Impetus webinar presentation ‘Accelerating Growth through Big Data and Analytics’ available at http://lf1.me/dU/

1. Accelerating growth through Big Data
and Analytics
December 6, 2012
Presented by:
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2. Key points we want to make today
I. Overview on Luminar and Impetus
II. Shift from the status quo
III. How Big data is helping advance how we market to Latinos
IV. The journey, implementation approach
V. A new business model supporting clients
VI. Lessons Learned
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3. ● Luminar is an analytics and modeling company focused on
helping clients become more efficient in targeting the US
Hispanic market
● Company was established Spring 2012
● Luminar is a business unit of Entravision Communications
(NASDAQ: EVC)
● Based in Denver with operations in LA, DC, Buenos Aires,
Argentina; and Mexico City
● Key client segments include: Retail, CPG, Financial Services,
Media & Entertainment, and Publishing
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4. Big Data…What it is
“Big Data is NOT just analytics. It's NOT just about storage. It's
NOT just about anything - it's about everything. It's about
tossing out the old way of doing things because those ways
simply won't work in the world of BIG.”
- Steve Duplessie, founder and senior analyst at ESG
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5. The value of Big Data varies by company
Improve Operational Grow Sales & Empower New
Efficiencies Profitability Business Models
● Save Time ● Actionable Customer ● Competitive
● Lower Complexity Insights Differentiation
● Self Service ● Reduce Churn ● Data As a Service
● Predictive Analytics ● Data Science Services
● Improve Customer ● Incubate New
Experience Ventures
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6. Understanding the magnitude of the US
Hispanic Population – some basic facts…
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7. Understanding why Luminar decided to
make a Big Data play
Four key factors influenced this decision:
1. We wanted to shift from the current marketing
paradigm targeting Latinos focused on sample data
2. We recognized that Hispanic consumers are under
represented with most marketing approaches
3. Our service offerings are synergistic to our parent
company
4. Our model would necessitate ingesting vast amounts
of diverse data that required a robust analytics
environment
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8. Underserved market – what we
saw in the marketplace
● Brands are making marketing investment
decisions on limited information
● Targeting assumptions based mostly on survey or
sample methods (i.e. “Latinos over-index on
mobile usage”)
● No real insights or true performance of program
● Campaigns mostly based on just ethnically-
coded data
● Stereotype approach; they speak Spanish,
consume Spanish media, heavy online
users…therefore…good target
● Little or no cultural relevancy
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9. Actionable insights is an evolving process
• Testing market • Retains Hispanic • Increased ability • Use analytics to
• No in-language agency to capture, prescribe actions
experience • Use focus group aggregate and • Effective at sharing
analyze data information and
• Mass media, single • Multi-channel, often
channel not integrated • Use analytics to insights
guide actions • Strong ability to
• No dedicated • Top Hispanic DMAs
• Growing use of capture, aggregate,
• Hispanic team • Bilingual experience analyze or share
insights to guide
• Lack of • Qualitative data use future strategy information
understanding how • Strong use of
to use analytics insights to guide
day-to-day
operations
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10. Big Data brings a high-value offering
● Ability to more precisely support customers across the entire
marketing value chain:
- Move from a media & communications discussion to a
business challenge discussion
- Help identify growth opportunity within the Hispanic market
- Improve measurement of Hispanic market investments
- Demonstrate ROI
- Help accelerate growth through empirical data insights
● Transformative in the way we approached business and marketing
needs
● Leverage big data environment and 3rd party data sources across
business units
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11. Winning executive buy-in was critical
● It’s was a significant investment and commitment that required CEO vision
and support
● Developed detailed roadmap for success:
- Prepared comprehensive plan detailing operations, resources, level of
investment and implementation path
- We weighted the need for big data as new revenue source for EVC
- We identified “packaged solutions” for a big data offering
- And, we clearly defined how big data fulfilled an underserved market
and provided a shift from sample-based research to empirical
analytics
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12. Result – Luminar was created as a new
Entravision business unit
New business unit was created dedicated to serving
Hispanic-focused analytics and insights
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14. Luminar Big Data would need to support
these needs
● Analytics-as-a-Service platform
● Aggregate multiple sources of data from diverse sources
- Licensed data
- EVC data
- Unstructured social data
- Client data
● Offer an advanced and unique focused analytics service
- Provide insights into Hispanic consumer behavior
- Targeting customers in retail, financial services, insurance and auto
segments
● Future offerings
- Platform as a Service
- White Label Services
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15. Importance of aligning our vision with the
right technology partner
● Proven track record – vendor had to have a demonstrable
experience in the implementation of big data solutions
● Technology agnostic – We needed a technology partner that
could help plan and deploy a solution architecture that was
not married to any one vendor
● Experience with multiple technology providers/suppliers
– We needed a partner that could understand the big data
landscape now, in 6 moths and 18 months from today
● Blended team approach – Our ideal partner had to clearly
understand that they would be operating in a blended
client/vendor team environment
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16. Deployment Objectives
● Build a best-of-breed model based on Luminar requirements
- Take a vendor neutral approach
- Lowest Total Cost of Ownership
- No requirement to integrate with any legacy systems but SQL
data migration
● Cloud based architecture
● Maximize “re-use” of vendor experience in Big Data
● Scalability for future data requirements
● Data security requirements
● Visualization
● Start with a “shoestring” approach
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17. Build the right foundation for growth
● Impetus lead solution architecture and vendor selection process
● We established a solution framework that delivers four client offerings
● We architected a solution that defined all major technology Key
Performance Indicators (KPIs) and SPOF
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18. Advisors Experience. Architects Expertise. Applications Excellence.
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19. Use Case Discovery and Implementations
Verticals
Big Data
Analytics
Value
Use-Case Drivers and
Patterns Functional areas
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20. Solution Creation Approach - Steps
• Understand Data, ETL and Analytical/Reporting &
1: Initial roadmap requirements
Phase • Prepare comprehensive/ long list of candidates
• Finalize assessment criteria and weightage factors
2: Finalize • Compare and recommend short list of
POC candidates after detailed evaluation
Candidates including vendor meetings
• Implement, execute and
benchmark critical use cases
3: POC • Execute POC candidates in
parallel if possible
4: Final • Assessment report
Phase • Recommend best
solution fit
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21. Vendor selection based on weighted scoring
We created a custom-scoring matrix used for evaluating
vendors pros and cons, defining requirements, and
weighting against Luminar’s objectives
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22. Technology – Hybrid architecture
Data Sources
Amazon AWS
BI Display Outputs
ETL / ELT Security Module BI Tools/ Data
Licensed & Compliance Software Services
Data Web-based
Internal
Data Workstation
Talend
Tableau
Client
Data Data Tablet
Stream Statistical
Processing Modeling
Unstructured (LADAP)
Data Mobile
Other
Data Sources Notebook
Revolution
/Hive
Luminar Data Store Cluster
(HDFS on EBS + P+G + 53 + RDS + Hdbase)
Horton Works
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23. Implemented solution overview
● Hadoop Cluster provisioned on
Amazon EC2 in under four hours
● Original data sets imported from
MySQL to HDFS/Hive using
Sqoop and Talend
● Existing R scripts were modified
to work with Hive for data
analysis. Minimal code
modification required
● Tableau work books modified to
connect to Hive via Hortonworks
ODBC driver
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24. Our value proposition formula
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25. How we do It
Data Sources Data Management & Staging Insight Solutions
Customer Visualization Application
Luminar
Luminar Data
Insight App
Customer
Decision Engine
Client Data Files
Real-Time
Cloud Insights
3rd Party Data
Unstructured Data Big Data Analytics
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26. Luminar Business Insights
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27. Closing remarks…Four key takeaways
1 Make a strategic connection to Big Data… In Luminar’s
case, it provided a clear strategic path to a new
marketing approach
2 Big Data initiative requires holistic approach bringing
business and IT together to stitch all the parts
3 While IT and business need to work together, the
business must own the initiatives
4 Have a flexible approach to your roll-out strategy
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28. Accelerating Growth through Big
Data and Analytics
Q&A
December 6, 2012
Presented by:
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