How to Get Your Brand Elected: Big Data Presentation at NRF by David Selinger
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Transcript of How to Get Your Brand Elected: Big Data Presentation at NRF by David Selinger
How Relevance Can GetYour Brand Elected
DAVID SELINGER
www.richrelevance.com
RAYID GHANI
www.rayidghani.com
© 2014 RichRelevance, Inc. All Rights Reserved. Confidential.#richrelevance
© 2014 RichRelevance, Inc. All Rights Reserved. Confidential.#richrelevance
Our Platform
1. The History of Data
2. How Data Make Money, Win Elections and Other Such Nonsense
3. Demystifying the Mystical Creature, Big Data
4. Next Steps: How to Take the Next Steps
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The History of Data
SECTION 1
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A Brief History of Data
1980 1990 2000 Today1900
STATISTICS
PROBABILITY “DATA MINING”
MACHINE LEARNING
“BIG DATA”
DATA WAREHOUSES
BI
FRAUD PREDICTION
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The 3 Tools:Getting Value from Data
1
2
3
Analytics
Prediction & Interruption
Optimization
{Relevance}
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Getting Value from Data
Getting Elected with Data
SECTION 2
OR
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Getting Elected with Data
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I am a data geek.
Intelligence
Obsession
Social Ineptitude
Nerd
Dweeb
Geek Dork
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What am I good at?
How can I make a
difference?
Data Scientist for
Obama
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Everyone’s NOT a Winner… It’s Binary
The Campaign
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Legacy Orgs Are Nothing Like Start-ups
The Campaign
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Don’t Disdain Successful Evergreen Channels
Key Campaign Lessons
EMAILDIRECT MAIL ADVERTISING
1
2
3
Analytics
Prediction & Interruption
Optimization
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Perfect predictions do not necessarily alter hearts and minds.
Predicting vs. changing behaviorKey Campaign Lessons
1
2
3
Analytics
Prediction & Interruption
Optimization
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Influencing the Right PeopleKey Campaign Lessons
1
2
3
Analytics
Prediction & Interruption
Optimization
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A: The people who are just like you
Q: Who are the best people to persuade and influence?
Key Campaign Lessons
1
2
3
Analytics
Prediction & Interruption
Optimization
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DONATE
Turning supporters into advocates
Key Campaign Lessons
1
2
3
Analytics
Prediction & Interruption
Optimization
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The 3 Tools In an ElectionGetting Value from Data
1
2
3
Analytics
Prediction & Interruption
Optimization
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David Selinger
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Legacy Shopping Engagement
Source: Omni-channel Retail: The Future of Shopping (A study in partnership with Paco Underhill)
Awareness Consideration
PreferencePurchase
Clarity
Signage
Flow
RetentionRetention
Conversion
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Source: Omni-channel Retail: The Future of Shopping (A study in partnership with Paco Underhill)
Omni-Channel Engagement
Purchase
Consideration
Preference
Awareness
Retention
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research
1
2
3
Analytics
Prediction & Interruption
Optimization
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© 2014 RichRelevance, Inc. All Rights Reserved. Confidential.#richrelevance
Big Data: The 3 V’s
Velocity Volume Variety
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Surprise!
HOW WE GOT ELECTED WITH DATA:
Lots of good ol’ data integration with sprinkles of Hadoop dust and other” big data” technologies when appropriate.
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Big Data vs. Smart Data
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Let’s Get Going
SECTION 4
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Where are we?
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BIGDATA
IQ
DATA IQ
Catalogers/DR Marketers
NicheeCommerce
Upstarts
AdvancedeCommerce
Vendors
TraditionaleCommerce
Vendors
eCommercePureplays
Omnichannel Retailers
Where are we?
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How do we apply these to our businesses?
• Getting Started: Learn Quickly
• Getting Integrated: Bite-sized Chunks
• Measure yourself: The right metrics
• Experiment, Experiment, Experiment.
• Scale: Rinse and Repeat or “Big Project”
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SUCCESSFUL• Multi-channel marketing
attribution• Social network
demographics for content targeting
• Multi-channel customer profile creation for analytics, targeting, personalization
• Datamart creation
UNSUCCESSFUL• Data warehouse
replacement• “Big Data Experiment”• Online
analytics/segmentation • Board of Directors “Check
the Box” Project: “Yes, ma’am we’ve got Big Data”
Some Initial Big Data Retail Projects
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Closing Thoughts
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ACCESS THE HANDOUT:
richrelevance.com/nrfCONTINUE THE CONVERSATION:
DAVID SELINGER
www.richrelevance.com
RAYID GHANI
www.rayidghani.com
Thank you!
To track the progress of Big Data for
retailers, subscribe to our regular newsletter “Big Data for Retailers”