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FUTURE OF PUBLISHING IN THE DATA ERA

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Lesson made at NABA in Milan https://www.naba.it/en
Talk focused on data and publishing.

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FUTURE OF PUBLISHING IN THE DATA ERA

  1. 1. FUTURE OF PUBLISHING IN THE DATA ERA MARCO VIGANO’ - DIGITAL CHIEF TECHNOLOGY OFFICER - @SASHA0423
  2. 2. 1. Condé Nast Italia System Who is CN? 2. Media integrative platforms Let’s talk about content production, social and experience 3. Data Strategy Advertising, Content, Machine Learning, Analytics 4. What’s next? EXPERIENCE IS 1 MILLION FB AND IG INTERACTIONS PER MONTH 8% IG ENGAGEMENT RATE +10 MILLIONS TOTAL REACH PER MONTH CONDE’ NAST NETWORK 15 MIO TOTAL FAN BASE CN 50 MIO INSTANET MONTHLY IMPRS* AGENDA
  3. 3. Who is CN?
  4. 4. CONDÉ NAST ITALIA SYSTEM CONDÈ NAST fanbase 500k event participation NUMBERS readers● MAGAZINE ● DIGITAL ● SOCIAL MEDIA ● EVENTS PLATFORMS • a unique selection of brands in terms of content, numbers and positioning; • a selected, diversified audience; • a system of media and distribution platforms constantly evolving; • a high level of engagement. > > > TARGET MARKET > > > >visitors affluent millennians business dandy print professionals smart elite digital page views 3.2 interactions per second 25 main events per year circulation 5,6mio 1,3mio 27mio 177mio 16,4mio Source: Audipress 2019/1, ADS moving average 3/19, Webtrekk average Jan-May 2019, Social 05-2019
  5. 5. Source: Webtrekk, AVG 2019, update May 2019 / Social Platforms, Update May 2019 NEWS, CELEBRITIES & ENTERTAINMENT 12 MIO UNIQUE VISITORS 2.7 MIOTotal Fan Base TECHNOLOGY & INNOVATION 5 MIO UNIQUEVISITORS 1.4 MIOTotal Fan Base BEAUTY,WELLNESS & FASHION 2,3 MIO UNIQUE VISITORS 1,1 MIOTotal Fan Base FOOD & CONVIVIALITY 4,1 MIO UNIQUE VISITORS 1. 5 MIOTotal Fan Base FASHION, STYLE FOR MEN 1,3 MIO UNIQUE VISITORS 822 KTotal Fan Base FASHION, BEAUTY, CULTURE & INFLUENCING 2,1 MIO UNIQUE VISITORS 6,6 MIOTotal Fan Base BRANDS INTERIOR DESIGN & ART 220.000 UNIQUE VISITORS 130.000Total Fan Base
  6. 6. MAGAZINE AUDIENCE DIGITAL SOCIAL EVENTS
  7. 7. *Source: Google Analytics AGE GENDER 19% 25% 23% 23% 25% 10% 30% 34% 27% 28% 28% 18% 27% 21% 20% 20% 20% 17% 24% 20% 30% 19% 27% 55% VANITYFAIR WIRED VOGUE GQ GLAMOUR LCI 44+ 35-44 25-35 18-24 81% 35% 81% 23% 89% 72% 19% 65% 19% 77% 11% 28% VANITYFAIR WIRED VOGUE GQ GLAMOUR LCI Female Male Best target markets for having influence AUDIENCE
  8. 8. media integrative platforms
  9. 9. CONTENT PRODUCTION SOCIAL EXPERIENCE DATA media integrative platforms
  10. 10. CONTENT PRODUCTION
  11. 11. CONTENT PRODUCTION CONTENT PRODUCTI ON IN HOUSE PRODUCTIONS WHITE LABEL • BRANDED CONTENT • NATIVE ADVERTISING • MULTI-PLATFORM PROJECTS • ADVERTORIAL CON SHOOTING • VIDEO PRODUCTIONS • SHAREABLE • VIDEO PRODUCTIONS • TAILOR-MADE CREATIVITY
  12. 12. SOCIAL
  13. 13. SOCIAL SOCIAL EXPERIENCE IS
  14. 14. IDENTIKIT 18 – 26 main target market, that goes all the way to the 15-30 age range Mostly girls with some boys as well 92% Italian CHARACTERISTICS • Always seeking what’s new; • Desire to reflect what they see; • More difficult to engage in a conversation but won over. EXPERIENCE IS 1 MILLION FB AND IG INTERACTIONS PER MONTH 8% IG ENGAGEMENT RATE +10 MILLIONS TOTAL REACH PER MONTH CONDE’ NAST NETWORK 15 MIO TOTAL FAN BASE CN 50 MIO INSTANET MONTHLY IMPRS* *InstaNetricopre social-media posts embedded in the pages of women’s sites: VanityFair, Vogue Experience is
  15. 15. Has been a success since the first two modules* were launched: CN Social Academy CONDÉ NAST along with SDA BOCCONI “We believe in the culture of Social Media Marketing for successful digital strategies, and this is why we want to invest in tomorrow’s content creators” 3 PARTNERS 40 Students trained More than 2,000 SIGNED UP FOR EACH MODULE more than 200 content pieces created *Theme 1° module: Beauty ;Theme 2 ° module: Lifestyle & Men’s Passions
  16. 16. CN Social Talent Agency WHERE IS THE MARKET GOING? • Talents as content creators; • Quality content; • Storytelling; • Authentictiy, reputation and credibility. ▼ HOW IS CONDE’ NAST REACTING? ▼ Integrated approach with CN properties Opportunity to be published on CN properties Great Customer Base Promotion on CN properties (for microinfluencers & Academy) Integrated Communicatio ns project High-end target ▲ ▲ FOR THE TALENTFOR THE CLIENT 6 TALENTS SOCIAL ACADEMY 21 TALENTS NEW TALENT RECRUITMENT
  17. 17. EXPERIENCE
  18. 18. EXPERIENCE EVENTS • Sponsorship • Partnership • Tailor Made Events EXPERIENC E CROSS MEDIA PROJECTS (Print_Web_Socia l and Local) CO-MARKETING ACTIVITIES
  19. 19. Frame Experience Store EXPERIENCE STORE able to “frame” the trends, projects and most interesting content to experiment with. A store based on the idea of INCLUSION where offering new ways of interacting among readers, users, followers, the editors and brands. An INSPIRATIONAL SPOT where brands can evangelize via: Online is becoming more and more important but at the same time stores must become a real experience. “ ”  CULTURE  HERITAGE  INNOVATI ON  MEET UPS  INTERACTI ON  SHOPPING and…EXPERIENCES
  20. 20. BE HUNGRY FOR DATA
  21. 21. DATA DATA UNIT PRINT CIRCULATIO N DIGITAL CIRCULATIO N ON SITE REGISTRATIONS
  22. 22. DATA DATA From “UNDRESSED” to “PROFILED” Creating a UNIQUE DATABASE based on FIRST DATA from Condé Nast’s user base. 1- DATABASE MARKETING CLIENT – USER CENTRIC Integration of print and digital free pass subscribers, registered users, those signed up to event response of users. Integration of social-world world. 2- DATA ENRICHMENT Enriching profiles with new information (demographic, behavior, preferences and interests). Update GDPR consent forms. 3- ENGAGEMENT Dedicated newsletters and initiatives (event invitations, partner communications, etc..). 4- LIFE CYCLE MANAGEMENT Campaigns for managing life cycle of subscribers, Welcome for new users, Retention for those about to expire, Reactivation for those already expired. MILESTONES  Engage and maintain active users  Increase user base  Creating quality audience  Building a user-centric vision
  23. 23. ADVERTISING CONTENT MACHINE LEARNING ANALYTICS data strategy
  24. 24. ADVERTISING
  25. 25. • RTB (RealTime Bidding) * CPM – Cost Per Mille = Cost for 1,000 adv impression advertising • Programmatic buying adv placement based on user interests, age, sex, for example: - demographics (male / female / 18 – 45), - geography (3 min from a beach), - time of day (7-10PM) - weather (sunny) - device (smartphones) Programmatic & RTB IAB = Interactive Advertising Bureau DSP = Demand-Side Platform SSP = Sell-Side Platform
  26. 26. advertising DMP: Data Management Platform • Unified and centralized technology platform used for collecting, organizing, and activating large sets of data from disparate sources • DMP is built on five interconnected pillars: collection, unification, organization, activation, and analytics. • DMP + Programmatic and RTB sell better advertising based on clustering and user behaviour
  27. 27. CONTENT
  28. 28. content Wow!!!!!
  29. 29. content Publishing forecast (Data from La Cucina Italiana) • Editors: What is the best time for publishing content? • IT: How I can saving money in my infrastructure? Predictable workload Unique Users 27 Jun – 4 Jul
  30. 30. content Heatmaps demo
  31. 31. MACHINE LEARNING
  32. 32. IMAGES TEXT USERS machine learning PILLARS
  33. 33. IMAGES TEXT USERS machine learning PILLARS autotagging celebs recognition discover old content natural language processing history recomendation
  34. 34. machine learning Images: Computer Vision Computer vision allows computers to gain an understanding of digital images and videos. It seeks to automate tasks that human vision can achieve. This involves methods of acquiring, processing, analyzing and understanding digital images, and extraction of data from the real world to produce information. For CN: Image tagging + Celeb recognition = help editorial team What is CV?
  35. 35. machine learning Images: Computer Vision autotagging celebs recognition discover old content 1. Shaq 2. Shaquille 3. Diesel 4. The Big Fella 5. Shaquile O’Neal
  36. 36. demo machine learning Amazon Rekognition and Google Cloud Vision APIs
  37. 37. machine learning Text: Natural Language Processing Natural language processing is the ability of a computer program to understand human language as it is spoken. For CN: Topic extraction + article tagging = help editorial team What is NLP?
  38. 38. machine learning NLP: TEXT CLEANING Remove: frequent words, stop words, symbols, and numbers. Keep: nouns, verbs, adjectives, and adverbs.
  39. 39. machine learning Facendo le dovute proporzioni se il Napoli allo scudetto ci dovesse arrivare davvero la festa durerebbe giorni o settimane. Il metro di paragone è la notte appena passata. Passata sveglia in tutta la città e con più di 10 mila persone all’aeroporto ad aspettare gli eroi. Erano quasi le tre del mattino quando è atterrato l’aereo che riportava a casa la squadra partenopea dalla vittoria di Torino che ha sancito il ritorno a meno uno in classifica dalla Juventus a quattro giornate dalla fine del campionato. È una vittoria che vuol dire speranza e che è stata festeggiata a Napoli con un delirio fare dovuto proporzione napoli scudetto arrivare davvero festa durare giorno settimana metro paragone essere notte appena passato passato sveglio città molto persona aeroporto aspettare eroe quasi mattino atterrare aereo riportare casa squadrare partenopeo vittoria torino sancire ritornare menare classificare juventus giornata fino campionare essere vittoria diro speranza festeggiare napoli delirio collettivo fuoco artificio rievocazione maradona tuffo NLP: TEXT CLEANING EXAMPLE
  40. 40. demo machine learning Google NLP
  41. 41. machine learning CV + NLP = RECOMMENDATION: COLLABORATE FILTERING
  42. 42. ANALYTICS
  43. 43. analytics Measure everything - Measure and track results accross time - Understand users behavior - Design products and servirvices that maximize customer acceptance - Measure ROI - … Why is important?
  44. 44. demo analytics Google Analytics
  45. 45. WHAT’S NEXT?
  46. 46. What’s next? NEW Publisher Digital Transformation: Content everywhere - Again… Data become crucial to understand impact of content distribution - Content must follow users / customers - Kill Homepages? (5% - 10% total traffic) - Kill Website? From Mobile first to Content Distribution first!!!
  47. 47. What’s next? Choose your destiny!
  48. 48. What’s next? Choose your destiny! Google Ecosystem for a Publisher: - Google SERP (Search Engine Results Pages) - Google News - Google app - Chrome sugestion
  49. 49. What’s next? Facebook Tango Down (45 minutes)
  50. 50. MAYTHEVOICE BEWITHYOU
  51. 51. Smart speakers
  52. 52. La Cucina Italian Skill • Alexa, apri La Cucina Italiana • Alexa, apri La Cucina Italiana e dammi la ricetta di {oggi} • Alexa, apri La Cucina Italiana e trova una ricetta con {tagliatelle} e {funghi}
  53. 53. La Cucina Italiana Custom Skill
  54. 54. VOICE DATA
  55. 55. IL FUTURO DEL PUBLISHING NEL MONDO DEI DATI Thank you MARCO VIGANO’ - DIGITAL CHIEF TECHNOLOGY OFFICER - @SASHA0423

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