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1.
Wearables and smartphones
in digital marketing.
Interactive and Digital Marketing
9th of November 2015
Dr Lukasz Piwek
@motioninsocial
2.
PEOPLE WITH SMARTPHONES GLOBALLY*
1.8 billion
ADULTS WITH SMARTPHONES IN UK**
80%
EVERYONE WILL HAVE SMARTPHONE BY***
2025
*eMarketer (2014)
**Ofcom (2014)
***Portio Research (2011)
3.
Number of patent claims
potentially at stake
in each smartphone
250,000
4.
activity tracking
location
personal assistants
social networking
5.
Jawbone 3 Misfit ShineFitbit FlexNike Fuelband
Basis B1 Apple Watch Moto 360 Microsoft Band
7.
WEARABLE SHIPMENT VOLUMES IN 2014*
19 million
*IDC (2014)
**Juniper Research (2014)
global spending $700 million**
WEARABLE SHIPMENT VOLUMES IN 2018*
111.9 millionglobal spending $2.4 billion**
PREDICTED
8.
we continuously
generate
digital traces
of our behaviour
such as
search / click / view / like /
share / follow / check in /
passive tracking
9.
The Age of
Personal Big Data
tracking and profiling
digital consumption
(and virtual currency)
“digital dark side”
10.
tracking
and
profiling
digital consumption
(and virtual currency)
“digital dark side”
11.
Call logs
Phone status logs
SMS logs
App use logsMicrophone
Accelerometer
Light sensor
Camera
Touchscreen
Speaker
Vibration
GPSCellular network WiFi BluetoothNFC time
location
proximity to devices
ambient light intensity
spatial orientation
movement level
feedback
control
input
12.
analysis of call logs
can reveal exact structure
and strength of your
social ties
Onnela et al. (2007)
Eagle et al. (2009a)
13.
only four points
from cell towers
are enough
to uniquely identify
95% of the individuals
de Montjoye et al. (2013a)
14.
stress level can be detected
with 80% accuracy
by capturing only a few words
with microphone
Lu et al. (2012)
15.
call logs, SMS logs,
Bluetooth scans,
and application usage
can be used to predict
personality traits
with up to 70% accuracy
de Montjoye et al. (2013b)
Chittaranjan et al. (2013)
16.
accelerometers
can be used to predict
whether you’re sleeping,
walking, sitting, jogging,
cycling, or driving
with up to 95% accuracy
Khalil & Glal, 2009
He & Li, 2013
Behar et al. (2013)
17.
wall content depends on
likes, clicks,
comments,
scrolling speed,
cursor location
Bakshy et al., 2015
18.
collection of your likes
can reveal your:
age
gender
ethnicity
happiness
intelligence
use of drugs
personality traits
parental separation
religious and political views
Kosinski et al., 2013
Kross et al., 2013
Lambiotte et al., 2015
19.
recommendation system
based on
viewer demographics,
history, ratings,
browsing patterns
20.
habit detection
and suggestion system
based on movement,
location, payment history,
activity/sleep
21.
consumer habits with
past history of purchases
22.
digital
consumption
(and virtual
currency)
tracking and profiling
“digital dark side”
23.
freemium
premium
preview
trial
subscription
digital rental
embedded advertisement
pay-as-you-go
streaming-without-ownership
digital media sale tactics
25.
NUMBER OF APPS IN APPLE AND GOOGLE STORES*
2.4 million
*Apple/Google (2014)
**Kantar Media (2014)
MEAN VALUE OF APPS INSTALLED ON iOS DEVICES**
£60
26.
NUMBER OF CONSUMERS USING THEIR
SMARTPHONE WHILE SHOPPING IN-STORE
70%
NUMBER OF CONSUMERS WHO CALL OR VISIT
A BUSINESS AFTER LOOKING FOR LOCAL INFO
ON THEIR PHONE
77%
(2013)
(2012)
28.
MORE VIDEOS UPLOADED TO
every 60 days
than the top three broadcasters produced
in 60 years
(2014)
ONLINE VIDEO ADS RECEIVED
18.3%more viewer attention than TV commercials
(2012)
29.
AVERAGE NUMBER OF USERS
PIRATING WORLDWIDE PER DAY
Game of Thrones
Breaking Bad
The Big Bang Theory
Orange is the New Black
True Detective
House of Cards
Masters of Sex
Modern Family
Fargo
Homeland
0 75,000 150,000 225,000 300,000
(data: Times)
30.
“digital
dark side”
tracking and profiling
digital consumption
(and virtual currency)
34.
major usability & technical issues
data discrepancies
low build quality and design issues
short battery life
overcomplicated interface
issues with bluetooth synch
no intelligent feedback
problem with raw data access (closed API)
problem with data ownership