webinar presentation- accelerating growth through big data and analytics

7/30/2019 Webinar Presentation- Accelerating Growth through Big Data and Analytics

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Accelerating growth through Big Data

and Analytics

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December 6, 2012

Presented by:


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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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Luminar is an analytics and modeling company focused onhelping 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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Big DataWhat it is

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BigData is NOT just analytics. It's NOT just about storage. It's

NOT just about anything - it's about everything. It's abouttossing 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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The value of Big Data varies by company

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Save Time

Lower Complexity

Self Service

Improve OperationalEfficiencies

Grow Sales &Profitability

Empower NewBusiness Models

Actionable Customer

Insights

Reduce Churn

Predictive Analytics

Improve Customer

Experience

Competitive

Differentiation

Data As a Service

Data Science Services

Incubate New

Ventures


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Understanding the magnitude of the US

Hispanic Population some basic facts

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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 underrepresented with most marketing approaches

3. Our service offerings are synergistic to our parent

company

4. Our model would necessitate ingesting vast amountsof diverse data that required a robust analytics

environment

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Underserved market what we

saw in the marketplace

Brands are making marketing investmentdecisions 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 onlineusersthereforegood target

Little or no cultural relevancy

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Actionable insights is an evolving process

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Testing market

No in-language

experience

Mass media, singlechannel

No dedicated

Hispanic team

Lack of

understanding how

to use analytics

Retains Hispanic

agency

Use focus group

Multi-channel, oftennot integrated

Top Hispanic DMAs

Bilingual experience

Qualitative data use

Increased ability

to capture,

aggregate and

analyze data Use analytics to

guide actions

Growing use of

insights to guide

future strategy

Use analytics to

prescribe actions

Effective at sharing

information andinsights

Strong ability to

capture, aggregate,

analyze or share

information

Strong use of

insights to guideday-to-day

operations


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Big Data brings a high-value offering

Ability to more precisely support customers across the entiremarketing 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 marketingneeds

Leverage big data environment and 3rd party data sources across

business units

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Winning executive buy-in was critical

Its was a significant investment and commitment that required CEO visionand 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 empiricalanalytics

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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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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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Importance of aligning our vision with the

right technology partner

Proven track record vendor had to have a demonstrableexperience in the implementation of big data solutions

Technology agnostic We needed a technology partner thatcould 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 blendedclient/vendor team environment

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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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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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AdvisorsExperience. Architects Expertise. Applications Excellence.

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Use Case Discovery and Implementations

Verticals

Use-Case

Patterns

ValueDrivers and

Functional areas

Big Data

Analytics

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Solution Creation Approach - Steps

1: Initial

Phase

Understand Data, ETL and Analytical/Reporting &roadmap requirements

Prepare comprehensive/ long list of candidates

Finalize assessment criteria and weightage factors

2: Finalize

POC

Candidates

Compare and recommend short list ofcandidates after detailed evaluationincluding vendor meetings

3: POC

Implement, execute andbenchmark critical use cases

Execute POC candidates inparallel if possible

4: FinalPhase

Assessment report Recommend bestsolution fit

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Vendor selection based on weighted scoring

We created a custom-scoring matrix used for evaluating

vendors pros and cons, defining requirements, andweighting against Luminars objectives

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Licensed

Data

Internal

Data

Client

Data

Unstructured

Data

OtherData Sources

Data Sources

Luminar Data Store Cluster(HDFS on EBS + P+G + 53 + RDS + Hdbase)

Horton Works

ETL / ELT Security Module

& Compliance Software

Data

Talend

Stream

Processing

(LADAP)

Tableau

BI Tools/ Data

Services

Revolution

/Hive

Statistical

Modeling

Web-based

Workstation

Tablet

Mobile

Notebook

BI Display OutputsAmazon AWS

Technology Hybrid architecture

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Implemented solution overview

Hadoop Cluster provisioned on

Amazon EC2 in under four hours

Original data sets imported from

MySQL to HDFS/Hive usingSqoop 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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Our value proposition formula


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How we do It

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Customer Visualization Application

Big Data Analytics

Data Management & StagingData Sources

Client Data Files

Unstructured Data

Luminar Data

Insight Solutions

3rd Party Data

Customer

Decision Engine

Luminar

Insight App

Real-Time

Cloud Insights


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Luminar Business Insights


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Closing remarksFourkey takeaways

Make a strategic connection to Big Data In Luminarscase, it provided a clear strategic path to a new

marketing approach

Big Data initiative requires holistic approach bringing

business and IT together to stitch all the parts

While IT and business need to work together, the

business must own the initiatives

Have a flexible approach to your roll-out strategy

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Accelerating Growth through Big

Data and Analytics

Q & A

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December 6, 2012

Presented by:

webinar presentation- accelerating growth through big data and analytics

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