Delivered at Casual Connect USA 2016. Smartphones are stuffed with data about the people who use them. Still, mobile users are faced with irrelevant, intrusive ads and scared of losing privacy. As a result, developers get less ad revenue and advertisers fail to interact with their market. In this talk, Itamar will share why switching to the user-centric approach is a must and how transforming ads into personalized in-app experiences can take your advertising to the next level.

1. WHY
PERSONA-BASED
MARKETING IS A
MUST

2. VP STRATEGY & APPS
ITAMAR BENEDY
NICE TO MEET YOU!

3. OVERVIEW
2008
Founded in
Berlin
7
Offices
Around the Globe
180
Countries with Active
Campaigns
1B
Active Users / Month
400B
Ad Impressions /
Month
+550
Global Brands
& App Dev.

4. THE AVERAGE U.S CONSUMER SPENDS
2 HOURS, 42 MINUTES
A DAY USING THEIR MOBILE DEVICES

5. GROWTH IN DIGITAL MEDIA TIME
SPENT IN MINUTES (MM)
Source: comScore
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
Dec-13 Dec-14 Dec-15
Desktop Smartphone Tablet
+30%
vs. 2013
+78%
vs. 2013
-1%
vs. 2013

6. # OF U.S. USER APP DOWNLOADS/ MONTH
Source: Quartz
0
apps
1
app
2
apps
3
apps
4
apps
5-7
apps
8+
apps
65.5% 8.4% 8.9% 6.2% 3.7% 4.8% 2.4%

7. MOBILE AD SPENDING IS SET TO REACH
$100 BILLION
IN 2016

8. BUT USERS ARE CONCERNED ABOUT...
COLLECTED
DATA
INTRUSIVE
ADS
PRIVACY
ISSUES

9. AD BLOCKING APP DOWNLOADS:
GROWING FAST
0
10
20
30
40
50
60
70
80
Source: Tune
%
%
%
%
%
%
%
%
%

10. SOMETHING GOES WRONG IN THE
ADVERTISING WORLD...
PUBLISHERSADVERTISERS USERS
$

11. DO YOU REALLY KNOW
WHO YOUR USERS ARE?
Geo
location
In-app
activity
Device
type
OS
version

13. CAN YOU LEARN
YOUR USERS
BETTER?

14. YES!
tracking behavioral data for user-centric approach
using advanced machine learning technologies
How?
How?

15. MACHINE LEARNING
THE SCIENCE OF TEACHING MACHINES
TO MAKE THEIR OWN DECISIONS

16. DEEP LEARNING
MACHINE LEARNING “ON STEROIDS”

17. COMPUTER VISION
AUTOMATIC EXTRACTION, ANALYSIS AND UNDERSTANDING OF
USEFUL INFORMATION FROM IMAGES OR HIGH-DIMENSIONAL DATA

18. What users like
What users do
What users dislike
When users do it
USE TECHNOLOGIES TO DEFINE…

19. IDENTIFY DATA YOU NEED AND...

20. ...LEVERAGE IT

21. …DIG DEEPER

22. PERSONAS

23. PERSONAS
IS OPTIMAL TO START WITH
3-5

24. GO PERSONAL!
Gender: Male
Age: 18-26
GEO: USA
Interests: Tech, Mobile
OS: iOS
Time active: 6 pm - 2 am
Channel
Time
Message
Frequency
Tech
Savvy

25. IS PRIVACY AN ISSUE?

26. CONSUMER PERCEPTION: HOW MUCH
DATA DO ADVERTISERS COLLECT
Source: Tune
No Answer
19%
Too Much/
Everything
21%
None
30%
I Don’t Know
30%

27. PRIVACY POLICY
Be transparent
Inform about the gathering activity
Provide an opt-out option for your users
Avoid collecting personally identifiable information

28. FRAUD?

29. AD FRAUD IS
PROJECTED TO COST
$7.2 BLN
GLOBALLY IN 2016

30. THE MORE PRECISELY TARGETED AD
CAMPAIGNS YOU LAUNCH...
...THE LOWER CHANCES ARE TO
ENCOUNTER FRAUD

31. Advertisers PublishersUsers
#

32. LET’S LOOK AT SOME DATA…

33. Itamar.benedy@glispamedia.com
linkedin.com/in/itamarbenedy
@BenedyItamar
benedy.itamar