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Reader Analytics & Smart Book Metrics (Observing Readers in their Natural Habitat)

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How to better understand book readers and developing deep audience insights

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Reader Analytics & Smart Book Metrics (Observing Readers in their Natural Habitat)

  1. 1. Reader Analytics & Smart Book Metrics Observing Readers in their Natural Habitat @arhomberg @jellybooks
  2. 2. 2 The Inside of a Book is a Black Box • As a publisher or author, do you know how users read your books? • ebooks (ePub) enable reading data to be recorded, but... • ... Amazon, Google, Apple etc. don’t share that data with publishers and authors: they operate black boxes that we cannot peer into...published by John Wiley & Sons, Inc 2
  3. 3. 3 Amazon gives us no data, so is there a way for publishers to collect reading data themselves from third party reading apps so that publishers and authors can get to know their readers better and make better decisions? + 3
  4. 4. 4 Getting to know Readers Quantitatively measure reader engagement We want to understand both audiences (groups) as well as readers (individuals) • How many readers finish a book? • How fast do they read the books? • When do they read the books? • Do they rave about the books they read? • How is their engagement influenced by cover, description and other factors? 4
  5. 5. 5 I. JavaScript software (candy.js) is integrated into the ebook file (ePub 3 format) II. candy.js interacts with the ebook reading app APIs, retrieving key reading events III. Candy.js stores the events into local storage on the device (reader can be online) IV. event data is extracted when the reader clicks on a pre-integrated ebook link (button) Technology behind Reader Analytics 5
  6. 6. 6 Supported Reading Applications iBooks (Apple) – Bookshelf (Vitalsource) – Bookvia (Datalogics) – Kobo (Rakuten) Digital Editions (Adobe) – Ebook Reader (ebooks.com) –Helicon ereader (Helicon) - Bookari (Mantano) Cloudshelf Reader (Bluefire) – Media Console (Overdrive) – Lisa (Art Book magazine) - Azardi (Azardi) 6
  7. 7. 7 Readings Streams for Individuals 7
  8. 8. 8 Do Readers Finish the Books they start? 0% 20% 40% 60% 80% 100% F 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 B B B Example of a highly successful title with 80% completion rate 8
  9. 9. 9 0% 20% 40% 60% 80% 100% F 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 B … or do they quit in droves? A title that could not hold on to its readers… 9
  10. 10. Popularity Engagement Audience AnalysisWord-of-mouth 10 … and lot of other data, too!
  11. 11. 11 Key Performance Indicators (KPIs) Audience Engagement summarized in 5 Simple Numbers 1 Completion Rate 2 Satisfaction Index 3 Recommendation factor 4 Cover-match factor 5 Velocity 11
  12. 12. A|B Cover test • Pulling Power Does the cover elicit interest? • Signaling What expectations are raised? • Honesty Does it deliver on its promise? How to Pick the Right Cover? 12
  13. 13. Case Study: 5 Titles – 1 was A|B tested 13 Reader Test Group A Reader Test Group B
  14. 14. If Cover A is shown 35% of test readers picked this title, but only 26%, if cover A. Das Traumbuch A (Welle) Das Traumbuch B (Nautilus) +37% A|B Test Covers – Reader’s don’t know 14
  15. 15. < 13 13–17 18–25 26–35 36–45 46–55 56–65 66–75 > 75 Did Age Influence Cover Preference? 15
  16. 16. 16 CR 50% RF 62 CMF 38 Velocity 6.8 CR 43% RF 36 CMF 24 Velocity 13.5 completion rate recommendation factor cover-match factor velocity Cover Effect on Audience KPIs 16
  17. 17. 1. Deciding on which books should be lead titles 2. Allocating marketing budgets to the right book 3. Creating consensus around ‘difficult’ books 4. Optimizing, cover, title and description 5. Defining core audience & target demographic 6. Positioning difficult books 7. Why did a book with expectations dissapoint? 8. Measuring strength of an author’s platform 17 Use Cases When do publishers use reader analytics? 17
  18. 18. Jellybooks Clients Top Trade Publishers in UK, US and Germany 18
  19. 19. 19 Book Candy for Readers Data Candy for Publishers

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