Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

From Big Social Data to Smart Social Data

747 views

Published on

Research Arena 2012

  • Be the first to comment

From Big Social Data to Smart Social Data

  1. 1. Big Social Data From Big Data to Smart Data Prinzie Anitasolutions-2 Belgium www.solutions2.be +32(0)9 242 50 40Solutions-2 London www.solutions2.co.uk +44 (0)20 7608 9300
  2. 2. OR50,000 100,000 125,000 150,000
  3. 3. OR650,000 100,000 48 hours 28,000
  4. 4. http://datasift.com/stream/13991/mcdonalds#app1-preview
  5. 5. ORGANIC DATA DESIGNED DATA CREATED TO GAIN SPECIFIC INSIGHTS DATA RICHNESS Based on Bob Groves, former US Census director
  6. 6. BIG DATA SMART DATA
  7. 7. Define business objective
  8. 8. LISTENING MONITORINGBrand perceptions Detecting crisisBrand positioning Complaints & malfunctionsConsumer segments New product launchMedia campaign success ...... Based on Jasper Snyder Converseon
  9. 9. Identify relevant data
  10. 10. BUSINESS OBJECTIVELISTENING PURPOSE MONITORING PURPOSE RELEVANT DATA Who? Which platforms? Which conversations? How long?
  11. 11. WhoFit with business objective IPhone 5 UK launch success (Vision Critical) “Should we listen to FB conversations of people not wanting the IPhone 5?” Battery 8 3 44 65 Negative Neutral Positive 53 27 All Want IPhone 5 11
  12. 12. Who & Which platformsProfile on channel usage & engagement 15-24 25-34 35-54 55-99 Actively engaging on FB, Twitter, blogs?
  13. 13. Which conversationsFind the most relevant ones B2B Software Adoption Journey Focus groups with B2B customers Dictionary of typical actions during different phases of the software adoption process Scoring all Twitter/blog conversations on the software adoption phases. Software adoption journey-conversations 13
  14. 14. How long?Find relevant time window Natural time window ‘Enough conversations’ time window New product launch: 90 days before and after (Microsoft) 14
  15. 15. Clean &Preprocess
  16. 16. Clean and PreprocessKeep goal in mind Keep emoticons and markup for detecting crisis Context specific normalization and annotation (e.g. Convey API) 16
  17. 17. Analyse withobjectivein mind
  18. 18. MONITORING PURPOSE SENTIMENT ANALYSISDetecting crisis Detecting gradations of negative and evolutionsIdentifying complaints Detecting gradations ofand malfunctions negativeMonitoring response to Detecting gradations ofnew product launch positive and negative 18
  19. 19. Remedies Correct 80% Cost-sensitive learning Undersampling of neutral class 90% Use recall, precision and F1 measure to 7% 3% evaluate modelPositive Neutral Negative 19
  20. 20. Validateresults
  21. 21. Validate social media results Denali The evolution of the Microsoft software adoption index did O365 follow known success/failure trends for past software launches. 21
  22. 22. BIG DATA Define business objective Identify relevant data Clean & preprocess Analyse with objective in mind Validate resultsSMART DATA
  23. 23. Anita Prinzie anita@solutions2.be @AnitaPrinzie If you have anyrisingquestions
  24. 24. References http://www.domo.com/blog/2012/06/how-much-data-is-created- every-minute/ Snyder, J. (2012), Enriching Social Data for Market Research Converseon, New MR Webinar, Social Media Research, October 9th 2012. Woolmer, J. (2012), Is it real? Using conventional research to validate and quantify social media findings, Vision Critical, New MR Webinar, Social Media Research, October 9th 2012. 24

×