This document summarizes the value of mining (big) data for marketing purposes without alarming customers. It discusses challenges such as data being unstructured and needing cleaning. Case studies show data can provide brand and consumer insights, understand effective messaging, better target communications, market a company's own data as an asset, and enable continued experimentation. Examples include sentiment analysis of sedan forums, messaging lift for luxury brands vs. American brands, and Target's predictive customer analytics. The document stresses the importance of statisticians and data scientists being quantitative, technical, curious, skeptical, and good communicators.

1. The Value of Mining (Big) Data…
Without Scaring Your Customers
Matthew Quint
Director, Center on Global Brand Leadership
Columbia Business School
gsb.columbia.edu/globalbrands
@mattquint
Data Driven Marketing Conference – August 20, 2013
[NOTE: Images for Prof. Netzer, Everyday Health, The Weather
Company, and Target, have hyperlinks to video talks on the topic!]

2. …and not everything that counts
can be counted
- Prof. William Bruce Cameron
Not everything that can be
counted counts…

3. Data Bigger Data BIG Data
Small
No integration
Unit collected
Large
Some integration
Firm collected
Massive
Heavy integration
Firm and external

4. All marketers want to be
DATA-DRIVEN
Believe successful brands use
data to drive marketing decisions
91%
But many are NOT COLLECTING
the data they need
say their own company’s data
are collected too infrequently
39%
Marketing ROI in the Era of Big Data: 2012 BRITE-NYAMA Marketing Measurement in Transition Study
David Rogers and Prof. Don Sexton, Columbia Business School

5. TOO LITTLE
TOO INFREQUENT
NOT SHARED
NOT SPECIFIC
DON’T PERSONALIZE

6. “The evidence is clear:
Data-driven decisions tend to be
better decisions.
In sector after sector, companies that embrace this
fact will pull away from their rivals.”
- Erik Brynjolfsson and Andrew McAfee, MIT (Harvard Business Review)

7. Five key
CHALLENGES of (Big) Data

8. Everywhere

9. Unstructured

10. Needs cleaning

11. Storage and processing

12. Privacy and security

13. Case studies on
THE VALUE of (Big) Data

14. 1. Gain insights on brands or consumers
2. Understand what messaging works
3. Better target your communications
4. Your data becomes an asset to market
5. Continue experimenting

15. Brand and consumer
INSIGHTS from (Big) Data

16. Brand insights
Prof. Oded Netzer

17. Edmunds.com sedan forum
<Brand>Honda</Brand>
<Model>Honda Accord</Model>
<Model>Toyota Camry</Model>
<Brand>Toyota </Brand>
<Term>Best</Term>
<Term>Sedans</Term>
<Term>Competent</Term>
<Term>Price</Term>
<Term>Love</Term>
<Term>Best selling</Term>
<Term>Best</Term>
Honda Accords and Toyota Camrys are nice
sedans, but hardly the best car on the road
(for many people). It's just that they are very
compentant in their price range. So, a love
fest of the best selling may not tell you what
is "best".
Text mining

18. Network analysis

19. MODEL SENTRA COROLLA CIVIC
Commonalities
Differentiators
Economy | Small-car | Subcompact | Compact
Power
Performance
College
Mileage
Plastic parts
Mom/Daughter
VTEC Engine
Hatchback
Mud guards
Edmunds.com brand sentiment

20. Consumer insights

21. MESSAGING EFFECTIVENESS
from (Big) Data

22. AmericanLuxury
Messaging effectiveness
Prof. Oded Netzer

23. Based on JD Power PIN Data
Brand-switching map

24. 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1/1/2001
4/1/2001
7/1/2001
10/1/2001
1/1/2002
4/1/2002
7/1/2002
10/1/2002
1/1/2003
4/1/2003
7/1/2003
10/1/2003
1/1/2004
4/1/2004
7/1/2004
10/1/2004
1/1/2005
4/1/2005
7/1/2005
10/1/2005
1/1/2006
4/1/2006
7/1/2006
10/1/2006
Lift
Import Luxury
American Brands
Linear (Import Luxury)
Linear (American Brands)
Brands mentioned alongside Cadillac
AMERICAN brands
LUXURY imports

25. 0
0.2
0.4
0.6
0.8
1
1.2
1.4
1/1/2001
4/1/2001
7/1/2001
10/1/2001
1/1/2002
4/1/2002
7/1/2002
10/1/2002
1/1/2003
4/1/2003
7/1/2003
10/1/2003
1/1/2004
4/1/2004
7/1/2004
10/1/2004
1/1/2005
4/1/2005
7/1/2005
10/1/2005
1/1/2006
4/1/2006
7/1/2006
10/1/2006
1/1/2007
4/1/2007
7/1/2007
10/1/2007
1/1/2008
4/1/2008
7/1/2008
10/1/2008
Lift
Import Luxury
American
Linear (Import Luxury)
Linear (American)
Brands traded-in for Cadillac
AMERICAN brands
LUXURY imports

26. Demonstrate ROI

27. TARGETING improvements from
(Big) Data

28. Better ad targeting

29. Cross-Platform Campaign Ratings

30. MARKET YOUR OWN
(Big) Data

31. Stores care about the weather

32. Get acquired because of data

33. Continue
EXPERIMENTING

34. A/B Testing for Obama Campaign

35. DON’T FREAK OUT
your customers

36. Charles Duhigg, “How Companies Learn Your Secrets,” The New York Times (Feb 16, 2012)
Target’s predictive analytics

37. Tracking your whereabouts

38. “I keep saying that the sexy job in
the next 10 years will be
statisticians. And I’m not kidding.”
- Hal Varian, chief economist at Google.

39. The data scientist
1.Quantitative
2.Technical
3.Curious and creative
4.Skeptical
5.Communicative and collaborative

40. Questions?
Matthew Quint
Director, Center on Global Brand Leadership
Columbia Business School
matthew@globalbrands.org
Data Driven Marketing Conference – August 20, 2013