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The future of
Community
Insights
Michael Conroy
Senior Analyst, Tempero
@mickyconroy
So who am I then?
Mick Conroy
Senior Social Media Analyst
Tempero
Tempero’s Clients
Let’s get started!
1. Network Analysis
2. Machine Learning (A.I.)
3. Natural Language Processing
CM and Insights together
Community
Mapping
(Networks)
Conversation
Analysis
(Text Mining)
Community
Engagement
Community
Outreach
(M. Learning)
Community
Insights
Community
Management
Network Analysis
Community Analysis
Thinking in “networks”
Social networks are networks. Duh.
If we start analysing social media
like networks new and useful
insights arise
This is the New York Times
...and this is Forbes
Positive
Negative
Neutral
Fans @mention
Barlow their love
of the new track
“Barlow can stick
those Percy
Pigs...” gets RTs
Behind the scenes
photos shared
Barlow like
“slashing the Mona
Lisa with a
platinum machete”.
Conversation Analysis
The power of networks
Influencers can be found by their
place in the network
Spread content in a way that it gets
seen
The power of networks
Virality can be measured
Going viral can be induced
Going viral is a science
Going viral is a science
Find the influencers that connect
different communities
Subcommunities: Find any interests
groups within your community to
determine how to seed your content
The tools
mappr
Start with a tight community
Influence the connectors
10% is enough
Be relevant
Your message should be closely
related to the purpose that brings
the target network together
Acknowledge the group’s mindset
by bringing in some novelty
London’s Social Media Map
mappr
UK Fashion and Lifestyle
Luxury
Fashion
Brands
Home and
Decor
Magazine/
Design
companies
Art auction
houses ,
publications
and a
museum
Luxury
Travel
Blogs, sites
Luxury
watch
brands,
blogs
mappr
Summary
Network analysis helps us find the
people and conversations that
matter, and take the right actions
By knowing the structure of the
network, you can better model how
to influence it
Machine Learning
This is the Internet
You look at this much
Intro to Machine Learning
Train artificial intelligence on a
big dataset
AI makes a prediction based on
what it currently “knows”
AI recalculates what it “knows”
based on the new information
Get feedback on whether the prediction
was right or wrong
It’s everywhere!
It’s also handy for Social
Social media monitoring 2.0
AI can be trained to look for
concepts or themes in text,
independent of keywords
Human level intelligence – at scale
Humans and Algorithms...
A few tagging examples
The new way
Train the AI around a bespoke
classification taxonomy
One trained, the AI is able to tag
mentions autonomously
Keep human experts on hand for
minor corrections
Sample Taxonomy
Message-type
News
Technical Query
Comparison
Automated share
Advert
Contest
Opinion
Product Fault
Customer Service
Product Characteristics
Price
Ease-of-use
Compatibility
design/build
Availability
Energy Efficiency
Person
Customer
Rival customer
Press
Public
Staff/Sony
Product feature
sound
set up
internal software
apps
Screen Size
Resolution/Picture
Quality
Media Playback
Mobile/tablet/PC
connectivity
Brand
Sony Bravia
Samsung
LG
Sharp
Product-type
3D
Smart/Internet TV
LED TV
Plasma
The end result
Sony Samsung Apple
Customer Service Sentiment by
Brand Worldwide
The end result
Purchase Intent by Competitor
0
5000
10000
15000
20000
25000
28-Aug 29-Aug 30-Aug 31-Aug 01-Sep 02-Sep 03-Sep 04-Sep 05-Sep
Sony Samsung Panasonic
Deep insights, at a glance
Summary
Human-level intelligence at scale
Deep insights; Fast.
The basis for a laser-targeted
engagement strategy
Natural Language Processing
We’re all drowning in text!
CM’s have their fair share
Twitter status updates
Facebook comments
…there’s interaction data
everywhere
It’s not just “us”…
Introducing Overview
overview.ap.org
And these drones…
Acquire your data
How Overview works
Activists use drones to track
endangered wildlife
Apple rejects iPhone App
Overview in action
Represent the data
0
50
100
150
200
250
300
350
Represent the data
Football fans at a match
Summary
A new toolset is needed to analyse
the sheer volume of conversation
we all have to deal with
Natural Language Processing
algorithms are an ideal way to find
meaning in the clutter
In closing...
Pulling it all together
Community
Mapping
(Networks)
Conversation
Analysis
(Text Mining)
Community
Engagement
Community
Outreach
(M. Learning)
Community
Insights
Community
Management
There’s a lot more to it
We can help!
Michael Conroy
Senior Analyst, Tempero
@mickyconroy

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