SlideShare une entreprise Scribd logo
1  sur  7
Télécharger pour lire hors ligne
C A S E S T U D Y
vente-privee uses digital analytics data to
successfully drive its mobile strategy
2AT INTERNET / CASE STUDY / vente-privee
INTRODUCTION
The creator and leader of online flash sales in France, vente-
privee is a true success story on the European digital scene.
With 1.7 billion euros of gross revenue in 2014, vente-privee
counts 24 million members and is present in 8 countries.
Initially specialising in overstock sales for fashion and
homeware, vente-privee today positions itself as a generator of
visibility for brands.
With 3.5 million unique visitors every day, and 10,000 new
members per day across all of Europe, vente-privee generates
more than 50% of its sales revenue via mobile – which repre-
sents about 70% of its total visits.
Client
vente-privee
Industry:
E-commerce
Vendor:
AT Internet
Key numbers:
• 24 million members
• 3.5 million unique visitors per day
• €1.7B gross revenue in 2014
Solutions
• Analytics Suite
Benefits:
• Optimise user journey across all 	
devices (smartphones, computers and 	
tablets)
• Improve user friendliness of product 	
sheets and visuals
• Add new mobile-specific features
JULIEN BIZET
Projects and Innovation Manager
Marketing – vente-privee
Case study developed in participation with:
3AT INTERNET / CASE STUDY / vente-privee
NUMBERS
CHALLENGE
With its business model built around flash sales, vente-privee has shaken up the traditional sales
model by associating cost savings for the customer, the notion of urgency (short sale periods,
limited quantities), and regularly programmed sale start times.
This winning combination has also driven (and accelerated) the sharp increase of visits from
mobile and tablet.
vente-privee’s central challenge is therefore understanding and anticipating how users are truly
behaving across devices – and the impact of that behaviour on conversions – in order to make
the most of each device’s specific characteristics and optimise the overall customer journey
across channels.
Tracking customer experiences and touchpoints across devices has indeed become one of the
major challenges in digital, and for many companies, it remains complex due to gaps in user
identification across devices. vente-privee has managed to avoid this pitfall: Its members must
log in to access site content and sales, no matter the device used.
By identifying users throughout the entire purchase process via their login, vente-privee can
not only track members’ cross-device behaviours, but also connect each user journey to the
corresponding member profile.
vente-privee’s marketing teams can then track the entirety of consumer interactions on different
devices (smartphones, tablets and computer).
24 MILLION MEMBERS WHO ARE
. Highly engaged
. Brand-conscious
. E-shoppers
10,000 NEW MEMBERS
Per day in Europe
3.5 MILLION
Unique visitors per day in Europe
53 MINUTES SPENT ON THE SITE
In France per month per person
49% OF REVENUE, 70% OF VISITS
From mobile devices
4AT INTERNET / CASE STUDY / vente-privee
KEY OBJECTIVES
• Deeply analyse purchase behaviours: time of day products are viewed, particularities of how 	
each device is used, preferred product themes…
• Target vente-privee’s different user profiles
• Determine recommendations for improving usability based on collected data
SOLUTIONS
With AT Internet’s solution, vente-privee can consolidate its audience measurement on
smartphone, tablet and computers. This unified view is also fed by vente-privee’s datamart,
thanks to API technology.
Data is exchanged between both systems, to both enrich the CRM database with traffic volume
and behavioural data, and also feed the AT Internet solution with user profile data (socio-econo-
mic category, age, gender, etc.).
Data mining teams can also carry out advanced correlations by product line, by spend per spe-
cific product, by brand, and more.
Let’s now take a detailed look at the analysis of consumer usage by device.
Thanks to the connection between AT Internet’s API and our datamart, we have
a view that’s quantitative, behavioural and also segmented, in marketing terms.
LOGGED-
IN MEMBER
Desktop
Tablets
Web/Apps
Smartphones
Web/Apps
VENTE-PRIVEE
DATA MINING
5AT INTERNET / CASE STUDY / vente-privee
ANALYSES AND RESULTS
Based on data collected by AT Internet tools, we’ll share here a few analyses of vente-privee
members’ usage and behaviours.
A DEVICE FOR EACH TIME OF DAY
On the vente-privee website, we see a peak in sales revenue from mobile phones at 7am. This
time corresponds to the opening of sales on the site, and can be explained by the limited stock
quantities and “first come, first served” system. In the below graph, we observe the predomi-
nance of mobile phone usage, due to on-the-go purchasing from users during their morning
commute.
On the other hand, in the evenings and on weekends, we see that the tablet is largely favoured
over the other devices.
EACH DEVICE HAS ITS OWN PARTICULARITIES
By analysing data by device, vente-privee’s marketing teams have identified actionable learnings.
Many different uses
For the same group of users, behaviours can vary greatly from one device to another.
• On mobile, the “immediate opportunity detection“ mode dominates: Users peruse a large 	
number of sales and products in a short time.
• On computers, behaviour is more exploratory: Users spend even more time on a limited 	 	
number of sales.
In the end, even if apps represent 70% of mobile traffic, we observe that conversion rates drop
with screen size.
Distribution of visits by time and by device
Mobile Computer Tablet
Revenue from phones peaks
when sales open Tablets used in
the evening Tablets
Mobile
Computer
Distribution of visits by day and by device
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Tablets used on the
weekend
Tablets
Mobile
Computer
6AT INTERNET / CASE STUDY / vente-privee
User profiles are clearly identified
• Smartphone users are often young men
• Tablet users (especially iPads) are from high socio-economic categories
Merchandising  devices
The device used proves to be a differentiating factor when it comes to the nature of products
viewed and especially purchased. For example, a shorter path to purchase on mobile encourages
low-commitment, more generalised purchases like fashion accessories or home décor, whereas
travel packages and more expensive purchases are more often made via desktop and tablet.
CONCLUSION
Thanks to enriched analytics data, vente-privee can detect and identify behavioural and
transactional specificities of each device, and how they are used by each customer profile.
Mobile has shown its capacity to profoundly change customer usage, in terms of how often
users view products and when, the nature of the products viewed and bought, and whether that
purchase is impulsive or planned.
Based on these analyses, vente-privee drives its mobile strategy and has implemented actions to:
• Adapt its merchandising to each specific device.
• Streamline and shorten the purchase process on mobile.
• Develop features designed for mobile: app for Apple Watch, “Le Pass” application, or “Le Voyage”
application, for example.
Growth in terms of visits is clear, with mobile representing more than 70% of
traffic in 2015, across all different countries.
BENEFITS
• Optimise user journey across all devices (smartphones, computers and tablets).
• Improve user friendliness of product sheets and visuals.
• Add new mobile-specific features.
About AT Internet
One of the world’s major players in digital intelligence since
1996, AT Internet helps companies measure their audience
and optimise their digital performance across all channels.
AT Internet’s expertise extends from collecting raw data to
treating it in real time and delivering it for analysis and the
sharing of insights. Applications in AT Internet’s Analytics Suite
provide reliable, contextualised and actionable information.
Scalable and completely modular, AT Internet’s offering adapts
to businesses in all industries: e-commerce, media, finance/
banking, corporate institutions. Easy-to-use and accessible to all
individuals within a company, these solutions help address the
challenges facing both novices and experts in digital analytics
and data mining. The power of AT Internet’s Analytics Suite and
the quality of its services (consulting, training and support) are
recognised worldwide. AT Internet counts more than 3,800
customers around the globe, in all industries. With more than
200 employees, the group is present in 32 countries via its
customers, subsidiaries and partners.
About vente-privee.com
vente-privee.com (“private sales” in French) founded the online
sales event concept and is the global leader in the sector. Spe-
cialising in selling brand overstock since 2001, online sales are
exclusive to its 24 million members in Europe. Registration to
the vente-privee.com club is free and with no purchase obliga-
tion. Sales are for a limited time only (3 to 5 days) and are orga-
nised in close collaboration with over 2,700 major international
brands in all product categories: ready-to-wear, homewares,
wine, toys, sports, high-tech, etc… Thanks to its partnerships
with brands, vente-privee.com offers irresistible discounts (50-
70% off). With 2,500 employees in 8 European countries, vente-
privee.com generated €1.7 billion gross turnover in 2014.
Request a demo at www.atinternet.com
DISCOVER YOUR DATA’S TRUE POTENTIAL
BORDEAUX - HAMBURG - LONDON - MOSCOW - MUNICH - PARIS - SÃO PAULO - SINGAPORE
Follow us on
TWITTER
Follow us on
YOUTUBE
Follow us on
DA BLOG
Follow us on
SLIDESHARE
Follow us on
LINKEDIN

Contenu connexe

Plus de AT Internet

[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité
[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité
[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialitéAT Internet
 
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...Reeport Partner presentation - Mixing site- and ad- centric data despite the ...
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...AT Internet
 
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...AT Internet
 
Altice Média Customer Success - App store optimisation
Altice Média Customer Success - App store optimisationAltice Média Customer Success - App store optimisation
Altice Média Customer Success - App store optimisationAT Internet
 
L'Équipe Customer Success - Using analytics to fuel efficient personalisation
L'Équipe Customer Success - Using analytics to fuel efficient personalisationL'Équipe Customer Success - Using analytics to fuel efficient personalisation
L'Équipe Customer Success - Using analytics to fuel efficient personalisationAT Internet
 
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...AT Internet
 
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018AT Internet
 
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matter
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matterReeport @ Digital Analytics Forum 2018: Defining KPIs that matter
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matterAT Internet
 
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmenté
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmentéOnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmenté
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmentéAT Internet
 
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...AT Internet
 
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018AT Internet
 
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...Le Digital Analytics, arme de conversion massive pour les sites marchands - P...
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...AT Internet
 
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018AT Internet
 
AT Internet & Mazeberry : de la data analytics au mix marketing maitrisé
AT Internet & Mazeberry : de la data analytics au mix marketing maitriséAT Internet & Mazeberry : de la data analytics au mix marketing maitrisé
AT Internet & Mazeberry : de la data analytics au mix marketing maitriséAT Internet
 
AT Internet & Mazeberry: from analytics to a fully optimised marketing mix
AT Internet & Mazeberry: from analytics to a fully optimised marketing mixAT Internet & Mazeberry: from analytics to a fully optimised marketing mix
AT Internet & Mazeberry: from analytics to a fully optimised marketing mixAT Internet
 
[DAF 2017] Digital Analytics 4.0: Are You Ready?
[DAF 2017] Digital Analytics 4.0: Are You Ready?[DAF 2017] Digital Analytics 4.0: Are You Ready?
[DAF 2017] Digital Analytics 4.0: Are You Ready?AT Internet
 
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)AT Internet
 
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)AT Internet
 
[DAF 2017] Analytics Suite 2 - Data you can trust
[DAF 2017] Analytics Suite 2 - Data you can trust[DAF 2017] Analytics Suite 2 - Data you can trust
[DAF 2017] Analytics Suite 2 - Data you can trustAT Internet
 
[DAF 2017] Analytics Suite 2 - Insights for everyone
[DAF 2017] Analytics Suite 2 - Insights for everyone[DAF 2017] Analytics Suite 2 - Insights for everyone
[DAF 2017] Analytics Suite 2 - Insights for everyoneAT Internet
 

Plus de AT Internet (20)

[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité
[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité
[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialité
 
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...Reeport Partner presentation - Mixing site- and ad- centric data despite the ...
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...
 
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...
Présentation partenaire OnCrawl - Comment ouvrir l’Appétit des Moteurs de Rec...
 
Altice Média Customer Success - App store optimisation
Altice Média Customer Success - App store optimisationAltice Média Customer Success - App store optimisation
Altice Média Customer Success - App store optimisation
 
L'Équipe Customer Success - Using analytics to fuel efficient personalisation
L'Équipe Customer Success - Using analytics to fuel efficient personalisationL'Équipe Customer Success - Using analytics to fuel efficient personalisation
L'Équipe Customer Success - Using analytics to fuel efficient personalisation
 
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...
Cas client Credit Agricole - Approche data-driven : de la stratégie au déploi...
 
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018
RGPD & Data Privacy : la CNIL au Digital Analytics Forum 2018
 
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matter
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matterReeport @ Digital Analytics Forum 2018: Defining KPIs that matter
Reeport @ Digital Analytics Forum 2018: Defining KPIs that matter
 
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmenté
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmentéOnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmenté
OnCrawl @ Digital Analytics Forum 2018 : le référencement naturel augmenté
 
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...
Kamp'n @ Digital Analytics Forum 2018 : la puissance d'AT Internet dans vos F...
 
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018
Machine Learning in Marketing - Jim Sterne @ Digital Analytics Forum 2018
 
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...Le Digital Analytics, arme de conversion massive pour les sites marchands - P...
Le Digital Analytics, arme de conversion massive pour les sites marchands - P...
 
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018
Analytics et SEO : les clés d'une stratégie réussie - We Love SEO 2018
 
AT Internet & Mazeberry : de la data analytics au mix marketing maitrisé
AT Internet & Mazeberry : de la data analytics au mix marketing maitriséAT Internet & Mazeberry : de la data analytics au mix marketing maitrisé
AT Internet & Mazeberry : de la data analytics au mix marketing maitrisé
 
AT Internet & Mazeberry: from analytics to a fully optimised marketing mix
AT Internet & Mazeberry: from analytics to a fully optimised marketing mixAT Internet & Mazeberry: from analytics to a fully optimised marketing mix
AT Internet & Mazeberry: from analytics to a fully optimised marketing mix
 
[DAF 2017] Digital Analytics 4.0: Are You Ready?
[DAF 2017] Digital Analytics 4.0: Are You Ready?[DAF 2017] Digital Analytics 4.0: Are You Ready?
[DAF 2017] Digital Analytics 4.0: Are You Ready?
 
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Ludivine Lille (EY)
 
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)
[DAF 2017] RGPD 2018 : Êtes-vous prêt ? par Clémence Scottez (CNIL)
 
[DAF 2017] Analytics Suite 2 - Data you can trust
[DAF 2017] Analytics Suite 2 - Data you can trust[DAF 2017] Analytics Suite 2 - Data you can trust
[DAF 2017] Analytics Suite 2 - Data you can trust
 
[DAF 2017] Analytics Suite 2 - Insights for everyone
[DAF 2017] Analytics Suite 2 - Insights for everyone[DAF 2017] Analytics Suite 2 - Insights for everyone
[DAF 2017] Analytics Suite 2 - Insights for everyone
 

Dernier

Heart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectHeart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectBoston Institute of Analytics
 
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)jennyeacort
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.natarajan8993
 
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfMultiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfchwongval
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 
Call Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceCall Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceSapana Sha
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Seán Kennedy
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]📊 Markus Baersch
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Cantervoginip
 
Machine learning classification ppt.ppt
Machine learning classification  ppt.pptMachine learning classification  ppt.ppt
Machine learning classification ppt.pptamreenkhanum0307
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryJeremy Anderson
 
Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024Colleen Farrelly
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...Florian Roscheck
 
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...Boston Institute of Analytics
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Jack DiGiovanna
 
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理科罗拉多大学波尔得分校毕业证学位证成绩单-可办理
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理e4aez8ss
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdfHuman37
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsVICTOR MAESTRE RAMIREZ
 
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一F sss
 

Dernier (20)

Heart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis ProjectHeart Disease Classification Report: A Data Analysis Project
Heart Disease Classification Report: A Data Analysis Project
 
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
 
Multiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdfMultiple time frame trading analysis -brianshannon.pdf
Multiple time frame trading analysis -brianshannon.pdf
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 
Call Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceCall Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts Service
 
Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...Student profile product demonstration on grades, ability, well-being and mind...
Student profile product demonstration on grades, ability, well-being and mind...
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Canter
 
Machine learning classification ppt.ppt
Machine learning classification  ppt.pptMachine learning classification  ppt.ppt
Machine learning classification ppt.ppt
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
 
Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024Generative AI for Social Good at Open Data Science East 2024
Generative AI for Social Good at Open Data Science East 2024
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
 
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
 
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理科罗拉多大学波尔得分校毕业证学位证成绩单-可办理
科罗拉多大学波尔得分校毕业证学位证成绩单-可办理
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
 
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
 

Vente-privee uses digital analytics data to successfully drive its mobile strategy

  • 1. C A S E S T U D Y vente-privee uses digital analytics data to successfully drive its mobile strategy
  • 2. 2AT INTERNET / CASE STUDY / vente-privee INTRODUCTION The creator and leader of online flash sales in France, vente- privee is a true success story on the European digital scene. With 1.7 billion euros of gross revenue in 2014, vente-privee counts 24 million members and is present in 8 countries. Initially specialising in overstock sales for fashion and homeware, vente-privee today positions itself as a generator of visibility for brands. With 3.5 million unique visitors every day, and 10,000 new members per day across all of Europe, vente-privee generates more than 50% of its sales revenue via mobile – which repre- sents about 70% of its total visits. Client vente-privee Industry: E-commerce Vendor: AT Internet Key numbers: • 24 million members • 3.5 million unique visitors per day • €1.7B gross revenue in 2014 Solutions • Analytics Suite Benefits: • Optimise user journey across all devices (smartphones, computers and tablets) • Improve user friendliness of product sheets and visuals • Add new mobile-specific features JULIEN BIZET Projects and Innovation Manager Marketing – vente-privee Case study developed in participation with:
  • 3. 3AT INTERNET / CASE STUDY / vente-privee NUMBERS CHALLENGE With its business model built around flash sales, vente-privee has shaken up the traditional sales model by associating cost savings for the customer, the notion of urgency (short sale periods, limited quantities), and regularly programmed sale start times. This winning combination has also driven (and accelerated) the sharp increase of visits from mobile and tablet. vente-privee’s central challenge is therefore understanding and anticipating how users are truly behaving across devices – and the impact of that behaviour on conversions – in order to make the most of each device’s specific characteristics and optimise the overall customer journey across channels. Tracking customer experiences and touchpoints across devices has indeed become one of the major challenges in digital, and for many companies, it remains complex due to gaps in user identification across devices. vente-privee has managed to avoid this pitfall: Its members must log in to access site content and sales, no matter the device used. By identifying users throughout the entire purchase process via their login, vente-privee can not only track members’ cross-device behaviours, but also connect each user journey to the corresponding member profile. vente-privee’s marketing teams can then track the entirety of consumer interactions on different devices (smartphones, tablets and computer). 24 MILLION MEMBERS WHO ARE . Highly engaged . Brand-conscious . E-shoppers 10,000 NEW MEMBERS Per day in Europe 3.5 MILLION Unique visitors per day in Europe 53 MINUTES SPENT ON THE SITE In France per month per person 49% OF REVENUE, 70% OF VISITS From mobile devices
  • 4. 4AT INTERNET / CASE STUDY / vente-privee KEY OBJECTIVES • Deeply analyse purchase behaviours: time of day products are viewed, particularities of how each device is used, preferred product themes… • Target vente-privee’s different user profiles • Determine recommendations for improving usability based on collected data SOLUTIONS With AT Internet’s solution, vente-privee can consolidate its audience measurement on smartphone, tablet and computers. This unified view is also fed by vente-privee’s datamart, thanks to API technology. Data is exchanged between both systems, to both enrich the CRM database with traffic volume and behavioural data, and also feed the AT Internet solution with user profile data (socio-econo- mic category, age, gender, etc.). Data mining teams can also carry out advanced correlations by product line, by spend per spe- cific product, by brand, and more. Let’s now take a detailed look at the analysis of consumer usage by device. Thanks to the connection between AT Internet’s API and our datamart, we have a view that’s quantitative, behavioural and also segmented, in marketing terms. LOGGED- IN MEMBER Desktop Tablets Web/Apps Smartphones Web/Apps VENTE-PRIVEE DATA MINING
  • 5. 5AT INTERNET / CASE STUDY / vente-privee ANALYSES AND RESULTS Based on data collected by AT Internet tools, we’ll share here a few analyses of vente-privee members’ usage and behaviours. A DEVICE FOR EACH TIME OF DAY On the vente-privee website, we see a peak in sales revenue from mobile phones at 7am. This time corresponds to the opening of sales on the site, and can be explained by the limited stock quantities and “first come, first served” system. In the below graph, we observe the predomi- nance of mobile phone usage, due to on-the-go purchasing from users during their morning commute. On the other hand, in the evenings and on weekends, we see that the tablet is largely favoured over the other devices. EACH DEVICE HAS ITS OWN PARTICULARITIES By analysing data by device, vente-privee’s marketing teams have identified actionable learnings. Many different uses For the same group of users, behaviours can vary greatly from one device to another. • On mobile, the “immediate opportunity detection“ mode dominates: Users peruse a large number of sales and products in a short time. • On computers, behaviour is more exploratory: Users spend even more time on a limited number of sales. In the end, even if apps represent 70% of mobile traffic, we observe that conversion rates drop with screen size. Distribution of visits by time and by device Mobile Computer Tablet Revenue from phones peaks when sales open Tablets used in the evening Tablets Mobile Computer Distribution of visits by day and by device Monday Tuesday Wednesday Thursday Friday Saturday Sunday Tablets used on the weekend Tablets Mobile Computer
  • 6. 6AT INTERNET / CASE STUDY / vente-privee User profiles are clearly identified • Smartphone users are often young men • Tablet users (especially iPads) are from high socio-economic categories Merchandising devices The device used proves to be a differentiating factor when it comes to the nature of products viewed and especially purchased. For example, a shorter path to purchase on mobile encourages low-commitment, more generalised purchases like fashion accessories or home décor, whereas travel packages and more expensive purchases are more often made via desktop and tablet. CONCLUSION Thanks to enriched analytics data, vente-privee can detect and identify behavioural and transactional specificities of each device, and how they are used by each customer profile. Mobile has shown its capacity to profoundly change customer usage, in terms of how often users view products and when, the nature of the products viewed and bought, and whether that purchase is impulsive or planned. Based on these analyses, vente-privee drives its mobile strategy and has implemented actions to: • Adapt its merchandising to each specific device. • Streamline and shorten the purchase process on mobile. • Develop features designed for mobile: app for Apple Watch, “Le Pass” application, or “Le Voyage” application, for example. Growth in terms of visits is clear, with mobile representing more than 70% of traffic in 2015, across all different countries. BENEFITS • Optimise user journey across all devices (smartphones, computers and tablets). • Improve user friendliness of product sheets and visuals. • Add new mobile-specific features.
  • 7. About AT Internet One of the world’s major players in digital intelligence since 1996, AT Internet helps companies measure their audience and optimise their digital performance across all channels. AT Internet’s expertise extends from collecting raw data to treating it in real time and delivering it for analysis and the sharing of insights. Applications in AT Internet’s Analytics Suite provide reliable, contextualised and actionable information. Scalable and completely modular, AT Internet’s offering adapts to businesses in all industries: e-commerce, media, finance/ banking, corporate institutions. Easy-to-use and accessible to all individuals within a company, these solutions help address the challenges facing both novices and experts in digital analytics and data mining. The power of AT Internet’s Analytics Suite and the quality of its services (consulting, training and support) are recognised worldwide. AT Internet counts more than 3,800 customers around the globe, in all industries. With more than 200 employees, the group is present in 32 countries via its customers, subsidiaries and partners. About vente-privee.com vente-privee.com (“private sales” in French) founded the online sales event concept and is the global leader in the sector. Spe- cialising in selling brand overstock since 2001, online sales are exclusive to its 24 million members in Europe. Registration to the vente-privee.com club is free and with no purchase obliga- tion. Sales are for a limited time only (3 to 5 days) and are orga- nised in close collaboration with over 2,700 major international brands in all product categories: ready-to-wear, homewares, wine, toys, sports, high-tech, etc… Thanks to its partnerships with brands, vente-privee.com offers irresistible discounts (50- 70% off). With 2,500 employees in 8 European countries, vente- privee.com generated €1.7 billion gross turnover in 2014. Request a demo at www.atinternet.com DISCOVER YOUR DATA’S TRUE POTENTIAL BORDEAUX - HAMBURG - LONDON - MOSCOW - MUNICH - PARIS - SÃO PAULO - SINGAPORE Follow us on TWITTER Follow us on YOUTUBE Follow us on DA BLOG Follow us on SLIDESHARE Follow us on LINKEDIN