1. Implementing Marketing Analytics

2. Outline
Potential benefits of and skepticism toward marketing analytics
Performance implications of deploying marketing analytics
Putting it all together

3. Challenges faced by today’s marketing decision makers
Global, hypercompetitive business environment.
More demanding customers served by a greater number of competitors on a global scale.
Exploding volume of data
“We’re drowning in data. What we lack are true insights.”
Need for faster decision making
Information overload and lack of time, yet decisions have to be made all the time.
Higher standards of accountability
Marketing expenditures have to be justified in the same way as other investments.

4. Need for better marketing decision making
Intuitive decision making
In a world characterized by rapid change, information overload, greater accountability, etc. intuition is unlikely to generate superior results;
Data- and model-based decision making
Marketing Engineering: “A systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported interactive decision process” (LRB, p. 2)
Yet, “paralysis through analysis” and other criticisms of marketing analytics

5. Marketing Engineering
Marketing Environment
Automatic scanning, data entry,
subjective interpretation
Data
Database management, e.g..,
selection, sorting, summarization,
report generation
Information
Engineering
Marketing
Mental models,
Decision models
Insights
Judgment under uncertainty,
e.g.., modeling, communication,
introspection
Decisions
Financial, human, and other
organizational resources
Implementation

6. Germann, Lilien, and Rangaswamy (2013)

7. Response models in the decision loop
Marketing actions
(inputs)
Competitive actions
Observations
(outputs)
Product design,
Price, Advertising,
Selling effort, etc.
Response
Model
Awareness
Preferences
Sales
Environmental
Conditions
Objectives
Control, Adaptation
Evaluation

8. A simple (linear) response model

9. A nonlinear response model

10. The profit equation

11. STP – Segmentation, Targeting, Positioning
Product
Price
Communication
Distribution
All consumers
in the market
Target
market
segment(s)
Marketing mix
Marketing strategies
of competitors
Target marketing
and positioning

12. Market segmentation Discriminant analysis Segment B Cluster analysis
Response
Who’s this?
Segment B B2
Cluster analysis A2
Segment A A1 B1
Who’s this?
marketing
variable x1 x2

13. Positioning
What are the central dimensions that underlie customers’ perceptions of brands in the product class?
How do customers view our brand on these dimensions?
How do customers view our competitors?
How do perceptions relate to preferences?

14. Positioning map with perceptions and preferences

15. The company’s profit chain
Choice models
Conjoint analysis
Customer
value
Customer
satisfaction
Customer
loyalty
Company
profitability
Analyzing and managing
customer satisfaction
CLV

16. The digital revolution
digital marketing provides many new opportunities for interacting with customers and exploiting the traces of these interactions
search analytics
a lot of unstructured data is available in the online world and marketers can extract useful information from these online conversations by their customers;
text analysis