Marketing Analytics. Stephan Sorger

Transcription

1 Stephan Sorger 2016; Marketing Analytics: Cover Page Marketing Analytics Stephan Sorger 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

2 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 Stephan Sorger 2016; Marketing Analytics: Introduction 1

3 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) Models Metrics Techniques and tools to provide actionable insight - Models - Metrics Decision tools, such as spreadsheets 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 Marketing Analytics: Introduction 2

4 Models and Metrics Metrics = Gauges: - Monitor situation - Diagnose problems Models = GPS: - Representation of Reality - Decide on course of action Stephan Sorger Marketing Analytics: Introduction 3

5 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 Marketing Analytics: Introduction 4

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

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

8 Models: What is a Model? Topic Description Model Purpose Decisions Simplified representation of reality to solve problems Example: Advertising effectiveness model Evaluate impact of input variables Example: Assess how advertising impacts sales 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 Marketing Analytics: Introduction 7

9 Styles: Verbal, Pictorial, Mathematical Topic Verbal Pictorial Mathematical Description Expressed in words Sales is influenced by advertising Expressed in pictures Chart or graph of phenomenon Expessed in equation Sales = a + b * Advertising Verbal Pictorial Mathematical Sales = f(advertising) Stephan Sorger Marketing Analytics: Introduction 8

10 Models: Forms Topic Descriptive Predictive Normative Description Characterize (describe) marketing phenomenon Identify causal relationships and relevant variables Example: Sales = a*advertising + b*features +c* Determine likely outcomes given certain inputs Classic What If? spreadsheet exercise Example: Sales forecast model 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 Predictive Normative Sales Advertising This Way Stephan Sorger Marketing Analytics: Introduction 9

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

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

13 Metrics Topic Definition Purpose Metrics Families Metrics Dashboards Description Business-oriented key performance indicators Examples: Sales per channel, Cost per sale Monitor and improve marketing effectiveness Take corrective action as necessary Example: Marketing expense as percentage of sales 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 Dashboard Stephan Sorger Marketing Analytics: Introduction 12

14 Let s Get Started! Participant Introductions - Name Say your name clearly so others can hear you - Reason for being here What you hope to learn in the course - Geographical area Desired geographical area for team meetings Listen for your area during introductions During Class Break - Meet with Others Meet with others from your area during break - Contact Info Exchange addresses & phone numbers - Get to Know Familiarize yourself with others during cases Example: North Bay Team 1: SF-Marina 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 Marketing Analytics: Introduction 13