ANALYSEDE DONNÉESDE TRAFICWEB POUR LA PRÉDICTION … · ecommerce / crm 2-analyse a +15 persons...
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MATLAB EXPO 2016 21/06/16 ANALYSE DE DONNÉES DE TRAFIC WEB POUR LA PRÉDICTION SOCIODÉMOGRAPHIQUE
Transcript of ANALYSEDE DONNÉESDE TRAFICWEB POUR LA PRÉDICTION … · ecommerce / crm 2-analyse a +15 persons...
MATLAB EXPO 2016
21/06/16
ANALYSEDE DONNÉES DE
TRAFICWEB POUR LA
PRÉDICTION
SOCIODÉMOGRAPHIQUE
PAGE 2CRM DATA
SOMMAIRE
LE FIGARO – START-UP DEPUIS 1826
LA DATA DANS LES MÉDIAS
FIG DATA
CAS D’USAGE: PRÉDICTIONSSOCIO-DÉMOGRAPHIQUES
PAGE 3CRM DATA
LE GROUPE LE FIGARO: UNE START-UP DEPUIS 1826
1826 1999 2015
FROM A WEEKLY NEWSPAPER TO THE FIRST
PAID NATIONAL DAILY NEWSPAPER
BRAND DIGITALIZATION + CCM BENCHMARK’S
CREATION
SERVICES &E-COMMERCE
DIVERSIFICATION
BUY-OUTOF CCM
BENCHMARK
1978
FROM A DAILY NEWSPAPER TO
MAGAZINES
LAUNCH OF FIGARO
CLASSIFIEDS
20102000
PAGE 4CRM DATA
LE MEILLEUR DES MONDESPlurimedia coverage(thousands)
Digital coverage(thousands)
• Crossmedia 2015 – MNR fixe novembre 2015
PAGE 5CRM DATA
1ER GROUPE DIGITAL FRANÇAIS
Mediamétrie/Netratings - Internet global, janvier 2016 - TOP 4
GOOGLE 40 133 000
36 735 000
31 084 000
35 465 000
MICROSOFT
PAGE 6CRM DATA
UN ACTEUR MAJEUR: 80% DE REACH SUR SES CIBLES
More than 40,4M individuals
Almost 80% of frenchpopulation is reachedeach month
CrossMedia 2015
PAGE 7CRM DATA
FIGARO - SOCIAL PLAYER
10 M SOCIAL FOLLOWERS
1 COMMENT
EVERY 6 SECONDS
1ST BRAND MENTIONNED
+ 2 400 QUOTES EVERY MONTH
PAGE 8CRM DATA
A TECHNOLOGICAL PUBLISHER
ONBOARDING TALENTS
+900 COLLABORATORS
+350 WEB DEVELOPERS
+10M€ INVESTMENT / YEAR
INVESTING IN TECHNOLOGY
DMP, ADSERVER FULL STACK, SSP, PROGRAMMATIC, TRADING DESK, DATA LAKE
START UP BUY-OUT
PAGE 9CRM DATA
WITH A PROVEN IMPACT ON THE MODEL
1/2 34% 72%
PAGE 10CRM DATA
DE LA DATA POUR CHAQUE DOMAINE D’ACTIVITÉ
PUBLICITAIRE
E-COMMERCE
EDITORIAL
PAGE 11CRM DATA
EXPLOITATION DE LA DONNÉE CLIENT
1986 20092005 2013
NEWSLETTER
& EMAILING
CONDITIONAL
ADVERTISMENT
DEDICATED
DATA ARCHITECTURE
HOME
DELIVERY
2016
DATA :+15 INDIVIDUALS(DATA scientist, DATA analyst…)
PROGRAMMATIC : +5 INDIVIDUALS(directeur des opérations, programmatic manager…)
TRADING DESK : +20 INDIVIDUALS(trader, online advertising sales manager…)
DATA MARKETING : +20 INDIVIDUALS(CRM manager, Email & CRM executive…)
PAGE 12CRM DATA
LA DATA DANS LES MEDIAS
PAGE 13CRM DATA
TOUT MESURER
PAGE 14CRM DATA
ANALYSER / DÉFINIR LES KPI’S
PAGE 15CRM DATA
DONNER DU SENS À LA DONNÉE
PAGE 16CRM DATA
PASSER DU SEGMENT À L’INDIVIDU
Segmentation Personnalisation
PAGE 17CRM DATA
GO LIVE!
PAGE 18CRM DATA
CHALLENGER SES PRATIQUES
Source: http://www.kameleoon.com/
PAGE 19CRM DATA
IDENTIFIER LES PROFILS UNIQUES
20MUNIQUE
PROFILES
Cibler les visiteurs tout au long de leur navigation
Ajuster le capping, la fréquence, advertising
story
Identification anonyme
FIG DATA
PAGE 21CRM DATA
LA DONNÉE CLIENT À TRAVERS LES ÂGES
• 1980 : ce qu’il achète cartes de fidélité
• 1990 : qui il est études, segments
• 2000 : ce qu’il fait cookies, analytics
• 2010 : ce qu’il veut algorithmes
PAGE 22CRM DATA
ORGANISATION DE LA DONNÉE CLIENT
PII Data Non PII Data
PAGE 23CRM DATA
COLLECTER LA DONNÉE DE TOUTES LES SOURCES
PAGE 24CRM DATA
DONNÉE 1ST PARTY – VALEUR UNIQUE
Interests and hobbies
Likes & share
URL, referer, keywords
Advertising interactions
Content DATA
CRM onboarding
PAGE 25CRM DATA
ENRICHISSEMENT DE LA DONNÉE : 3RD PARTY DATA
Weather DATA
Cart content
Geolocation
Open DATA
PAGE 26CRM DATA
LA DMP ET LE DATA LAKE
DMP DATA lakeD
ATA
st
ora
geC
on
ne
ctio
ns
DA
TA
acti
vati
on
s
Non PII DATADATA from customers and prospects only
All the DATA:- PII DATA from customers and prospects- activities DATA (pricing, margins, products…)
Connected in real-time to the ecosystem-adservers-3rd parties
Cross-platform identificationManual segmentation only
Multi-sources DATA processingand dynamic segmentation
Indirectly connected in real-time to the ecosystem(through DMP and CRM)
Operationnal DATAActivation
Comprehensive DATAKnowledge and intelligence
PAGE 27CRM DATA
LE DATA LAKE
A place to store every type of DATA
With processing capabilities
Virtualy unlimited by using a bigDATA infrastrucure(distributed approach potentially in the cloud)
Storing unstructrured DATA at an optimized cost
And processing it at record paces for exploitation
A technological opportunity for customer insights and business optimizations
PAGE 28CRM DATA
UNE COMBINAISON D’INGÉNIEURS ET D’ANALYTICS
Behavioral DATA Customer DATA Products DATA Cross-canal DATA 3rd party DATA
Data-lakeStoring and using the DATAPredictive analysis, scoring, pairing, machine learning, normalization, etc.
DMP
ANALYSIS/ REPORTING ACTIVATION / USE CASES ADVERTISING, CONTENTS, E-COMMERCE, CUSTOMER EXPERIENCE
PAGE 29CRM DATA
3-QUALIFY+60M QUALIFIED PROFILES
+20M CROSS SCREEN PROFILES
+30M CRM MEMBERS
DE LA COLLECTEÀ L’ACTIVATION
TIRER PARTI DE LA DMP ET DU DATA LAKE
1-COLLECTNAVIGATION /
ECOMMERCE / CRM
2-ANALYSEA +15 PERSONS DEDICATED TEAM
4-ACTIVATECMS
DIRECT AND PROGRAMMATIC ADSERVERS
TRADING DESKS
EMAILS
CATALOGUE AND CUSTOM-MADE
SEGMENTS
RETARGETING
LOOK ALIKE
DCO
AUDIENCE EXTENSION
PAGE 30CRM DATA
POUR LES BESOINS MÉTIERS
UNE SEGMENTATION ADRESSÉE
Gentleman930 000 profiles9 000 opt-in Sponsor
Fashionista800 000 profiles18 000 opt-in Sponsor
Affluent urban200 000 profiles3 000 opt-in Sponsor
Ideal husband150 000 profiles3 000 opt-in Sponsor
Businessman950 000 profiles22 000 opt-in Sponsor
Senior-itas180 000 profiles2000 opt-in Sponsor
Business woman650 000 profiles11 000 opt-in Sponsor
Highly educated1 500 000 profiles 12 000 opt-in Sponsor
Epicurean1 500 000 profiles12 000 opt-in Sponsor
Investor1 500 000 profiles29 000 opt-in Sponsor
Madame Rive Gauche800 000 profiles18 000 opt-in Sponsor
Bride-to-be300 000 profiles12 000 opt-in Sponsor
PAGE 31CRM DATA
CREATINGYOUROWNSPECIFICTARGETS
DES SEGMENTS COLLABORATIFS FAITS SUR-MESURE
CUSTOM-MADECOLLABORATIVE SEGMENTS
CRMSURF KEYWORD SEARCH PIXEL ADVERTISER WEBSITE
ADVERTISER DATA
CRME COMMERCE
DATAFIGARO CCM
PAGE 32CRM DATA
COLLABORATIVE DATA
AU PLUS PROCHE DE L’ AUDIENCE DE L’ANNONCEUR
CHANEL.COMVISITORS
CHANEL.COM AUDIENCESEGMENTATION
WEBSITEVISITORS
PERFUME SECTIONVISITORS
BLEU DE CHANELVISITORS
RETARGETING ‘BLEU DE CHANEL’ ON FIGARO CCM GROUP WEBSITES
LOOK ALIKE AND PRETARGETING ONPERFUME INTENT BUYERS
LOOK ALIKE WITH CHANEL.COM WEB USERS
Pixeladvertiser website
PAGE 33CRM DATA
DU SUR-MESURE GRÂCE À LA DATA SCIENCE
Target Core target
Interest
Wellness and skincare Natural care
Targeting through a keyword selection (ex:oil, spa, dry skin, beauty habits, lines , pollution)
Focus on specific keywords (dry oil, cell renewal, jojoba,… )
Lifestyle
Active city worker Active city worker with wellness
concerns Women living in big and medium cities, focused on their career and family
Inspiration
Events and celebrities Icons Interests for events and people close to the brand
universe (Fashion week, Oscars, Coachella)
Competition
Brands Products
Women with interests for competing brands(Nuxe, Kielhs, Nivea…)
Women living in big and medium cities, focused on their career and family
+ Major concern about personal care and family wellness
Women with interests for the brand icons (Leila Bekthi, Bianca Balti, Blake Lively, Eva
Longoria…)
Women with interests for competing products(ex: Nuxe huile prodigieuse …)
L’Oréal
New facial oil
launch
PAGE 34CRM DATA
ATTEINDRE LES BONNES CIBLES, AUGMENTER LE ROI
EMAILING CONTEXTUALISÉ
Prospect’s targeting thanks to
key words identified in articles and
navigation DATATargeted emails sent
Transaction on advertiser’s website
or first contact through landing
page
PAGE 35CRM DATA
MAXIMISERLE REACHGRÂCE À NOTRE DSP
EXTENSION D’AUDIENCE
THIRD PARTY PREMIUM INVENTORY
ROI IS KING
PAGE 37CRM DATA
ACQUISITION MADE PERSONAL
LE FIGARO DISPONIBLE DES [22H] A [ LONDRES ]Sur web, mobile, tablette
ABONNEMENT 100% NUMERIQUE
PAGE 38CRM DATA
EMAIL EXCELLENCE WITH BEHAVIORAL DATA
PAGE 39CRM DATA
EMAIL EXCELLENCE WITH BEHAVIORAL DATA
CAS D’USAGE
PRÉDICTIONS SOCIO-DÉMOGRAPHIQUES
PAGE 40CRM DATA
LE ROI EST DANS LA DONNÉE QUALIFIÉE
?
PAGE 41CRM DATA
DONNER DU SENS À LA DONNÉE
PASSER DE L’INFORMATION A LA CONNAISSANCE
PAGE 42CRM DATA
IDENTIFIER LES TRACES DES CLIENTS VS LES INCONNUS
PAGE 43CRM DATA
PRÉDICTIONS
CRM
AGE / GENRE / PCS REVENU / ETUDE / …
Visiteur appairé (client)
Visiteur inconnu
CONNAISSANCE
10 M VISITEURS
JUMEAUX STATISTIQUES
NAVIGATION WEB(700 k URL)
INFRASTRUCTURE
PAGE 45CRM DATA
INFRASTRUCTURE
INTERNE
AMAZON
> CALCULS
MATLAB
• Database• Neural Network• Parallel Computing• Statistics and Machine Learning
ToolBoxes
MDCS • 16 CPUs• 128 CPUs
• 2 serveurs• 16 CPUs / 96Go RAM
• 8 instances EC2• 128 CPUs / 1To RAM
CLUSTER
CLUSTER
> CLUSTERS
PERFORMANCES & RESULTATS
PAGE 47CRM DATA
PERFORMANCES
10 min800 M de lignes25 Gb
700 k URL10 M de cookies
PAGE 48CRM DATA
RÉSULTATS (EXTRAITS)
Etudiant NC PCS_MoinsMoins PCS_Moins PCS_Plus PCS_PlusPlus Retraite
PCSCRM
PREDIT
0 : 5 15 : 20 20 : 25 25 : 30 30 : 35 35 : 40 40 : 45 45 : 50 50 : 55 55 : 60 60 : 65 65 : 70 70 : 75 75 : 80 80 : 85 85 : 90
AGE
CRM PREDIT
ETUDIANT NC PCS - - PCS - PCS + PCS ++ RETRAITE
MERCI POUR VOTRE ATTENTION
GUILLAUME [email protected]
SAMUEL [email protected]
CDO – Dir. Data & CRM-
Groupe Le Figaro CCM BENCHMARK
Chief Data Officer-
Groupe Crédit Agricole Consumer Finance
