Presented by Jean-Francois Belisle at the eMetrics Tour in Montreal on January 16th 2013.

1. WHEN WORLDS COLLIDE - BIG
DATA & WEB ANALYTICS IN 2013
Presented by
Jean-François Bélisle
Director – Consulting Services
@K3Media
K3 MEDIA INC. | 204 du Saint-Sacrement, 7ème étage | Montréal (Québec) | H2Y 1W8 T : 514.861.3332 | F : 514.861.3398

2. GAME PLAN
1. Where is my money? 4
2. Off-line Customer Intelligence 14
3. On-line Customer Intelligence 23
4. Conclusion 34
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3. THE GUY IN FRONT
Jean-François (JF) Bélisle
Director - Consulting Services @ K3 Media
Formation B.Sc. Economics, Université de Montréal
M.Sc. Marketing, HEC Montréal
Award of Achievement, Web Analytics, University of British Columbia
Ph.D Studies, Marketing & Computational Stats , McGill University
Executive Training in Customer Analytics, University of Pennsylvania (Wharton)
Experience
Jean-François is the Director – Consulting Services at K3 Media. He is responsible for: (1) New Business
Development, (2) Training partners, and (3) Supervising the Consulting Services team.
He has a background in Economics and Computational Statistics, and used to be a Lecturer at HEC Montréal
where he created the eMarketing class. He is also a web expert who has given more than 100 conferences.
He has solid critical thinking and analytical skills and more than 8 years of experience as a consultant gained
as a Manager at AIR MILES and as an independent consultant. He has worked for clients such as P&G, Bell,
Jean Coutu, Rona and the Quebec Government to name a few, where he used his knowledge in Interactive
Marketing, CRM and Data Mining.

4. SECTION 1
WHERE IS MY MONEY?

5. 1 – WHERE IS MY MONEY?
ASK BRIAN OR HIRE A PRO?
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6. 1 – WHERE IS MY MONEY?
4 AREAS = 1 GOAL
1. Business Intelligence: Designates the ways, tools and methods used to
collect, consolidate, model and restore the material or immaterial
business data used to support the decision making process and help the
decision maker have a better overview of the activity.
2. Customer Intelligence: The Customer part of Business intelligence.
3. Big Data Analytics: Analytics with humongous datasets –> When the
data doesn’t fit in an Excel file (thx @shamelCP).
4. Web Analytics: What most of us are doing here!
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7. 1 – WHERE IS MY MONEY?
LINKS BETWEEN AREAS, NOW!
Business Intelligence
Customer Intelligence
Web Analytics
Big Data Analytics
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8. 1 – WHERE IS MY MONEY?
LINKS BETWEEN AREAS, TOMORROW!
Business Intelligence
Customer Intelligence Web Analytics
Big Data Analytics
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9. 1 – WHERE IS MY MONEY?
WHO’S GROWING FASTER?
1. Big Data Analytics
2. Web Analytics
3. Customer Intelligence
4. Business Intelligence
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10. 1 – WHERE IS MY MONEY?
GALACTIC DATA EXPLOSION
More Data
≠
More Insights
Source: 2011 IBM Global Chief Marketing Officer: From Streched to Strengthened (www.ibm.com/cmostudy)
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11. 1 – WHERE IS MY MONEY?
…CLEAN RELATIONAL DATABASES
Social &
Mobile Customer Attributes
and Interactions
Traffic Off-line
Sources Interactions
Lifetime Systems of
Website Record
Behavior
Source: IBM Customer Profiles (LIVE) terminology
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12. 1 – WHERE IS MY MONEY?
ADVANCED CUSTOMER INTELLIGENCE
A dichotomy:
Off-line Customer Intelligence
–> Manual analysis by an analyst (or any other Take your
type of humans) time for
• Supervised methods (predictive analysis) analysis
• Non-supervised methods
On-line Customer Intelligence (real-time)
–> Algorithmic Recommendation Systems Real-time
May include algorithms based on off-line supervised analysis
methods (predictive analysis) and non-supervised methods
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13. SECTION 2
OFF-LINE CUSTOMER
INTELLIGENCE

14. 2 – OFF-LINE CUSTOMER INTELLIGENCE
SOFTWARE
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15. 2 – OFF-LINE CUSTOMER INTELLIGENCE
SUPERVISED METHODS
•
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16. 2 – OFF-LINE CUSTOMER INTELLIGENCE
SUPERVISED METHODS
Churn analysis: Type of analysis that helps
detecting beforehand customers that have the
highest probability of churning.
Supervised statistical methods:
1. Multinomial Logit (MNL)
2. Linear Discriminant Analysis (LDA) 9. Support Vector Machines (SVM)
3. Quadratic Discriminant Analysis (QDA) 10. Classification and Regression
4. Flexible Discriminant Analysis (FDA) Trees (CART)
5. Penalized Discriminant Analysis (PDA) 11. Bagging
6. Mixture Discriminant Analysis (MDA) 12. Boosting
7. Naïve Bayes Classifier (NBC) 13. Random Forests
8. K-Nearest Neighbor (KNN) 14. Neural Networks
9. Support Vector Machines with multiple
Kernels (SVM)
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17. 2 – OFF-LINE CUSTOMER INTELLIGENCE
SUPERVISED METHODS
A few application:
1. Identify customers who have a higher
probability of buying a product based on
their tastes and previous purchases.
2. Isolate the impact of advertising campaigns
on sales (taking in consideration
cannibalization)
3. Compute the impact of each communication
channel on sales
4. Identify the characteristics of the
respondents vs. Non-respondents in an
email offer.
5. Identify the causes (X) of (Y)
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18. 2 – OFF-LINE CUSTOMER INTELLIGENCE
NON-SUPERVISED METHODS
X = multiple independent variables (all the variables we
can collect: navigation data, psychographics,
sociodemographics)
Example 1 – Segmentation through clustering
Question: Based on the independent variables
available, how can we segment our market?
Segmentation: Strategy that involves creating groups of
customers based on similar caracteristics in a way that
every segment created is different from the others.
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19. 2 – OFF-LINE CUSTOMER INTELLIGENCE
NON-SUPERVISED METHODS
Example 2 – RFM Analysis
Segmentation method that allows the
creation of a classification of customers
based on their buying habits. The RFM
classification is based on 3 criteria:
(1) Recency: date of the last purchase or the
last customer contact,
(2) Frequency: frequency of the purchased
on a given reference period, and
(3) Monetary: cumulated amount of
purchases on that period.
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20. 2 - OFF-LINE CUSTOMER INTELLIGENCE
NON-SUPERVISED METHODS
Example 3 - Affinity analysis
Analysis that helps uncovering relations of
cooccurrences between activities realized by
customers or groups of customers.
Other examples
1. Personas Optimization
2. Market Basket Analysis
3. Front page flyer optimization
4. Assortment optimization
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21. SECTION 3
ON-LINE CUSTOMER INTELLIGENCE

22. 3 – ON-LINE CUSTOMER INTELLIGENCE
RECOMMENDATION SYSTEMS
Définition: Specific form of filtering that seeks to present elements of
information (movies, music, books, news, pictures, web pages, etc.) that
should be of interest to a user.
Generally, a recommendation system allows the comparaison of a user’s
profile to certain reference features and seeks to offer informations that are
as relevant as possible to the user using predictive algoritmns.
Those features can come from :
1. The object itself -> Content-Based Approach
2. The user
3. The social environment-> Collaborative Filtering
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23. 3 – ON-LINE CUSTOMER INTELLIGENCE
AMAZON.COM’S PATENT
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24. 3 – ON-LINE CUSTOMER INTELLIGENCE
… BASED ON PURCHASE HISTORY
Recommendations based on the purchase history
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25. 3 – ON-LINE CUSTOMER INTELLIGENCE
… BASED ON A REQUEST
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26. 3 – ON-LINE CUSTOMER INTELLIGENCE
… BASED ON SIMILARITY
Recommendations based on the similarity with the purchases of
other users
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27. 3 – ON-LINE CUSTOMER INTELLIGENCE
GOING FOR THE BUNDLE
Bundle: combining several products in one offer based on the
similarity between your purchase and those of other customers.
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28. 3 – ON-LINE CUSTOMER INTELLIGENCE
MORE RECOMMENDATION SYSTEMS
1. Avail Intelligence
2. Barilliance
3. Baynote
4. Certona
5. Peerius
6. Predictive intent
7. RichRelevance
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29. 3 – ON-LINE CUSTOMER INTELLIGENCE
…AND THE INTEGRATION WITH THE CMS
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30. 3 – ON-LINE CUSTOMER INTELLIGENCE
… AND WEB ANALYTICS SOLUTIONS
• IBM Intelligent Offer generates personalized product recommendations for each
visitor based on current session and historical browsing, shopping and purchasing
data collected by IBM.
• An offer is a collection of settings that includes the type, algorithm affinity
weighting, data analysis time period, and business rules that generates a list of
recommended items.
• The offers can be on the:
• Homepage
• Product page
• Shopping card
• Email
• Search results page
Source: 2011 IBM Coremetrics Intelligent offer guide
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31. 3 – ON-LINE CUSTOMER INTELLIGENCE
REMARKETING
Remarketing: Action taken on by companies to reintroduce
a product or service to the market in response to declining sales. The
company remarkets the product as something that has been improved to
reignite interest and hopefully improve sales. (businessdictionary.com)
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32. SECTION 4
CONCLUSION

33. 4 – CONCLUSION
THE FUTURE IS BRIGHT
Possibilities related to customer Intelligence are countless. The only thing
needed for a strategist is to understand the potential of the methods (off-
line and on-line) to generate ideas and then try to convince the HiPPO.
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34. 4 – CONCLUSION
GET SOME TRAINING … IN FRENCH
http://www.k3media.com/services/formation-google-
analytics/
PROMO CODE = EMETRICS for 20%
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35. THANKS AND I HOPE YOU’VE
APPRECIATED!
Jean-François (JF) Bélisle
Phone number: 514-861-3332 ext 50
Email: jfbelisle@k3media.com
Corp.: www.k3media.com
LinkedIn: Linkedin.com/in/jfbelisle
Twitter: @jfbelisle
Site: jfbelisle.com
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Any Questions ? 