The Council for Research Excellence

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The Council for Research Excellence Consists of 35+ senior-level research professionals Represents advertisers, agencies, networks, cable companies, and station groups Seeks to advance the knowledge and practice of methodological research

Media Consumption and Engagement Committee Co-Chairs: Joanne Burns, 0 th Television Members: Laura Cowan, LIN Media Jordan Breslow, Direct TV Shari Brill Tim Brooks Chris Edwards, 0 News Janet Gallent, NBCU Hadassa Gerber, SNTA Tanya Giles, Viacom Sara Grimaldi, ESPN Greg Iocco, Scripps Jennie Lai, Nielsen Redjeb Shah, Univision Ceril Shagrin, Univision Susie Thomas, Palisades Emily Vanides, MediaVest Jack Wakshlag, Turner Richard Zackon, CRE 3

TV Untethered Measuring the Shifting Screen Photo *-pt black border shadow Laura Cowan Research Director LIN Media Christopher Neal VP, Tech and Telecom Practice Chadwick Martin Bailey

Video Usage on Smartphones Increasing Monthly Minutes (000) Mobile Video Watching 4,000,000,000,000 0,000,000 8,000,000 4,909,95,49,375 0,83,057 8,76,760 6,000,000 4,000,000,000,000 6,44,080 4,764,669,6,40 3,070,080 009-Q 00-Q 0-Q 0-Q 03-Q 5 Source: Nielsen Mobile Device Insights, Q 03

Study Objectives Gain a Better Understanding of Mobile Video Usage to Provide Insight for Cross Platform Measurement Quantify time spent watching TV on mobile devices How much How often Determine what motivates consumers to watch TV on mobile devices Profile mobile viewing occasions what kinds of conditions correlate with mobile viewing through which sources are mobile TV viewers getting programming 6

Who We Surveyed Sample Group Definitions US 5-64 yrs olds Broadband Internet access at home Watch 5+ hours of TV per week Group Group Group 3 No mobile devices Own mobile devices Do notwatch TV on mobile devices Own mobile devices DOwatch TV on mobile devices N=,9 respondents N=,58 respondents N=3,067 respondents Sample Size N=65,756 viewing occasions N=96,95 viewing occasions N=30,506 viewing occasions 7

Respondent Experience Respondents Completed a Screening Survey, Journaled Their TV Viewing Behavior for 7 Days, Followed by a Post-Journal Attitudinal Survey Screening Survey Mobile Journaling Diary Attitudinal Survey Online survey identifying respondents and developing profiling information Census-balanced click-throughs at first to size the market accurately 7 day journaling of TV viewing occasions by device and viewing preferences Based on four time blocks per 4 hour period Fielded January 4 th 7 th 03 Post journaling, online survey to better understand motivations and behaviors associated with decision making for watching TV programming Additional profiling questions 8

How Much and How Often? Group : Own mobile device No mobile TV Watch 5+ hrs TV a week Of those in addressable market: Group 47% Group 3 3% Group % Group 3: Mobile TV viewers Group : No smartphones/ tablets 9 Sources: US Population and Age Buckets (census.gov); High-speed internet access at home (PEW: pewinternet.org); Watch 5+ hours TV a week (Survey screener data from census balanced click throughs).

All Viewers: Only % of All TV Hours Logged Were on Mobile Devices % of Total TV Hours Watched On Each Device Among TOTAL ADDRESSABLE MARKET 00% 90% 89 TV Computer Tablet Smartphone 80% 70% 60% 50% 40% 30% 0% 0% 0% 8 % Mobile Viewing 0

The Remainder of the Presentation Focuses Solely on Mobile Viewers Group 3: Mobile TV Viewers = 3%

Mobile Viewers: Even Among Them, Mobile Viewing Is a Minority of Total TV Hours % Of Total TV Hours Watched On Each Device Among MOBILE VIEWERS (GROUP 3) 00% 90% 80% 84 TV Computer Tablet Smartphone 70% 60% 50% 40% 7% Mobile Viewing 30% 0% 0% 0% 9 4 3

Mobile TV Viewers: Younger, Higher Income Group No Mobile Devices Group No Mobile TV Viewing Group 3 Mobile TV Viewers Demographics Tend to be older (mean age 44) HH income is lower More likely Caucasian More unemployed and retired Age falls in between Group and Group 3 (mean age 40) More likely Caucasian HH income similar to Group 3 More employed professionals Tend to be younger (mean age 35) HH income is higher Ethnic Skew Asian-American African-American English Dominant Hispanic More employed professionals More graduate/prof degrees 3

4% of Mobile TV Viewers Currently Have No Pay TV Service at Home Mobile Viewers with No Pay TV Yes = 86% No = 4% Younger (under 35 years of age) Lower HH income More likely to live in the West region of the US More likely to live by themselves More likely to rent primary residence More likely to be Asian-American 4 Base: All mobile TV viewers (Group 3) SCQ: Which of the following providers do you currently use for pay TV at your primary place of residence? ( No = % who selected None of the above: I do not currently subscribe to any pay TV service ).

The Majority of Mobile Viewing Takes Place in the Home % of TV Viewing Occasions TV Computer Tablet Smartphone In own home At work / at the office At another's residence In transit/commuting Commercial location Doctor s/dentist s/waiting At school At a hotel On a plane At an airport Other type of travel 5 6 5 0 0 0 90 4 5 0 8 8 6 4 3 8 3 9 8 6 64 0% 50% 00% 0% 50% 00% 0% 50% 00% 0% 50% 00% 5 Base: Total positive TV viewing occasions. JOURNAL_Q7: Where did you watch TV on a device other than a traditional TV set during this time? (Select all that apply.)

Most Mobile Viewing Is through Online Services % of TV Viewing Occasions TV Computer Tablet Smartphone Live Online subscription service Broadcast/cable net site, free TV service provider site/app TV aggregator site - free itunes or similar service DVR On demand (TV/website/app) Unofficial app or website TV program: online source DVD of TV series 7 4 3 80 6 3 4 7 7 49 6 0 4 4 3 3 54 6 0 6 3 4 8 64 0% 50% 00% 0% 50% 00% 0% 50% 00% 0% 50% 00% 6 Base: Total positive viewing occasions. JOURNAL Q6/Q8/Q0/Q/Q4: What was the source of TV shows or movies that you watched on a [DEVICE] during this time? All data is within Group 3.

Mobile Viewing: Dramas, Comedies, Adult Animation on Smartphones in Particular % of TV Viewing Occasions TV Computer Tablet Smartphone News / Business 3 9 5 Drama 30 36 3 7 Comedies 9 9 0 4 Sports 9 7 9 4 Movie / Mini-series 8 3 5 6 Adult animation 0 0 7 *Top 5 genres shown for all devices 0% 50% 00% 0% 50% 00% 0% 50% 00% 0% 50% 00% Base: Total positive viewing occasions. JOURNAL Q3: During which time(s) did you watch TV, specifically?

Mobile Viewing More Commonly Occurs During Daytime, Prime and Late Fringe % of TV Viewing Occasions TV Computer Tablet Smartphone OVERNIGHT: :00am - 4:59 am 3 3 5 EARLY MORNING: 5:00am - 8:59 am 0 6 9 4 DAYTIME: 9:00am - :59 pm 9 6 8 EARLY FRINGE: 3:00pm - 4:59 pm 8 9 8 0 EARLY NEWS: 5:00pm to 6:59pm 8 7 8 M-SAT PRIME ACCESS: 7:00pm - 7:59 pm 7 5 5 3 M-SAT PRIME: 8:00pm - 0:59 pm 5 0 4 SUNDAY PRIME: 7:00pm - 0:59 pm 6 4 4 LATE NEWS: :00pm - :9 pm 4 5 6 5 LATE FRINGE: :30pm - :59 am 9 5 0% 50% 00% 0% 50% 00% 0% 50% 00% 0% 50% 00% 8 Base: Total positive viewing occasions. JOURNAL Q3: During which time(s) did you watch TV, specifically?

Convenience and Multi-Episode Availability Drive Mobile Viewing Why Chose to Watch Program on Device Other Than TV Set More convenient on this device 49 0 0 Wanted to watch multiple episodes 3 7 Watch other show during TV commercials 8 0 7 Fewer ads 5 9 Enjoy viewing experience better 4 6 8 More personal viewing experience 4 9 0 Inappropriate content for others 4 8 5 Program looks better 3 0% 0% 40% 60% 80% 00% Top Reason Second Reason Third Reason Ad avoidance is not a primary motivator 9 Base: Those who watched on device other than TV set (Group 3 Mobile Viewers). QADQ0: Why did you choose to watch television programming on a [DEVICE] instead of on a TV set?.

Mobile TV Viewing Is Driven by Necessity in Larger HH % of TV Viewing Occasions One Two Three Four 40 4 46 33 37 43 4 5 0 8 When they choose to watch on a TV set, it is more commonly because they want to watch with others. 0 Base: Total positive TV viewing occasions.

Mobile TV Viewing Is Driven by Program Availability in Single Person HH % of TV Viewing Occasions Top motivations for device selection: One Two Three Four 6 59 57 55 Base: Total positive TV viewing occasions.

The Smaller the Device, the More Focused Viewers Are While Watching TV % of TV Viewing Occasions TV Computer Tablet Smartphone 50% 45% 40% 45 44 35% 30% 5% 0% 5 3 5 4 30 5% 0% 5% 0% Darker bars: second screen activity, unrelated Lighter bars: second screen activity, related Base: Total positive TV viewing occasions. JOURNAL Q9: What activities did you do at the same time on these devices while you were watching TV?

In Summary 3 ) Mobile TV viewing total volume is still small, though many people now do it The mobile revolution makes TV viewing more convenient and more personalized for more occasions, but the majority of viewing still happens on TV sets ) Convenience is by far the most common motivation for mobile viewing Even inside the home, mobile can be the more convenient (or the only way) to watch a show Screen multiplier: enables household members to watch different shows at the same time Immediacy: mobile spurs spontaneous viewing and enables instant gratification even when consumers cannavigate to the same shows through a television set 3) TV content distribution source is the biggest mobile vs. television set difference Online subscription services currently dominate mobile TV viewing 4) Dramas, comedies, movies and adult animation are the most common mobile genres 5) Daytime, Prime and Late Fringe are the most common dayparts for mobile 6) Mobile viewers are more focused than television set viewers

Additional White Paper This study also resulted in substantial learnings about best practices for online mobile journaling research, such as Recruiting techniques, incentive structures and alert notification systems that maximize inthe-moment participation rates on a mobile journaling app Journaling research design and mobile app interface considerations for high data quality Data QC, integration and analytical considerations for occasion-based journaling data Additionally, we learned much about the implied impact of mobile TV viewing on overall TV viewing as well as television set viewing through TreeNet predictive analytics (and compared these modeling results with more conventional OLS regression models) Further details are available in the accompanying white paper for this presentation 4