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

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.Comment: 10 pages, KDD 201

Recommendations for Responsible Food Marketing to Children

The marketing of unhealthy foods to children and youth is a major public health concern. Children in the United States grow up surrounded by food and beverage marketing, which primarily promotes products with excessive amounts of added sugar, salt, and fat, and inadequate amounts of fruits, vegetables, and whole grains. This document provides a comprehensive set of model definitions for food marketing practices directed to children. The recommendations, developed by a national panel of experts convened by Healthy Eating Research, define the child audience range as birth to 14 years of age; address the range of food marketing practices aimed at children; and specify the strategies, techniques, media platforms, and venues used to target children. When paired with sound nutrition criteria, these recommendations will help support responsible food marketing to children by addressing current loopholes in food marketing definitions and self-regulatory efforts that allow companies to market unhealthy foods and beverages to children

Affinity Paths and Information Diffusion in Social Networks

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.Comment: 11 pages, 7 figure

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches