Cloud 5 context and behaviours
-
Upload
john-griffiths -
Category
Business
-
view
229 -
download
0
description
Transcript of Cloud 5 context and behaviours
Cloud 5: Behaviours and Context
John GriffithsFeb 1st 2011
Cloud of Knowing
Going to be talking about
Internet content is poor grade content But outstanding contextual and behavioural
data – so let’s treat it as such And it’s a free resource..
content vsbehaviours vs
? What does online data deliver best
Cloud of Knowing
The research task: finding the fruit
Cloud of Knowing
Qualitative technique
Separation of process from content in qual
Reportage –– no separation – pulp!
Cloud of KnowingDoes context matter?
ArbitraryArtificial
Cloud of Knowing
Dictionary definitions
Source businessdictionary.com
Cloud of Knowing
Dictionary definitions
Source businessdictionary.com
Cloud of Knowing
Dictionary definitions
Source businessdictionary.com
The part of a text or statementthat surrounds a particular word or passage and determines its meaning.
Cloud of KnowingDoes context matter?
Imposing frames of reference
Cloud of Knowing
Thinking about advertising you have seen in the last 2 weeks..
When you make gravy.. What do you usually?
Cloud of Knowing
Why focus on context?
Because found online contentdoesn’t match offline research content
What people spontaneously post is richer in behavioural and contextual data
Play to your strengths
Cloud of Knowing
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
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
Cloud of Knowing
Not everybody online is equal
Creators
Fans
Viewers
Bystanders
Curators
Cloud of Knowing
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
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
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
camcorders
Cloud of Knowing
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
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
Social media currencyfor 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
Conclusions Contextual and behavioural data is so
much more than online behaviour Probably needs an offline research
study to identify interesting behaviours Once identified behaviours and context
can be tracked. Frequency and change over time can be
automatically monitored
Discuss!!