Marketing Analytics
Stephan Sorger
www.StephanSorger.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
Outline/ Learning Objectives
Topic
Description
Definitions
Styles and Forms
Metrics
© 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
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
Marketing
Analytics
Advantages
Focus on revenue impact from marketing
Correlation between spending & results
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
Variable whose value impacts 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
© 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
Outline/ Learning Objectives
Topic
Description
Definitions
Styles and Forms
Metrics
© Stephan Sorger 2016; www.StephanSorger.com; Marketing Analytics: Introduction 1
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