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Transformation of Media Business Using Big DATA

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Transformation of Media Business Using Big DATA

  1. 1. Copyright © infobahn All Rights Reserved. Transformation of Media Business Using Big DATA
  2. 2. << FEMALE MALE >> YOUNGER>><<OLDER MEDIA SET 136,000,000 Monthly Page Views 19,000,000Monthly Readers
  3. 3. Agenda 1. What is target media? 2. Programmatic 3. Content Marketing & Native Ads 4. Using Big Data 5. To be succeed in Media Business
  4. 4. What is target media?
  5. 5. 69,000,000 PAGEVIEWS (60% MOBILE DEVICE ACCESS) 7,400,000 UNIQUE USER 125,000+ FANS 219,000+ FOLLOWERS THE GADGET GUIDE
  6. 6. PC 92% Smart Phone 86% TABLET 66% CAMERA 62% Consumer Electronics 66% DIY 54% ROBOT 50% CAR & BIKE 59% SCIENCE 77% ART 64% PROFILE | AUDIENCE INTEREST INTEREST
  7. 7. Male Female 88% 10% Under 20 21-25 26-30 31-35 36-40 41-45 46-50 50+ 10% 14% 15% 15% 14% 15% 10% 8% Single Married 63% 37% インターネット ソフトウェア 情報通信 マスコミ・広告 小売・外食・卸 官公庁・公務 教育・医療 サービス 建設・不動産 メーカー その他 7% 13% 4% 7% 7% 3% 8% 10% 4% 15% 22% AUDIENCE 88% MALE 63% SINGLE 59% 26-45 15% MAKER 経営 プログラマー・SE マーケティング・広 報 研究・開発 記者・編集 デザイナー アーティスト 総務・人事・経理 営業 学生 9% 18% 5% 14% 2% 10% 1% 8% 14% 19% 18% ENGINEE R 社長・経営者 役員クラス 部長クラス 課長クラス 係長クラス 社員 その他 10% 2% 4% 7% 9% 44% 24% 16% DECISION MAKER
  8. 8. 1-5 6-14 15-50 51-100 101- 200 201+ 34% 11% 18% 10% 10% 19% BOOKMARK Facebook Official Apps Twitter News Apps Google+ mixi 59% 19% 17% 12% 11% 2% 1% 何かを待っているとき 移動時間 ベッドで 通勤途中 テレビを観ながら トイレで 誰かといるとき 会議や授業中 買い物中 60% 42% 33% 27% 23% 16% 9% 5% 1% 39% HEAVY USER 59% BOOKMARK 60% WAITING SOMETING 3ヶ月未満 半年未満 1年 3年以上 5% 8% 27% 60% 60% MORE THAN 3 YEARS Lifehacker Kotaku TABROID AppBank ASCII CNET Engadget ITmedia TechCrunch WIRED Yahoo! News 51% 18% 20% 19% 25% 24% 28% 49% 12% 19% 46% 51% LIFEHACK ER ※ひと月あたりのサイト訪問回 数 ※購読方法 ※アクセスするシチュエーショ ン HOME OFFICE TRAIN OTHER 88% 47% 36% 5% 88% HOME ※どこからアクセスするか ※ギズモードのファン歴 ※よく見るWebサイト PROFILE | AUDIENCE DEMOGRAPHIC AUDIENCE
  9. 9. ECO System of Information diffusion on internet カジュアル世論 ファン/フォロワー OWNED MEDIA PORTAL MEDIA CONTENT RECCOMMEND CURATION MEDIA TARGET MEDIA FAN FOLLOWER 80,000REACH 14,400REACH 17,465VISIT Approx 850,000REACH 3,300,000REACH CONTENT FAN FOLLOWER
  10. 10. TARGET MEDIA  Have Highly Engaged Audience  Hub of the eco system of information spread
  11. 11. PROGRAMMATIC Display Ads Ruined the value of Target Media.
  12. 12. Good old days: The publishers had direct control of advertisers and ad-revenue Traditional advertising: • Direct relationship between publisher and advertiser • Publisher with 100% of ad revenue Display Ads
  13. 13. 50-80% of publisher advertising revenue lost to middlemen, and to…
  14. 14. OTHERS DISPLAY ADS
  15. 15. CONTENT MARKETING Target Media
  16. 16. What is CONTENT MARKETING? BRANDED CONTENT 共感 Message Sympathy BRAND STORY SHARE & SPREAD
  17. 17. BRANDED CONTENT NATIVE ADVERTISING × Emotion Provoking Distribution Emotion Provoking Action Share & Spread
  18. 18. IPG Media Lab and Sharethrough “Native advertising” is a form of paid media where the ad experience follows the natural form and function of the user experience in which it is placed.
  19. 19. 最新デジタル 美容・コスメ Visit to brand site, with sympathy and knowledge of brand. With Story written by Editors Know about the brand. to engaged fan of Media What Brand would like to tell. Emotion Provoking ACTION! Visit to brand site, without enough knowledge of brand. With attractive banners. To coincidence visitors Of the site. What Brand would like to tell. With little interest. ! ! ACTION! Display Ads Native Ads
  20. 20. 36% ❝36% of Conversion were from Sponsored post of GIZMODO. − MARKETING METRICS Lab. Native Ads of GIZMODO can make audience to buy
  21. 21. ADNETWORK SPONSORED POST BRAND SITE Targeting using audience data ×10
  22. 22. Publisher has to hold their own audience data
  23. 23. Big data=Big chance for Target Media Publisher has to hold their own audience data
  24. 24. Audience data Collect Divide Analyze What media can do with DMP?
  25. 25. DIVIDEANALYZE DMP Classify the AUDIENCE according to the interest. AUDEIENCE DATA BEAUTY FOOD INETRIOR GAME GADGET MONEY AUDIO
  26. 26. DMP Distribute ads only when the audience data matches. BEAUTY FOOD INTERIOR GAME GADGET MONEY AUDIO AD DISTRIBUTION BRAND PUBLISHER DATA Match DISTRIBUTE
  27. 27. DMP Distribute the contents only when the audience data matches. BEAUTY FOOD INTERIOR GAME GADGET MONEY AUDIO CMS AUDIENCE RECOMMEND DATA Match DISTRIBUTE
  28. 28. The power of user insight … and how it defines the future of online business Who? When ? What? Where ?
  29. 29. 29 SIGHT TARGETING GEO TARGETING DAY AND TIME TARGETING KEYWORD TARGETING CATEGORY TARGETING DEVICE TARGETING RETARGETING METRIX
  30. 30. Effective and Comprehensive Data Capture Cxense Script Tag • Automatic capture of a wide range of user and traffic data • Automatic download and semantic analysis of each individual page REALTIME Content insight Audience insight Predictable insight
  31. 31. 31 Real-time analytics and editorial insight UNDERSTAND HOW PEOPLE INTERACT WITH YOUR CONTENT CAPTURE DATA IN REAL-TIME ACROSS ALL DEVICES SLICE AND DICE DATA FOR DEEP INSIGHTSANALYZE TRENDS AND POPULARITY MONITOR AND TAKE ACTION ON TRAFFIC PATTERNS CAPTURE CUSTOM EVENTS FROM YOUR BUSINESS SYSTEMS STAY IN CONTROL WITH CUSTOMIZABLE REAL-TIME WALLBOARDS
  32. 32. New solution for monetizing media We produce Premium Content Delivery Advertising, using audience data from selected targeting media.
  33. 33. Network Premium Targeting Media DATA MEDIA NEWS MEDIA BUSINESS MEDIA LIFESTYLE MEDIA GADGET MEDIA
  34. 34. Ad Network focus on Content 34 Native ad placed on Recommendation space.
  35. 35. Placement ad with Content match or Audience match CONTENT MATCH 100 AUDIENCE MATCH 100 CONTENT50: AUDIENCE50 BRAND
  36. 36. Feature 1. Content ad network controlled by publishers. 2. NATIVE ADS placed in line and between reliable content. 3. Optimize the display using audience data and content match data.
  37. 37. Empower publishers to maximize advertising revenues through big data and audience insight
  38. 38. Publishers need to regain control of the advertiser value chain
  39. 39. Audience data will change the game.
  40. 40. Thank you.
  41. 41. ■Contact Us http://www.infobahn.co.jp/ask ■Careers https://js01.jposting.net/infobahn/u/regular/job.phtml

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