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Early Lessons Learned in Applying Big Data To TV Advertising,[object Object],ARF September 12, 2011,[object Object],Jack Smith, Chief Product Officer, Simulmedia,[object Object]
About Us,[object Object],Who We Are,[object Object],We are a New York based start-up. We are venture backed by Avalon Ventures, Union Square Ventures and Time-Warner.,[object Object],Where We Have Been,[object Object],Our 35 person team has veterans of:,[object Object],What We Believe,[object Object],Television is still the most powerful advertising medium in the world. While addressability will come, we’re not waiting for it. We’ve taken a few strategies we learned from the Internet and are applying it to linear TV advertising, today.,[object Object],Through partnerships with major data providers, we have assembled the world’s largest set of actionable television data.,[object Object],How We Do It,[object Object],How We Make Money,[object Object],We sell television advertising. With inventory in over 106 million US households, we can cost-effectively extend reach into high-value target audiences across virtually any advertiser category. We use big data and science to do this.,[object Object]
Why Did We Leave The Web?,[object Object],Television remains the dominant consumer medium,[object Object],(a) Nielsen US TV Viewing AudicenceTraditional Live-Only TV based on average monthly viewing during 1Q2011.  Internet and Online Video based on average monthly consumption during July 2011.  Video on Demand based on consumption during May 2011.,[object Object]
TV Spend Is Increasing,[object Object],Source: MAGNAGLOBAL,[object Object]
Audience Is Fragmenting,[object Object],Source: Nielsen via TVbythenumbers.com,[object Object]
Campaign Reach Is Declining,[object Object],Impossible for measurement and planning tools to keep pace ,[object Object],Source: Simulmedia analysis of data from SQAD, Nielsen and TVB,[object Object]
Big Data,[object Object]
Big Data Is Driving Growth,[object Object],“We are on the cusp of a tremendous wave of innovation, productivity and growth, as well as new modes of competition and value-capture – all driven by Big Data.”,[object Object],- McKinsey Global Institute, May 2011,[object Object],“For CMOs,Big Data is a very big deal.”,[object Object],- Alfredo Gangotena, CMO, Mastercard, July 2011,[object Object]
Size Is Relative,[object Object],1 byte x 1000 = 1 kilobyte,[object Object],…x 1000 = 1 megabyte,[object Object],…x 1000 = 1 gigabyte,[object Object],…x 1000 = 1 terabyte,[object Object],…x 1000 = 1 petabyte,[object Object],…x 1000 = 1 exabyte ,[object Object]
Size Is Relative,[object Object],Telegram = 100 bytes,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
Size Is Relative,[object Object],Page of an Encyclopedia = 100 kilobytes,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
Size Is Relative,[object Object],Pickup truck bed full of paper = 1 gigabyte ,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
Size Is Relative,[object Object],Entire print collection of the Library of Congress = 10 terabytes,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
Size Is Relative,[object Object],All hard drives produced in 1995 = 20 petabytes ,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
Size Is Relative,[object Object],All printed material = 200 petabytes ,[object Object],Data © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm,[object Object]
But Big Data Is More Than Size,[object Object],What happened?,[object Object],Why did it happen?,[object Object],BIG DATA,[object Object],What’s going to happen next?,[object Object],Time:,[object Object],Past,[object Object],Future,[object Object],Focus:,[object Object],Reporting,[object Object],Prediction,[object Object],Supports:,[object Object],Human decisions,[object Object],Machine decisions,[object Object],Structured,[object Object],Aggregated,[object Object],Unstructured,[object Object],Unaggregated,[object Object],Data:,[object Object],Dashboards,[object Object],Excel,[object Object],Discovery,[object Object],Visualization,[object Object],Statistics & Physics,[object Object],Human Skills:,[object Object]
Accelerating The Push To Big Data,[object Object],Hadoop, cloud computing, Facebook, Yahoo, quants, Bittorrent, machine learning, Stanford, large hadron collider, Wal-Mart, text processing, Amazon S3 & EC2, open source intelligence, NoSQL, social media, Google, commodity hardware, Hive, fraud detection, trading desks, MapReduce, natural language processing ,[object Object]
What Can It Mean For TV Advertising?,[object Object],Big data drove the rise of web & search advertising,[object Object],[object Object]
Better predictions about consumer interests
Real time return path
Automation
Interim step for addressability
More diligence around consumer privacy
Media buyers and sellers rethinking their approach to audience packaging, campaign planning, technology, data assembly and people,[object Object]
Australian Bureau of Statistics: 250 tb1
AT&T: 250 tb1
Nielsen: 45 tb1
Adidas: 13 tb1
Wal-Mart: 1 pb2Data Lakes,[object Object],[object Object]
Yahoo: 22 pb4
Google: ???1 Oracle F1Q10 Earnings Call September 16, 2009 Transcript,[object Object],2Stair, Principles of Information Systems, 2009, p 181,[object Object],3 Dhruba Borthakur, Facebook, December 2010, http://www.facebook.com/note.php?note_id=468211193919,[object Object],4 Simulmedia estimate,[object Object]
Our Idea of Big Data,[object Object],Bringing the data set together in a single platform,[object Object],Our (comparatively modest) data set:,[object Object],[object Object]
113,858,592 daily events
Approximately 402,301 weekly ads
Double capacity every 6 months…And we don’t load every data point across all data sets, yet,[object Object]
Rethinking Media Data Architecture,[object Object],Applying big data to television required us to rethink what our technical architecture should be,[object Object],Commodity Hardware,[object Object],[object Object]
Expect hardware failure
Learn from those who have done it
Participate in the Open Source communityOpen Source Software,[object Object],Write Your Own Software,[object Object],[object Object]
Meddle
Machine learningScience,[object Object],[object Object]
Experimentation,[object Object]
The People We Needed,[object Object],A different approach required different skill sets,[object Object],[object Object]
Pattern recognition
Visualization
Technology
Experimentation
Where do you find hard to find tech skills?
You don’t find them. You make them.
A dedicated Science team
Non traditional researchers (Brain imaging, bioinformatics, economic modeling, genetics)
People who watch a lot of television,[object Object]
Some Things To Know, First,[object Object],[object Object]
Time shifting lessons is a whole other presentation
Time shifting + live viewing lessons is a whole other other presentation
Video on demand is a whole other other other presentation
We name names and provide numbers where clients and data partners permit
Client confidentiality is important to us
None of this work would’ve been possible without the help of our clients and partnersThis box will contain important information about the graphs on each page.,[object Object],Read me…,[object Object]
60% of TV Viewers Watch 90% of TV,[object Object]
Where The Other 40% Are,[object Object],Networks with relatively fewer lighter viewer impressions ,[object Object],Networks with relatively more lighter viewer impressions ,[object Object],Vertical: Ratio of Heavy Viewers to light viewer impressions. ,[object Object],Horizontal: Low rated to Highly rated networks Call outs: Ratio is the number of Heavier Viewer impressions you would deliver to reach a Lighter Viewer on a given network,[object Object],Higher rated networks,[object Object],Lower,[object Object],rated networks,[object Object],Sources: Nielsen & Simulmedia’s a7,[object Object]
Where The Other 40% Are,[object Object],To capture light viewers, media planning and measurement tools must quickly apply new methods to emerging data sets,[object Object]
Quality Control Is A Full Time Job,[object Object]
When Data Goes Missing,[object Object],Automation of error checking/quality control is essential,[object Object],Reuse the data to solve other problems,[object Object],Occasionally observe missing data,[object Object],Three choices:,[object Object],[object Object]
Estimate missing fields
Work around the missing dataTime series of SYFY network. 10645 observations from 2010.02.28 at 7:00pm Eastern to 2010.10.14 at 12:30pm Eastern,[object Object],Source: Simulmedia’s a7,[object Object]
More Data Really Is Better,[object Object]
Disambiguation: The Madonna Problem,[object Object],OR,[object Object],Pop Icon?,[object Object],Religious icon?,[object Object]
The Revolution of Simple Methods,[object Object],More data beats better algorithms.,[object Object],The best performing algorithm underperforms the worst algorithm when given an order of magnitude more data. ,[object Object],Simple algorithms at very large scale can help better predict audience movement.,[object Object],Peter Norvig | Internet Scale Data Analysis | June 21, 2010,[object Object],Original graph sourced from: Banko & Brill, 2001. Mitigating the paucity-of-data problem: exploring the effect of training corpus size on classifier performance for natural language processing,[object Object]
Packaging Reach,[object Object],Very large data sets better predict TV audience movements,[object Object],Peter Norvig | Internet Scale Data Analysis | June 21, 2010,[object Object]

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Early Lessons Learned in Applying Big Data To TV Advertising

Editor's Notes

  1. The revolution will be televised.
  2. Audience fragmentation is going from bad to worseThis fragmentation is wrecking effective campaign reach and creating a massive frequency imbalanceAudience re-aggregation will be key for brand advertisers to maintain scaleTV is not going to the web. The web is going to television.
  3. Audience fragmentation is going from bad to worseThis fragmentation is wrecking effective campaign reach and creating a massive frequency imbalanceAudience re-aggregation will be key for brand advertisers to maintain scaleTV is not going to the web. The web is going to television.
  4. The Huntington copy is one of eleven surviving copies printed on vellum, and one of three such copies in the United States. An additional thirty-six copies printed on paper also survive.
  5. Our claim of the world's largest actionable set of TV viewing data at 75tb would be hard for anyone to challenge. The fact that we link schedule information, set-top box data and ratings data makes it even more difficult to challenge.  The most interesting discovery was that we're 3x larger than Nielsen's biggest single instance transactional datastore. (Netezza has similar kinds of multiplying factors as our data storage scheme, Hadoop.) The Numbers:Wal-Mart: 1 petabyte (800 million transactions/day across 7000 stores globally) (3)  (This is probably in a combination of HP Neoview and Teradata.)Yahoo!: 700 terabytes (1)  (Doesn't include their Hadoop cluster which is approx 15 petabytes.)Australian Bureau of Statistics: 250 terabytes (1)AT&T: 250 terabytes (1)AC Nielsen: Largest single instances: Netezza: 20 tera, Oracle: 10 tera (500 terabytes TOTAL in Netezza, 45 tera in Oracle) Most are distributed databases with client data. (1)(2)Adidas: 13 terabytesLargest Hadoop cluster (4):Facebook: 30 petabytes of storage---------------------------------------------The fine print----------NOTES:(1) From Oracle F1Q10 Earnings Call September 16, 2009 5:00 pm ET Transcript (Charles E. Phillips Jr.)Yahoo!: 700 terabytes Australian Bureau of Statistics: 250 terabytesAT&T: 250 terabytesAC Nielsen: 45-terabyte data [mart], they called itAdidas: 13 terabytes2) DBMS2:September 29, 2009What Nielsen really uses in data warehousing DBMSIn its latest earnings call, Oracle made a reference to The Nielsen Companythat was — to put it politely — rather confusing. I just plopped down in a chair next to Greg Goff, who evidently runs data warehousing at Nielsen, and had a quick chat. Here’s the real story.The Nielsen Company has over half a petabyte of data on Netezza in the US. This installation is growing.The Nielsen Company indeed has 45 terabytes or whatever of data on Oracle in its European (Customer) Information Factory. This is not particularly growing. Nielsen’s Oracle data warehouse has been built up over the past 9 years. It’s not new. It’s certainly not on Exadata, nor planned to move to Exadata.These are not single-instance databases. Nielsen’s biggest single Netezza database is 20 terabytes or so of user data, and its biggest single Oracle database is 10 terabytes or so.Much (most?) of the rest of the installations are customer data marts and the like, based in each case on the “big” central database. (That’s actually a classic data mart use case.) Greg said that Netezza’s capabilities to spin out those databases seemed pretty good.That 10 terabyte Oracle data warehouse instance requires a lot of partitioning effort and so on in the usual way.Nielsen has no immediate plans to replace Oracle with Netezza.Nielsen actually has 800 terabytes or so of Netezza equipment. Some of that is kept more lightly loaded, for performance.(3) Stair, Principles of Information Systems, 2009, p 181.(4) Dhruba Borthakur who is the Hadoop Engineer for Facebook.30petabytes in December 2010.  This is really interesting....  http://www.facebook.com/note.php?note_id=468211193919In May 2010The Datawarehouse Hadoop cluster at Facebook has become the largest known Hadoop storage cluster in the world. Here are some of the details about this single HDFS cluster:21 PB of storage in a single HDFS cluster2000 machines12 TB per machine (a few machines have 24 TB each)1200 machines with 8 cores each + 800 machines with 16 cores each32 GB of RAM per machine15 map-reduce tasks per machineThat's a total of more than 21 PB of configured storage capacity! This is larger than the previously known Yahoo!'s cluster of 14 PB. Here are the cluster statistics from the HDFS cluster at Facebook:
  6. BioinformaticsFederalist papersPhysicsBusinessdevelopement
  7. Two reasons for light viewing:Modality. People have busy lives.Fragmentation to lower measured networksThe heaviest viewers watch 3X the volume of television of the average viewer.The lightest viewers watch 5% the volume of television of the average viewer.60% of the television audience accounts for 90% of television viewing (and therefore ad impressions).  Call them the Heavier Viewers.The remaining 40% of the viewers account for only 10% of total attention to television.  These Lighter Viewers’ attention to television generates less than 1/10 the volume of impressions that a Heavier Viewer does.Without careful planning based on the best possible data resource, every 12 impressions an advertiser buys will yield one unit of reach against the 40% of the audience that are Lighter Viewers.Ratio of Heavier Viewer viewing to Lighter Viewer viewing varies by network.  Networks with a relatively greater share of viewing attributable to heavier viewers will tend to accumulate audience more slowly that networks with lower share of viewing attributable to heavier viewers.  All else equal, impressions on networks with more heavier viewer viewing will create more frequency and less reach than networks with less heavier viewer viewing.
  8. SYFY 2010.02.28 7:00:00PM to 2010.10.14 12:30PM10645 Observations for 514 stationsSometimes easy to spotFiles corruptedWhat about inconsistency in field level data?Possibly a logging problem at the STB level?Possibly an aggregation problem?
  9. Learning the difference between “bank” of a river vs “bank” as a place where you put your money.In search we called this the “Madonna problem” Madonna the religious icon vs Madonna pop culture icon
  10. Learning the difference between “bank” of a river vs “bank” as a place where you put your money.In search we called this the “Madonna problem” Madonna the religious icon vs Madonna pop culture icon
  11. Learning the difference between “bank” of a river vs “bank” as a place where you put your money.In search we called this the “Madonna problem” Madonna the religious icon vs Madonna pop culture icon
  12. Nielsen has Over The Air, Analog, Digital
  13. Nielsen has Over The Air, Analog, Digital
  14. Nielsen has Over The Air, Analog, Digital
  15. Nielsen has Over The Air, Analog, Digital
  16. Nielsen has Over The Air, Analog, DigitalImputed Nielsen’s numbers
  17. The first chart shows the Fraction of view time for women of ages 18-54 (F18-54) as fraction of view time for all tv viewers for week 2 vs the same fraction for week 1 (two weeks in January). The data is for three markets Philadelphia in blue, Atlanta in red and Chicago in green. Each point represents a zip code in one of these markets. The second chart is similar but for men 18-54 (M18-54).The distance of a point away from the diagonal line represents the variation from one week to the next for that zip code. The separation along the diagonal line represents the varying fraction of adult women between the zip codes. As an example, if there had been no change from the first week to the second, all points would have been along the diagonal.We see strong overlap of all three markets and they can't be separated in these views. However, we see significant spread of the fraction of the F18-54 group and M-18-54 group between the zip codes that compose these markets.  Women appear to show more geographically variation in their viewing habits
  18. Audience fragmentation is going from bad to worseThis fragmentation is wrecking effective campaign reach and creating a massive frequency imbalanceAudience re-aggregation will be key for brand advertisers to maintain scaleTV is not going to the web. The web is going to television.
  19. Audience fragmentation is going from bad to worseThis fragmentation is wrecking effective campaign reach and creating a massive frequency imbalanceAudience re-aggregation will be key for brand advertisers to maintain scaleTV is not going to the web. The web is going to television.
  20. Merci.