Big data has been hyped so heavily that CMO’s are expecting it to be ‘the’ miracle solution in today’s complex marketing environment. However, what we’re actually seeing, is that most companies are already struggling with the small amount of data they already have accumulated. The trouble is most obvious on these three levels: companies don’t know how to manage the data, companies don’t know how to analyze the data so as to yield insights, companies don’t know how to act upon the new insights. Of course technology is needed. But even more so, a cultural shift in how CMO’s run their daily marketing operations is definitely required. The good news is that, once CMO’s have accomplished this cultural change, they usually don’t go back. Because they realize they now have a huge competitive advantage. Now, those forward-thinking CMO’s are able to use customer data to their advantage by delivering more targeted and relevant messages to people. During this session, you will discover how to embrace the power of data and turn it into gold for your company.

1. Wifi: LivingTomorrow PW:
#bbdoconnect

2. +

3. Tim Nagels, Business Lead Microsoft Dynamics

4. Franky Willekens, Head of Data Analytics BBDO

5. Forget about big data. Think big about any data.

6. October 2012: Franky goes to Las Vegas

7. Big Data. Customer Engagement. Marketing Accountability. DMA2012 Brings It All Together.

8. Survey participants DMA2012

9. 91% wants to
34% is able to

10. What is the level of data maturity DATA in your company?

11. The Data Maturity Stairway
I consolidate customer data
into one customer database
I capture customer data
across different touch points
I deliver customized interactions at
point of impact across touch points
I know what type of offer, channel and time
is best for different customer segments
I analyze historical customer data
(purchases, interactions, motivations)
I uncover hidden patterns in customer data
to predict what they are likely to do next
Gather Data
Aggregate Data
Customer Insight
Targeted
Communications
Predictive
Modeling
Real-time
contextual interactions

12. Today’s story

15. Relevance + Utility =

16. Customer
Experience Leaders
+43.0%
S&P 500 Index
+14.5%
Customer
Experience Laggards
-33.9%
Reason 1

17. Reason 2
86% of customers are willing to pay more
for a better customer experience

18. Silos in the organization

19. Data Silos
Marketing Sales Customer Service Billing Dept.

20. We live in the age of the customer.
A 20-year business cycle in which the most successful enterprises
will reinvent themselves to systematically understand and serve
increasingly more powerful consumers.
Forrester

21. Bigger role for the CMO
• Focus of the CMO should be on creating and
safeguarding the customer experience!
• Fueling innovation and new business models:
CMO as firestarter...
• Become owner of customer data that will guide and
enable your company strategy.

22. Overview
Managing the data Understanding the data Acting on the data

23. Managing the data Understanding the data Acting on the data

24. Volume - Variety - Velocity - Value

25. Value for Value
• 47% of women would share their mobile phone location
with a retailer in return for a $5 credit
• 83% would do so for a $25 credit
Research Now

26. Value for value in data gathering
Two different data sources:
‣ Own data
Ask the customer (explicitly)
Auto-populate data (implicitly)
‣ External data
Paid
Open
Any data

36. Managing the data Understanding the data Acting on the data

37. Personas are a vivid description
of your customer database records.

38. Socio-demo
Habits
Attitudes
Consumption
Media
Technology

39. Socio-demo
Habits
Attitudes
Consumption
Media
Technology

40. Customer Behavior Data
Research data
Social listing data
Media reports data
Third party data
…
ANALYZE
SYNTHESIZE

42. Analyze Customer Data
Registration Data Web Browsing Email Response User Actions

43. Clustering Database Data
Engagement level
User action 1
User action 2
max min

44. Third Party Data
Shopper Panel data
Socio-demographic & Lifestyle data

45. Different who?
Engaging Emily
• X% in db population
• Socio-demographic profile
• Shopping attitudes
• A-Brands
• Coupon usage
• P&G category spend
• Lifestyle data
Inactive Iris
Different what?
• Content and offers
• Frequency

52. Site engagement
• Repeat visits x2
• Time on site x9
Sales
• Double-digit growth with FlavorPrint users

53. Predictive Analysis

55. “We found that 74% of the time, our model
could correctly predict the exact address.”
Uber

57. HISTORICAL CUSTOMER DATA
CLIENT ID BIRTH DATE LOCATION … # TRANS JAN # TRANS FEB # TRANS MRCH
567678 25/11/1976 3400 8 2 0
566777 23/09/1987 3245 4 8 0
567789 11/08/1945 6700 6 8 6
445566 21/03/1967 9000 8 9 3
CURRENT CUSTOMER DATA
CLIENT ID BIRTH DATE LOCATION … # TRANS APR # TRANS MAY # TRANS JUNE
567898 25/08/1956 2440 6 1 0
589777 13/09/1977 3000 4 8 0
467789 11/09/1969 2431 5 2 0
445578 12/05/1988 1000 8 9 2
TODAY
CHURN FLAG
YES
NO
NO
NO
T + X
CHURN
PREDICTION
YES
NO
YES
NO
LEARN
APPLY
How it works

58. What’s the most likely model of interest when repurchasing?

59. Attributes
Initial enquiry data
(date, model, method, previous
car…)
Purchase data
(date, model, engine type, options,
…)
Driver data
(birthdate, location, dealer…)
Satisfaction data
(survey completion, …)
Predicted Model of Interest

60. DM
pack
Customized customer experience
Email
DM
pack
Es-mated
Repurchase
Date
Targets
within
buying
window
Non-­‐responders
Non-­‐converted
Test
Drivers
A
A
B
B
C

61. FMCG Company
Which is the most likely coupon offer combination that will trigger redemption?

62. Attributes
Household data
(family size, age, …)
Online response data
(email open/click behaviour,…)
Profile data
(brand consumption, …)
Redemption data
(coupon redemption, …)

64. Democratization of data science

66. Power to the marketeer

68. Managing the data Understanding the data Acting on the data

69. Relevance + Utility =

70. Customer Journey Planning
“A pre-planned series of integrated, targeted communications,
content or services designed to deliver a personal experience
for the consumer across all touch points.”

71. From campaigns (ads) to customer moments (value)
1. What are the make or break moments?
2. How can this be a positive experience?
3. What data do we need to help deliver the
experience?

73. • Persona
• Time
• Place
• Device
• External Data Context {

74. 5 golden rules for creating ‘contextual’ customer connections

75. #1 - Make it easy to interact

77. It’s CMR

78. #2 - Combine data and collaborate

80. Context creates new connections

81. #3 - Create value, not campaigns

85. "We are moving more and more toward service,
personalization, [and] customization."
Guive Balooch, global director of L'Oréal's Connected Beauty Incubator

86. #4 - Make real-time data a reality

88. Data and technology ‘yes’, adding creativity and imagination ‘woohoo’

89. #5 - Think big about any data

90. Key takeaways

91. Requirements:
• Data driven culture
• People & Technology
Relevance + Utility =

92. Data-driven company culture
Strategy Data
Optimize + Innovate
User Experience

93. People & Technology

94. Next 48 hours?

95. 1. What’s your next move on the data maturity stairway?
2. Who in your own organisation will you address and involve
in order to be able to move?
3. What value will you be offering beyond your product or
service?

96. Think big about any data

97. Thank you!

98. October 9th

99. IAB ThinkData
November 20th