Social media analytics powered by data science
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The document discusses social media analytics using big data and data science. It begins by defining social media and its importance for businesses, as well as big data analytics. It then explains how data science can be leveraged in social media analytics to gain powerful insights through techniques like sentiment analysis, social network analysis, and identifying top influencers. Specific use cases are presented for industries like finance and opportunities discussed for applying these techniques globally. Examples are provided of analyzing social media data for companies like banks and Legoland park.
Social media analytics powered by data science
- 1. Social Media Analytics Powered by Data Science Presented by Navin Manaswi
- 2. 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
- 3. Global Digital Snapshot UAE Digital Snapshot
- 5. Social Media Data has Volume Velocity Veracity Variety We need to use Big Data and Data Science to make use of it
- 6. 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
- 7. Social Media in Finance
- 8. Glimpse of Social Media Analytics : Sentiment Analysis, Opinion Mining, Social Network Analysis, Wordcloud, Top Influencer
- 9. What is Social Media Data ? Any data available on social media which can be leveraged to get actionable insights Example of social media data: Shares Likes Mentions Impressions Hashtag usage URL clicks Keyword analysis New followers Comments
- 10. 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”
- 11. Social Media Types Social Media Types Social Networking Micro blogging Social News Media Sharing Blog Comments and Forums Social Bookmarking
- 13. Advantages of Big Data Key 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
- 14. 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 stocks Improve retail store performance and inventory turns Improve demand planning and reduce wastages. Improve campaign target segment response Increase sales and market share Improve customer loyalty and brand affinity Improve customer segmentation Understand customers’ need better Increase upsell dramatically Increase cross-sell dramatically Big Data Analytics Use cases Business Outcome
- 15. 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
- 16. 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" 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 ?
- 17. Big Data Analytics Achievement Google famously showed that they could predict flu outbreaks based upon when and where people were searching for flu-related terms :
- 18. Big Data Opportunity in World
- 20. Hadoop : Overview A 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
- 21. Power of Data Science in Social Media Analytics 1. 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
- 22. Social Media Analytics : For Legoland
- 23. Social Network Analysis : For Legoland Social Media Analytics : For Legoland
- 28. 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
- 30. Thank You! Ready for your Questions