This document summarizes a collaboration between Innovate UK and Penguin Random House UK to develop a solution called Reader Analytics that collects reading data from ebooks and third-party reading apps. It allows publishers to analyze how and how fast readers consume ebooks, whether they finish books or quit early, and how reading data can predict sales. The tool was then expanded to support data collection from multiple publishers and reading applications. A/B testing with cover images showed one cover increased choice and faster reading while doubling the recommendation factor.

1. Reader Analytics
Leser besser kennen lernen!
A story of collaboration presented
by @arhomberg founder of @jellybooks

2. 2
Three years ago,
on a grey and foggy morning
in London’s East End…

3. 3
It all started with a collaboration
between Innovate UK, a part of the UK
government that sponsors research and
development, and Penguin Random House UK.
The two came together and jointly sponsored a
competition for start-ups to come up with new
ideas to a specific challenge in book publishing.
+

4. 4
Amazon gives us no data,
so is there a way for
publishers to collect
reading data from
3rd party reading apps
so publishers can get to
know their readers better?
The Penguin Challenge:

5. 5
„Google Analytics für E-Books”
ebook
+
candy.js
tracking 1-click
sync
analytics

6. 6
Involve the reader in the process!

7. 7
How and how fast do they read?
Reader A
Reader B
Reading time: 24 hours
Velocity: 9 days
Reading time: 18 hours
Velocity: 27 days

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

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?

10. 10
45%
-11
28 d
56
14 d
65%Completion Rate
Net Promoter Score
Velocity
Sold units (eBook)
Sold units (PB)
3.000
18.000
45.000
135.000
Audience Insights Can Predict Sales!

11. 11
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. 12
Case Study: 5 Titles – 1 was A|B tested

13. < 13
13–17
18–25
26–35
36–45
46–55
56–65
66–75
> 75
1. Cover A was 37% more likely to be chosen and this preference
held for every age group.
2. The book with cover A was finished sooner (3 days earlier)
than the book (same content!) with cover B.
3. Most importantly though, the recommendation factor was
more than twice as high for cover A compared to cover B.
Cover
A
Cover
B

14. 14
F
F
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
B
B
F
F
1
4
7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
B
B
Completion Rates Almost Identical

15. 15
Reader analytics also facilitates
the collaboration between
author and publisher

16. 16
Multi-publisher Reader Analytics Campaigns

17. 17
Supported Reading Applications
iBooks (Apple) – Bookshelf (Vitalsource) – Bookvia (Datalogics)
Digital Editions (Adobe) – Ebook Reader (ebooks.com) – Bookari (Mantano)
Cloudshelf Reader (Bluefire) – Media Console (Overdrive) – Azardi (Azardi)

18. 18
Tolino & Kobo reading apps are not yet supported,
due to issues with ePub3 support.

19. So in the spirit of collaboration, we ask Michael Tamblyn of Kobo:
Help us help publishers to get to know their readers better.
19

20. 20
Book Candy for Readers
Data Candy for Publishers