DEGREE PROJECT IN MEDIA TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2016 Customising Linear-TV SIMON ROTH KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION Title: Customising Linear-TV Author: Simon Roth [email protected] Degree project subject: Media Technology Programme: Master of Science in Engineering in Media Technology Master's programme in Media Management Supervisors: Matti Zemack (C More) Christopher Rosenqvist (Stockholm School of Economics) Examiner: Haibo Li Principal: Peter Gudmundson Date: 19.09.2016 Abstract The purpose of this thesis was to explore whether we can customise and personalise linear TV. The approach from the author was first to do an exten- sive research into the available literature and statistics, ex- amining the current technology and reasons and behaviour behind media consumption. Thereafter, the author interviewed ten individuals about their media habits, and got insight into how modern young professionals consume their media content. Finally, the author conducted an reverse engineering ex- periment on current SVOD services, to get a better under- standing of how refined the current recommendation sys- tems are. After analysing the resulting data and discussing it, the author concludes that although one can customise a linear TV service, doing so would not be beneficial to media com- panies with current technology and the media habits of the target group. Sammanfattning Användaranpassning av linjär-TV Syftet med denna avhandling var att undersöka om vi kan användaranpassa linjär-TV. Tillvägagångssättet från författaren var först att göra en omfattande forskning om tillgänglig litteratur och statistik, att undersöka den nuvarande tekniken och motiv och bete- ende bakom media konsumtion. Därefter intervjuade författaren tio personer om deras me- dievanor, och fick inblick i hur moderna unga yrkesverk- samma konsumerar sitt medieinnehåll . Slutligen har författaren genomfört ett reverse engineering experiment på nuvarande SVOD tjänster för att få en bättre förståelse för hur förfinade nuvarande rekommendationssy- stem är. Efter att ha analyserat resulterande data och diskuterat det, drar författaren slutsatsen att även om man kan an- passa en linjär-TV-tjänst, så skulle detta inte vara fördel- aktigt för medieföretagen att genomföra detta med dagens teknik och målgruppens konsumtionsmönster. Acknowledgments I would like to thank my teachers, professors, lecturers and many more during my years at KTH, for helping me grow academically. I would also like to thank my com- pany supervisor Matti Zemack, and my thesis supervisor Christopher Rosenqvist, for taking me on and guiding me during the thesis. Finally, I would like to thank my family and friends, for help and support. Especially thanks to my brother, Max, whom helped me with the final format of the thesis. The author, June 2016, Stockholm Contents 1 Introduction 1 1.1 Introduction................................ 1 1.2 Keywords and acronyms . 1 1.3 Purpose and goal . 2 1.4 Researchquestions ............................ 2 1.4.1 Thesisquestions ......................... 2 1.4.2 Support questions . 2 1.5 Delimitations ............................... 3 2 Background 5 2.1 Actors................................... 5 2.1.1 CMore .............................. 5 2.1.2 Netflix............................... 5 2.1.3 Filmnet .............................. 5 2.1.4 Other actors . 5 2.2 Previous research / literature study . 6 2.2.1 Context recommendations . 6 2.2.2 Behavior and motivation behind TV-viewing . 7 2.2.3 Statistical research . 9 3 Methodology 13 3.1 Introduction................................ 13 3.2 Literature review . 13 3.3 Interviews................................. 14 3.4 Reverseengineering............................ 14 4 Technology 15 4.1 The CLSVOD Concept . 15 4.2 Difference between differenttypesofmediaservices. 16 4.3 CLSVOD design proposal . 18 5 Results and analysis 21 5.1 Interviews................................. 21 5.1.1 Serendipity ............................ 21 5.1.2 Background noise . 22 5.1.3 Binge watching . 23 5.2 Reverseengineering............................ 24 5.2.1 Netflix . 24 5.2.2 SVT Flow . 27 5.2.3 HBO Nordic . 27 5.2.4 TV4 Play Premium . 27 5.2.5 Filmnet .............................. 27 5.2.6 SVT Play . 34 5.2.7 Hulu+ . 34 5.2.8 Showtime ............................. 34 5.2.9 EpixHD . 34 5.2.10 VUDU . 34 5.2.11 Cloadload . 34 6 Discussion 35 6.1 Results................................... 35 6.1.1 Findings . 35 6.1.2 Interviews............................. 36 6.1.3 Reverse engineering . 36 6.2 Meta - methodology and approach, lessons learned . 37 7 Conclusion 39 7.1 Support questions . 39 7.1.1 Where is linear media heading? . 39 7.1.2 Can we customise a linear SVOD, based on user behaviour collected from Big Data? . 39 7.2 Thesisquestions ............................. 40 7.2.1 What is a CLSVOD service? . 40 7.2.2 Is there a market for a CLSVOD? . 40 8 Implications and recommendations for stakeholders 41 8.1 Linear TV companies . 41 8.2 Academia ................................. 41 References 43 9 Appendix 45 9.1 Appendix A - Reverse engineering guide . 45 9.1.1 Purpose . 45 9.1.2 Services .............................. 45 9.1.3 Methodology/MO . 45 9.2 Appendix B - Results reverse engineering . 47 9.2.1 Netflix . 47 9.3 AppendixC-Interviewquestions. 54 9.3.1 Media habits . 54 9.3.2 SVT-Flow, linear VOD . 54 9.4 AppendixD-Fullinterviews . 55 9.4.1 Interviewee #1: Male, 30. FH . 55 9.4.2 Interviewee #2: Male, 28. JR . 56 9.4.3 Interviewee #3: Male, 29. F . 57 9.4.4 Interviewee #4: Male, 25. NA . 57 9.4.5 Interviewee #5: Male, 24. NF . 59 9.4.6 Interviewee #6: Male, 25. JN . 61 9.4.7 Interviewee #7: Male, 24. NB . 63 9.4.8 Interviewee #8: Female, 26. JTA . 65 9.4.9 Interviewee #9: Female, 24. ACA . 67 9.4.10 Interviewee #10: Female, 26. FM . 68 Chapter 1 Introduction The introduction chapter covers some formalities regarding the thesis, such as key- words, the purpose of the thesis and the research questions for the thesis. 1.1 Introduction The entertainment industry is in an age of change. Customers no longer want to wait for several months before they get their entertainment fix; they want it now. Gone are the days when the only way to consume motion picture at home was to either watch linear TV or rent a DVD. Streaming Video on Demand (SVOD) is the current channel, with Netflix being the forerunner. And with SVOD comes a new previously untapped well of information: Big Data. It is used to make recommendations on what the customer should watch, based on their own behaviour. There is a source of Big Data from the motion pictures themselves; they can be described by meta tags, that gives a high precision during a search. This leads to the basis of this thesis; the problem of connecting these two data sets in a meaningful way. 1.2 Keywords and acronyms Linear TV - Television broadcasting, e.g. TV4, SVT, TV6 SVOD - Streaming Video on Demand, e.g. Netflix EPG - Electronic Programming Guide Time Shift TV - Being able to watch content at a later time, for example by record- ing it to a hard disk Customised linear SVOD - A SVOD that appears to be functioning like linear media, but that in fact can be paused and manipulated like expected of a SVOD. There is a continuous stream that is based on user behaviour. One could also call 1 CHAPTER 1. INTRODUCTION this a Customised EPG. Big Data - data collected about the user of a service, usually through cookies on the web. The data is then often aggregated together with the fitting demographics, and the data collector can use it however they want, often being advertisement or improving the service provided. NDP - Next Day Premiere ACM - Association for Computing Machinery, http://www.acm.org/ UGC - User Generated Content CDN - Content Delivery Network 1.3 Purpose and goal The purpose of this thesis was to examine the role of linear media in the future. Will it still be a factor in 10-15 years, or will something else have taken it’s place? Furthermore, the goal is to have the thesis work to culminate in an system design proposal, that considers both user data and metadata from the digital content. This system design could then form the basis for further research and thesis work. To accomplish all this, a literature study, interviews and reverse engineering were conducted. 1.4 Research questions 1.4.1 Thesis questions What is a CLSVOD service? Is there a market for a CLSVOD? 1.4.2 Support questions Where is linear media heading? Will it be a factor in a few years? Can we customise a linear SVOD, based on user behaviour collected from Big Data? How should it be designed? 2 1.5. DELIMITATIONS 1.5 Delimitations To limit the work to something manageable that can be achieved alone during a 20 week period, the media is restricted to SVOD, the geographical area to Stockholm and the interviewees to persons aged 18-35. 3 Chapter 2 Background The background chapter presents the actors concerned in this thesis, while also pre- senting the literature study and relevant statistics. 2.1 Actors 2.1.1 C More C More entertainment is a media company focusing on sport and entertainment broadcasting. Examples of the sport channels are football, hockey, tennis, while examples of the entertainment channels are action, drama and Film HD. There is also a SVOD service available, called C More Play. C More have been active in Sweden since 1997, known back then as Canal+ (owned by the french company of the same name). TV4 bought C More in July 2008. In 2010, TV4 sold 35 % of the ownership to Telenor. C More is operated from within TV4-huset at Gärdet, Stockholm 2.1.2 Netflix Netflix is an American streaming service, having its roots in a rental DVD service.