stijn geuens - i know what you’ll buy next summer
TRANSCRIPT
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KU Leuven:Commercial
Engineer
U Gent:Master Marketing
Analysis
Dexia/Belfius:Data Miner
La Redoute: Marketing Analyst
IESEG: PhD Candidate
Who am I?2005 2010 2011 2014
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La Redoute: Home Shopping Retailer
• Multi Product
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La Redoute: Home Shopping Retailer
• Multi Channel
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La Redoute: Facts and Figures
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La Redoute: Facts and Figures
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La Redoute: Strong Competition
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Mass personalization society• Mass Consumption
• Personalized offering
• Mass personalization Personalized offering with the advantages of mass consumption. People expect mass personalization. Globalization and e-commerce are factors
making this shift possible.BAQMaR Conference: Futures Festival
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Mass personalization society• Paradox of choice+ Client are better informed
• Globalization and internet• Enormous choice possibilities
+ A customer could find a product/service satisfying his/her needs
– Client is overwhelmed by the enormous choice• Hard to make an (optimal) purchase decision
→Recommendation SystemsBAQMaR Conference: Futures Festival
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Recommendation Systems: Examples
In …. Travel sector
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Recommendation Systems: Examples
In …. Media services
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Recommendation Systems: Examples
In …. Social media
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Recommendation Systems: Examples
In …. FMCG
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Recommendation Systems: Examples
In …. E-commerce
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Recommendation Systems: La Redoute
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Recommendation Systems?
• A definition [Ricci et al. 2011]
Recommender Systems are software tools and techniques providing personalized suggestions for items to be of use to a user. The suggestions, based a customer’s profile or his behavior, relate to various decision-making processes.
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Recommendation Systems?
• A data science description A recommendation system is a customer centric
view to recommend personalized sets of products to clients using data science. This contrasts to the classical PTB models which are applied in product centric approaches.
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What is it not?
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What is it not?
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What is it not?
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What data to use?
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Identification MenSportBlue
MenSuitsAccessoryRedMen SuitsVest Dark Blue
Men SuitsShoesBrown
MenSportBlueMen
SuitsAccessory
Red
Men SuitsVest Dark Blue
Men
SuitsShoes
Brown
Identification
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Identification
Socio DemographicsIdentification
Socio Demographics
Raincoat
MenSportBlueMen
SuitsAccessory
Red
Men SuitsVest Dark Blue
Men
SuitsShoes
Brown
Identification
Men JacketRaincoat Red
Men JacketRaincoat Blue
Men JacketRaincoat Blue
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Combining different algorithms and/or data sources
Hybrid RecSys
Identification
Identification
Socio Demographics
Raincoat
MenSportBlueMen
SuitsAccessory
Red
Men SuitsVest Dark Blue
Men
SuitsShoes
Brown
Men JacketRaincoat Red
Men JacketRaincoat Blue
Men JacketRaincoat Blue
Behavioral history and Look-a-likes
Collabotative RecSys
Socio-demographics and segmentation
Demographic RecSys
Social media Social RecSys
Product Information Content-based RecSys
Context (location weather, etc.)
Context-based RecSys
Real-time path analysis Knowledge-based RecSys
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Recommendation Zone
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Applied to La Redoute• E-mail personalization system: returning visit
• Let’s do the math: 7 million unique visitors a month 233 k visitors a day 150 k identified (e-mail) 250 k products, of which 50 k in recommendation scope 7.5 billion recommendation scores on average a day
• Possible extension to the site Real-time recommendations
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• Input Data
• Modeling techniques
Applied to La Redoute
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Socio-demographics and segmentation
Demographic RecSys
Behavioral history and Look-a-likes
Collabotative RecSys
Product Information Content-based RecSys
Hierarchical Hybrid Model
Combination of scoresSVM, LR, DT, PM
Factorization Machine
Use all different data sources as input for Factorization Machine
Applications
….
Pre-installed
….
Applied to La Redoute
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(R)JDBC
Virtual Machine
CDH
To install
SQOOP
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Application Examples
Car Loan
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Added Value• Customer
• Company> 75% of items watched on Netflix 50% of LinkedIn connections
Thank you for your Attention
Contact:Stijn Geuens (0)3.20.545.892
IESEG School of Management [email protected] Rue de la Digue fr.linkedin.com/pub/stijn-geuens/
F-59000 Lille stijn.geuens