BIG DATA M E D I A I N T E L L I G E N C E

Transcription

1 BIG DATA M E D I A I N T E L L I G E N C E

2 EVOLUTION MODEL OF THE TV SUPPLIER Content Content Partners / Distributors Analog TV use of the content network for maximum reach, very small contact with the customer Digital TV - content in the center of the output from the content and search for mass audience Digital TV - the recipient in the center of the exit from the interests of the recipient and matching the right content PAST From 90s Now and the FUTURE 2 2

3 MILLIONS OF INTERACTION GENERATE MULTIPLE OF UNCURRENT DATA clickstream Smart TV Set-top-box Mobile device Social Media Demography Marketing campaigns Customer Service Advets comsuption Customers Transaction Identity 3

4 FROM "WHAT HAPPENED?" TO "WHY IS THIS EVEN? Traditional report approach Slow, late, look at the past, the product in focus How many people watched the program? In what social groups? How many new subscribers last month? How many subscribers have left? What is average income per customer? How profitable is the whole business? Approach with Media Intelligence Fast, real-time, look at what's going on, forecasting, personalization, customer focus Who saw what and why? In what micro-segments? Which marketing strategy has praven to be effective Where is the risk of leaving? Do we have any problems? Which offers and what content increases profits? Which customers are the most profitable? Let s get tchem. 4

5 WHY WE DECIDE BIG DATA? Collect data from multiple sources and store them in an efficient analytics store to build awareness of your audience and their preferences. Build user profiles for their dynamic, multi-dimensional segmentation Launch of recommendation and content recommendation engines Personalization of advertising materials 5

6 WHY WE DECIDE BIG DATA? NEW TECHNOLOGY 6

7 CURRENT STATE Data sources: Audience measurements, Data warehouse, Www statistics, logs other... 7

8 analytics EXPECTATIONS IN OUT Communications: Set-top-box, www, mobile Customer indentificanion: Subscriber account, Billing system, IP, Cookies, Google+, FB, Twitter Communication: Set-top-box, www, mobile Personalized presentation for user 8

9 WHAT CAN YOU GET THROUGH BIG DATA? Support in marketing strategy planning Optimization of the offer - personalization Construction of an advert personalization engine Support in planning the production process and purchase of content Support data exploration and detection of new relationships between data Other 9

10 S O L U T I O N S 10

11 HIGHARCHITECTURE Early detection Events and warnings Remarks analisysis recommendations Structured data Event engine Data Reservoir Rules and inference Virtual knowledge base Reporting CRM Production Billing system Input data Events And data Dependency detection Detected dependencies 11 11

12 FUNCTIONALITY Dynamic recommendation Behavioral Profiling Analytics for relevance a nd results Structuret date CRM Production Algorithms Silnik zdarzeń Preferences (eg: time, behavioral) Data Reservoir Rules and inference Dynamic Attribute Virtual knowledge base Reporting Recipients Segmentation Billing system Input data Events and data Identification demo graphic features Identification of preferences Detected dependences 12 12

13 RECOMMENDATION Personalized program recommendation Proposals tailored to the characteristics of the user Recommendation in Real Time and Batch mode. Use full data with the time of day for building a household profile.

14 RECOMMENDATION - ADDITIONS Enrich the program with additional information. Share information in Real Time and Batch mode. Information about the cast, rankings, trivia, comments. Analysis of sentiment on social media for programs.

15 RECOMMENDATION - OFFER Calculation of segments, users Use of full audience data for construction, segments monetization, Use full audience data to build profile characteristics.

16 FROM LOGS TO SEGMENTATION OF RECEIVERS AND RECOMMENDATIO VOD service infrastructure, set-top box. Reservoir Data Inference engine Audience seg mentation Database Data analysis and link search (Data Scientist / Data Engineer) Entrance to the platform Knowledge Base seg ments, profiles 16

17 EXAMPLE OF YOUR INTERFACE 17

18 EXAMPLE OF YOUR INTERFACE 18

19 PRODUKTY BIG DATA AS A SERVICE Ready-made products with short time to market Full of possibilities for high-scale platform Advanced analyst Real Time and Batch processing Cloud and on-site products 19

20 K o n i e c 20