Cloud 5 context and behaviours

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presented at Cloud 5 Feb 2011 - open source meet about content analytics and market research. Argues that online data may be more usefully interpreted as if it was purely behavioural or contextual rather than as content. The earliest presentation where I talk about the failure of research to properly address context - something I write about regularly in 2012

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Cloud 5 context and behaviours

  1. 1. Cloud 5: Behaviours and Context John Griffiths Feb 1st 2011
  2. 2. Going to be talking about ? What does online data deliver best content vs behaviours vs Internet content is poor grade content But outstanding contextual and behavioural data – so let’s treat it as such And it’s a Knowing resource.. Cloud of free
  3. 3. The research task: finding the fruit Cloud of Knowing
  4. 4. Qualitative technique  Separation of process from content in qual  Reportage –– no separation – pulp!Cloud of Knowing
  5. 5. Arbitrary ArtificialDoes context matter? Cloud of Knowing
  6. 6. Dictionary definitions Source businessdictionary.comCloud of Knowing
  7. 7. Dictionary definitions Source businessdictionary.comCloud of Knowing
  8. 8. Dictionary definitions The part of a text or statement that surrounds a particularSource businessdictionary.com word or passage and Cloud of Knowing determines its meaning.
  9. 9. Imposing frames of referenceDoes context matter? Cloud of Knowing
  10. 10. Thinking about advertising you have seen in the last 2 weeks.. When you make gravy.. What do you usually?Cloud of Knowing
  11. 11. Why focus on context? Because found online content doesn’t match offline research content What people spontaneously post is richer in behavioural and contextual data Play to your strengths Cloud of Knowing
  12. 12. Ocean vs jellyfish  Jellyfish 99% water and mostly transparent – people are unreliable at reporting recall, behaviour, let alone culture changes  Are we researching the jellyfish – or are we using the jellyfish to understand the ocean? Cloud of Knowing
  13. 13. Wineglass vs Mattress Remarkably difficult to start a movement that travels the length of the mattress – LOTS of post rationalisation – mostly with non commercial virals Behaviours and contextual data spread faster than contentCloud of Knowing
  14. 14. Not everybody online is equal CreatorsCurators Fans Viewers Bystanders Cloud of Knowing
  15. 15. Grading data: the curator curve  Some people know a lot more than others,  Some post a lot more content than others  A significant proportion of non commercial web content is published by a relatively few sources  How much you have posted affects the content you post – and what you say.  We need to factor in a measure for curation for every piece of data we examine  And identify those who create and the fans who link comment and forwardCloud of Knowing
  16. 16. Grading data: the attention curve  We pay a lot more attention to some information than others  Currency comes from lots of people perceiving that others are perceiving it too  It affects how we talk about it  Much of the desire to reach large numbers of people comes from brand manager’s desire to locate and aggregate an audience  When we track how many people have paid attention we need to identify creators and fans separately from audience and bystanders  Bystanders received it but didn’t pay attentionCloud of Knowing
  17. 17. camcorders Usage: Mothers with babies and toddlers  Behaviour  Stills uploaded – where uploaded  uploaded clips: clip length, number  Verbatims about trips with young children – camera mentions  Camcorder vs mobile usage/repertoire  Geographical context  Geotagging – where clips being shot  Geo distribution of images/clipsCloud of Knowing
  18. 18. More usage..  Social Context  Subject matter  What the children are doing  What is said about what the children are doing  Social media context  Who photos clips are mailed to  Who comments  Who forwarded to  Keywords usedCloud of Knowing
  19. 19. Purchase  Triggers to purchase  Camcorder/camera repairs search enquiries  Visits to camera camcorder websites  Competitor models considered, sort criteria  Features searched for  Social media context  Asking for advice about cameras used  Who comments  Who forwarded to  Keywords usedCloud of Knowing
  20. 20. Social media currency for each item of data  Social metrics  Audience curve:  Size and scale of audience – index against other types of clip  Curator curve:  Frequency and regularity of posting or commenting on this topic compared with others.Cloud of Knowing
  21. 21. Conclusions  Contextual and behavioural data is so much more than online behaviour  Probably needs an offline research study to Disc identify interesting behaviours us s !!  Once identified behaviours and context can be tracked.  Frequency and change over time can be automatically monitoredCloud of Knowing

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