WHEN WORLDS COLLIDE - BIG                           DATA & WEB ANALYTICS IN 2013                           Presented by   ...
GAME PLAN    1. Where is my money?                 4    2.   Off-line Customer Intelligence   14    3.   On-line Customer ...
THE GUY IN FRONT                 Jean-François (JF) Bélisle                 Director - Consulting Services @ K3 MediaForma...
SECTION 1WHERE IS MY MONEY?
1 – WHERE IS MY MONEY?    ASK BRIAN OR HIRE A PRO?5
1 – WHERE IS MY MONEY?       4 AREAS = 1 GOAL    1. Business Intelligence: Designates the ways, tools and methods used to ...
1 – WHERE IS MY MONEY?       LINKS BETWEEN AREAS, NOW!                                Business Intelligence    Customer In...
1 – WHERE IS MY MONEY?       LINKS BETWEEN AREAS, TOMORROW!                                Business Intelligence    Custom...
1 – WHERE IS MY MONEY?       WHO’S GROWING FASTER?    1. Big Data Analytics    2. Web Analytics    3. Customer Intelligenc...
1 – WHERE IS MY MONEY?        GALACTIC DATA EXPLOSION                                                                    M...
1 – WHERE IS MY MONEY?      …CLEAN RELATIONAL DATABASES            Social &            Mobile                             ...
1 – WHERE IS MY MONEY?     ADVANCED CUSTOMER INTELLIGENCE A dichotomy: Off-line Customer Intelligence –> Manual analysis b...
SECTION 2OFF-LINE CUSTOMER  INTELLIGENCE
2 – OFF-LINE CUSTOMER INTELLIGENCE     SOFTWARE14
2 – OFF-LINE CUSTOMER INTELLIGENCE     SUPERVISED METHODS     •15
2 – OFF-LINE CUSTOMER INTELLIGENCE     SUPERVISED METHODS Churn analysis: Type of analysis that helps detecting beforehand...
2 – OFF-LINE CUSTOMER INTELLIGENCE     SUPERVISED METHODS A few application: 1. Identify customers who have a higher    pr...
2 – OFF-LINE CUSTOMER INTELLIGENCE     NON-SUPERVISED METHODS X = multiple independent variables (all the variables we can...
2 – OFF-LINE CUSTOMER INTELLIGENCE     NON-SUPERVISED METHODS Example 2 – RFM Analysis Segmentation method that allows the...
2 - OFF-LINE CUSTOMER INTELLIGENCE     NON-SUPERVISED METHODS Example 3 - Affinity analysis Analysis that helps uncovering...
SECTION 3ON-LINE CUSTOMER INTELLIGENCE
3 – ON-LINE CUSTOMER INTELLIGENCE     RECOMMENDATION SYSTEMS Définition: Specific form of filtering that seeks to present ...
3 – ON-LINE CUSTOMER INTELLIGENCE     AMAZON.COM’S PATENT23
3 – ON-LINE CUSTOMER INTELLIGENCE     … BASED ON PURCHASE HISTORY                 Recommendations based on the purchase hi...
3 – ON-LINE CUSTOMER INTELLIGENCE     … BASED ON A REQUEST25
3 – ON-LINE CUSTOMER INTELLIGENCE     … BASED ON SIMILARITY     Recommendations based on the similarity with the purchases...
3 – ON-LINE CUSTOMER INTELLIGENCE     GOING FOR THE BUNDLE      Bundle: combining several products in one offer based on t...
3 – ON-LINE CUSTOMER INTELLIGENCE      MORE RECOMMENDATION SYSTEMS 1.   Avail Intelligence 2.   Barilliance 3.   Baynote 4...
3 – ON-LINE CUSTOMER INTELLIGENCE     …AND THE INTEGRATION WITH THE CMS29
3 – ON-LINE CUSTOMER INTELLIGENCE        … AND WEB ANALYTICS SOLUTIONS     • IBM Intelligent Offer generates personalized ...
3 – ON-LINE CUSTOMER INTELLIGENCE       REMARKETING     Remarketing: Action taken on by companies to reintroduce     a pro...
SECTION 4CONCLUSION
4 – CONCLUSION       THE FUTURE IS BRIGHT     Possibilities related to customer Intelligence are countless. The only thing...
4 – CONCLUSION     GET SOME TRAINING … IN FRENCH             http://www.k3media.com/services/formation-google-            ...
THANKS AND I HOPE YOU’VE          APPRECIATED!         Jean-François (JF) Bélisle     Phone number: 514-861-3332 ext 50   ...
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eMetrics_big_data_customer_intelligence

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

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  • http://www.e-marketing.fr/Definitions-Glossaire-Marketing/Remarketing-6280.htm
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  • eMetrics_big_data_customer_intelligence

    1. 1. WHEN WORLDS COLLIDE - BIG DATA & WEB ANALYTICS IN 2013 Presented by Jean-François Bélisle Director – Consulting Services @K3MediaK3 MEDIA INC. | 204 du Saint-Sacrement, 7ème étage | Montréal (Québec) | H2Y 1W8 T : 514.861.3332 | F : 514.861.3398
    2. 2. GAME PLAN 1. Where is my money? 4 2. Off-line Customer Intelligence 14 3. On-line Customer Intelligence 23 4. Conclusion 342
    3. 3. THE GUY IN FRONT Jean-François (JF) Bélisle Director - Consulting Services @ K3 MediaFormation 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. 4. SECTION 1WHERE IS MY MONEY?
    5. 5. 1 – WHERE IS MY MONEY? ASK BRIAN OR HIRE A PRO?5
    6. 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!6
    7. 7. 1 – WHERE IS MY MONEY? LINKS BETWEEN AREAS, NOW! Business Intelligence Customer Intelligence Web Analytics Big Data Analytics7
    8. 8. 1 – WHERE IS MY MONEY? LINKS BETWEEN AREAS, TOMORROW! Business Intelligence Customer Intelligence Web Analytics Big Data Analytics8
    9. 9. 1 – WHERE IS MY MONEY? WHO’S GROWING FASTER? 1. Big Data Analytics 2. Web Analytics 3. Customer Intelligence 4. Business Intelligence9
    10. 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)10
    11. 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) terminology11
    12. 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 methods12
    13. 13. SECTION 2OFF-LINE CUSTOMER INTELLIGENCE
    14. 14. 2 – OFF-LINE CUSTOMER INTELLIGENCE SOFTWARE14
    15. 15. 2 – OFF-LINE CUSTOMER INTELLIGENCE SUPERVISED METHODS •15
    16. 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)16
    17. 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)17
    18. 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.18
    19. 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.19
    20. 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 optimization20
    21. 21. SECTION 3ON-LINE CUSTOMER INTELLIGENCE
    22. 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 Filtering22
    23. 23. 3 – ON-LINE CUSTOMER INTELLIGENCE AMAZON.COM’S PATENT23
    24. 24. 3 – ON-LINE CUSTOMER INTELLIGENCE … BASED ON PURCHASE HISTORY Recommendations based on the purchase history24
    25. 25. 3 – ON-LINE CUSTOMER INTELLIGENCE … BASED ON A REQUEST25
    26. 26. 3 – ON-LINE CUSTOMER INTELLIGENCE … BASED ON SIMILARITY Recommendations based on the similarity with the purchases of other users26
    27. 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.27
    28. 28. 3 – ON-LINE CUSTOMER INTELLIGENCE MORE RECOMMENDATION SYSTEMS 1. Avail Intelligence 2. Barilliance 3. Baynote 4. Certona 5. Peerius 6. Predictive intent 7. RichRelevance28
    29. 29. 3 – ON-LINE CUSTOMER INTELLIGENCE …AND THE INTEGRATION WITH THE CMS29
    30. 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 guide30
    31. 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)31
    32. 32. SECTION 4CONCLUSION
    33. 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.33
    34. 34. 4 – CONCLUSION GET SOME TRAINING … IN FRENCH http://www.k3media.com/services/formation-google- analytics/ PROMO CODE = EMETRICS for 20%34
    35. 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.com35 Any Questions ? 

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