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Unt consumer big data (30 march2016)

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Unt consumer big data (30 march2016)

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Unt consumer big data (30 march2016)

  1. 1. Turning Open and Big Data into Business Context (朴漢雨)
  2. 2. van Dijk (2012, p.220)
  3. 3. 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
  4. 4. http://www.youtube.com/watch?feature=player_embedded&v=iIKPjOuwqHo http://science.sciencemag.org/content/332/6025/60
  5. 5. 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
  6. 6. 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
  7. 7. According to IBM, we as a society create 2.5 quintillion bytes of data every single day.
  8. 8. 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)
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
  15. 15. https://www.youtube.com/watch?v=DS310JMdu2s
  16. 16. https://www.youtube.com/watch?v=DS310JMdu2s
  17. 17. http://www.wsj.com/articles/the-mistakes-companies-make-with-big-data-1455075886?imm_mid=0e0878&cmp=em-data-na-na-newsltr_20160217
  18. 18. LESSONS LEARNED FROM 10 TERRIFIC TWEETS https://www.facebook.com/1586563929/posts/10204260510944722
  19. 19. 10 BEST SOCIAL MEDIA WINS OF 2014 http://www.clickz.com/clickz/news/2386712/10-best-social-media-wins-of-2014
  20. 20. From Pre-travel To In-Market for Chinese Tourists
  21. 21. MCN & Open Business : Merchandising
  22. 22. Data Insights: New Ways to Visualize and Make Sense of Data , 2012 by Hunter Whitney
  23. 23. 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
  24. 24. 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
  25. 25. Dutch Airlines’ Advertising including Korean Instant Messenger in Beijing Airport
  26. 26. 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
  27. 27. President Harry S. Truman holds up a copy of the Tribune after his presidential election, arguably one of the most famous headline mistakes.
  28. 28. Data as a Public Resource
  29. 29. Open Data Companies Under Study
  30. 30. 고객 기업의 지출과 관련된 문제를 해결하기 위해 원시 데이터를 실행 가능한 정보로 변환하여 의사결정할 수 있도록 도움을 줌.
  31. 31. 자금과 관련된 데이터를 통해 자금 관리를 도와줌.
  32. 32. Social-Explorer 지형학적 데이터 수집을 통해 기업에 정보 제공을 하며, 데이터 정보를 활용할 수 있는 툴을 제공.
  33. 33. Geolytics 지리 데이터, 인구학적 데이터, 시장 연구 데이터를 사회 연구자와 기업에 제공.
  34. 34. Lumesis 경제 및 인구학적 데이터를 통해 경제 관련 문제를 해결.
  35. 35. Merrill 신뢰성 있는 데이터를 통해 고객의 비즈니스 과정에 개입하여 니즈를 충족시키도록 도움.
  36. 36. OvertureTechnologies 대출 관련 데이터들을 통해 대출 관련 정보들을 비교해 볼 수 있도록 도움을 주는 소프트웨어를 제공.
  37. 37. Quandl 금융, 경제, 사회 등 다양한 분야의 데이터들을 이용가능하도록 도와주는 소프트웨어를 제공.
  38. 38. Towerdata 기업의 고객 이메일을 통해 마케팅에 필요한 데이터들을 제공.
  39. 39. Equilar 기업의 내부 데이터를 통해 의사결정에 필요한 정보를 제공하고 임원들과의 계약시 임금 기준을 마련해 줌.
  40. 40. Barchart 금융데이터를 고객에게 제공함
  41. 41. Priceweave 상품과 관련된 데이터를 제공함으로써 소비자의 구매활동 도와줌
  42. 42. Quid 데이터 분석을 통해 비즈니스와 관련된 투자 정보를 주며, 인구통계학적 데이터를 통해 마케팅과 관련된 아이디어를 생산할 수 있게 도와줌.
  43. 43. 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

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