I know what you will buy next summer

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

11/24/2015 BAQMaR Conference: Futures Festival 2

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

11/24/2015 BAQMaR Conference: Futures Festival 2

La Redoute: Home Shopping Retailer

BAQMaR Conference: Futures Festival 311/24/2015


• Multi Product



• Multi Product



• Multi Channel



• Multi Channel



• Multi Channel



• Multi Channel



• Multi Channel


La Redoute: Facts and Figures






La Redoute: Strong Competition


La Redoute: Strong Competition


Mass personalization society (Wind, 2001, JIM)

• Mass Consumption

• Personalized offering

• Mass personalization




















• Mass personalization Personalized offering with the advantages of mass consumption.





• Mass personalization Personalized offering with the advantages of mass consumption.

People expect mass personalization. Globalization and e-commerce are factors making this shift possible.


Mass personalization society

• Paradox of choice (Tamaki 2005, LATimes; Girish 2007 JIM)




+ 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







– Client is overwhelmed by the enormous choice• Hard to make an (optimal) purchase decision

→Recommendation SystemsBAQMaR Conference: Futures Festival 911/24/2015

Recommendation Systems: Examples

BAQMaR Conference: Futures Festival 10

In …. Travel sector

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In …. Media services

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In …. Social media

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In …. FMCG

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In …. E-commerce

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Recommendation Systems: La Redoute


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.


Recommendation Systems?

• A data science description

A recommendation system is a customer centric view to recommend personalized sets of productsto clients using data science. This contrasts to the classical PTB models which are applied in product centric approaches.


What is it not?


What is it not?


What is it not?


What is it not?


What is it not?


What is it not?


What data to use?


What data to use?


What data to use?


What data to use?


What data to use?





Identification


Identification


Identification MenSportBlue

Men SuitsAccessory Red

Men SuitsVest Dark Blue

Men SuitsShoes Brown


MenSportBlue




Identification


Identification

Socio Demographics

MenSportBlue




Identification


Identification

Socio Demographics

MenSportBlue




Identification


Identification

Socio Demographics

MenSportBlue




Identification


Identification

Socio Demographics

Raincoat

MenSportBlue




Identification


Identification

Socio Demographics

Raincoat

MenSportBlue




Identification

Men JacketRaincoat Blue


Identification

Socio Demographics

Raincoat

MenSportBlue




Identification



Identification

Socio Demographics

Raincoat

MenSportBlue




Identification



Identification

Socio Demographics

Raincoat

MenSportBlue




Identification

Men JacketRaincoat Red



Identification

Socio Demographics

Raincoat

MenSportBlue




Identification




Identification

Socio Demographics

Raincoat

MenSportBlue




Identification





Identification

Socio Demographics

Raincoat

MenSportBlue




Identification





Identification

Socio Demographics

Raincoat

MenSportBlue




Identification





Identification

Identification

Socio Demographics

Raincoat

MenSportBlue








Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue







Socio-demographics and segmentation

Demographic RecSys


Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue








Demographic RecSys

Social media Social RecSys


Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue







Behavioral history and Look-a-likes

Collabotative RecSys


Demographic RecSys



Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue










Demographic RecSys


Product Information Content-based RecSys


Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue










Demographic RecSys




Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue










Demographic RecSys



Context (location weather, etc.)

Context-based RecSys


Recommendation Zone

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Identification

Identification

Socio Demographics

Raincoat

MenSportBlue










Demographic RecSys





Real-time path analysis Knowledge-based RecSys


Recommendation Zone

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Combining different algorithms and/or data sources

Hybrid RecSys

Identification

Identification

Socio Demographics

Raincoat

MenSportBlue










Demographic RecSys





Real-time path analysis Knowledge-based RecSys


Recommendation Zone

11/24/2015

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




• 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


• Input Data

• Modeling techniques



• Input Data





Demographic RecSys




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• Input Data





Demographic RecSys




Hierarchical Hybrid Model

Combination of scoresSVM, LR, DT, PM

Factorization Machine

Use all different data sources as input for Factorization Machine

11/24/2015

Applications

….

Pre-installed

….


27

(R)JDBC

Virtual Machine

CDH

To install

SQOOP

Application Examples






Car Loan

11/24/2015



Added Value

• Customer

• Company


Added Value

• Customer

• Company


Added Value

• Customer

• Company


> 75% of items watched on Netflix 50% of LinkedIn connections

11/24/2015

Thank you for your Attention

Contact:Stijn Geuens (0)3.20.545.892

IESEG School of Management [email protected]

3 Rue de la Digue fr.linkedin.com/pub/stijn-geuens/

F-59000 Lille stijn.geuens

Data & Analytics

I know what you will buy next summer