Decision Trees for Uplift Modeling with Single and Multiple Treatments
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Uplift Modeling in Retail Marketing Using SAS
Uplift Modeling in Retail Marketing using SAS Aashish Arjun Bugalia, Oklahoma State University, Stillwater, OK Agastya Komarraju, Oklahoma State University, Stillwater, OK Abstract Direct marketing campaigns are often targeted to randomly selected customers which results in huge costs for the company. Also, such campaigns result in customer frustration and makes them less likely to react to further communications in future. Companies involved in direct marketing campaigns often use a random response model to target customers for the campaigns. An alternative approach can be through uplift modeling - precise targeting of the beneficial customers resulting in greater return on invested money and resources by the company. Based on the marketing and finance literature, this article looks at high prediction accuracy for the probability of purchase based on a sample of customers, to whom a pilot campaign has been sent. Uplift modeling analyzes the causal effect of an action such as a marketing campaign on a given individual by considering difference in response rate between a treated group and a randomized control group. The resulting model can then be used to select individuals for whom the action will be most profitable. This article aims at predicting beneficial customers to an online retailer with the implementation of several statistical, machine learning and deep learning methods in SAS. Through above-mentioned methods this paper will also help to know effectiveness of the campaign by determining incremental gains, thus resulting in greater return on invested money and resources by the company. Keywords: Direct marketing, Uplift Modeling, Decision Tree, Logistic Regression, Random Forest, Neural network, Decision tree, Ensemble methods 1 Introduction A marketing campaign comprises of a time bound offer sent to a specific set of people to observe a specific behavior and has a specific reward attached to it. -
ES-Working Paper No. 12
FACULTEIT ECONOMISCHE EN SOCIALE WETENSCHAPPEN & SOLVAY BUSINESS SCHOOL ES-Working Paper no. 12 THE CASE FOR PRESCRIPTIVE ANALYTICS: A NOVEL MAXIMUM PROFIT MEASURE FOR EVALUATING AND COMPARING CUSTOMER CHURN PREDICTION AND UPLIFT MODELS Floris Devriendt and Wouter Verbeke April 30th, 2018 Vrije Universiteit Brussel – Pleinlaan 2, 1050 Brussel – www.vub.be – [email protected] © Vrije Universiteit Brussel This text may be downloaded for personal research purposes only. Any additional reproduction for other purposes, whether in hard copy or electronically, requires the consent of the author(s), editor(s). If cited or quoted, reference should be made to the full name of the author(s), editor(s), title, the working paper or other series, the year and the publisher. Printed in Belgium Vrije Universiteit Brussel Faculty of Economics, Social Sciences and Solvay Business School B-1050 Brussel Belgium www.vub.be The case for prescriptive analytics: a novel maximum profit measure for evaluating and comparing customer churn prediction and uplift models a, a Floris Devriendt ⇤, Wouter Verbeke aData Analytics Laboratory, Faculty of Economic and Social Sciences and Solvay Business School, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium Abstract Prescriptive analytics and uplift modeling are receiving more attention from the business analyt- ics research community and from industry as an alternative and improved paradigm of predictive analytics that supports data-driven decision making. Although it has been shown in theory that prescriptive analytics improves decision-making more than predictive analytics, no empirical evi- dence has been presented in the literature on an elaborated application of both approaches that allows for a fair comparison of predictive and uplift modeling. -
Effective Extraction of Small Data from Large Database by Using Data Mining Technique
International Journal of Scientific & Engineering Research, Volume 7, Issue 4, April-2016 372 ISSN 2229-5518 Effective Extraction of Small Data from Large Database by using Data mining Technique. Mr. Parag Satish Kulkarni Miss. Prajakta Arjun Jejure B.E, A.M.I.E, D.M.E, B.com, PGDOM, B.E, M.B.A Student M.B.A, Student Department of Information Technology Department of Operations Management, K.K.W.I.E.R COE, Symbiosis Institute of Operations Management Savitribai Phule Pune University. Symbiosis International University. Abstract: The demand for extracting meaningful patterns in various applications is very necessary. Data mining is the process of automatically extracting meaningful patterns from usually very large quantities of seemingly unrelated data. When used in conjunction with the appropriate visualization tools, data mining allows the researcher to use highly advanced pattern-recognition skills and knowledge of molecular biology to determine which results warrant further study. Data mining is an automated means of reducing the complexity of data in large bioinformatics databases and of discovering meaningful, useful patterns and relationships in data. Data mining is one stage in an overall knowledge-discovery process. It is an iterative process in which preceding processes are modified to support new hypotheses suggested by the data. The process of data mining is concerned with extracting patterns from the data, typically using classification, regression, link analysis, and segmentation or deviation detection. Keywords: KDD, Computational process, Artificial Intelligence, Data pre processing, Data mining. —————————— —————————— Introduction: Data mining is the process of creating insightful, interesting, and novel patterns, also descriptive, predictive modelsIJSER and understandable from large size data. -
Machine Learning for Marketing Decision Support
Machine Learning for Marketing Decision Support Doctoral Thesis to acquire the academic degree of doctor rerum politicarum (Doctor of Economics and Management Science) submitted to School of Business and Economics Humboldt-Universität zu Berlin by M.Sc. Johannes Sebastian Haupt President of Humboldt-Universität zu Berlin: Prof. Dr.-Ing. Dr. Sabine Kunst Dean of the School of Business and Economics: Prof. Dr. Daniel Klapper Reviewers: 1. Prof. Dr. Stefan Lessmann 2. Prof. Dr. Daniel Guhl Date of Colloquium: 24 June 2020 2 Abstract The digitization of the economy has fundamentally changed the way in which companies interact with customers and made customer targeting a key intersection of marketing and information systems. Marketers can choose very specifically which customers to serve with a marketing mes- sage based on detailed demographic and behavioral information. Building models of customer behavior at the scale of modern customer data requires development of tools at the intersection of data management and statistical knowledge discovery. The application of these models for successful targeting requires deep understanding of the underlying marketing decision problem and awareness of the ethical implications of data collection. This dissertation widens the scope of research on predictive modeling by focusing on the in- tersections of model building with data collection and decision support. Its goals are 1) to develop and validate new machine learning methods explicitly designed to optimize customer targeting decisions in direct marketing and customer retention management and 2) to study the implications of data collection for customer targeting from the perspective of the company and its customers. The thesis addresses the first goal by proposing methods that utilize the richness of e-commerce data, reduce the cost of data collection through efficient experiment design and address the tar- geting decision setting during model building. -
Predictive Analytics Using R
Predictive Analytics using R Dr. Jeffrey Strickland is a Senior Predictive Analytics Consultant with over 20 years of Predictive Analytics expereince in multiple industiries including financial, insurance, defense and NASA. He is a subject matter expert on mathematical and statistical modeling, as well as machine using R learning. He has published numerous books on modeling and simulation. Dr. Strickland resides in Colorado. This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling, both statistical (parametric and nonparametric), as well as machine learning. We define predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics (statistical inference, models, tests, etc.) — this is an advanced book. Some theoretical foundations are laid out (perhaps subtlety) but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror at http://www.r-project.org/. An introduction to R is also available at http://cran.r-project.org /doc/manuals/r-release/R-intro.html. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. -
Machine Learning Based Prediction and Classification for Uplift Modeling
DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Machine Learning Based Prediction and Classification for Uplift Modeling LOVISA BÖRTHAS JESSICA KRANGE SJÖLANDER KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES Machine Learning Based Prediction and Classification for Uplift Modeling LOVISA BÖRTHAS JESSICA KRANGE SJÖLANDER Degree Projects in Mathematical Statistics (30 ECTS credits) Degree Programme in Applied and Computational Mathematics (120 credits) KTH Royal Institute of Technology year 2020 Supervisor at KTH: Tatjana Pavlenko Examiner at KTH: Tatjana Pavlenko TRITA-SCI-GRU 2020:002 MAT-E 2020:02 Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci Abstract The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investigated. The statistical machine learning methods applied are Random Forests and Neural Networks along with the standard method Logistic Regression. The data is collected from a well established retail company and the purpose of the project is thus to investigate which uplift modeling approach and statistical machine learning method that yields in the best performance given the data used in this project.