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Wearables and smartphones
in digital marketing.
Interactive and Digital Marketing
9th of November 2015
Dr Lukasz Piwek
@mo...
PEOPLE WITH SMARTPHONES GLOBALLY*
1.8 billion
ADULTS WITH SMARTPHONES IN UK**
80%
EVERYONE WILL HAVE SMARTPHONE BY***
2025...
Number of patent claims
potentially at stake
in each smartphone
250,000
activity tracking
location
personal assistants
social networking
Jawbone 3 Misfit ShineFitbit FlexNike Fuelband
Basis B1 Apple Watch Moto 360 Microsoft Band
Withings Pulse
Sproutling
Baby Monitor
Nuubo NECG
Minder
NeuroSky
MindWave DuoFertility
Withings Aura
Withings
Smart Scale
WEARABLE SHIPMENT VOLUMES IN 2014*
19 million
*IDC (2014)
**Juniper Research (2014)
global spending $700 million**
WEARABL...
we continuously
generate
digital traces
of our behaviour
such as
search / click / view / like /
share / follow / check in ...
The Age of
Personal Big Data
tracking and profiling
digital consumption
(and virtual currency)
“digital dark side”
tracking
and
profiling
digital consumption
(and virtual currency)
“digital dark side”
Call logs
Phone status logs
SMS logs
App use logsMicrophone
Accelerometer
Light sensor
Camera
Touchscreen
Speaker
Vibratio...
analysis of call logs
can reveal exact structure
and strength of your
social ties
Onnela et al. (2007)
Eagle et al. (2009a)
only four points
from cell towers
are enough
to uniquely identify
95% of the individuals
de Montjoye et al. (2013a)
stress level can be detected
with 80% accuracy
by capturing only a few words
with microphone
Lu et al. (2012)
call logs, SMS logs,
Bluetooth scans,
and application usage
can be used to predict
personality traits
with up to 70% accur...
accelerometers
can be used to predict
whether you’re sleeping,
walking, sitting, jogging,
cycling, or driving
with up to 9...
wall content depends on
likes, clicks,
comments,
scrolling speed,
cursor location
Bakshy et al., 2015
collection of your likes
can reveal your:
age
gender
ethnicity
happiness
intelligence
use of drugs
personality traits
pare...
recommendation system
based on
viewer demographics,
history, ratings,
browsing patterns
habit detection
and suggestion system
based on movement,
location, payment history,
activity/sleep
consumer habits with
past history of purchases
digital
consumption
(and virtual
currency)
tracking and profiling
“digital dark side”
freemium
premium
preview
trial
subscription
digital rental
embedded advertisement
pay-as-you-go
streaming-without-ownershi...
mobile
is the new
shop window
NUMBER OF APPS IN APPLE AND GOOGLE STORES*
2.4 million
*Apple/Google (2014)
**Kantar Media (2014)
MEAN VALUE OF APPS INSTA...
NUMBER OF CONSUMERS USING THEIR
SMARTPHONE WHILE SHOPPING IN-STORE
70%
NUMBER OF CONSUMERS WHO CALL OR VISIT
A BUSINESS AF...
consumers
co-create
advertising
and branding
MORE VIDEOS UPLOADED TO
every 60 days
than the top three broadcasters produced
in 60 years
(2014)
ONLINE VIDEO ADS RECEIVE...
AVERAGE NUMBER OF USERS
PIRATING WORLDWIDE PER DAY
Game of Thrones
Breaking Bad
The Big Bang Theory
Orange is the New Blac...
“digital
dark side”
tracking and profiling
digital consumption
(and virtual currency)
Data breaches
Darknet
Privacy issues
and identity theft
major usability & technical issues
data discrepancies
low build quality and design issues
short battery life
overcomplicat...
lukasz.piwek@uwe.ac.uk
@motioninsocial
motioninsocial.com
thank you!
Wearables and smartphones in digital marketing.
Wearables and smartphones in digital marketing.
Wearables and smartphones in digital marketing.
Wearables and smartphones in digital marketing.
Wearables and smartphones in digital marketing.
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Wearables and smartphones in digital marketing.

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Guest lecture for 'Interactive and Digital Marketing' module at the University of West of England (UWE) in Bristol on 9th of November 2015.

Published in: Technology
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Wearables and smartphones in digital marketing.

  1. 1. Wearables and smartphones in digital marketing. Interactive and Digital Marketing 9th of November 2015 Dr Lukasz Piwek @motioninsocial
  2. 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. 3. Number of patent claims potentially at stake in each smartphone 250,000
  4. 4. activity tracking location personal assistants social networking
  5. 5. Jawbone 3 Misfit ShineFitbit FlexNike Fuelband Basis B1 Apple Watch Moto 360 Microsoft Band
  6. 6. Withings Pulse Sproutling Baby Monitor Nuubo NECG Minder NeuroSky MindWave DuoFertility Withings Aura Withings Smart Scale
  7. 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. 8. we continuously generate digital traces of our behaviour such as search / click / view / like / share / follow / check in / passive tracking
  9. 9. The Age of Personal Big Data tracking and profiling digital consumption (and virtual currency) “digital dark side”
  10. 10. tracking and profiling digital consumption (and virtual currency) “digital dark side”
  11. 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. 12. analysis of call logs can reveal exact structure and strength of your social ties Onnela et al. (2007) Eagle et al. (2009a)
  13. 13. only four points from cell towers are enough to uniquely identify 95% of the individuals de Montjoye et al. (2013a)
  14. 14. stress level can be detected with 80% accuracy by capturing only a few words with microphone Lu et al. (2012)
  15. 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. 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. 17. wall content depends on likes, clicks, comments, scrolling speed, cursor location Bakshy et al., 2015
  18. 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. 19. recommendation system based on viewer demographics, history, ratings, browsing patterns
  20. 20. habit detection and suggestion system based on movement, location, payment history, activity/sleep
  21. 21. consumer habits with past history of purchases
  22. 22. digital consumption (and virtual currency) tracking and profiling “digital dark side”
  23. 23. freemium premium preview trial subscription digital rental embedded advertisement pay-as-you-go streaming-without-ownership digital media sale tactics
  24. 24. mobile is the new shop window
  25. 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. 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)
  27. 27. consumers co-create advertising and branding
  28. 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. 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. 30. “digital dark side” tracking and profiling digital consumption (and virtual currency)
  31. 31. Data breaches
  32. 32. Darknet
  33. 33. Privacy issues and identity theft
  34. 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
  35. 35. lukasz.piwek@uwe.ac.uk @motioninsocial motioninsocial.com thank you!

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