Collecting Ebook Reading Data - Moneyball for Publishers or how we research the way we read

1. 1
Collecting Reading Data
presented by
Andrew Rhomberg
CEO Jellybooks
@arhomberg
@Jellybooks
©Antonio Roselló

2. Part I
Why?©Li Dandan

3. The Unknown Reader…
3
Despite the digital
transformation of the
publishing industry,
authors & publisher
still cannot measure
reader engagement!
Do consumers read
the books they buy?

4. It doesn‘t really matter, does it?
4
If readers buy and pay
for books (revenue)
does it matter if they
read them or not
(engagement)?
Ah, but are publishers
selling paper or are they
offering entertainment
and education?©Frederic Bordoni

5. Part II
How to Collect Reading Data?

6. Online Content = Web Analytics
6
• Google Analytics
• Kissmetrics
• Mixpanel
• Optimizely
• Adobe Site Catalyst
• Flurry Analytics
Use of log files
(Server), tracking
pixels und Javascript
tags for the collection
of data.

7. What is an E-Book?
• An E-Book is 90% HTML = “web page”
• Reading application (iBooks, Kindle) = “Browser”
But, but, but:
• server log files = Amazon, Apple, Google, etc.
And content is processed/read offline
• application logs = Apple, Amazon etc.
7

8. 8
Amazon, Apple & Google collect
vast amounts of reading app/device data,
but don‘t share this with publishers.

9. 9
Solution is…
3

10. 10
3
=
HTML 5 + CSS 3 + JS
A key feature of ePub 3 is the support for Javascript,
as well as HTML5 support for offline storage of data.

11. ... and the data connection?
Engage readers!
When the reader clicks on a
link at end of chapter/book
(styled as a button), a data
connection with Jellybooks is
established = user opt-in
11

12. Readers use familiar Apps
Test reader are recruited based on whether they
already used one of the apps supported by JBKS
12
iBooks
iOS
ADE
Windows
Ebook Reader
Android

13. “Google Analytics for Ebooks”
13
1. Modified ebook (ePub3 format) distributed to users,
who read on their existing reading device or app
2. Embedded Javascript software tracks (offline) reading
3. Readers click on button at end of chapter/end of
ebook to upload/sync data to Jellybooks
4. Data Visualisation for Author/Agent/Publisher

14. Part III
Execution

15. ePub Modification by Jellybooks
• We receive a regular ePub 3
file from publisher
• We modify it with candy.js to
record reading data
• We automatically insert sync
buttons to extract data
• We make sure user is aware
of the modification via
identifiers like “candy stripe”
15

16. Format 1: Distribution via Email
16

17. Format 2: Invitation-only Web Page
17

18. Survey Follow-up
18
not finishedfinished

19. Part IV
Visualising Data

20. KPIs of a Book
20
1. Completion Rate
how many readers finish?
2. Velocity
fast read or slow read?
3. Recommendation factor
do readers rave or rant?
and more…

21. KPI – 1
Completion Rate
21

22. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
F
F
2
4
6
8
10
12
14
16
18
20
22
24
26
28
B
B
Displayed like Table of Contents
Each horizontal bar = completion
rate for a particular chapter
narrative
front- and back matter
22
Completion Ratebookchapters

23. 23
Completion Rate - Example
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
B
B
B
20% drop off
within first 5
chapters - “not
my kind of book”
80% finish book,
straight through

24. 24CR: 80% excellent CR: 62% very good
Completion Rate – Examples
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
B
B
B
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
B

25. 25
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
F
1
3
5
7
9
11
13
15
17
19
B
B
CR: 30% good CR: 20% poor
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
F
F
1
3
5
7
9
11
13
15
17
19
21
23
25
27
B
B
B
Completion Rate – Examples

26. 26
Completion rate – Example
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
F
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
B
A lot of “flipping” (jumping between
chapters), which is typical of book titles
readers find “have to read” but that
don’t hold their attention…
Rapid decline of reader
engagement in first 100
pages

27. Completion Rate
• Does the book keep
reader’s attention?
• Rate is measured as
percentage relative to
those who start reading
• Unlike ratings
(subjective), this is an
observational KPI
27

28. KPI - 2
Velocity
28

29. Example A – Fast Read
29
The majority of users read this ebook on a smartphone
female, 27 female, 25 female, 48

30. Example B - slow read
30
male, 26

31. 31
3.7
9.1
10.1
10.4
12.4
12.5
12.8
13.1
14.1
14.4
15.1
16.1
17.5
18.5
20.8
21.6
24.7
26.2
26.8
27.6
32.1
0 7 14 21 28 35
Ranking by Velocity
Days
Quick
Slow

32. Velocity
• Measure of whether
readers glued to the pages
• Are readers coming back
daily/hourly or are they
distracted by other books,
movies, social media etc.?
• Measure of how
“digestible” content is for
readers.
32

33. KPI - 3
Recommendation Factor
33

34. 34
Would you recommend this book
to a friend?
0 = not all likely 5 = neutral 10 = Extremely likely
0 1 2 3 4 5 6 7 8 9 10
Recommendation
Factor
= Promoters (%)
(9s and 10s)
Detractors (%)
(0 through 6s)
-

35. 35
NPS for Individual Books
“The book was great and I have read
other books by this author. In addition,
I have probably recommended this
book to at least a dozen people
because it was that good.”
Promoters (9,10s) 97 81%
Neutrals (7s, 8s) 10 8%
Detractors (0-6s) 13 11%
Recommendation Factor
(Net Promoter Score) 70%

36. Audience Insights
Demographic Factors
36

37. 37
Demographic Breakdown of CR
Average CR 35%
Gender
male 34%
female 35%
Age
<35 0%
35-45 25%
>45 54%

38. Profile of a Book
Everything in one Slide
38

39. An Anonymous Example
“I really want to read the next book.“
“Even as a forty-something woman, I find myself really
enjoying YA dystopian fiction.”
39
Male CR
29%
NPS
32%
Velocity
10 d
Price Tolerance
$6.27
Completion rate
61%
female CR
66%
<35 CR
50%
>45 CR
78%
35-45 CR
56%
gripping (32%) entertaining (32%) good (20%)great (28%)

40. Get a taste for data candy…