4. 22 MIN.
60.3 MIN.AMOUNT OF TIME PER DAY
THE AVERAGE US MOBILE
CONSUMER SPENDS WITH
APPS.
00:22
The amount of time
the average US
mobile consumer
spends per day with
apps:
AMOUNT OF TIME PER
DAY THE AVERAGE US
CONSUMER SPENDS ON
THE MOBILE WEB.
Nielsen & Comscore, 2014
8. 19%
23%
29%
42%
48%
68%
71%
Forced social logins
Privacy concerns
Intrusive ads
Bad UI/UX
Freezing
Complex registration
Annoying notifications
TOP 7 REASONS WHY PEOPLE
UNINSTALL MOBILE APPS*
*AS A % OF ALL
RESPONDENTS.
EACH
PARTICIPANT
MENTIONED
THREE
REASONS.
26. 3% of broadcast push
messages are clicked
7% of targeted push
messages are clicked
15% of users converted 54% of users converted
Broadcast: Targeted:
Segment your audience
vs
28. Broadcast: Targeted:
3% of 100,000 users =
3,000 opened messages
7% of 100,000 users =
7,000 opened messages
15% of 3,000 opened
messages =
450 converted users
54% of 7,000 opened
messages =
3,780 converted users
vs.
Segment your audience
vs
29. Maximize user value through engagement
• Segmentation
• Channels to the customer
• Push
• In-App
• Remarketing
• Email
30. Bring them back and keep them engaged with Push
Motivate inactive users to
return to your app with
targeted, carefully timed, and
well-written copy
88% MORE
Users with push enabled have
app launches.
Source: Localytics, 2014
31. Increase Push audience, increase success
52% of app users have push
enabled on their phones
Industry Averages
32. Increase Push audience, increase success
52% of app users have push
enabled on their phones
48% of app users don’t have
push enabled on their phones
Industry Averages
33. Bad Example
- Ask them to opt in
immediately after launching
the app for the first time
Increase Push audience, increase success
(first launch)
34. - Welcome your users with a
sequence of introductory,
how-to screens to show
value
1 2 32 3
Increase Push audience, increase success
Good example
35. Good example
- Welcome your users with a
sequence of introductory,
how-to screens to show
value
- THEN, ask them to opt in
with a unique, well-designed
in-app message
Increase Push audience, increase success
36. In-App Messages – Drive Conversions
Move users further along
funnels to ultimate in-app
action with beautiful, branded,
in-app creatives
4X HIGHER
In-app messages presented based
on an event have
conversion rates.
37. Remarketing – Reaching Existing Users
Source: Litmus, 2015
Show current users ads based
on how they’ve previously
engaged with your brand
Great for reaching the
who opt out of push notifications
48% OF USERS
38. Email – Cross Channel Marketing
Treat users with richer, longer
form content
Source: Copyblogger, 2014
43. Still not fulfilling the promise of big data
But still… 50% of all
Data Science
Projects Fail
44. Apps Create a New Opportunity
Apps generating massive
amounts of data AND
have marketing channels
embedded
Advances in computing have
made machine learning more
accessible
Users Demand Better
Experiences
45. Pillars of Predictive App Marketing
Predic5ve
Segmenta5on
• The
dynamic
grouping
of
users
into
segments
which
will
behave
in
similar
ways
Marke5ng
Auto-‐Op5miza5on
• The
automa8c
tes8ng
and
op8miza8on
of
a
marke8ng
strategy
across
mul8ple
channels
Na5ve
Personaliza5on
• The
1:1
matching
of
users
to
content,
products,
with
which
they
have
the
greatest
affinity
46. Keys to Successful Predictive App Marketing
Define
the
specifics
of
the
objec8ve
-‐
Churn
Take
ac8on
via
the
app
(via
push,
in-‐app
msg,
etc.)
Establish
Baseline
and
iden8fy
user
paIerns
of
user
behavior
and
correlated
characteris8cs
47. Define
objec8ve
–
Churn
=
users
who
have
visited
the
app
at
least
twice,
but
not
in
the
last
30
days
Predictive Churn Example for a Sports App
48. *Measured
as
%
ac8ve
users
with
no
ac8vity
in
past
30
days.
Auto-‐segmented
new
users
into
the
at
risk
buckets
and
sent
personalized
push
messages
to
drive
users
back
into
the
app
Predictive Churn Example for a Sports App
49. Control Group Experimental Group
Users! 190,930! 189,900!
Returned! 115,243! 120,112!
Churn %*! 39.3%! 36.8%!
Improvement — 6.6%
Users Rescued — 4,928
*Measured
as
%
ac8ve
users
with
no
ac8vity
in
past
30
days.
Predictive Churn Example for a Sports App
50. *Measured
as
%
ac8ve
users
with
no
ac8vity
in
past
30
days.
Predictive Churn Example for a Sports App
51. Control Group Experimental Group
Users! 3,383,031! 381,723!
Returned! 565,930! 102,500!
Churn %*! 83.3%! 73.1%!
Improvement — 14%
Users Rescued — 38,644
*Measured
as
%
ac8ve
users
with
no
ac8vity
in
past
30
days.
Predictive Churn Example for a Lifestyle App
65. “In 2008, everyone thought apps were a fad.
They couldn’t have been more wrong. Apps
have become the dominant way we interact
with information – and the world.
Raj Aggarwal
CEO, Localytics