that users see a reaction gain of up to 17% on Facebook and 4% on Twitter when the recommended posting times are used. We open the dataset used in this study, which includes timestamps for over 144 million posts and over 1.1 billion reactions. The personalized schedules derived here are used in a fully deployed production system to recommend posting times for millions of users every day.Comment: 10 pages, to appear in KDD201