Big Data Technologies for Social Media Analytics- Impetus Webinar

8/3/2019 Big Data Technologies for Social Media Analytics- Impetus Webinar

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Big Data Technologies forSocial Media Analytics

Recorded version available at

http://www.impetus.com/webinar_registration?event=archived&eid=48
http://www.impetus.com/webinar_registration?event=archived&eid=48http://www.impetus.com/webinar_registration?event=archived&eid=48


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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




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Social Analytics

Reports and

Statistics

Sentiment

AnalysisData visualization

via Interactive

Interface

Recommendation

Engine

Social Media Sources




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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

Social Analytics provides smarter ways of data tracking, powerful analytics and

metrics for informed decision making


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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 customers opinions and addresses their needs

Competition benchmarking

Proactive on demographic changes




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Convergence of Data Sources

Social Media Website Traffic Analysis(On-site web analytics)

Internal CSR Logs, Customer QueriesAutomated Agent discussionsComplaints and ResolutionsEmployee Insights

External Data Sources(Off-site web analytics)

Industry ReportsMarket Research

Social Media Analytics

Social Media Analytics effectively converges on-site, social media and thirdparty data to extract useful information

Data Sources




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Technical Tenets of Social Media Analytics

Social Media Website Traffic Analysis(On-site web analytics)

Internal CSR Logs, Customer QueriesAutomated Agent discussionsComplaints and ResolutionsEmployee Insights

External Data Sources(Off-site web analytics)

Industry ReportsMarket Research

Social Media Analytics

Data Sources

Clustering Classification Sequential classification

Entity extraction Event extraction Communication graph




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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




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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




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Collecting Twitter Feed (Streaming feed) using filter fire hose Tweets for keywords

Based on brand, product, category, industry, product segment, specialoffers and marketing buzz words

Streaming API and HBASE based sink for high writes

Collect/create training data

Standalone Tweets for individual keywords




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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




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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 orframework




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iLaDaP

Impetus Large Data Analytics Platform




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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.




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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 Nave Bayesian algorithm forclassification/sentiment analysis

Clustering using k-means algorithm of Mahout

Apache Mahout based recommendation engine

Benefits Realised

Better product recommendations


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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


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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 SocialAnalytics EDW using Big Data

Easy extension of the existing Data Warehouse and Analyticsinfrastructure is possible to leverage existing investments




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Impetus Technologies

We offer innovative product engineering

and technology R&D services




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Questions

Please send in your questions

using the chat panel




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Thank you

Mail us at [email protected] visit bigdata.impetus.com



@impetuscalling
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Documents

Big Data Technologies for Social Media Analytics- Impetus Webinar