Transcript ppt - Stephan Sorger

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
Outline/ Learning Objectives
Topic
Description
Introduction
Models
Metrics
Marketing analytics definition, drivers, and advantages
Definition, styles, forms, and variables of models
Definition, families, and dashboards of metrics
© Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.2
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch.1.3
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 Ch. 1.4
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
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.6
Marketing Analytics Advantages
Drive Revenue
Persuade Executives
Marketing as cost center
Marketing as profit center
Correlation between spending and results
Marketing
Analytics
Advantages
Save Money
Focus on revenue impact from marketing
Correlation between spending & results
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.7
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 affects 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
Advertising
© Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.8
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.9
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.10
Models: Variables
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
© Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.11
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 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.12
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
Metrics Dashboards
Marketing automation systems
- Eloqua, Marketo, Pardot
Salesforce automation systems
- Netsuite, Salesforce.com
Metrics Dashboard
© Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.13
Check Your Understanding
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.
© Stephan Sorger 2015; www.StephanSorger.com; Marketing Analytics: Introduction Ch. 1.14