Tailor-made personalization and recommendation - Sailendra
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Transcript of Tailor-made personalization and recommendation - Sailendra
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Tailor-made personalization and recommendation
Sailendra
Tailor-made personalization
and recommendation
Tailor-made personalization and recommendation
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Tailor-made personalization and recommendation
Sailendra
15 years of research in AI
Collaboration:
INRIA and LORIA
Artificial intelligence
Machine Learning
Behavioural analysis
Recommendations
Activity sectorScientific experiences
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Tailor-made personalization and recommendation 3
Behavioral analysis and recommendations
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Tailor-made personalization and recommendation
Basics of solution
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1
• Collect users’ traces from client website:
• Navigation
• Visited pages (category page, product page …)
• Sales, add to basket, …
2• Convert traces into user-item digital rating
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Tailor-made personalization and recommendation
Basics of solution
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3
• Choose the appropriate algorithm and adjusting it:
• User based
• Item based
• Hybrid algorithms
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• Add new algorithms that fit to client activityspecifications
• Marketing filters
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Tailor-made personalization and recommendation
Costumers cases presentation
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Tailor-made personalization and recommendation
E-commerce
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e-Business
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Tailor-made personalization and recommendation
Paraforme
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Activity area : Online sales of parapharmaceutical
products
Problem : low users conversion
Objective : Realize additional sales
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Tailor-made personalization and recommendation
Recommendations on product page
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METHOD
• Generate recommendations usingdifferents algorithms according to the visited page
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Tailor-made personalization and recommendation
Recommendations on product page
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Proposing alternative options to augment satisfaction
- Cascade hybrid recommender
Content based similar item
Item based collaborative filtering
Products usually bought with the current product
GOAL
METHOD
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Tailor-made personalization and recommendation
Recommendations on basket pageParaforme
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Incite user to complet his basket
• Cascade hybrid recommender
• Products usually bought with the current product
• Popular products within the community of the active user
GOAL
METHOD
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Tailor-made personalization and recommendation
Recommendations on basket pageParaforme
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RESULT• + 20 % of conversion
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Tailor-made personalization and recommendation
E-commerce
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Business to Business
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Tailor-made personalization and recommendation
J. Milliet
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Activity area : drinks distributor for restaurants and
bars
Problem : low conversion on sales channels
Objective : Increase sales
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Tailor-made personalization and recommendation
Cross canal recommendations
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METHOD
• Personalized recommendations in the CRM and on commercials’ tablet
• Development of tailor-made filters
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Tailor-made personalization and recommendation
Cross canal recommendations
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• Customers knowlegde
• Developement of customers proximity
• Sales increase
• Propose innovative recommendation despite the presence of many strict rulesRESULTS
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Tailor-made personalization and recommendation
Banque
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Online Bank
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Tailor-made personalization and recommendation
BforBank
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Activity area : online bank
Problem : No direct relation with customers
(100% online)
Objective : Simplify and understand users’
navigation paths, and predict their intentions
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Tailor-made personalization and recommendation
Sales channels recommendations
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METHOD
• Analysis (clustering) of stream based on behaviors
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Tailor-made personalization and recommendation
RESULTS
• Optimize user navigation and simplify his path
• Predict user intentions and their time
• Anticipate costumer attrition and alert the bank
• Adapt marketing and communication strategies by community
Sales channels recommendations
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Other domains
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Tailor-made personalization and recommendation
Other domains
e-Health: Satelor
• Goal : secure sick and old people in
their place
• Way : A robot and a tablet application
to monitoring user daily activities
• Sailendra:
– Personalizing the tablet interface
– Pushing personalized behavioural
advices to improve health
e-Learning: Périclès
• Goal : recommend personalizedpedagogic resources for students
• Way : an integrated framework withinthe web-site of the university
• Sailendra:
– Participation in the conception and parametring recommendation algorithm
– Industrialization of algorithms stemmingfrom the project
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Tailor-made personalization and recommendation
Conclusion
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Conclusion
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Tailor-made personalization and recommendation
Personalized and strategical support
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Tailor-made support in relation with:
Sector of activity Structure of the website
Webmarketing strategy
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Tailor-made personalization and recommendation
Any questions ?
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www.sailendra.fr
+33 (0)3 72 47 03 37@SailendraSAS
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