Social Media Analytics.pptx

Editor's Notes

• #4: Social Media Analytics is the art and science of extracting valuable insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. In this chapter, we examine this new and constantly emerging field that continues to evolve as social media matures. Social Media Analytics is a science as it requires systematically identifying, extracting, and analyzing various social media data using a variety of sophisticated tools and techniques (this book will examine some of the tools and technology to extract and use social media data). However, Social Media Analytics is also an art, which requires analysts, stakeholders, and business owners to align the insights gained via the analytics with business goals and objectives. We should master both the art and science of Social Media Analytics to get full value from it.
• #7: Social media text analytics deals with the extraction and analysis of business insights from textual elements of social media content, such as comments, tweets, blog posts, and Facebook status updates. Text analytics is mostly used to understand social media users’ sentiments or identify emerging themes and topics.
• #8: Social media network analytics extract, analyze, and interpret personal and professional social networks, for example, Facebook, and Twitter. Network analytics seeks to identify infl uential nodes (e.g., people and organizations) and their position in the network.
• #9: Social media actions (Intermediate Metrics) analytics deals with extracting, analyzing, and interpreting the actions performed by social media users, including likes, shares, mentions, and endorsement. Actions analytics are mostly used to measure popularity, influence, and prediction in social media.
• #10: . Hyperlink analytics is about extracting, analyzing, and interpreting social media hyperlinks (e.g., in-links and out-links). Hyperlink analysis can reveal sources of incoming or outgoing web traffic to and from a webpage or website.
• #11: Mobile analytics is the next frontier in the social business landscape. Mobile analytics deals with measuring and optimizing user engagement through mobile applications (or apps for short). We shall discuss about Mobile Analytics in greater detain in our next lesson
• #12: Location analytics, also known as spatial analysis or geospatial analytics, is concerned with mining and mapping the locations of social media users, contents, and data.
• #13: Search engines analytics focuses on analyzing search engine data to gain valuable insights into a range of areas, including trends analysis, keyword monitoring, keyword research, search results and search engine marketing (text ads, etc.).
• #14: Based on Google Trends data, the term Social Media Analytics appeared over the Internet horizon during 2008, and interest in it (based on Internet searches for the term) has steadily increased since then. Social Media Analytics was present as a cottage industry,, as early as 2003 In 2008, Google Trends began to detect enough usage of the term “Social Media Analytics” to show up in its trend reporting, and the subject is becoming ever-more popular as we move towards 2020. No doubt, the growth in the development and usage of various social media channels spawned Social Media Analytics, as the means to better understand and harness “social data.” Social media has become one of the main ways people express themselves. Because of this activity, Social Media Analytics is gaining prominence among both the research and business communities.
• #16: The main purpose of Social Media Analytics is to enable informed and insightful decision-making by leveraging social media data The following are some sample questions that can be answered with social media analytics: • What are customers using social media saying about our brand or a new product launch? • Which content posted over social media is resonating more with clients or customers? • How can we harness social media data (e.g., tweets and Facebook comments) to improve our product/services? • Is the social media conversation about our company, product, or service positive, negative, or neutral? • How can we leverage social media to promote brand awareness? • Who are our infl uential social media followers, fans, and friends? • Who are our infl uential social media nodes (e.g., people and organizations) and what is their position in the network? • Which are the social media platforms driving the most traffi c to our corporate website? • Where is the geographical location of our social media customers? • What are the keywords and terms trending over social media? • How current is our business with social media, and how many people are connected with us? • Which websites are linked to our corporate website? • How are my competitors doing on social media? Note the Social media’s role informing each business function/stakeholder in this digram
• #17: The questions, use cases, and goals that inform social media can be measured using Key Performance Indicators such as share of voice and sentiment score
• #19: Descriptive analytics is mostly focused on gathering and describing social media data in the form of reports, visualizations, and clustering to understand a business problem. Actions analytics (e.g., number of likes, tweets, and views) and certain aspects of text analytics are examples of descriptive analytics. Social media text (e.g., user comments), for instance, can be used to understand users’ sentiments or identify emerging trends by clustering themes and topics. Currently, descriptive analytics accounts for most Social Media Analytics. Predictive analytics involves analyzing large amounts of accumulated social media data to predict a future event. For example, an intention expressed over social media (such as buy, sell, recommend, quit, desire, or wish) can be mined to predict a future event (such as a purchase). Alternatively, a business manager can predict sales fi gures based on past visits (or in-links) to a corporate website. The TweepsMap tool, for example, can help users determine the right time to tweet for maximum alignment with the right audience time zone While predictive analytics help to predict the future, prescriptive analytics suggest the best action to take when handling a situation or scenario. 4 For example, if you have groups of social media users that display certain patterns of buying behavior, how can you optimize your offering to each group? Like predictive analytics, prescriptive analytics has not yet found its way into social media data.
• #21: Social media data is high-volume, high-velocity, and highly diverse, which, in a sense, is a blessing regarding the insights it carries; however, analyzing and interpreting it presents several challenges. Analyzing unstructured data requires new metrics, tools, and capabilities, particularly for real-time analytics, that most businesses do not possess
• #22: These are some of the social media analytical tools that you can use .