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The document discusses big data and smart data. It provides examples of how to analyze advocacy and brand mentions in data to gain insights. Some key points:
- Advocacy, or brand advocates, can be measured and may move in opposite direction as sentiment but provides context to other metrics like sales.
- The most successful brands align with human needs and emotions. Different "types" appeal to different emotions.
- Categories of interests are often tribal and brands should understand the tribes.
- To get value from data, one should start with the right business challenge and data, focus on coding frameworks to extract insights, and tell a story with implications from the data analysis.
"Dynamic Success at the Polls" Presentation at ILA 2013 #ilaigniteEveryLibrary
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Librarian as candidate- EveryLibrary - pala2016 - 18 october 2016EveryLibrary
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"Dynamic Success at the Polls" Presentation at ILA 2013 #ilaigniteEveryLibrary
Melissa Gardner, Director at the Broadview (IL) Public Library, and John Chrastka, Executive Director at EveryLibrary, presented at the 2013 Illinois Library Association conference on planning and running successful library ballot campaigns in both Information-Only and Vote Yes settings.
Librarian as candidate- EveryLibrary - pala2016 - 18 october 2016EveryLibrary
As presented for APPLS / PaLA2016: Are you looking for new ways to engage and activate advocates for your library? Join EveryLibrary executive director John Chrastka for a discussion of innovative new techniques to energize, focus, and improve your library advocacy efforts. EveryLibrary supports library communities when they are on the ballot. He will share best practices from political campaigns that candidates use to reach – and activate – voters. Whether you are on the ballot or just looking to improve your fundraising, come and learn how librarians who see themselves as “the candidate” succeed.
Teaching Elephants to Dance (and Fly!): A Developer's Journey to Digital Tran...Burr Sutter
We can be brilliant developers, but we won’t succeed—and won’t lead our organizations to succeed—without a new perspective (if you will) and new assumptions about the components of the “technology ecosystem” that are fundamentally critical to our success. This includes the operators, QA team, DBAs, security folks, and even the pure business contingent—in most cases, each of these individuals and groups plays a critical role in the success of what we create and give birth to as developers. What we do in isolation might be genius, but if we insulate ourselves—especially with arrogance—from these colleagues, neither our code nor our organizations will realize their full potential, and most will fail. The bottom line is that our old ways are no longer viable, and as the elite within our industry, we will be the leaders and heroes who discard old assumptions and adopt a new perspective in this exciting journey to digital transformation—where the impossible can become reality.
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It's Not the Size of the Data, But What You Can Do with It by Zachary Nippert of MotiveQuest - Presented at Insight Innovation eXchange North America 2013
2. WHY?
Fearlessly Seeking the Reasons…
Between the birth of the
world and 2003, there were
five exabytes of information
created.
Huffington Post April 2013
3. WHY?
Fearlessly Seeking the Reasons…
Between the birth of the
world and 2003, there were
five exabytes of information
created.
We [now] create five
exabytes every two days
Huffington Post April 2013
12. 12
Multi-Media Sites Personal Blogs
Photo Sharing Sites Twitter
Forums &
Message Boards
Social
Networks
Personal
Finance
Entertainment &
Pop Culture
Automotive Health & Wellness Food & Dieting
Technology Retail & Style Travel
Sports & Fitness Digital Water Cooler Parenting
Environment &
Philosophy
13. 13
No Brand Mentions
95%
Brand Mentions 5% Brand Mentions 11%
No Brand Mentions
50%
No Brand Mentions
89%
Brand Mentions 50%
Health ConversationsFood Conversations Car Conversations
Lowest share of brand mentions Highest share of brand members
15. 15
The power of custom data and
why you get what you pay for…
16. 16
• Curators should screen all data sources
for quality.
• Most listening tools buy the same data.
• Algorithms should be used to detect
and track consistency, ensuring that
data sources do not have gaps or
holes, alerting us to site infrastructure
changes.
• Channel breadth and organization are
most important.
• Beyond Twitter and Facebook, custom
templates are needed to extract each
site’s full text, comments, and historical
activity.
17. 17
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求助啊,搞了两天了,EASY-UI+STRUTS一直没数据。。。。。。大牛们。。。2 2 0
93 40 14 2 37
MAJOR DATA PROVIDER– 40% VALID
19. 19
Example 1: The Advocacy Framework
Change in advocates (prior month) vs. Change in Sales• Measures the number of
people, not conversations.
• Developed with
Northwestern University’s
Kellogg Business School.
• Enforces choice. When
individuals mention
multiple brands over time
(even when not in the
same conversation), the
Advocacy algorithm
determines while brand
they speak most positively
about and considers them
advocates of that brand
only.
20. 20
Advocacy may move in the opposite direction as
sentiment.
0
000
000
000
000
000
000
NETFLIX
ADVOCATES
21. 21
Advocacy provides context to other metrics.
Brand advocates
Unprompted brand mentions
Content creation (YT videos)
Responses to brand Facebook posts
Sentiment of engagement metrics
Facebook likes on brand posts
Re-tweets of brand mentions
Comments on YT videos
Sentiment of participation metrics
Brand Facebook page likes
Brand Twitter handle followers
Subscribers to brand YouTube channel
LowtoHighEffort
Advocacy
Brand/ Product Advocate
Engagement
More effort to interact
Participation
Minimal effort to interact
Awareness
Passive contact with brand mentions
or messages
23. 23
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
The most successful brands align themselves clearly
with human needs.
HERO
FEEL ACCOMPLISHED
“where there’s a
will, there’s a way”
MAGICIAN
FEEL MAGICAL
“it can happen!”
OUTLAW
FEEL SINFUL
“rules are meant to be
broken”
SAGE
FEEL WISE
“the truth will set you free”
INNOCENT
FEEL REJUVENATED
“free to be your younger
yourself”
EXPLORER
FEEL ADVENTUROUS
“let me find my own way”
CREATOR
FEEL CREATIVE
“if it can be imagined, it
can be created”
CAREGIVER
FEEL PROTECTIVE
“love your neighbor as
yourself”
RULER
FEEL ELITE
“power isn’t
everything, it’s the only
thing”
REGULAR GUY/GAL
FEEL CONNECTED
“all men and women
are created equal”
JESTER
FEEL FRIVOLOUS
“if I can’t dance, I don’t
want to be part of your
revolution”
LOVER
FEEL SENSUAL
“I only have eyes for you”
24. 24
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
I feel accepted
I feel amused
I feel competitive
I feel creative
I feel rebellious
I feel happy
I feel loved
I feel smart
I feel prestigious
I feel adventurous
I feel responsible
I feel productive
Categories are often tribal.
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
I feel accepted
I feel amused
I feel competitive
I feel creative
I feel rebellious
I feel happy
I feel loved
I feel smart
I feel important
I feel adventurous
I feel responsible
I feel productive
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
I feel accepted
I feel amused
I feel competitive
I feel creatives
nsible
I feel productive
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Big data, its simplest form, is just that, lots and lots of data.It took us from the birth of the world till 2003, when I started my company, to generate five exabytes of data ..…we now create the same amount every 2 days. That is a lot of web, Facebook, twitter, forum, sales stuff to wade through
Big data, its simplest form, is just that, lots and lots of data.It took us from the birth of the world till 2003, when I started my company, to generate five exabytes of data ..…we now create the same amount every 2 days. That is a lot of web, Facebook, twitter, forum, sales stuff to wade through
Today I am going to share what I learned in that moment1 big insight about how to deal with big data2 Real world Businesscases4 tips for you to take home and use
OK so here is the secret. Ready
The ugly, emperor tailor myth, is that you don’t need all that data.You don’t need big data at all.You actually need the opposite.
What you really need is small data.
What is small data? Small data is just the right amount of data to solve the specific problem that you have today.
What is small data? Small data is just the right amount of data to solve the specific problem that you have today.
OK so here is the secret. Ready
So what are the 4 secrets you can use when you go home?