Big Data represent an opportunity for organizations with data analysis needs. Companies need to prepare a number of functions to address the Big Data Challenge. The following presentation describes the Big Data landscape for marketing technology, introducing several applications, and describing the three key aspects a media agency must focus on when dealing with Big Data analysis applications.

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Óscar Muñoz-García
Project Manager, Havas Media Group

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Big Data and Marketing Technology
Challenges and Case Studies for Media Agencies

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What are the 3 things that a company needs to focus on?
1. Technology
Huge volume of heterogeneous data
Heavyweight data analysis proceses

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Data Sources
Social Media
Highly unstructured / Heterogeneous
Fine grained (seconds, posts/events)
~ 1,200 M posts/year (1.56 TB)
~ 0.5 M social ad events/year (250 GB)
Digital Display Advertising
Structured / Heterogeneous
Fine grained (seconds, events)
~ 145,000 M ad serving events (11 TB)
Search Engine Marketing
Structured / Heterogeneous
Fine grained (seconds, events)
~ 191 M search events (20 GB)
Site Analytics
Structured / Heterogeneous
Data aggregated (daily, weekly, monthly)
~ 51 M records (11 GB)
Off-line Advertising
Structured / Highly Heterogeneous
Data aggregated (minutes, hourly, daily,
weekly, monthly, …)
Not So Big Data
(but require to be integrated with the rest
of data sources)
Customer data
(CRM, Sales, Visits to Store, …)
Structured? / Highly Heterogeneous
Fine grained and aggregated
Requires to be integrated with the rest of
data sources)

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Data Sources
 Data Heterogeneity
 From structured to unstructured formats
 Formats non-standardised
 Different levels of aggregation
 From fine-grained posts and events
 To time-series aggregated at different time periods (every second, hourly, daily, etc.)
 Different data sizes
 Big Data vs. “not so big” Data (both useful)
 Data Integration is a challenge
 Even for the same kinds of data (Twitter vs. Facebook, Google vs. Affiliate Networks)

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Example Applications
 Brand Reputation Monitoring
 Market Research
 Social CRM
 Measuring the Performance of Communication
 Supporting Digital Media Planning and Buying

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What are the 3 things that a company needs to focus on?
2. Alignment between technology and specific
customer needs
From data observations to actionable knowledge
Deliverables must go beyond basic metrics extracted from
social media monitoring

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SoA: Brand Reputation Monitoring
 Obtain brand reputation KPIs and monitor its evolution over time
 Brand Popularity
 Volume of mentions to a brand in social media
 Brand Valuation
 Sentiment of the opinions mentioning the brand
 Brand Attributes
 Topics mentioned when talking about the brand

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Brand Reputation Monitoring (data processing workflow)
Content Search
Author
Reputation
Analysis
Data
Warehousing &
Query
Content
Extraction
Unshorten
Links
Language
Detection
Sentiment
Analysis
Opinion
Clipping
Topic
Identification
Spam
Detection
POS-Tagging

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SoA: Brand Reputation Monitoring
So what?
Are these basic insights enough?
Which is the status of my market by segments of
consumers?
How can I focus CRM in social media on the appropriate
targets?
Which is the performance of my advertising campaigns
on paid media?

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From brand reputation to market monitoring
McKinsey (2009). The Consumer Decision Journey
 The Consumer Decision Journey + Socio-demographic Segmentation
80% 20%
75% 25%
Gender
Age
Location
Purchase Intention
75% 25%

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 Data intensive analysis processes of social media content and metadata
combined with site analytics

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Social CRM
 Challenge
 Handling thousands of brand consumers in social media
 Community managers cannot handle every social media
user
 Approach
 Dealing with communities
 Focusing communication on relevant consumers
 Discovering information propagation paths
 Big graph analysis techniques
 Thousands of brand consumers (nodes)
 Millions of relationships (friend of, follows, …)

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Measuring the Performance of Communication
 Challenge
 Measure the influence of paid media
over earned media
 Approach
 Generate time series for buzz and
advertising pressure
 Detect events on time series
 Find explanations to the events
 Correlate buzz with advertisement
pressure
Brand popularity
Campaign GRPs

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What are the 3 things that a company needs to focus on?
3. Human Resources & Methodology
It is only technology?
Skills & Workflow

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Cannot be automatized
Business objectives and
application success criteria
must be defined by the
Organization Strategist and
Customers
BIG Data application
requirements
Cross Industry Standard Process for Data Mining
Business Understanding

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Data Understanding
Cannot be automatized
Domain Experts and Data
Analysts
To become familiar with the data
To identify data quality problems
and solutions
To asses that the data is valid for
achieving business objectives
Cross Industry Standard Process for Data Mining

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Data preparation
Can be automatized
Examples:
 Remove spam from content gathered
 Filter content according to its
language
 Filter content according to is context,
etc.
Application Developers
 BIG Data Warehousing skills (e.g.,
HIVE, PIG, …)
 AI skills (e.g., NLP)

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Modeling
 Can be automatized
 Application Developers with expertise in
Data Mining and AI
 Data mining frameworks
 Machine learning classifiers
 Clustering techniques
 Statistical analysis tools
 …

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Evaluation
 Cannot be automatized
 Data Scientists are required to validate
the models
 Assessing the correctness of the model
 Validating that a correlation found imply
causality
 Assessing that the sample used is
representative
 …

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Deployment
 Cannot be automatized
 Knowledge obtained must be translated
into a final report aligned with business
for consumers
 Data insights enhanced by Consultants
work
 From observations to decision support
 From KPIs to recommendations