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Marketing Analytics
Stephan Sorgerwww.StephanSorger.com
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: Cover Page
Chapter 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 1
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 spreadsheetsExample: Bass Forecasting
Metrics:KPIs to monitor business,like charts and graphsExample: Sales/ Channel
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 2
Models and Metrics
Metrics = Gauges:- Monitor situation- Diagnose problems
Models = GPS:- Representation of Reality- Decide on course of action
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 3
Metrics Gone WrongMilitary 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 4
Trends Driving Marketing Analytics Adoption
Before:Huge budgets
Now:Tiny budgets
MarketingAnalyticsAdoption
Online Data Availability
Reduced Resources
Massive Data
Accountability
Data-Driven Presentations
Improve productivityReduce costs“What gets measured gets done”
Data to back up proposalsPredict success of plans
Initiatives to capture customer informationWhat to do with all that data?
Cloud-based data storageOnline = speedOnline = convenience
Do more with lessScrutinized budgetsMarketers must show outcomes
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 5
Marketing Analytics Advantages
MarketingAnalytics
Advantages
Persuade Executives
Side-step Politics
Encourage Experimentation
Drive Revenue
Save Money
Marketing as cost centerMarketing as profit centerCorrelation between spending and results
Old way: Execute campaign guess outcomeNo longer tolerate such an approachNew way: Predict outcome
Test multiple scenarios before proceedingRun simulationsPredict which will work best
Focus on revenue impact from marketingCorrelation between spending & results
Some CEOs do not appreciate marketingShow impact of efforts with metrics
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 6
Topic Description
Model Simplified representation of reality to solve problemsExample: Advertising effectiveness model
Purpose Evaluate impact of input variablesExample: Assess how advertising impacts sales
Decisions Models provide guidance on marketing actionsExample: Decide on ad budget to achieve objectives
Models: What is a Model?
Advertising Effectiveness:Response (sales revenue)increases with increasing ad budgetuntil Point A, then decreases
Sales
time
A
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 7
Topic Description
Verbal Expressed in words“Sales is influenced by advertising”
Pictorial Expressed in picturesChart or graph of phenomenon
Mathematical Expessed in equationSales = a + b * Advertising
Styles: Verbal, Pictorial, Mathematical
Verbal Pictorial Mathematical
Sales = f(advertising)
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 8
Topic Description
Descriptive Characterize (describe) marketing phenomenonIdentify causal relationships and relevant variablesExample: Sales = a*Advertising + b*Features +c*…
Predictive Determine likely outcomes given certain inputsClassic “What If?” spreadsheet exerciseExample: 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
Descriptive Predictive NormativeSales
Advertising
This Way
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 9
Topic Description
Variable Quantity that can be changed, or variedExamples: Advertising budget, Sales
Independent Variable Variable whose value impacts dependent variable (x)Controllable: Advertising budgetNon-controllable: Customer age
Dependent Variable Variable representing marketing objective (y, or output)Responds to changes in independent variableFor-profit: Revenue, Profit; Not-for-profit: Donations
Models: Variables
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 10
Models: Terminology: Linear Response Model
Y = a + b * X
Y = Sales (Dependent Variable) (Output)a = Parameter: Y-interceptb = Parameter: Slopex = Advertising (Independent Variable) (Input)
1
b
Slope = rise/run = b/1
X (Advertising)Independent Variable
Y (Sales)DependentVariable
Y-intercept(Sales levelwhen advertisingspending =0)
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 11
Topic Description
Definition Business-oriented key performance indicatorsExamples: Sales per channel, Cost per sale
Purpose Monitor and improve marketing effectivenessTake corrective action as necessaryExample: Marketing expense as percentage of sales
Metrics Families Groups of control metrics; Diagnostic & predictive infoExample: Sales metrics: sales/industry; sales/product
Metrics Dashboards Marketing automation systems- Eloqua, Marketo, PardotSalesforce automation systemsNetsuite, Salesforce.com
Metrics
Metrics Dashboard
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 12
Let’s Get Started!
East Bay
South Bay
Peninsula
SF
North BayExample: Team 1: SF-MarinaTeam 2: SF-DowntownTeam 3: East BayTeam 4: North BayTeam 5: Peninsula/ South Bay
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 email addresses & phone numbers- Get to Know Familiarize yourself with others during cases
© Stephan Sorger 2015 www.StephanSorger.com; Marketing Analytics: Introduction 13