Unt consumer big data (30 march2016)

1. Turning Open and Big Data
into Business Context
(朴漢雨)

3. van Dijk (2012, p.220)

4. Overview of data scale from megabytes to yottabytes (log scale).
http://www.researchtrends.com/wp-content/uploads/2012/09/Research_Trends_Issue30.pdf
1,048,576 rows by 16,384 columns in Excel

6. http://www.youtube.com/watch?feature=player_embedded&v=iIKPjOuwqHo
http://science.sciencemag.org/content/332/6025/60

8. 8
Automatic
Consumer Type
Analytics
• Analyze a customer’s gender and age in real time through the cameras which is placed at the
POS-integrated system. More than 2,000 convenience stores are using our solution to
analyze consumer pattern.
• Everyday 200,000 consumer’s data generate from over 2,500 stores in Korea and
there data enables retailers reduce expenses and use for planning marketing activities.
HOW FACIAL RECOGNITON SYSTEM BEING USED FOR RETAIL MARKET
380
25
Man
Chocolate
Number of
Visitors
Age
Sex
Goods

9. Mobile Detector
Mobile Detector
Mobile Detector
Mobile Phone Mobile Sensing Detector CCTV Camera
Mobile Detector
Mobile Detector
Analyze consumer
Send analyzed meta data to central monitoring system
①
②
Store consumer’s pattern and
behavior
③
• WI-FI sensing detector continually measures the mobile signal of passerby near your location.
• By communicating with facial recognition’s data server in real time, it analyze consumer’s type
: All WI-FI and Facial Recognition system has been realized to secure consumer’s anonymity.
• WI-FI Detection system calculate people’s flow, staying time, returning rate and footfall around stores as out-door positioning system.
• Facial recognition system analyze consumer’s type such as gender and age.
Real-Time Retail Analytics by WI-FI Signal Detection and Facial Recognition

10. According to IBM, we as a
society create 2.5 quintillion
bytes of data every single day.

11. 11
• Han Woo Park
- “hidden” and “relational” data about web-based
content (e.g., text, images, audio-visual objects, and
hyperlinks) and actors including individuals, groups,
and nation-states
• Lev Manovich
- “surface” data about lots of people (i.e., statistical,
mathematical or computational techniques for analyzing data)
- “deep” data about the few individuals or small groups (i.e.,
hermeneutics, participant observation, thick description,
semiotics, and close reading)

12. Start small
Don’t get too far into the weeds, lest you make someone feel like they’re being stalked.
• it’s best to start with simpler data sets, like
location – weather dictates fashion, after all.
• Limit your behavioral marketing to things
people have actually done on your site, not
things you’ve gleaned about them
elsewhere.
• “For a media company, asking your shoe
size has no relevance to what your favorite
radio station is. Asking for information
that’d be appropriate based on what your
business is and what your users expect
from you is a good place to start,”
Data for dummies: Four places to get started

13. Six Sources of Big Data
• Web Mining
• Search Information
• Social Media
• Crowd Sourcing
• Transactional Data
• Mobile
https://datafloq.com/read/how-big-data-drives-digital-marketing-success/1469

15. Girls catch sight of The Beatles, Los Angeles, 1964
http://www.huffingtonpost.com/2015/07/23/smartphones-are-hideous-and-sad-kind-of_n_5234216.html

16. Girls catch sight of One Direction, London, 2013
http://www.huffingtonpost.com/2015/07/23/smartphones-are-hideous-and-sad-kind-of_n_5234216.html

20. Malia and Sasha Obama stand together at
the Inaugural Parade, 2009 and 2013
http://www.huffingtonpost.com/2015/07/23/smartphones-are-hideous-and-sad-kind-of_n_5234216.html

21. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

22. https://www.youtube.com/watch?v=DS310JMdu2s

23. https://www.youtube.com/watch?v=DS310JMdu2s

24. http://www.wsj.com/articles/the-mistakes-companies-make-with-big-data-1455075886?imm_mid=0e0878&cmp=em-data-na-na-newsltr_20160217

25. LESSONS LEARNED FROM
10 TERRIFIC TWEETS
https://www.facebook.com/1586563929/posts/10204260510944722

26. 10 BEST SOCIAL MEDIA WINS OF 2014
http://www.clickz.com/clickz/news/2386712/10-best-social-media-wins-of-2014

27. From Pre-travel To In-Market for Chinese Tourists

28. MCN & Open Business : Merchandising

33. Data Insights: New Ways to Visualize and Make Sense of Data , 2012
by Hunter Whitney

34. The Future of Analytics Is Prescriptive, Not
Predictive
The descriptive analytics tell us
which way to go, while the device's
predictive analytics tell us when we
are likely to get there. If our GPS has
prescriptive analytical capabilities, it
will monitor that forecast in real time
against my objective, which is usually
to get somewhere as quickly as
possible. If, on the basis of the
feedback, it finds that another
solution will be better, it will
recommend that I change my
current route and take a different
route instead. It tells me what I
should do.
In the marketing world, descriptive
analytics would tell me that a
customer has churned. Predictive
analytics will tell me that a customer
is likely to churn. Prescriptive
analytics will tell me that a customer
is likely to churn and what the
appropriate intervention strategy
should be, based upon my
objectives and constraints at that
time.
Mason, N. (2015, July 10). The future of analytics is prescriptive, not predictive. Clickz.com

35. The Future of Analytics Is Prescriptive, Not
Predictive
Some argue that there is no real
difference between predictive and
prescriptive analytics. From an
analytical perspective, I can see
where they are coming from. The
algorithms are predominantly the
same, with some additional
optimization ingredients thrown into
the mix. However, I think there is a
real difference when one looks at
the operational readiness required to
successfully deploy a prescriptive
analytical capability.
In terms of the data - which will be
predominantly real time, consist of
multiple and integrated data sources,
and be both structured and
unstructured - the analytics will be
integrated into the technologies. The
algorithms will also need to be
adaptive, meaning that there needs
to be a feedback mechanism in
place. Finally, workflow and
governance must exist around the
data and technology to ensure that
objectives and constraints are in
place.
Mason, N. (2015, July 10). The future of analytics is prescriptive, not predictive. Clickz.com

36. Dutch Airlines’ Advertising including
Korean Instant Messenger in Beijing Airport

37. Deepen Understanding of Differential Pricing
However, individualized pricing based on estimates of cost or
riskiness can raise concerns about fairness, particularly when
consumers are unaware of the data or methods that companies
employ.
The CEA report finds that many companies already use big data
for targeted marketing, and some are experimenting with
personalized pricing, though examples of personalized pricing
remain fairly limited.
Key Recommendations-The big data and privacy working group report identified six specific
policy recommendations as deserving prompt action, White House (2015). Big Data

38. President Harry S. Truman holds up a copy of the Tribune after his
presidential election, arguably one of the most famous headline mistakes.

40. Data as a Public Resource

41. Open Data Companies Under Study

44. 고객 기업의 지출과 관련된 문제를 해결하기 위해 원시 데이터를 실행 가능한 정보로
변환하여 의사결정할 수 있도록 도움을 줌.

45. 자금과 관련된 데이터를 통해 자금 관리를 도와줌.

46. Social-Explorer
지형학적 데이터 수집을 통해 기업에 정보 제공을 하며, 데이터 정보를 활용할 수
있는 툴을 제공.

47. Geolytics
지리 데이터, 인구학적 데이터, 시장 연구 데이터를 사회 연구자와 기업에 제공.

48. Lumesis
경제 및 인구학적 데이터를 통해 경제 관련 문제를 해결.

49. Merrill
신뢰성 있는 데이터를 통해 고객의 비즈니스 과정에 개입하여 니즈를 충족시키도록
도움.

50. OvertureTechnologies
대출 관련 데이터들을 통해 대출 관련 정보들을 비교해 볼 수 있도록 도움을 주는
소프트웨어를 제공.

51. Quandl
금융, 경제, 사회 등 다양한 분야의 데이터들을 이용가능하도록 도와주는
소프트웨어를 제공.

52. Towerdata
기업의 고객 이메일을 통해 마케팅에 필요한 데이터들을 제공.

53. Equilar
기업의 내부 데이터를 통해 의사결정에 필요한 정보를 제공하고 임원들과의 계약시
임금 기준을 마련해 줌.

54. Barchart
금융데이터를 고객에게 제공함

55. Priceweave
상품과 관련된 데이터를 제공함으로써 소비자의 구매활동 도와줌

56. Quid
데이터 분석을 통해 비즈니스와 관련된 투자 정보를 주며, 인구통계학적 데이터를
통해 마케팅과 관련된 아이디어를 생산할 수 있게 도와줌.

57. The McDonaldization of Society by G. Ritzer
• Velvet cage: People who enjoy fast, quick, convenient,
and predictable products and services, and are
comforted by the rationalization and the predictability
the McDonaldization brings to society.
• Rubber cage: People who try to find ways to
temporarily escape the process of McDonaldization, but
at the same time like other aspects of it.
• Iron cage: People are more pessimistic and try to fight
back against the McDonaldization process.
http://www.anglohigher.com/key_announce/key_announce_detail/11#ixzz441JXdyXp