ݺߣ

ݺߣShare a Scribd company logo
@siimulation
Data driven social media
“It seems reasonable to envision, for a time 10 or 15 years
hence, a 'thinking center' that will incorporate the
functions of present-day libraries together with anticipated
advances in information storage and retrieval.
The picture readily enlarges itself into a network of such
centers, connected to one another by wide-band
communication lines and to individual users by leased-wire
services. In such a system, the speed of the computers would
be balanced, and the cost of the gigantic memories and the
sophisticated programs would be divided by the number of
users.“
[J.C.R. Licklider said in 1960…]
…So Licklider was able to see into the future with great accuracy
and partially because he himself played a critical role in making
that future happen by developing ARPANET (predecessor of
the Internet).
Rather than machines taking over human’s role, he foresaw a
vision for man and machine symbiosis
What are people great at? What are machines great at?
Creativity
Intuition
Empathy
Context*
Pattern recognition*
*might not be for long till machines are better
[Humans and Machine]
Calculations
Scale and volume
Simulations
[Thinking of data driven marketing, its less about…]
How can technology solve my marketing problem?
How to store our data, compute and access it?
[And more about…]
Decision
making
process
How can
we save
time?
Marketing
objectives
What data
we need?
~200,000 manually coded social conversations since 2009
1,000 days
of coding
174 brands 26 languages
[STATSIT has been “symbiotic” since 2009]
This is what the usual brand message on FB looks like…
Promotional Discount driven No story 1 - way
While most discussions are on products, positive emotional
connection appears to be the key component in the
ecosystem evolving from 1-way (brand leading) to 2-way
(consumer leading)
STATSIT, N=3,000 manual coding
But before being able to engineer the right Energy, we have
to understand what drives the audience
STATSIT, N=10,000 parents in Indonesia, text mining + N=200 manual coding
FunctionalEmotional
Gasoline
70%30%
Tickets
Wife/Husband
Safety VouchersChild
Holiday Book
Qurban Baldness
Food
House
improvement
Motorbikes
2
2
3
7
9
10
17
25
26
29
32
34
36
70
94
Look: Litter
Look: Package
Feel
Liquid absorbation
Flushability
Dust creation
Stacking
Clumpability
Change interval
Price
Disposal / Recycling
Acceptance and suitability
Transport, Storage, Weight
Material
Odor
…and if an key decision factor analysis can be done for the
cat litter category, there is a good chance its doable for you
STATSIT, N=674, manual coding
3.1
6.9
5.4
max 10
15.5
Max 30
- People: reach FB 2mil+, Twitter reach of 238,000
- Energy: great variety of content, frequent videos (17%)
- Influence: best influence from observed brands, avg Twitter follower’s followers 124,000
Opportunities:
- Real brand engagement with Fans is almost missing
- Utilize brand’s social projects in content, to gain longer term effect of branding
- Utilize existing customer UGC to personalize the brand image
- Consider increasing post frequency to increase engagement (currently 0.5 per day)
RE
69%
FA
18% DE
1%
39%Illness and pain
26%
Death
19%
Accidents
4%
Diabetes
3%
Getting
fired
9%
Poverty
Universal fears Specific to Millennial audiences
[Millennial’s fears]
STATSIT, N=10,000 Indonesian youth, text mining + N=200 manual coding
22%78%
EmotionalFunctional
15%85%
EmotionalFunctional
24%76%
EmotionalFunctional
20%80%
EmotionalFunctional
10% 7%33% 22%
COMMITTED
TO A TRIP
CAME BACK
FROM THE TRIP
THINKING OF
TRAVELING
ACTUALLY
ON THE TRIP
+60%
Increase in Emotionality
+46%
Increase in Emotionality
[Emotionality peaks while considering and on the trip]
STATSIT, N=2,500 manual coding
87.52
Average
conversations per
day
18.40
Thereof
Consideration
13.08
Thereof
Buying
127.98
Average
conversations per
day
20.33
Thereof
Consideration
21.15
Thereof
Buying
94.33
Average
conversations per
day
11.94
Thereof
Consideration
10.06
Thereof
Buying
8%
-23%
-35%
PRE DURING AFTER
46%
11%
62%
1.05-17.06.2012 18.06-10.08.2012 11.08-31.10.2012
STATSIT, N=18,847, text mining
[Campaign metrics drop significantly in post]
Segment 1: Intends to
switch away from iPhone
Segment 2: Considering
other smartphones
Segment 3: Not considering
a smartphone purchase
Segment 4: Waiting for
iPhone 5
High
Motivation
Low
Motivation
Low Ability High Ability
Activation Threshold
(Below this line, there is
insufficient motivation or ability for
Behavior to occur)
Increase Ability or Motivation to move
consumer past the Activation Threshold.
1
Sufficient Ability
Enough
Motivation
2
3
4
Triggers will likely
result in desired
behavior if above
the threshold
[Focus on audiences with ability and intention to switch]
Drive brand value with social intelligenceThank you
@siimulation

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Data driven social media

  • 3. “It seems reasonable to envision, for a time 10 or 15 years hence, a 'thinking center' that will incorporate the functions of present-day libraries together with anticipated advances in information storage and retrieval. The picture readily enlarges itself into a network of such centers, connected to one another by wide-band communication lines and to individual users by leased-wire services. In such a system, the speed of the computers would be balanced, and the cost of the gigantic memories and the sophisticated programs would be divided by the number of users.“ [J.C.R. Licklider said in 1960…]
  • 4. …So Licklider was able to see into the future with great accuracy and partially because he himself played a critical role in making that future happen by developing ARPANET (predecessor of the Internet). Rather than machines taking over human’s role, he foresaw a vision for man and machine symbiosis
  • 5. What are people great at? What are machines great at?
  • 6. Creativity Intuition Empathy Context* Pattern recognition* *might not be for long till machines are better [Humans and Machine] Calculations Scale and volume Simulations
  • 7. [Thinking of data driven marketing, its less about…] How can technology solve my marketing problem? How to store our data, compute and access it?
  • 8. [And more about…] Decision making process How can we save time? Marketing objectives What data we need?
  • 9. ~200,000 manually coded social conversations since 2009 1,000 days of coding 174 brands 26 languages [STATSIT has been “symbiotic” since 2009]
  • 10. This is what the usual brand message on FB looks like… Promotional Discount driven No story 1 - way
  • 11. While most discussions are on products, positive emotional connection appears to be the key component in the ecosystem evolving from 1-way (brand leading) to 2-way (consumer leading) STATSIT, N=3,000 manual coding
  • 12. But before being able to engineer the right Energy, we have to understand what drives the audience STATSIT, N=10,000 parents in Indonesia, text mining + N=200 manual coding FunctionalEmotional Gasoline 70%30% Tickets Wife/Husband Safety VouchersChild Holiday Book Qurban Baldness Food House improvement Motorbikes
  • 13. 2 2 3 7 9 10 17 25 26 29 32 34 36 70 94 Look: Litter Look: Package Feel Liquid absorbation Flushability Dust creation Stacking Clumpability Change interval Price Disposal / Recycling Acceptance and suitability Transport, Storage, Weight Material Odor …and if an key decision factor analysis can be done for the cat litter category, there is a good chance its doable for you STATSIT, N=674, manual coding
  • 14. 3.1 6.9 5.4 max 10 15.5 Max 30 - People: reach FB 2mil+, Twitter reach of 238,000 - Energy: great variety of content, frequent videos (17%) - Influence: best influence from observed brands, avg Twitter follower’s followers 124,000 Opportunities: - Real brand engagement with Fans is almost missing - Utilize brand’s social projects in content, to gain longer term effect of branding - Utilize existing customer UGC to personalize the brand image - Consider increasing post frequency to increase engagement (currently 0.5 per day) RE 69% FA 18% DE 1%
  • 15. 39%Illness and pain 26% Death 19% Accidents 4% Diabetes 3% Getting fired 9% Poverty Universal fears Specific to Millennial audiences [Millennial’s fears] STATSIT, N=10,000 Indonesian youth, text mining + N=200 manual coding
  • 16. 22%78% EmotionalFunctional 15%85% EmotionalFunctional 24%76% EmotionalFunctional 20%80% EmotionalFunctional 10% 7%33% 22% COMMITTED TO A TRIP CAME BACK FROM THE TRIP THINKING OF TRAVELING ACTUALLY ON THE TRIP +60% Increase in Emotionality +46% Increase in Emotionality [Emotionality peaks while considering and on the trip] STATSIT, N=2,500 manual coding
  • 17. 87.52 Average conversations per day 18.40 Thereof Consideration 13.08 Thereof Buying 127.98 Average conversations per day 20.33 Thereof Consideration 21.15 Thereof Buying 94.33 Average conversations per day 11.94 Thereof Consideration 10.06 Thereof Buying 8% -23% -35% PRE DURING AFTER 46% 11% 62% 1.05-17.06.2012 18.06-10.08.2012 11.08-31.10.2012 STATSIT, N=18,847, text mining [Campaign metrics drop significantly in post]
  • 18. Segment 1: Intends to switch away from iPhone Segment 2: Considering other smartphones Segment 3: Not considering a smartphone purchase Segment 4: Waiting for iPhone 5 High Motivation Low Motivation Low Ability High Ability Activation Threshold (Below this line, there is insufficient motivation or ability for Behavior to occur) Increase Ability or Motivation to move consumer past the Activation Threshold. 1 Sufficient Ability Enough Motivation 2 3 4 Triggers will likely result in desired behavior if above the threshold [Focus on audiences with ability and intention to switch]
  • 19. Drive brand value with social intelligenceThank you @siimulation