MarketingAnalytics_Ch1_Introduction_v1.ppt
- 1. Introduction Disclaimer: • All logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch.1.1
- 2. Outline/ Learning Objectives © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.2 Topic Description Introduction Marketing analytics definition, drivers, and advantages Models Definition, styles, forms, and variables of models Metrics Definition, families, and dashboards of metrics
- 3. 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 Marketing Analytics: Models, Metrics & Measurements Models: Decision tools, like spreadsheets Example: Bass Forecasting Metrics: KPIs to monitor business, like charts and graphs Example: Sales/ Channel © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch.1.3
- 4. Models and Metrics Metrics = Gauges: - Monitor situation - Diagnose problems © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.4 Models = GPS: - Representation of Reality - Decide on course of action
- 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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.5
- 6. Trends Driving Marketing Analytics Adoption Before: Huge budgets Now: Tiny budgets © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.6 Marketing Analytics Adoption Online Data Availability Reduced Resources Massive Data Accountability Data-Driven Presentations Improve productivity Reduce costs “What gets measured gets done” Data to back up proposals Predict success of plans Initiatives to capture customer information What to do with all that data? Cloud-based data storage Online = speed Online = convenience Do more with less Scrutinized budgets Marketers must show outcomes
- 7. Marketing Analytics Advantages © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.7 Marketing Analytics Advantages Persuade Executives Side-step Politics Encourage Experimentation Drive Revenue Save Money Marketing as cost center Marketing as profit center Correlation between spending and results Old way: Execute campaign guess outcome No longer tolerate such an approach New way: Predict outcome Test multiple scenarios before proceeding Run simulations Predict which will work best Focus on revenue impact from marketing Correlation between spending & results Some CEOs do not appreciate marketing Show impact of efforts with metrics
- 8. Topic Description Model Simplified representation of reality to solve problems Example: Advertising effectiveness model Purpose Evaluate impact of input variables Example: Assess how advertising affects sales Decisions Models provide guidance on marketing actions Example: Decide on ad budget to achieve objectives Models: What is a Model? Advertising Effectiveness: Response (sales revenue) increases with increasing ad budget until Point A, then decreases © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.8 Advertising Sales time A
- 9. 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 Styles: Verbal, Pictorial, Mathematical © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.9 Verbal Pictorial Mathematical Sales = f(advertising)
- 10. 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 Models: Forms © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.10 Descriptive Predictive Normative Sales Advertising This Way
- 11. Topic Description Variable Quantity that can be changed, or varied Examples: Advertising budget, Sales Independent Variable Variable whose value affects dependent variable (x) Controllable: Advertising budget Non-controllable: Customer age Dependent Variable Variable representing marketing objective (y, or output) Responds to changes in independent variable For-profit: Revenue, Profit; Not-for-profit: Donations Models: Variables © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.11
- 12. Y = a + b * X Y = Sales (Dependent Variable) (Output) a = Parameter: Y-intercept b = Parameter: Slope x = Advertising (Independent Variable) (Input) 1 b Slope = rise/run = b/1 X (Advertising) Independent Variable Y (Sales) Dependent Variable Y-intercept (Sales level when advertising spending =0) © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.12 Models: Terminology Linear Response Model
- 13. 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 Metrics Dashboards Marketing automation systems - Eloqua, Marketo, Pardot Salesforce automation systems - Netsuite, Salesforce.com Metrics Metrics Dashboard © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.13
- 14. Number Question 1 Describe how marketing analytics models are analogous to automotive global positioning system (GPS) units. 2 Explain how marketing accountability is driving the adoption of marketing analytics. 3 Describe how marketing analytics approaches can help persuade executives. 4 Identify the type of style a model expressed in pictures represents. 5 Identify the form of model used in standard computer spreadsheet programs. 6 Understand the difference between controllable & non-controllable independent variables. 7 Understand the basic form of a linear response model: Y = a + b*X 8 Identify the types of systems that typically include metrics dashboards. Check Your Understanding © Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.14