1) The document discusses using online data to understand behaviors and context, which it argues provides richer insights than just content alone. 2) It emphasizes the importance of considering factors like who created or curated the content, how many people paid attention to it, and differences between active online participants versus casual viewers or bystanders. 3) As an example, it outlines how tracking behaviors, geographical and social contexts around mothers' use of camcorders with young children could provide insights beyond just the content itself.

1. Cloud 5: Behaviours and Context
John Griffiths
Feb 1st 2011

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..
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3. The research task: finding the fruit
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4. Qualitative technique
 Separation of process from
content in qual
 Reportage –– no separation –
pulp!
Cloud of Knowing

5. Arbitrary
Artificial
Does context matter?
Cloud of Knowing

6. Dictionary definitions
Source businessdictionary.com
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7. Dictionary definitions
Source businessdictionary.com
Cloud of Knowing

8. Dictionary definitions
The part of a text or statement
that surrounds a particular
Source businessdictionary.com word or passage and
Cloud of Knowing determines its meaning.

9. Imposing frames of reference
Does context matter?
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10. Thinking about advertising you
have seen in the last 2
weeks..
When you make gravy.. What
do you usually?
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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. 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. 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 content
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14. Not everybody online is equal
Creators
Curators
Fans
Viewers
Bystanders
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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 forward
Cloud of Knowing

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 attention
Cloud of Knowing

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/clips
Cloud of Knowing

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 used
Cloud of Knowing

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 used
Cloud of Knowing

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. 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 monitored
Cloud of Knowing