Introduction To Algorithmic Marketing: Artificial .
I LYA K AT S O VINTRODUCTION TOALGORITHMIC MARKETING
Introduction to Algorithmic Marketingby Ilya KatsovVisit the book’s website at https://algorithmicweb.wordpress.comfor additional resources.ISBN 978-0-692-98904-3Copyright 2018 Ilya KatsovAll rights reserved. The digital version of the book is available fordownload on algorithmicweb.wordpress.com and can be shared onlythrough a link to the landing page. No part of this book may bemodified, reposted, emailed, or made available for download on othersites or channels, without the prior written permission of the author.
v“At a time when power is shifting to consumers, while brands and retailersare grasping for fleeting moments of attention, everyone is competing on dataand the ability to leverage it at scale to target, acquire, and retain customers.This book is a manual for doing just that. Both marketing practitioners andtechnology providers will find this book very useful in guiding them throughthe marketing value chain and how to fully digitize it. A comprehensive andindispensable reference for anyone undertaking the transformational journeytowards algorithmic marketing.”—Ali Bouhouch, CTO, Sephora Americas“If you’re tired of the vague fluff about AI in marketing, and you want tounderstand the real substance of what’s possible today and how it works, thenyou must read An Introduction to Algorithmic Marketing. This is the bestbook in the field of marketing technology and operations that I’ve read yet.”—Scott Brinker,Author of Hacking Marketing, Editor of chiefmartec.com“Its all possible now. This book brings practicality to concepts that just afew years ago would have been dismissed as mere theory. It features principledframing that captures what the best marketers innately feel but cannot express.Elegant math articulates the important relationships that are so elusive to traditional business modeling. The book is unapologetic for its lack of spreadsheetexamples – much of the world can not be represented linearly in just a fewdimensions and devoid of uncertainty. Instead, the book embraces rigorousframing that yields better insights into real phenomenon. It’s written neitherfor the data scientist nor the marketer, but rather for the two combined! Itsthis partnership between these two departments that will lead to real impact.This book is where that partnership should begin.”—Eric Colson, Chief Algorithms Officer, Stitch Fix
vi“This book is a live portrait of digital transformation in marketing. It showshow data science becomes an essential part of every marketing activity. Thebook details how data-driven approaches and smart algorithms result in deepautomation of traditionally labor-intensive marketing tasks. Decision-makingis getting not only better but much faster, and this is crucial in our everaccelerating competitive environment. It is a must-read for both data scientistsand marketing officers–even better if they read it together.”—Andrey Sebrant, Director of Strategic Marketing, Yandex“Introduction to Algorithmic Marketing isn’t just about machine learningand economic modeling. It’s ultimately a framework for running business andmarketing operations in the AI economy.”—Kyle McKiou,Sr. Director of Data Science, The Marketing Store“This books delivers a complete end-to-end blueprint on how to fully digitizeyour company’s marketing operations. Starting from a conceptual architecturefor the future of digital marketing, it then delves into detailed analysis of bestpractices in each individual area of marketing operations. The book gives theexecutives, middle managers, and data scientists in your organization a set ofconcrete, actionable, and incremental recommendations on how to build betterinsights and decisions, starting today, one step at a time.”—Victoria Livschitz, founder and CTO, Grid Dynamics
vii“While virtually every business manager today grasps the conceptualimportance of data analytics and machine learning, the challenge of implementing actual competitive solutions rooted in data science remains quitedaunting. The scarcity of data scientist talent, combined with the difficultyof adapting academic models, generic open-source software and algorithmsto industry-specific contexts are among the difficulties confronting digitalmarketers around the world. This book by Ilya Katsov draws from the deepdomain expertise he developed at Grid Dynamics in delivering innovative,yet practical digital marketing solutions to large organizations and helpingthem successfully compete, remain relevant, and adapt in the new age of dataanalytics.”—Eric Benhamou,founder and General Partner, Benhamou Global Ventures;former CEO and Chairman of 3Com and Palm“This book provides a much-needed collection of recipes for marketing practitioners on how to use advanced methods of machine learning and data scienceto understand customer behavior, personalize product offerings, optimize theincentives, and control the engagement – thus creating a new generation ofdata-driven analytic platform for marketing systems.”—Kira Makagon, Chief Innovation Officer, RingCentral;serial entrepreneur, founder of RedAril and Octane
CONTENTS1introduction1.1 The Subject of Algorithmic Marketing . . . . . . . . . . .1.2 The Definition of Algorithmic Marketing . . . . . . . . . .1.3 Historical Backgrounds and Context . . . . . . . . . . . .1.3.1 Online Advertising: Services and Exchanges . . . . .1.3.2 Airlines: Revenue Management . . . . . . . . . . . . .1.3.3 Marketing Science . . . . . . . . . . . . . . . . . . . . .1.4 Programmatic Services . . . . . . . . . . . . . . . . . . . .1.5 Who Should Read This Book? . . . . . . . . . . . . . . . .1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 review of predictive modeling2.1 Descriptive, Predictive, and Prescriptive Analytics . . . .2.2 Economic Optimization . . . . . . . . . . . . . . . . . . . .2.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . .2.4 Supervised Learning . . . . . . . . . . . . . . . . . . . . . .2.4.1 Parametric and Nonparametric Models . . . . . . . .2.4.2 Maximum Likelihood Estimation . . . . . . . . . . . .2.4.3 Linear Models . . . . . . . . . . . . . . . . . . . . . . .2.4.3.1 Linear Regression . . . . . . . . . . . . . . . . . .2.4.3.2 Logistic Regression and Binary Classification . .2.4.3.3 Logistic Regression and Multinomial Classification . . . . . . . . . . . . . . . . . . . . . . . . . . .2.4.3.4 Naive Bayes Classifier . . . . . . . . . . . . . . . .2.4.4 Nonlinear Models . . . . . . . . . . . . . . . . . . . . .2.4.4.1 Feature Mapping and Kernel Methods . . . . . .2.4.4.2 Adaptive Basis and Decision Trees . . . . . . . .2.5 Representation Learning . . . . . . . . . . . . . . . . . . .2.5.1 Principal Component Analysis . . . . . . . . . . . . .2.5.1.1 Decorrelation . . . . . . . . . . . . . . . . . . . . .2.5.1.2 Dimensionality Reduction . . . . . . . . . . . . .2.5.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . .2.6 More Specialized Models . . . . . . . . . . . . . . . . . . .2.6.1 Consumer Choice Theory . . . . . . . . . . . . . . . .2.6.1.1 Multinomial Logit Model . . . . . . . . . . . . . .2.6.1.2 Estimation of the Multinomial Logit Model . . .2.6.2 Survival Analysis . . . . . . . . . . . . . . . . . . . . .2.6.2.1 Survival Function . . . . . . . . . . . . . . . . . .2.6.2.2 Hazard Function . . . . . . . . . . . . . . . . . . .2.6.2.3 Survival Analysis Regression . . . . . . . . . . . 246495252545758606264ix
xcontents2.6.3 Auction Theory . . . . . . . . . . . . . . . .2.7 Summary . . . . . . . . . . . . . . . . . . . . . .3 promotions and advertisements3.1 Environment . . . . . . . . . . . . . . . . . . . .3.2 Business Objectives . . . . . . . . . . . . . . . .3.2.1 Manufacturers and Retailers . . . . . . . . .3.2.2 Costs . . . . . . . . . . . . . . . . . . . . . .3.2.3 Gains . . . . . . . . . . . . . . . . . . . . . .3.3 Targeting Pipeline . . . . . . . . . . . . . . . . .3.4 Response Modeling and Measurement . . . . .3.4.1 Response Modeling Framework . . . . . . .3.4.2 Response Measurement . . . . . . . . . . .3.5 Building Blocks: Targeting and LTV Models . .3.5.1 Data Collection . . . . . . . . . . . . . . . .3.5.2 Tiered Modeling . . . . . . . . . . . . . . . .3.5.3 RFM Modeling . . . . . . . . . . . . . . . .3.5.4 Propensity Modeling . . . . . . . . . . . . .3.5.4.1 Look-alike Modeling . . . . . . . . . .3.5.4.2 Response and Uplift Modeling . . . . .3.5.5 Segmentation and Persona-based Modeling3.5.6 Targeting by using Survival Analysis . . . .3.5.7 Lifetime Value Modeling . . . . . . . . . . .3.5.7.1 Descriptive Analysis . . . . . . . . . . .3.5.7.2 Markov Chain Models . . . . . . . . . .3.5.7.3 Regression Models . . . . . . . . . . . .3.6 Designing and Running Campaigns . . . . . . .3.6.1 Customer Journeys . . . . . . . . . . . . . .3.6.2 Product Promotion Campaigns . . . . . . .3.6.2.1 Targeting Process . . . . . . . . . . . .3.6.2.2 Budgeting and Capping . . . . . . . . .3.6.3 Multistage Promotion Campaigns . . . . .3.6.4 Retention Campaigns . . . . . . . . . . . . .3.6.5 Replenishment Campaigns . . . . . . . . .3.7 Resource Allocation . . . . . . . . . . . . . . . .3.7.1 Allocation by Channel . . . . . . . . . . . .3.7.2 Allocation by Objective . . . . . . . . . . . .3.8 Online Advertisements . . . . . . . . . . . . . .3.8.1 Environment . . . . . . . . . . . . . . . . . .3.8.2 Objectives and Attribution . . . . . . . . . .3.8.3 Targeting for the CPA-LT Model . . . . . .3.8.3.1 Brand Proximity . . . . . . . . . . . . .3.8.3.2 Ad Response Modeling . . . . . . . . .3.8.3.3 Inventory Quality and Bidding . . . . . . . . . . . . . 6149151152152
contents3.8.4 Multi-Touch Attribution . . . . . . . .3.9 Measuring the Effectiveness . . . . . . . .3.9.1 Randomized Experiments . . . . . . .3.9.1.1 Conversion Rate . . . . . . . . . .3.9.1.2 Uplift . . . . . . . . . . . . . . . .3.9.2 Observational Studies . . . . . . . . .3.9.2.1 Model Specification . . . . . . . .3.9.2.2 Simulation . . . . . . . . . . . . .3.10 Architecture of Targeting Systems . . . . .3.10.1 Targeting Server . . . . . . . . . . . . .3.10.2 Data Management Platform . . . . . .3.10.3 Analytics Platform . . . . . . . . . . .3.11 Summary . . . . . . . . . . . . . . . . . . .4 search4.1 Environment . . . . . . . . . . . . . . . . .4.2 Business Objectives . . . . . . . . . . . . .4.2.1 Relevance Metrics . . . . . . . . . . . .4.2.2 Merchandising Controls . . . . . . . .4.2.3 Service Quality Metrics . . . . . . . . .4.3 Building Blocks: Matching and Ranking .4.3.1 Token Matching . . . . . . . . . . . . .4.3.2 Boolean Search and Phrase Search . .4.3.3 Normalization and Stemming . . . . .4.3.4 Ranking and the Vector Space Model4.3.5 TF IDF Scoring Model . . . . . . . . .4.3.6 Scoring with n-grams . . . . . . . . . .4.4 Mixing Relevance Signals . . . . . . . . . .4.4.1 Searching Multiple Fields . . . . . . .4.4.2 Signal Engineering and Equalization .4.4.2.1 One Strong Signal . . . . . . . . .4.4.2.2 Strong Average Signal . . . . . . .4.4.2.3 Fragmented Features and Signals4.4.3 Designing a Signal Mixing Pipeline .4.5 Semantic Analysis . . . . . . . . . . . . . .4.5.1 Synonyms and Hierarchies . . . . . .4.5.2 Word Embedding . . . . . . . . . . . .4.5.3 Latent Semantic Analysis . . . . . . .4.5.4 Probabilistic Topic Modeling . . . . .4.5.5 Probabilistic Latent Semantic Analysis4.5.5.1 Latent Variable Model . . . . . . .4.5.5.2 Matrix Factorization . . . . . . . .4.5.5.3 pLSA Properties . . . . . . . . . .4.5.6 Latent Dirichlet Allocation . . . . . . 218219222224231232233236237237xi
xiicontents4.5.7 Word2Vec Model . . . . . . . . . . . . . . . . . . . . .4.6 Search Methods for Merchandising . . . . . . . . . . . . .4.6.1 Combinatorial Phrase Search . . . . . . . . . . . . . .4.6.2 Controlled Precision Reduction . . . . . . . . . . . . .4.6.3 Nested Entities and Dynamic Grouping . . . . . . . .4.7 Relevance Tuning . . . . . . . . . . . . . . . . . . . . . . . .4.7.1 Learning to Rank . . . . . . . . . . . . . . . . . . . . .4.7.2 Learning to Rank from Implicit Feedback . . . . . . .4.8 Architecture of Merchandising Search Services . . . . . .4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 recommendations5.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . .5.1.1 Properties of Customer Ratings . . . . . . . . . . . . .5.2 Business Objectives . . . . . . . . . . . . . . . . . . . . . .5.3 Quality Evaluation . . . . . . . . . . . . . . . . . . . . . . .5.3.1 Prediction Accuracy . . . . . . . . . . . . . . . . . . .5.3.2 Ranking Accuracy . . . . . . . . . . . . . . . . . . . . .5.3.3 Novelty . . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.4 Serendipity . . . . . . . . . . . . . . . . . . . . . . . . .5.3.5 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.6 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.7 The Role of Experimentation . . . . . . . . . . . . . .5.4 Overview of Recommendation Methods . . . . . . . . . .5.5 Content-based Filtering . . . . . . . . . . . . . . . . . . . .5.5.1 Nearest Neighbor Approach . . . . . . . . . . . . . . .5.5.2 Naive Bayes Classifier . . . . . . . . . . . . . . . . . .5.5.3 Feature Engineering for Content Filtering . . . . . . .5.6 Introduction to Collaborative Filtering . . . . . . . . . . .5.6.1 Baseline Estimates . . . . . . . . . . . . . . . . . . . .5.7 Neighborhood-based Collaborative Filtering . . . . . . . .5.7.1 User-based Collaborative Filtering . . . . . . . . . . .5.7.2 Item-based Collaborative Filtering . . . . . . . . . . .5.7.3 Comparison of User-based and Item-based Methods5.7.4 Neighborhood Methods as a Regression Problem . .5.7.4.1 Item-based Regression . . . . . . . . . . . . . . .5.7.4.2 User-based Regression . . . . . . . . . . . . . . .5.7.4.3 Fusing Item-based and User-based Models . . .5.8 Model-based Collaborative Filtering . . . . . . . . . . . . .5.8.1 Adapting Regression Models to Rating Prediction . .5.8.2 Naive Bayes Collaborative Filtering . . . . . . . . . . .5.8.3 Latent Factor Models . . . . . . . . . . . . . . . . . . .5.8.3.1 Unconstrained Factorization . . . . . . . . . . . .5.8.3.2 Constrained Factorization . . . . . . . . . . . . 318319322323324325327331335339
contents5.8.3.3 Advanced Latent Factor Models . . . . . . . . .5.9 Hybrid Methods . . . . . . . . . . . . . . . . . . . . . . .5.9.1 Switching . . . . . . . . . . . . . . . . . . . . . . . . .5.9.2 Blending . . . . . . . . . . . . . . . . . . . . . . . . .5.9.2.1 Blending with Incremental Model Training . .5.9.2.2 Blending with Residual Training . . . . . . . . .5.9.2.3 Feature-weighted Blending . . . . . . . . . . . .5.9.3 Feature Augmentation . . . . . . . . . . . . . . . . .5.9.4 Presentation Options for Hybrid Recommendations5.10 Contextual Recommendations . . . . . . . . . . . . . . .5.10.1 Multidimensional Framework . . . . . . . . . . . . .5.10.2 Context-Aware Recommendation Techniques . . . .5.10.3 Time-Aware Recommendation Models . . . . . . . .5.10.3.1 Baseline Estimates with Temporal Dynamics .5.10.3.2 Neighborhood Model with Time Decay . . . . .5.10.3.3 Latent Factor Model with Temporal Dynamics5.11 Non-Personalized Recommendations . . . . . . . . . . .5.11.1 Types of Non-Personalized Recommendations . . .5.11.2 Recommendations by Using Association Rules . . .5.12 Multiple Objective Optimization . . . . . . . . . . . . . .5.13 Architecture of Recommender Systems . . . . . . . . . .5.14 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .6 pricing and assortment6.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . .6.2 The Impact of Pricing . . . . . . . . . . . . . . . . . . . .6.3 Price and Value . . . . . . . . . . . . . . . . . . . . . . . .6.3.1 Price Boundaries . . . . . . . . . . . . . . . . . . . .6.3.2 Perceived Value . . . . . . . . . . . . . . . . . . . . .6.4 Price and Demand . . . . . . . . . . . . . . . . . . . . . .6.4.1 Linear Demand Curve . . . . . . . . . . . . . . . . .6.4.2 Constant-Elasticity Demand Curve . . . . . . . . . .6.4.3 Logit Demand Curve . . . . . . . . . . . . . . . . . .6.5 Basic Price Structures . . . . . . . . . . . . . . . . . . . .6.5.1 Unit Price . . . . . . . . . . . . . . . . . . . . . . . .6.5.2 Market Segmentation . . . . . . . . . . . . . . . . . .6.5.3 Multipart Pricing . . . . . . . . . . . . . . . . . . . .6.5.4 Bundling . . . . . . . . . . . . . . . . . . . . . . . . .6.6 Demand Prediction . . . . . . . . . . . . . . . . . . . . .6.6.1 Demand Model for Assortment Optimization . . .6.6.2 Demand Model for Seasonal Sales . . . . . . . . . .6.6.2.1 Demand Data Preparation . . . . . . . . . . . .6.6.2.2 Model Specification . . . . . . . . . . . . . . . .6.6.3 Demand Prediction with Stockouts . . . . . . . . . 399401406410414416419419420421xiii
xivcontents6.7 Price Optimization . . . . . . . . . . . . . . . . . . . .6.7.1 Price Differentiation . . . . . . . . . . . . . . . .6.7.1.1 Differentiation with Demand Shifting . . . .6.7.1.2 Differentiation with Constrained Supply . .6.7.2 Dynamic Pricing . . . . . . . . . . . . . . . . . .6.7.2.1 Markdowns and Clearance Sales . . . . . . .6.7.2.2 Markdown Price Optimization . . . . . . . .6.7.2.3 Price Optimization for Competing Products6.7.3 Personalized Discounts . . . . . . . . . . . . . . .6.8 Resource Allocation . . . . . . . . . . . . . . . . . . .6.8.1 Environment . . . . . . . . . . . . . . . . . . . . .6.8.2 Allocation with Two Classes . . . . . . . . . . . .6.8.3 Allocation with Multiple Classes . . . . . . . . .6.8.4 Heuristics for Multiple Classes . . . . . . . . . .6.8.4.1 EMSRa . . . . . . . . . . . . . . . . . . . . . .6.8.4.2 EMSRb . . . . . . . . . . . . . . . . . . . . .6.9 Assortment Optimization . . . . . . . . . . . . . . . .6.9.1 Store-Layout Optimization . . . . . . . . . . . . .6.9.2 Category Management . . . . . . . . . . . . . . .6.10 Architecture of Price Management Systems . . . . .6.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . .a appendix: dirichlet 81
ACKNOWLEDGEMENTSThis book would not have been possible without the support andhelp of many people. I am very grateful to my colleagues and friends,Ali Bouhouch, Max Martynov, David Naylor, Penelope Conlon, SergeyTryuber, Denys Kopiychenko, and Vadim Kozyrkov, who reviewed thecontent of the book and offered their feedback. Special thanks go toKonstantin Perikov, who provided a lot of insightful suggestions aboutsearch services and also helped with some of the examples.I am indebted to Igor Yagovoy, Victoria Livschitz, Leonard Livschitz,and Ezra Berger for supporting this project and helping with the publishing. Last,
—Andrey Sebrant, Director of Strategic Marketing, Yandex “Introduction to Algorithmic Marketing isn’t just about machine learning and economic modeling. It’s ultimately a framework for running business and marketing operations in the AI economy.” —Kyle McKiou, Sr. Director of Data Science, The Marketing Store
“algorithmic” from “software” has been also reflected in media studies. [2] “Drawing on contemporary media art practices and on studies of visual and algorithmic cultures, I would like to develop the idea of algorithmic superstructuring as a reading of aest
Algorithmic Trading Table: Proportions of trading volume contributed by di erent category of algorithmic and non-algorithmic traders in the NSE spot and equity derivatives segment (for the period Jan-Dec 2015) Custodian Proprietary NCNP Total Spot Market Algo 21.34% 13.18% 7.76% 42.28% Non-
aforementioned model. Thus, the Triangulation Algorithmic Model is provided and defined to aid in understanding the process of measurement instrument construction and research design. The Triangulation Algorithmic Model for the Tri-Squared Test The Algorithmic Model of Triangulation is of the form (Figure 2). Where,
In these notes we focus on algorithmic game theory, a relatively new field that lies in the intersection of game theory and computer science. The main objective of algorithmic game theory is to design effective algorithms in strategic environments. In the first part of these notes, we focus on algorithmic theory's earliest research goals—
What problems have beginners in learning algorithms/programming? Give some examples why algorithmic thinking is useful in other subjects/daily life Try to give a definition of Algorithmic Thinking : Title: Learning Algorithmic Thinking with Tangible Objects Eases Transition to Computer Programming
v. Who is doing algorithmic trading? Many algorithmic trading firms are market makers. This is a firm that stands ready to buy and sell a stock on a regular and continuous basis at a publicly quoted price. Customers use them to place large trades. Large quantitative funds (also called investment or hedge funds) and banks have an algorithmic .
United States by algorithmic trading. (3) An analysis of whether the activity of algorithmic trading and entities that engage in algorithmic trading are subject to appropriate Federal supervision and regulation. (4) A recommendation of whether (A) based on the analysis described in paragraphs (1), (2), and (3), any
MP Board Intermediate Syllabus 2020-Commerce Stream: Down below the MP Board Intermediate Syllabus 2020 for Commerce Stream is given which comprises three broad subjects i.e Economics, Business Studies, and Accountancy. The MP Board class 12th syllabus for Commerce stream can be accessed below. MP Board Intermediate Syllabus 2020 for Economics: P a r t Na me Un i t Na me .
