We are living in an era where people constantly interact with each other. With the rapid development of social computing and mushrooming of social media services, much of social interaction is mediated by information technology and takes place in the digital realm. An average Internet user consumes and shares large amounts of digital content every day through popular social online services, such as Facebook, Twitter, YouTube, Instagram and SnapChat.
The popularity of social media and computer-mediated communication has resulted in high-volume and highly semantic data about digital social interactions with diverse social uses and rich meanings which includes communication text, images and videos for entertainment and self-representation, sharing of news and other 3rd party content in social media . This Social data explosion has resulted in trends and studies about the emerging topic of Social Media analytics and Big Social Data.
Big Social Data refers to large data volumes that relate to people or describe their behavior and technology mediated social interactions in the digital arena whereas Social Media analytics is collecting information or data form the social media websites, blogs etc. and uses it in business purpose or decision making. The volume and semantic richness of such data opens enormous possibilities for utilizing and analyzing it for personal, commercial as well as social purposes
Big Data and Social Media
The most important concept in understanding Big Data‘s impact on social media marketing strategies is that social media is part of big data. That is, social media is one of Big Data’s most significant sources as 90 percent of the available data in the world was collected over just the previous two years comes from “unstructured” sources, like social media and this is a continuous process. This unending influx of content from social media is indeed what has allowed the data analytics to toss the coin of “Big Social Data Analytics.”
Big Data in Social Media Analytics
The main goal of the big data analytic is to help organization to make better business decision,future prediction, analysis large numbers of transactions that is done in organization and update the form of data that the organization uses . Example of big data Analytics are big online business website like Flipkart, snapdeal uses Facebook or Gmail data to view the customer information or behaviour. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable with the help of Big data dashboard based reporting services . Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze the previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions. It helps us to uncover hidden patterns, unknown correlations, market trends, customer preferences etc. It leads us to more effective marketing, revenue opportunities, better customer service etc.
This Social Big Data can be analyzed through predictive analytics, content based analytics, , Audio & Video based analytics, statistical analytics and data mining can be used in application areas for customers of :
The Benefits of Big Data in Social Media Analytics
Content is information, so are views, likes, shares, follows, retweets, comments, and downloads. When we think of Big Data in relation to social media, we must first realize that they are not separate from one and the same. Social media is no longer merely an option for businesses but a requisite component of success. So any analysis of social media marketing data, to be effective, must be viewed in the larger context of all of a business’s market penetration, brand engagement, and other return on investment metrics. This inseparability of social media and Big Data empowers new marketing strategies. The volume and scope of Big Data allows for the creation of more predictive approaches to analysis, marketers can now see with increasing clarity into the future to gauge the likely effectiveness of a strategy, rather than relying on past performance. This will foster the development of new approaches geared at predicting customer behavior, and can help limit the amount of costly and timely A/B testing a marketer would have to perform. By using Big Data ETL tools a retail business can potentially increase operating margins by over 60 percent (A report by McKinsey & Company)
This shift toward Big Data will also help in an era of customized algorithms, allowing individual companies to analyze their marketing efforts.The rapid increase of this new customized form of data analytics will empower small businesses with limited resources to compete on a more even playing field with even their bigger, wealthier competitors. More and more marketing success will be measured not by the quantity of interactions with your data but the targeted relevance of it in relation to your own specific goals and objectives.
Lessons from the past show that there are winners and losers in any move to a new change and there will also be winners and losers in this move to Big Data Services. The winners will be those who take advantage of the new technology, new methods of customer acquisition and engagement, and, more importantly, new possibilities for creating user experiences on emerging platforms.
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