Bigdatahuman

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Bigdatahuman

  1. 1. CI Labs - Interactive Master Class suresh.sood@uts.edu.au linkedin.com/in/sureshsood @soody http://www.slideshare.net/ssood/bigdatahuman 7 August 2014 Big Data Perspective on Human Centricity: Methods of Naturalistic Observation and Behavior
  2. 2. Areas for Discussion 1. Background studies – Datafication 2. How to put the human context into business? 3. The toolbox 4. Twitter 5. LIWC and RID psychological dictionaries 6. The predictive empathetic organisation 7. Internet of things and human tuning method 8. Signals of joy study (work in progress)
  3. 3. http://datafication.com.au/instagram/
  4. 4. MX , 19 July 2011 5
  5. 5. Theory and Research on Consumers’ Reports of Interactions with Brands and Experiencing Primal Forces, Suresh Sood, 2010 7
  6. 6. Social CRM integrates social data 8
  7. 7. …Blogs are like conversations with friends. You share what you feel and what excites you about certain things. It's almost as good as being there. The fact that others can Google your topic and read is like tuning into a television station. We all want to know what's out there. Who's doing what, shopping where and what products help others. Blogs are just another way to share all the great things, not so great things and just a part of who we are. An outlet if you will. The blogisphere community is all connect and we make contacts in many ways. Through posts, through twitter conversations, through smaller nit community's, live web casts, and through conferences that we met in person. We make many friends and help each other with lot of topics. Many of us are Mom bloggers who stay at home and have no way of making new friends or communicating with others until we found blogging. Blogging creates friendships and that's what makes us real and connected. 40 year old Mom blogger “nightowlmama” (#260)9
  8. 8. Datafication “Datafication refers to the fact that we’re looking at more aspects of life that we never actually understood as being informational before…So what we’re seeing with social media companies is they’re actually datafying aspects of the life that we never really saw that could be datafied. So for example Facebook datafies our friendships. Twitter datafies our whispers or maybe our stray thoughts. And LinkedIn datafies our professional contacts…what big data means is we are able to learn things about ourselves at the population level, at a huge scale, that we never could in the past. So lots of different disciplines, in one case sociology, totally gets upended. Because in the past you ran small studies on small groups, now you’re looking at it in population scale size. Kenneth Cukier, 2014, “Birth of Datafication”, http://bigthink.com/videos/the-birth-of-datafication
  9. 9. Datafication 2 : First National Study of Twitter Usage in Australia Australians send an average of 234 million tweets per month and 5,000 tweets per minute, a new Twitter study by advertising agency The Works has found. Aussie females are more likely to retweet than males and most retweets occur on Mondays, according to the agency's 'datafication' research project. Douglas Nicol, creative partner and director at The Works, said the study was designed to help marketers talk to consumers more effectively. “There’s a lot of hype around social media. Using research from datafication, we are able to equip Australian marketers with no nonsense practical advice,” Nicol said.“This in turn will help marketers appeal directly to an audience. We believe that in turn, this will boost the way people view and talk about a brand or product online.” Lovers, carers and jesters were identified as the top three archetypical personalities on Twitter. According to the study marketers can talk most effectively to lovers by being passionate, carers by being gentle and jesters by being mischievous.“If you understand what drives the motivations behind Australians you will be in a better position to connect with them,” Nicol said. Almost 11% of the Australian population is on Twitter and of those users 46% are male and 54% are females. The study also found that Sydney hosted the largest population of Twitter users while Hobart is responsible for the most tweets per capita. 'Datafication', which was supported by the University of Technology Sydney (UTS), analysed the most popular words used in Twitter over an eight week period to rank motivations and behaviours on the social site. Software created by Dr Suresh Sood, a social media expert at UTS, then analysed the data to produce the insights into what individuals are doing on Twitter. 'Datafication' is set to launch as a real-time service for the agency’s clients early next year.
  10. 10. Datafication 3- First Australian Instagram Study Conducted www.datafication.com.au
  11. 11. Analytic Insights from Millions of Instagram Images • Sunday at 5pm is the peak usage for Instagram in Australia while on weekdays 8pm is the most popular posting time • The average Aussie Instagram user posts 2.3 times a week with around 10 posts being made a month • Sydney, Brisbane and the Gold Coast are the ‘selfie’ capitals of Australia, with more pictures of people taking photos of themselves posted than any other category • In Melbourne images of food are the most popular Instagram subject, while in Perth its portrait piccies and in Adelaide it’s more artistic shots. • Brand recognition on Instagram is low. The most popular hashtag is #instagood with more than 1.6 million references, however brands such as McDonald’s, Nike and Holden have been hashtagged less than 15,000 times.
  12. 12. Experience Recommender Data collection Individual(Group) Analysis Find Preference and Behavior pattern(including Trajectory pattern) Recommendation Recommend right experience to right person ( or group) at right time and place Manual Automatic Recommndation
  13. 13. Driving decisions from big data has potential of dehumanizing interactions but balances with deep understanding of people (customers) to help and entertain them!
  14. 14. How to put the human context into the Business? • Behavior data  Links human emotions to business -> Analyse footprints left behind. • What really does customer satisfaction mean ? Is the person actually happy? • How do we take the emotional dimension into account for customer experience? • How do we recognize someone is dissatisfied? • How do we recognize a “distressed” person? • Do we use text and voice? Will sleeping patterns and eating habits help? • would you act differently if someone is happy? • How do you coach employees to see how someone sounds in emotional terms? • Understanding when distress exists and when a customer needs enhanced service • Behavior data reveals attitude and intent. This is more predictive of future opportunities and risk versus historical data
  15. 15. Honest Signals
  16. 16. “I've learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.” Maya Angelou
  17. 17. Challenge Today : Moving from Transactions Alone to Relationships and Empathy Current State = Transactions $$$ We do this stuff well e.g. Collect payments … Future State = Human Empathy (relationships) We don’t do this really e.g. User generated content, ratings, reviews, 1:1 dialogue, Distress Signals, Geolocation 20
  18. 18. Approach Combine design thinking with physiological frameworks to build and develop marketing activities with purpose and sympathetic of humans.
  19. 19. The toolbox Regressive Imagery Dictionary (RID) Twitter Linguistic Inquiry and Word Count (LIWC) Ethnography Scenarios Storytelling
  20. 20. Evil Plans: Having Fun on the Road to World Domination by Hugh MacLeod (Kindle Edition - Feb 17, 2011)
  21. 21. Twitter – “Found data and stray thoughts” • Twitter.com/barackobama,…/theellenshow • Search.twitter.com – Near: “taj mahal” within:1mi :) – Near: “taj mahal” within:1mi :( – Lang:pa near:”taj mahal” within:15mi – From:soody, to:soody and citations:@soody Mass opinion- Find questions people are asking by viewing tweets only with “?” and no links Keyword ? –filter:links lang:en
  22. 22. 25
  23. 23. Instagram Deception (Suspects outside of -20 & +20) Vine Deception (Suspects outside of -5 and +5)
  24. 24. The Newman Model of Deception (Pennebaker et al) Key word categories for deception mapping: 1. Self words e.g. “I” and “me” – decrease when someone distances themselves from content 1. Exclusive words e.g. “but” and “or” decrease with fabricated content owing to complexity of maintaining deception 1. Negative emotion words e.g. “hate” increase in word usage owing to shame or guilty feeling 1. Motion verbs e.g. “go” or “move” increase as exclusive words go down to keep the story on track
  25. 25. LIWC (analyzewords.com) • Reveal personality from word usage • Uses LIWC classification of words
  26. 26. TweetPsych (tweetpsych.com/) • Linguisitic analysis using: –RID –LIWC Note: TweetPsych is not without critics: http://psychcentral.com/blog/archives/2009/06/18/putting-cool-ahead-of-science-tweetpsych/
  27. 27. Photos with Faces (Bakhshi et al 2014) • Photos with faces – 38% more likely to be liked – 32% more likely to be commented – Age and gender does not drive engagement!
  28. 28. Twitter and Marketing Predictions • Tweets is “found data” without asking questions • More meaning than typical search engine query • • Large numbers of passive participants in natural settings • Twitter can predict the stock market (Lisa Grossman, Wired, Oct 19 2010) • Predict movie success in first few weekends of release – “…it also raises an interesting new question for advertisers and marketing executives. Can they change the demand for their film, product or service buy directly influencing the rate at which people tweet about it? In other words, can they change the future that tweeters predict?” Tech Review, http://www.technologyreview.com/blog/arxiv/25000/ 31
  29. 29. Roadmap – Evolution from Existing Operations to Predictive Empathetic Rigid Flexible Connected What if conversations continue? (Adapted from Solis, 2012 and Davenport 2007) Themes Silo, rigid Hoarding info Vs. collaboration Freely share info and Knowledge on internal basis acting social with customers 2 –way communications Connected internal and External. Listening and Learning. Internal and external engagement Shared via hub and Spoke. Employees Connected directly to Customers. Adaptive Agile, integrate customer Experiences and feedback Loops. Listening and Learning now become analyse and insights Makes sense of data And transforms into Intelligence. Respond in Real time Predictive Shift from reactive to Proactive and predictive Business uses social media heavily and is flexible, connected, adaptive and predictive in terms of customer experiences, distresses needs and new opportunities. Predict scenarios before they occur maximise opportunity and limit risk How can we help lead conversations and recognise the distress signals? (predictive recommendation with human focus) What conversations are next? Why are these conversations occurring? What actions are required? What are the sentiment of conversations? When and where are conversations taking place? What conversations are taking place? Business Intelligence 32
  30. 30. Beacon Active Card Shelf Shelf Shelf Gateway ServerMonitor Internet Supermarket control room
  31. 31. Smart Social Card System Reader/Wifi Gateway and Active Card
  32. 32. Smart Sandbag System smart-dove.com The first 3 columns are x, y, z axis of gyroscope, then x, y, z axis of accelerator. These are raw data of 40 repetitions of shoulder press exercise. Standard Deviation and moving average algorithm to build the chart and HMM to extract features and build model of exercise. All models are put into cloud for trainee exercise scoring.
  33. 33. Putting the Human into the Tuning (Method) 1. Get human insights (field observations) of trainer and trainee behavior and synchronise to output from system 2. Use data mining to develop models enhanced with human judgments versus using only log files 3. Sync log data to field observations 4. Distill meaningful data features for exercise environment based on qualitative study of output, experiences of field observers and past experience with other data sets 5. Develop automated detector using classification algorithm 6. Validate detector for new trainees
  34. 34. Method avoids limitations “our work is purely quantitative and based on observations we had from data…”
  35. 35. Signals of Joy Study (June 2014) • First Australian study of baby feeding experiences • Unpacks “Mother knows best” at feeding time • Naturalistic feeding videos (31 hours & 34 mums) • Exploratory versus Scientific hypothesis method • Basic drives at feeding time • Mother/care-giver generated video • Educators, parents and marketers • Paucity of research infant/toddler feeding in naturalistic settings • Signals babies “give off” near feeding time -> during –> after
  36. 36. Typical Signals (before,during and after) Crying Hug Grin Disorderly bite Nurse Play Stirring Stroke Suckle Mouth opening Turninghead With open arms
  37. 37. Signals as Percentage
  38. 38. Signal Distribution by Period Before feeding the baby follows signals of open mouth, cry, open arms, nurse and conversation During feeding the signals follow stir, turn head, stretch, increase movement, nurse and conversation. After feeding the baby is standoffish.
  39. 39. Signals by Household The signals vary by household as some parents or caregivers prefer to nurse or enter into more conversations with the infant relative to other parents.
  40. 40. Typical Signal Sequence (Behavior Pattern) Analysis of the signal sequence shows once a baby exhibits abnormal action or emotion the caregiver provides nursing to calm the baby.
  41. 41. Opportunities 1. Working theory/framework of feeding Include strategies for promoting communication and language of toddlers 2. Predictive Recommender System 3. Video Feeding Community (white label) 4. Smart Tin 5. Archetype Child and Parents
  42. 42. Video Analysis Methodology Video Exploration via Nvivio for Analysis and Mining of Video
  43. 43. The future is impossible to predict. However one thing is certain : The company that can excite it’s customers dreams is out ahead in the race to business success Selling Dreams, Gian Luigi Longinotti 47

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