social media analytics powered by data science

Social Media AnalyticsPowered by Data Science

Presented by Navin Manaswi

Flow of the Presentation● Social Media : What is it? How big is it? What are its types? How much important is it

for businesses? Use cases

● Big Data Analytics : What is it? How much important is it for businesses? How do we do it ? Use cases Success Stories Opportunities across globe

● Power of Data Science in Social Media (Big Data) Analytics : How can you leverage for your business? Powerful insights Sentiment Analysis Social Network Analysis Top Influencers Challenges

Global Digital Snapshot UAE Digital Snapshot

Social Media Data has

VolumeVelocityVeracityVariety

We need to use Big Data and Data Science to make use of it

Importance of Social Media in Industries Nowadays people tend to depend on the advice of friends and known people while making important decision related to any product and service. And, they are using social media in the form of social networking, social shopping and social bookmarking more than ever as a source to be able to make important decisions wisely

Visa, Wells Fargo, AMEX and JPM Chase try to move ahead in their quest for dominance, and so, the competition for the top slot is getting intense day by day

If you track these major banks on social media and analyze the buzz around them, you will come to know the INSIGHTS about these banks and their products and services. For the deep dive analysis, we focus on three key factors –

1. Share of Volume,

2. Sentiment Score and

3. Top Topics of Discussion about these brands

Social Media in FinanceUse case

Glimpse of Social Media Analytics :

Sentiment Analysis, Opinion Mining, Social Network Analysis,

Wordcloud, Top Influencer

What is Social Media Data ?Any data available on social media which can be leveraged to get actionable insightsExample of social media data:

SharesLikesMentionsImpressionsHashtag usageURL clicksKeyword analysisNew followersComments

How does Social Media Data help ?Once social media data is collected, it is measured or analyzed to get actionable insights for Digital Marketing Manager, Brand Manager, Digital Marketing Strategist, Social Media Manager, Event Manager and Product Manager

“Social media data acts as the ingredients to your meal and Social media analysis acts as your recipe”

Social Media Types

Social Media Types

Social Networking

Microblogging

Social News

Media Sharing

Blog Comments and Forums

Social Bookmarking

Advantages of Big DataKey areas where Big Data can help in Marketing are:

- Create customer segments based on huge data of transaction and other attributes

- Implement more targeted marketing campaign for specific geographies or individual customers

- Create upsell and cross-sell strategies based on transactional behaviours

- Identify which promotion strategy will yield the best results in a specific chain or cluster of stores

- Determine which new product options are the most profitable or the least risky to pursue

- Better assess product price elasticity before implementing price changes

Enable Micro- Market Campaign management

Send personalised Marketing messages to consumers based on algorithmix personalized recommendation engine so as to achieve high conversion ratio.

Optimize Promotions

Increase merchandising effectiveness by leveraging social sentiment insights across geographies over a period of time

Forecasting real time demands

Forecast demands by using machine learning algorithms more accurately than ever

Improve on-shelf performance and reduce out of stocksImprove retail store performance and inventory turnsImprove demand planning and reduce wastages.

Improve campaign target segment responseIncrease sales and market shareImprove customer loyalty and brand affinity

Improve customer segmentationUnderstand customers’ need betterIncrease upsell dramaticallyIncrease cross-sell dramatically

Big Data Analytics Use cases Business Outcome

Social Media Big Data : Analytics Process

Collection of Data from various sources

Extraction and Storage of Data

Data Preparation and Data Analytics

Data Visualization : Dashboards and Reports

Data Science, Machine Learning,Natural Language Processing

Hadoop Clusters,Hive, Pig on top.

Interactive Visualization, BI Tools

APIs, Flume

Advantage: If you get the insights, you can expect 10 times higher chance of clicking the ad when the ad is shown So you can achieve 10 times higher revenue

There are more than 200,000,000 Facebook users with college degrees, and they have been each served 100 ads.let's say that Facebook wants to know which ads work best for people with college degrees. Let's say there are 200,000,000 Facebook users with college degrees, and they have been each served 100 ads

That's 20,000,000,000 events of interest, and each "event" (an ad being served) contains several data points (features) about the ad: what was the ad for? Did it have a picture in it? Was there a man or woman in the ad? How big was the ad? What was the most prominent color? Let's say for each ad there are 50 "features"

This means you have 1,000,000,000,000 (one trillion) pieces of data to sort through. If each "piece" of data was only 100 bytes, you'd have about 93 GB of data to parse. That's pretty big (but still arguably not quite into "big data" territory), but you get the idea

Why is Big Data Analytics very important ?

Aim : To maximize click ads

Insights: Which features of ad are most effective in getting college grads to click ads ?

Big Data Analytics AchievementGoogle famously showed that they could predict flu outbreaks based upon when and where people were searching for flu-related terms :

Big Data Opportunity in World

Big Data Architecture

Hadoop : OverviewA scalable fault-tolerant grid system for data storage and processing

• Commodity hardware

• HDFS: Fault-tolerant high bandwidth clustered storage

• MapReduce: Distributed data processing

• Works with structured and unstructured data

Hadoop Design Principles

System shall manage and heal itself

• Automatically and transparently route around failure

• Speculatively execute redundant tasks if certain nodes are found to be slow

• Performance shall scale linearly

• Compute should move to data

• Simple core, modular and extensible

Power of Data Science in Social Media Analytics1. Sentiment Analysis

2. Social Network Analysis

3. Identification of Top Influencers

4. Identification of most related words

5. Understanding the main concerns of customers

6. Tracking public sentiments real time

7. Identification of social network

8. Tracking sentiments for rival products/services

Social Media Analytics : For Legoland

Sample of Social Media Analytics

Social Network Analysis : For Legoland

Social Media Analytics : For LegolandSample of Social Media Analytics

Take Away Points

1. What is Social Media?

2. Relevance of Social Media in Industries

3. What is Big Data ?

4. Social Media Big Data : What, How and Why ?

5. Data Science and Social Media Analytics

6. Use cases of Social Media Analytics

Thank You!

Ready for your Questions