Utilisation d'Hadoop chez Ericsson - HUG France (juin 2014)

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Franck Gallos de la société Ericsson enchaînera sur l’analyse des usages des services d’IP TV des grands opérateurs Telco. Franck détaillera comment la corrélation des données des usages IP TV avec des informations externes comme les données météorologiques ou sociales (événements politiques, sportif, vacances scolaires) permet de contextualiser les statistiques géo localisées pour un meilleur ciblage publicitaire. A noter que ce projet est arrivé second au Trophée de l’Innovation Big Data Paris 2014.

Hadoop User Group, le 11 Juin à la Tour Eiffel avec Infotel

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  • Slide no1: Growth in Mobile Broadband
    Let’s look at some numbers showing the tremendous growth in numbers of users and usage of mobile broadband.
    Subscriptions for mobile PCs and tablets are expected to grow from 250 million in 2012 to 750 million by 2019.
    Total smartphone subscriptions: Will reach 1.9 billion at the end of 2013 and are expected to grow to 5.6 billion in 2019.Around 1 billion smartphones were sold in 2013, representing close to 60 percent of all mobile phones sold in Q4 2013.
    Mobile data traffic is expected to grow at a CAGR of around 45 percent (2013-2019). This will result in an increase of around 10 times by the end of 2019.
    Global mobile broadband subscriptions: In Q4 2013, mobile broadband subscriptions grew by around 150 million to 2.1 billion (40% year-on-year increase). Are predicted to grow to 8 billion by 2019.
    Total Mobile subscriptions: 6.7 billion mobile subscriptions globally in Q4 2013. By the end of 2019 it’s expected to reach around 9.3 billion.
    While the pace of change may seem fast, in reality it will never be this slow again.
    This data boom means the end-user behavior shifts from being VOICE CENTRIC to DATA CENTRIC and creates a number of challenges for the operator.
  • NPS – Net Promoter Score = Measures the loyalty that exists between a Provider and a consumer
    -Considered to be a significant metric to measure the operators’ CEM initiatives
    -Easy to survey and analyze
    -In combination with other metrics. NPS is an effective final dependent variable in a customer data model.
    It is more important to understand what the factors are that make a customer a promoter or detractor than simply to know how many promoters an operator has.
    Operators are starting to use NPS as a way to understand the impact that their different touch points have on customer expectation via a simple metric
    NPS still evolving from a general overall score into an individual analysis. It needs to be analyzed in conjunction with other metrics across the business.
  • Focusing on the Right Use Cases, Driving Business Results
    Delivering Actionable Insights, Based on Domain Expertise, Realized in User-Specific Applications
    Empowered by an Agile, Real Time, Horizontal Big Data Analytics Platform
    Multi-Vendor, Multi-Data, Multi-Purpose
    Securing Customer Privacy

Transcript

  • 1. Ericsson Media Statistics Franck Gallos franck.gallos@ericsson.com Jérôme Antezak jerome.antezak@ericsson.com June 11st 2014
  • 2. Ericsson- HUG Paris | 2014-06-11 | Page 2 Agenda 1. Ericsson networked society & analytics 2. Projet Media statistics 3. Exemple : IPTV & meteo 1. Influence des facteurs métérologiques 2. Calculs prédictifs
  • 3. Ericsson- HUG Paris | 2014-06-11 | Page 3 Operator’s challenge and opportunity Growth in mobile broadband, users and usage 2013 to 2019… 250 M to 750 M PCs and tablets 6.7 BN to 9.3 BN Mobile subscriptions 1.9 BN to 5.6 BN Smartphone subscriptions 2.1BN to 8 BN Mobile broadband subscriptions 10 times 45% CAGR Mobile data traffic Growth opportunitiesData boom customer care Calls Problem Resolution time Source: Ericsson Mobility Report
  • 4. Ericsson- HUG Paris | 2014-06-11 | Page 4 Telecoms trails behind other industries in brand loyalty NPS Benchmark for US industry groups 2012 Source: Informa Telecoms NPS is a focus for telecom operators
  • 5. Ericsson- HUG Paris | 2014-06-11 | Page 5 improving customer experience across the lifecycle is crucial to increase NPS Key factors driving NPS Improving customer experience across the entire lifecycle is crucial By breaking down loyalty drivers, we can understand which areas that are most important for improving NPS Source: Keeping Smartphone Users Loyal, Ericsson ConsumerLab 2013 GET Simplicity, clarity, personalization FIND Availability, variety, relevance, transparency SET UP Accuracy, speed, efficiency USE Speed, quality, accessibility, reliability GET HELP Accessibility Speed Resolution PAY FOR Cost control Simplicity MODIFY Simplicity, clarity, personalization
  • 6. Ericsson- HUG Paris | 2014-06-11 | Page 6 Big data analytics is key to boost customer experience HOW OPERATORS RESPOND Idea-to- Implemen tation Plan-to- Provision Lead-to- Service Service-to- Cash Experience -to- Resolution PREPARATION DELIVERY OF CUSTOMER’S DESIRED EXPERIENCE BIG DATA ANALYTICS TAKE ACTON GAIN INSIGHT MEASURE FIND Availability, variety, relevance, transparency SET UP Accuracy, speed, efficiency USE Speed, quality, accessibility, reliability GET HELP Accessibility Speed Resolution PAY FOR Cost control Simplicity MODIFY Simplicity, clarity, personalization WHAT CUSTOMERS WANT
  • 7. Ericsson- HUG Paris | 2014-06-11 | Page 7 ANALYTICS WITH ERICSSON An agile, open, multi-vendor approach that converts big data and domain knowledge into real-time, actionable insights for a wide range of use cases Terminal Probes and DPI RAN Traffic Nodes Core Traffic Nodes Control Plane Product & Service Catalog Fault and Performan ce Trouble Ticket Charging & Billing CRM Social Network Marketing Engineering Customer Care Networks Mediation / Correlation / Filtering Knowledge Extraction / Business Logic / Data Mgmt Exposure / Insights / Action White Paper :http://www.ericsson.com/news/130819-big-data-analysis_244129227_c
  • 8. Ericsson- HUG Paris | 2014-06-11 | Page 8 Agenda 1. Ericsson networked society & analytics 2. Projet Media statistics 3. démonstration 4. Exemple : IPTV & meteo 1. Influence des facteurs métérologiques 2. Calculs prédictifs
  • 9. Ericsson- HUG Paris | 2014-06-11 | Page 9 Ericsson Media Statistics Media statistics User infos CSP, type,… Director Actors Analytics Logs, STBs Metadata Open data Comedy Action ….. Category Data report Reco. Engine Advertising Systems (box,portal,…)
  • 10. Ericsson- HUG Paris | 2014-06-11 | Page 10 Big data project › Cluster Hadoop › CORE IPTV › CDN LIVE/VOD › METEO STATIONS VOLUME VELOCITYVARIETY › 16 To › Terasort benchmark for 1 To Data Volume with a response time of 04’28s
  • 11. Ericsson- HUG Paris | 2014-06-11 | Page 11 Hadoop ecosystem SERDE
  • 12. Ericsson- HUG Paris | 2014-06-11 | Page 12 Agenda 1. Ericsson networked society & analytics 2. Projet Media statistics 3. Exemple : IPTV & meteo 1. Influence des facteurs métérologiques 2. Calculs prédictifs
  • 13. Ericsson- HUG Paris | 2014-06-11 | Page 13 CROSSING TV & WEATHER › 1 To of raw IPTV data › Data Meteo : 100 weather stations › 6 months of weather and IPTV logs data › Response time= 00:25:00
  • 14. Ericsson- HUG Paris | 2014-06-11 | Page 14 WEATHER INFLUENCE On VOD Consomptions › Monday Tuesday Wednesday thursday › friday saturday sunday › Such consomptions influences are not taken into account by marketing divisions › Such usage analytics data could now feed any recommendations operational systems
  • 15. Ericsson- HUG Paris | 2014-06-11 | Page 15 TOP « ONE day weather » INFLUENCE ON VOD USAGE › This Map displays the ranking influences between French locations › The lists indicates the names of the most influenced by weather French departments on Thursdays › THURSDAY rank department 1 Côtes d'Armor 2 Finistère 4 Ille-et-Vilaine 8 Loire atlantique 9 Mayenne 10 Morbihan 11 Maine et Loire 12 Vendée rank department 3 Corse 5 Var 6 Hérault 7 Bouches-du-Rhône
  • 16. Ericsson- HUG Paris | 2014-06-11 | Page 16 TOP « ONE day weather » INFLUENCE On VOD usage › Week end departures times in Paris suburb and weather do influence the VoD usages › FRIDAY › SUNDAY › Weather does not influence people on watching VoD when returning back home on Sundays in the north of France while it does in the south 
  • 17. Ericsson- HUG Paris | 2014-06-11 | Page 17 PrEDICTIVE ANALYTICS › Total numbers of VOD sessions per Sundays from July to December 2013 › Post-predictions validations obtained from 1To data volumes › Response time= 00:25:00
  • 18. Ericsson- HUG Paris | 2014-06-11 | Page 18 CAPACITY PLANNING › Total numbers of VOD sessions for each Sundays from July to December 2013 › Linear growth differences between real numbers and forecasted ones is 3% › The methodology used for this use case can be applied to individual TV channels (ex : by planning capacities thanks to EPGs for instance… )
  • 19. Ericsson- HUG Paris | 2014-06-11 | Page 19