Marketing Analytics Stephan Sorger www Stephan Sorger com

Marketing Analytics Stephan Sorger www. Stephan. Sorger. com Disclaimer: • All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only • Some material adapted from: Sorger, Stephan. “Marketing Analytics: Strategic Models and Metrics. Admiral Press. 2013. © Stephan Sorger 2016; www. stephansorger. com; Marketing Analytics: Cover Page

Chapter 1. Introduction Disclaimer: • All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only • Some material adapted from: Sorger, Stephan. “Marketing Analytics: Strategic Models and Metrics. Admiral Press. 2013. © Stephan Sorger 2016; www. stephansorger. com; Marketing Analytics: Introduction 1

Marketing Analytics: Models, Metrics & Measurements Topic Description Definition (Broad) Broad definition (but too vague): Data analysis for marketing purposes, from data gathering to analysis to reporting Definition (Applied) Techniques and tools to provide actionable insight - Models - Metrics Models Decision tools, such as spreadsheets Metrics Key performance indicators to monitor business Models: Decision tools, like spreadsheets Example: Bass Forecasting Metrics: KPIs to monitor business, like charts and graphs Example: Sales/ Channel © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 2

Models and Metrics = Gauges: - Monitor situation - Diagnose problems Models = GPS: - Representation of Reality - Decide on course of action © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 3

Metrics Gone Wrong Military leaders in World War II used metrics regarding airplane damage incorrectly “Reinforce damaged areas” Abraham Wald, a statistician skilled in analytics, said: Right Metrics, Wrong Conclusion “Reinforce non-damaged areas” (fixing selection bias from studying only airplances that returned) © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 4

Trends Driving Marketing Analytics Adoption Accountability Online Data Availability Improve productivity Reduce costs “What gets measured gets done” Cloud-based data storage Online = speed Online = convenience Marketing Analytics Adoption Data-Driven Presentations Reduced Resources Data to back up proposals Predict success of plans Massive Data Initiatives to capture customer information What to do with all that data? Before: Huge budgets Do more with less Scrutinized budgets Marketers must show outcomes Now: Tiny budgets © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 5

Marketing Analytics Advantages Drive Revenue Persuade Executives Marketing as cost center Marketing as profit center Correlation between spending and results Focus on revenue impact from marketing Correlation between spending & results Marketing Analytics Advantages Save Money Side-step Politics Old way: Execute campaign guess outcome No longer tolerate such an approach New way: Predict outcome Some CEOs do not appreciate marketing Show impact of efforts with metrics Encourage Experimentation Test multiple scenarios before proceeding Run simulations Predict which will work best © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 6

Models: What is a Model? Topic Description Model Simplified representation of reality to solve problems Example: Advertising effectiveness model Purpose Evaluate impact of input variables Example: Assess how advertising impacts sales Decisions Models provide guidance on marketing actions Example: Decide on ad budget to achieve objectives A Sales Advertising Effectiveness: Response (sales revenue) increases with increasing ad budget until Point A, then decreases time © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 7

Styles: Verbal, Pictorial, Mathematical Topic Description Verbal Expressed in words “Sales is influenced by advertising” Pictorial Expressed in pictures Chart or graph of phenomenon Mathematical Expessed in equation Sales = a + b * Advertising Verbal Pictorial Mathematical Sales = f(advertising) © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 8

Models: Forms Topic Description Descriptive Characterize (describe) marketing phenomenon Identify causal relationships and relevant variables Example: Sales = a*Advertising + b*Features +c*… Predictive Determine likely outcomes given certain inputs Classic “What If? ” spreadsheet exercise Example: Sales forecast model Normative Decide best course of action to maximize objective, given limits on input variables (constrained optimization) “Given X, what should I do? ” Example: Determine price using forecasts at diff. prices Descriptive Sales Predictive Normative This Way Advertising © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 9

Models: Variables Topic Description Variable Quantity that can be changed, or varied Examples: Advertising budget, Sales Independent Variable whose value impacts dependent variable (x) Controllable: Advertising budget Non-controllable: Customer age Dependent Variable representing marketing objective (y, or output) Responds to changes in independent variable For-profit: Revenue, Profit; Not-for-profit: Donations © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 10

Models: Terminology: Linear Response Model Y (Sales) Dependent Variable Y=a+b*X b Y-intercept (Sales level when advertising spending =0) 1 Slope = rise/run = b/1 Y = Sales (Dependent Variable) (Output) a = Parameter: Y-intercept b = Parameter: Slope x = Advertising (Independent Variable) (Input) X (Advertising) Independent Variable © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 11

Metrics Topic Description Definition Business-oriented key performance indicators Examples: Sales per channel, Cost per sale Purpose Monitor and improve marketing effectiveness Take corrective action as necessary Example: Marketing expense as percentage of sales Metrics Families Groups of control metrics; Diagnostic & predictive info Example: Sales metrics: sales/industry; sales/product Marketing automation systems - Eloqua, Marketo, Pardot Salesforce automation systems Netsuite, Salesforce. com Metrics Dashboards Metrics Dashboard © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 12

Let’s Get Started! Participant Introductions - Name - Reason for being here - Geographical area During Class Break - Meet with Others - Contact Info - Get to Know Say your name clearly so others can hear you What you hope to learn in the course Desired geographical area for team meetings Listen for your area during introductions Meet with others from your area during break Exchange email addresses & phone numbers Familiarize yourself with others during cases Example: Team 1: SF-Marina North Bay SF East Bay Team 2: SF-Downtown Team 3: East Bay Peninsula Team 4: North Bay South Bay Team 5: Peninsula/ South Bay © Stephan Sorger 2016 www. stephansorger. com; Marketing Analytics: Introduction 13