Big Data Technologies for Social Media Analytics - Impetus Webinar

1. Impetus Technologies Inc.
Big Data Technologies for Social
© 2014 1 Impetus Technologies
Media Analytics
Recorded version available at
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8

2. Outline
• Social Media Analytics- Need and Benefits
• Effective convergence of disparate data sources
• Big Data technologies to enable Social Analytics
• Our recommended approach
• Industry relevant use cases
© 2014 2 Impetus Technologies
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3. Social Analytics
Recommendation
Engine
© 2014 3 Impetus Technologies
Reports and
Statistics
Data visualization Sentiment Analysis
via Interactive
Interface
Social Media Sources
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4. Business Intelligence & Product Research
 Customer Analysis
 Identifies users from different geographies,
locations
 Tracks users activities to determine usage
patterns
 Feature Analysis
 Track the usage of various social features
 Product Growth Analysis
 Track customer feedback on products
 Target the right customers
 Recommendation Engine
 Related products and customers
 Third Party Data Analysis
 Analysis of customers on third party sites
© 2014 4 Impetus Technologies
Social Analytics provides smarter
ways of data tracking, powerful
analytics and metrics for informed
decision making
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5. How it Helps?
Outcome Based Approach
• Customer retention
• Brand building and recall (harvests/ address sentiment)
• Simplifies customer service
• Reduces operational cost
• Builds up the customer base
• Understands customer’s opinions and addresses their
needs
• Competition benchmarking
• Proactive on demographic changes
© 2014 5 Impetus Technologies
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6. Convergence of Data Sources
Data Sources
© 2014 6 Impetus Technologies
Website Traffic Analysis
(On-site web analytics)
Internal CSR Logs, Customer
Queries
Automated Agent discussions
Complaints and Resolutions
Employee Insights
External Data Sources
(Off-site web analytics)
Industry Reports
Market Research
Social Media
Social Media Analytics
Social Media Analytics effectively converges on-site, social media and third party data
to extract useful information
Recorded version available at
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7. Technical Tenets of Social Media Analytics
Data Sources
© 2014 7 Impetus Technologies
Website Traffic Analysis
(On-site web analytics)
Internal CSR Logs, Customer
Queries
Automated Agent discussions
Complaints and Resolutions
Employee Insights
External Data Sources
(Off-site web analytics)
Industry Reports
Market Research
Social Media
Social Media Analytics
Clustering Classification Sequential classification
Entity extraction Event extraction Communication graph
Recorded version available at
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8. Why Big Data for Social Analytics?
• Large data volumes in the order TBs and PBs
• Complex unstructured data from social sources
• Deeper insights into customers and trends
• Storing images, videos
• The bottom-line - $/TB
© 2014 8 Impetus Technologies
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9. Our Recommended Approach
Technologies
• Data collection - Social media data
– Live feeds
– Historical bulk data
• NLP (NLTK is a good option)
• Data preparation/ Mashup
– M/R, PIG, Hive, Oozie, R, Sqoop
• Classification/ Clustering (Mahout)
• Recommendation (Mahout)
• Loopback/ Feed output to live applications
• Analytical reporting and deep mining
© 2014 9 Impetus Technologies
Recorded version available at
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10. Our Recommended Approach
• Collecting Twitter Feed (Streaming feed) using filter fire
hose
– Tweets for keywords
– Based on brand, product, category, industry, product
segment, special offers and marketing buzz words
– Streaming API and HBASE based sink for high writes
• Collect/create training data
– Standalone Tweets for individual keywords
© 2014 10 Impetus Technologies
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11. Our Recommended Approach
• Creating or classifying text data and demographics
• Quantitative analytics
• Ascertaining daily trend
• General tweets v/s product-specific tweets
• Tweets targeted at competitors v/s own product
• Location based trends (for available data sets)
• Identifying and categorizing the output
• Sentiment analysis of own product - Good, Neutral,
Bad
• Use training data for classification - Mahout/NLTK
• Run trained models on Tweet data - Mahout/NLTK
© 2014 11 Impetus Technologies
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12. Our Recommended Approach
• Mash up Analytics from RDBMS with Social media
analytics
• Using customer data to recommend new/related
products
• Preparing mock customer data for Social ID mapping
• Running recommendations (item or user based) using
Mahout
• Analytical Reporting
• Demonstrates drill down reports on data generated by
Mahout
• Reports over Hive/MySQL using a traditional Reporting
product or framework
© 2014 12 Impetus Technologies
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13. © 2014 13 Impetus Technologies
iLaDaP
Impetus Large Data Analytics Platform

14. iLaDaP- Technology Stack
• Scalable data store
– Hadoop HDFS
– Hbase
• Connectors (In/Out)
– Flume
– Sqoop
– Messaging queue
– ESB- Apache Camel
• Analytics and ETL
– Mahout for NL and text mining
• Classification/ Clustering
• Recommendation
– Oozie for complex ETL and workflow
– JDBC/ODBC compliant Analytics tools – Intellicus, Jasper etc.
© 2014 14 Impetus Technologies
Recorded version available at
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15. Case Study- Financial Services
The Client
– Leading financial services company
Key Challenge
– Recommend products based on User profile/location
– Recommend alternate products based Social Media feedback
Impetus Solution
• Proposed iLaDaP based solution
• Sentiment Analysis using Naïve Bayesian algorithm for
classification/sentiment analysis
• Clustering using k-means algorithm of Mahout
• Apache Mahout based recommendation engine
Benefits Realised
• Better product recommendations
© 2014 15 Impetus Technologies
Recorded version available at
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16. Case Study- Online Retailer
The Client
– Leading online product retailer
Key Challenge
• Recommendation engine
• Cross product customer analysis
• Provide ‘Big Picture’ across business units
Impetus Solution
• Proposed iLaDaP based solution
• Clustering using k-means algorithm of Mahout
• Apache Mahout based recommendation engine
Benefits Realised
• True centralized business overview across product and business lines
© 2014 16 Impetus Technologies
Recorded version available at
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17. Summing Up
• Using Big Data technologies for Social Analytics needs a
well-thought of strategy
• Open source yields better results for social media data
• Hadoop based Big Data Analytics is a scalable and cost
effective option.
• Selecting the right tools is the key to build a successful
Social Analytics EDW using Big Data
• Easy extension of the existing Data Warehouse and
Analytics infrastructure is possible to leverage existing
investments
© 2014 17 Impetus Technologies
Recorded version available at
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18. © 2014 18 Impetus Technologies
About Impetus

19. • Strategic partners for software product engineering and
R&D
• Thought leaders in cutting-edge technologies
• Mature processes and practices that are methodical, yet
flexible
• Diverse domain expertise
© 2014 19 Impetus Technologies
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20. © 2014 20 Impetus Technologies
Q & A

21. © 2014 21 Impetus Technologies
Thank You
Write to us at inquiry@impetus.com
Follow us on Twitter @impetustech
Recorded version available at
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