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Tim Nagels, Business Lead Microsoft Dynamics
Franky Willekens, Head of Data Analytics BBDO
Forget about big data. Think big about any data.
October 2012: Franky goes to Las Vegas
Big Data. Customer Engagement. Marketing Accountability. DMA2012 Brings It All Together.
Survey participants DMA2012
91% wants to 
34% is able to
What is the level of data maturity DATA in your company?
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
Today’s story
Relevance + Utility =
Customer 
Experience Leaders 
+43.0% 
S&P 500 Index 
+14.5% 
Customer 
Experience Laggards 
-33.9% 
Reason 1
Reason 2 
86% of customers are willing to pay more 
for a better customer experience
Silos in the organization
Data Silos 
Marketing Sales Customer Service Billing Dept.
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
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.
Overview 
Managing the data Understanding the data Acting on the data
Managing the data Understanding the data Acting on the data
Volume - Variety - Velocity - Value
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
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
Managing the data Understanding the data Acting on the data
Personas are a vivid description 
of your customer database records.
Socio-demo 
Habits 
Attitudes 
Consumption 
Media 
Technology
Socio-demo 
Habits 
Attitudes 
Consumption 
Media 
Technology
Customer Behavior Data 
Research data 
Social listing data 
Media reports data 
Third party data 
… 
ANALYZE 
SYNTHESIZE
Analyze Customer Data 
Registration Data Web Browsing Email Response User Actions
Clustering Database Data 
Engagement level 
User action 1 
User action 2 
max min
Third Party Data 
Shopper Panel data 
Socio-demographic & Lifestyle data
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
Site engagement 
• Repeat visits x2 
• Time on site x9 
Sales 
• Double-digit growth with FlavorPrint users
Predictive Analysis
“We found that 74% of the time, our model 
could correctly predict the exact address.” 
Uber
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
What’s the most likely model of interest when repurchasing?
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
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
FMCG Company 
Which is the most likely coupon offer combination that will trigger redemption?
Attributes 
Household data 
(family size, age, …) 
Online response data 
(email open/click behaviour,…) 
Profile data 
(brand consumption, …) 
Redemption data 
(coupon redemption, …)
Democratization of data science
Power to the marketeer
Managing the data Understanding the data Acting on the data
Relevance + Utility =
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.”
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?
• Persona 
• Time 
• Place 
• Device 
• External Data Context {
5 golden rules for creating ‘contextual’ customer connections
#1 - Make it easy to interact
It’s CMR
#2 - Combine data and collaborate
Context creates new connections
#3 - Create value, not campaigns
"We are moving more and more toward service, 
personalization, [and] customization." 
Guive Balooch, global director of L'Oréal's Connected Beauty Incubator
#4 - Make real-time data a reality
Data and technology ‘yes’, adding creativity and imagination ‘woohoo’
#5 - Think big about any data
Key takeaways
Requirements: 
• Data driven culture 
• People & Technology 
Relevance + Utility =
Data-driven company culture 
Strategy Data 
Optimize + Innovate 
User Experience
People & Technology
Next 48 hours?
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?
Think big about any data
Thank you!
October 9th
IAB ThinkData 
November 20th

BBDO Connect Big Data

  • 1.
  • 2.
  • 3.
    Tim Nagels, BusinessLead Microsoft Dynamics
  • 4.
    Franky Willekens, Headof Data Analytics BBDO
  • 5.
    Forget about bigdata. Think big about any data.
  • 6.
    October 2012: Frankygoes to Las Vegas
  • 7.
    Big Data. CustomerEngagement. Marketing Accountability. DMA2012 Brings It All Together.
  • 8.
  • 9.
    91% wants to 34% is able to
  • 10.
    What is thelevel of data maturity DATA in your company?
  • 11.
    The Data MaturityStairway 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.
  • 15.
  • 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 theorganization
  • 19.
    Data Silos MarketingSales Customer Service Billing Dept.
  • 20.
    We live inthe 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 forthe 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 thedata Understanding the data Acting on the data
  • 23.
    Managing the dataUnderstanding 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 valuein 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 dataUnderstanding the data Acting on the data
  • 37.
    Personas are avivid 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? EngagingEmily • 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.
  • 55.
    “We found that74% 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 mostlikely model of interest when repurchasing?
  • 59.
    Attributes Initial enquirydata (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 Customizedcustomer 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 Whichis 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.
  • 66.
    Power to themarketeer
  • 68.
    Managing the dataUnderstanding the data Acting on the data
  • 69.
  • 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 rulesfor creating ‘contextual’ customer connections
  • 75.
    #1 - Makeit easy to interact
  • 77.
  • 78.
    #2 - Combinedata and collaborate
  • 80.
  • 81.
    #3 - Createvalue, not campaigns
  • 85.
    "We are movingmore and more toward service, personalization, [and] customization." Guive Balooch, global director of L'Oréal's Connected Beauty Incubator
  • 86.
    #4 - Makereal-time data a reality
  • 88.
    Data and technology‘yes’, adding creativity and imagination ‘woohoo’
  • 89.
    #5 - Thinkbig about any data
  • 90.
  • 91.
    Requirements: • Datadriven culture • People & Technology Relevance + Utility =
  • 92.
    Data-driven company culture Strategy Data Optimize + Innovate User Experience
  • 93.
  • 94.
  • 95.
    1. What’s yournext 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.
  • 97.
  • 98.
  • 99.