Analytics for Marketing Investment and Accountability

Transcription

1 Analytics for Marketing Investment and Accountability Professor: Michael A. Cohen, Ph.D. Office: 915 Tisch Hall, 40 West 4 th Street, NY, NY Hours: By appointment I Introduction Course Description This course introduces business professionals to the practice of applying marketing models to return on marketing investment oriented decision making. Emphasis is placed on the application of causal statistical models for cross-media-channel scheduling and tactical planning, as well as consumer behavior insights. Pre-Requirements Students should be familiar with the subject matter introduced in Statistics and Data Analysis and Firms & Markets (Managerial Economics). II Course Objectives The learning objectives for the course to: Establish a foundational understanding of marketing measurement models and their data requirements Discover modeling approaches, their underlying assumptions, and unifying themes Calibrate marketing measurement models and estimate marketing effectiveness using modern data streams Leverage measurement model results to drive business objectives efficiently III Benefits Derived i) Models Behavior, Uncertainty, and the Data Generation Process Models of Customer Behavior Linking Customer Behavior to Aggregate Measures of Sales Implications of the Inter-temporal Nature of Customer Behavior Page 1

2 ii) Model Assumptions and Measurement error Understanding the variance bias tradeoff Omitted variable bias, its many forms Functional form and parametric distributional assumptions Addressing measurement error iii) Consumer Behavior, What Can We Learn with Market Data and a Model? Experimental variation and Causal Identification Importance of Quality Data Simultaneously Determined Outcomes iv) Application of Models to Support Managerial Decisions Efficient allocation of Marketing Resources Prediction: Forecasting Future Sales Prediction: Using Models to Conduct Experiments Retrospective performance measurement, a.k.a. How Much Money was Left on the Table? v) Standard Client Deliverables Effectiveness Metrics: Elasticities, Volume Decomposition and Attribution Waterfall ROI Metrics: Marginal, Average, Total What-if (Counterfactual) Simulations Deploying Management Tools (Dashboards) IV Activities The course is constructed in modular form. It consists of a four part core, and a commutable set of case study modules used to modify the program to accommodate audience composition and emerging en vogue subject matter. The case study module listed below constitutes one suggested topic to provide a flavor for the case studies. Session I: Introductions, Motivations, and the Organization Role of Data in Marketing Management Introduction and Motivation Recitation: Manager Scientist, and the MBA Parable Motivational example: Measuring advertising effectiveness Lecture: Traditional organizational/institutional role and provision of Marketing Measurement/Mix Models (MMM) Lecture: Where does MMM fit in the Organization and why do we need it Session II: The Marketing Mix through a Model of Measurable Marketing Objectives Stochastic Specification Page 2

3 Recitation: Random Variables Intro to R statistical computing language Activity: Estimation and Results Visualization Model Specification Recitation: Model and Theory Model Activity: Specify a simple marketing model and implement it in R, code-in a marketing model and create a visualization Stochastic Model Specification Recitation: Measurement Model Activity: In R use your model to generate market data Session III: Strength, Limitation, and Measurement Confounds Causal Model Estimation Demonstration: RHS variables are often correlated with each other (multicollinearity) What does it matter? multicollinearity bias (R Demonstration) What can we do? principal components of variation (R Demonstration) Demonstration: Key variables are often unavailable (omitted variable bias) What does it matter? (R Demonstration) What can we do? (R Demonstration) Demonstration: RHS variables are often correlated with the unobserved (weak exogeneity) What does it matter? (R Demonstration) What can we do? (R Demonstration) Experiment: Hand-over R script, class experiments to discover the implications of confounds for measurement and summarize the practical managerial implications Class experiments with three confounds Summarize the discoveries on whiteboard Session IV: Preparing Data and Model Calibration Example Motivational Case Study You are approached by a digital-video streaming company. This company sells monthly subscriptions to their service. The Service is purchased exclusively at the company s consumer website, with an opt-out anytime option for subscribers. The company s finance team has indicated that the expected lifetime value of a conversion is $100. Page 3 The company would like to know how to improve their spending allocations against TV $2105 per, Targeted Digital $1.36 CPI, and Paid Search $0.01, to improve their weekly profitability. They have provided us with weekly subscription sales data for the last three years across eight geographic regions of the country, as well as their mix of television GRPs and total digital-display advertising impressions. The data set also includes the volume of paid search impressions, as well as the volume of website traffic. We also have an index on the adoption of technologies that enable users to best enjoy the digital video streaming service. This variable captures things like the market penetration of high-speed internet

4 connection, tablets, internet TVs, and the like. We ll call this trend variable Installed Base because this variable represents the installed base of technology relevant to digital-video-streaming. Preparing Data Recitation: Data-structure Motivate Clean Data - without this you cannot conduct a meaningful analysis Demonstration: Code book and variables - numeric, alphanumeric, time Lecture: Non workable and Incomplete files - not flat structure, graphs, and missing fields Discussion: Best Practices Never touch raw data Only manipulate data in code Model Calibration Recitation: Regression Analysis Activity: Model Specify Estimate (Use R to script regression commands) Recitation: Inference Activity: Inference Interpret Re-estimate/Calibrate (Use R to re-script regression commands) Report Final Model Results Session V: Processing Measurement Results for Consumer Insight, Decision Support Insight, and Delivery to Clients Recitation: Consumer Insights Interpreting consumers insights from the results generated by measurement models Demonstration: Interpreting consumer insights from model results Recitation: Decision Support Models Motivation, Framework, and Components Implementing a measurement model into a decision support system Recitation: Typical Deliverables What they are: Volume Decomposition, Waterfall, Marginal Analysis What they are not Recitation: Return on Investment Metrics Implement measurements to drive ROI oriented decisions. Recitation: Decision Support Applications What is it What goes into it What are Best Practices and Challenges to adoption Page 4

5 Session VI: Marketing Measurement Project Presentations The final session will be used for predetermined groups to deliver an assigned Marketing Mix Project. V Assessment of Learning Objectives Students are assessed based on two assignments (60%), a presentation (30%), and class participation (10%). The first assignment is a proposal reply to a Marketing Mix Modeling RFP (Request for Proposals). The second assignment is the final results delivered with the project presentation at the last class meeting. Page 5