4620 Course Pack.pdf - An Introduction to Social Media Analytics CHAPTER OBJECTIVES After reading this chapter readers should understand \u2022 \u2022 \u2022 \u2022

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Unformatted text preview: An Introduction to Social Media Analytics CHAPTER OBJECTIVES After reading this chapter, readers should understand: • • • • • • • Composition of the seven layers of Social Media Analytics Origin and history of Social Media Analytics Common goals, KPIs and use cases for Social Media Analytics Descriptive, predictive and prescriptive analytics for social media Differences between Business Analytics and Social Media Analytics Challenges to the efficient use of Social Media Analytics How to use the Social Analytics Vendor Assessment 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. Introducing the Seven Layers of Social Media Analytics In this book, we have posited that the analytics of social media is best understood as a series of data layers. Determining the best social data layer(s) to utilize for business issues is where the art and science of Social Media Analytics merge. 15034-0228d-1Pass-008-r01.indd 167 5/30/2017 5:03:56 PM 168 8 An Introduction to Social Media Analytics Figure 8.1 Seven layers of social media analytics. Source: Gohar F. Khan The science part of social media analytics requires a combination of skilled data analysts, sophisticated tools and technologies, and reliable/cleaned data. Getting the science right, however, is not enough. To effectively consume the results and put them into the action, the business must master the other half of analytics; that is, the art of interpreting and aligning analytics with business objectives and goals. Each layer of social media carries valuable information and insights that can be harvested for business intelligence purposes by using layer-specific Social/Text Analytics platforms as covered in this book. Out of the seven layers, some are visible or easily identifiable (e.g., text and actions), and others are mostly invisible (e.g., social media and hyperlink networks). The following are seven social media layers that will be discussed in detail in the subsequent chapters. 1. Text 2. Networks 3. Actions 15034-0228d-1Pass-008-r01.indd 168 5/30/2017 5:03:56 PM Analytics An Introduction to Social Media 4. 5. 6. 7. 8 169 Hyperlinks Mobile Location Search engines Definition of the Seven Layers Layer One: Text 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. Layer Two: Networks Social media network analytics extract, analyze, and interpret personal and professional social networks, for example, Facebook, Friendship Network, and Twitter. Network analytics seeks to identify influential nodes (e.g., people and organizations) and their position in the network. Layer Three: Actions Social media actions 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. The case study included at the end of the chapter demonstrates how social media actions (e.g., Twitter mentions) can be used for business intelligence purposes. Layer Four: Hyperlinks 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. Layer Five: Mobile 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). Later on, in this book, we will discuss mobile analytics and provides a practical tutorial on analyzing and understanding in-app purchases, customer engagement, and mobile user demographics. 15034-0228d-1Pass-008-r01.indd 169 5/30/2017 5:03:58 PM 170 8 An Introduction to Social Media Analytics Layer Six: Location 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. Layer Seven: Search Engine Analytics Search engines have underpinned how the Internet is used and leveraged for over two decades, and there is both a science “Search Analytics” and art in “Search Engine Analytics.” Although analytics results can be interpreted in many ways, some results are more meaningful and useful than others. We believe that domain-specific knowledge and training are necessary to extract the most useful interpretation from any data derived with analytics (of any kind). In fact, without a well-crafted and aligned social media strategy, most businesses will struggle to get the desired outcomes hoped for out of the analytics platforms being employed (regardless of the platforms). Furthermore, we believe the best way to actualize data is through the alignment of the analytics being deployed and very well-defined, and specific business goals; the alluded to alignment process will be discussed in detail in a later chapter. Emergence of Social Media Analytics 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, or a business or manufacturing activity carried on in a person’s home, as early as 2003 based on the authors’ personal experience. 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 Figure 8.2 Google Trends visualization showing the beginning of the use of the term “Social Media Analytics.” Source: WebMetricsGuru Inc. using Google Trends. 15034-0228d-1Pass-008-r01.indd 170 5/30/2017 5:03:58 PM Analytics An Introduction to Social Media 8 171 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. Some Popular Reasons for Using Social Media Analytics • • • • • • • Measure brand loyalty Generate business leads Drive traffic to owned media (Facebook pages, corporate blogs, company webpages, organizational microsites, specific mobile applications, etc.) Predictive business forecasting Demographics and psychographics around specific audiences and topics Business intelligence and market research Business decision-making However, it is hard to put a dollar value on the data without accurately tracking every step in the process of acquiring customers. Goals of Social Media Analytics The main purpose of Social Media Analytics is to enable informed and insightful decision-making by leveraging social media data.1 Figure 8.3 Social media’s role informing each business function/stakeholder. Source: WebMetricsGuru Inc. 15034-0228d-1Pass-008-r01.indd 171 5/30/2017 5:03:59 PM 172 8 An Introduction to Social Media Analytics 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 influential social media followers, fans, and friends? Who are our influential 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 traffic 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? Social Media Analytics KPIs 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 (see a list of suggested KPIs matched to business goals in Table 8.1). Social Media vs. Traditional Business Analytics While the premise of both social media and traditional business analytics is to produce actionable business, they do however slightly differ in scope and nature. Table 8.2 provides a comparison of social media analytics with traditional business analytics. As an emerging field, it may not be appropriate to use the term conventional for business analytics; we do so here for comparison purposes only. The most visible difference between the two types of information comes from the source, type, and nature of the data that is being mined. Unlike the traditional business analytics of structured and historical data, social media analytics involves the collection, analysis, and interpretation of semistructured and unstructured social media data to gain an insight into the contemporary issues while supporting effective decision-making.2 Social media data is highly diverse, high volume, realtime, and stored in third-party databases in a semi-structured and unstructured format. Structured business data, on the other hand, is mostly stored in databases and spreadsheets in machine-readable format (e.g., rows and columns). Thus it can be easily searched, computed, and mined. Unstructured and semi-structured social media data is not machine readable and can take a variety of forms, such as the 15034-0228d-1Pass-008-r01.indd 172 5/30/2017 5:04:00 PM 15034-0228d-1Pass-008-r01.indd 173 Table 8.1 Social media Key Performance Indicators matched to key business goals. Aligning Business Goals & KPIs (usually Intermediate Metrics, in the case of Social Media) for Business Success Business Goals KPI 1 KPI 2 KPI 3 KPI 4 KPI 5 Awareness Share of voice Social community growth Reach, volume of conversations Sentiment analysis (+/- neutral) Unique commenters Engagement Interactions per follower Daily active users % of community interacting Viral content spread Hashtag/meme use Lead Gen Cost to acquire leads from social Web referrals via social media Qualified sales leads via social Growth of reach in targeted audiences Number of downloads of select content Conversion Downloads via tracked links Revenue via tracked links Cost per acquisition (CPA) Increase % of social conversions Revenue attribution via Influencers Customer Support Cost savings Decreased time of issue resolution Sentiment change on support issues Number of issues resolved Resolution rate per issue/agent Advocacy Number of active advocates Volume of advocate conversations Volume of brand advocates conversations Influence score and reach of advocates Revenue attributed to advocates Innovation Number of ideas submitted related to products or services Number of ideas that are developed into products or services Number of bugs that are fixed in developed products or services Community feedback from development of products or services Engagement rate in product development forums Source: WebMetricsGuru INC. 5/30/2017 5:04:00 PM 174 8 An Introduction to Social Media Analytics Table 8.2 Social Media Analytics vs. conventional Business Analytics. Social Media Analytics Business Analytics Semi-structured and unstructured data Structured data Data is not analytical friendly Data is analytical friendly Real-time data Mostly historical data Public data Private data Stored in third-party databases Stored in business-owned databases Boundary-less data (i.e., Boundary within the Internet) Bound within the business intranet Data is high volume Data is medium to high volume Highly diverse data Uniform data Data is widely shared over the Internet Data is only shared within organizations More sharing creates greater value/ impact Less sharing creates more value No business control over data Tightly controlled by business Socialized data Bureaucratic data Data is informal in nature Data is formal in nature Source: Gohar F. Khan. contents of this book, Facebook comments, emails, tweets, hyperlinks, PowerPoint presentations, images, emoticons, videos, etc. Thus, it is not analytics-friendly and needs a lot of cleaning and transformation. Another visible difference comes from the way the information (i.e., text, photographs, videos, audio, etc.) is created and consumed. Social media data originates from the public Internet and is socialized in nature. Socialized data is provided for the benefit of humanity; it is created and consumed using various social media platforms and social technologies to maintain social and professional ties (e.g., Facebook, LinkedIn, etc.), to facilitate knowledge sharing and management (Wikipedia, blogs, etc.). Socialized data creates awareness (i.e., Twitter), or to exchange information in the form of text, audio, video, documents, graphics, to name a few.3 Social media data is generated by people communicating with each other through social media. Social media is not like the common Business Analytics data, which is structured and formal in nature and is often controlled by organizations and bound within an organizational network or intranet. The value of socialized data is determined by the extent to which it is shared with other social media accounts (e.g., people or organizations): the more it that is shared (i.e., socialized), the greater its overall value. However, it is important to point out that most social media metrics/ KPIs are engagement-based and do not yield a tangible Return on Investment (ROI); instead, social media produces intermediate, activity-based metrics that support traditional business metrics (but do not replace them). For example, the value/effect of 15034-0228d-1Pass-008-r01.indd 174 5/30/2017 5:04:00 PM Analytics An Introduction to Social Media 8 175 information can be considered an “Intermediate Metric” and is measured by the growth of followers (e.g., on Twitter or Facebook). On the other hand, most of the common business data and metrics are confined within an organization’s databases for use within the organization, and can serve as a source of competitive advantage for that organization. Types of Social Media Analytics Like any business analytics, social media analytics can take three forms: 1. Descriptive analytics 2. Predictive analytics 3. Prescriptive analytics Descriptive Analytics 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 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 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 figures 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 (see ). Alternatively, based on analyzing your social media users’ languages, it can suggest if it is time to create a new Twitter account for another language. Prescriptive Analytics While predictive analytics help to predict the future, prescriptive analytics suggest the best action take when handling a 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. 15034-0228d-1Pass-008-r01.indd 175 5/30/2017 5:04:00 PM 176 8 An Introduction to Social Media Analytics Social Media Analytics Cycle Social Media Analytics is a six-step irrelative process (involving both the science and art) of mining the desired business insights from social media data (see Figure 8.4). At the center of the analytics is the company. We want objectives that will inform each step of the social media analytics journal. Business goals are defined at the initial stage, and the analytics process will continue until the stated business objectives are fully satisfied. The steps may vary considerably based on the layers of social media data-mined (and the type of the tool employed). The following are the six general steps, at the highest level of abstraction, that involve both the science and art of achieving business insights from social media data. Interestingly, the steps of the Social Media Analytics cycle are like processes that are used to manage corporations, such as setting goals and objectives that are aligned with the business’s vision. Managing a business, which often involves identifying risks and controls, is like performing the same thing with data. Figure 8.4 The Social Media Analytics cycle. Source: Gohar F. Khan. 15034-0228d-1Pass-008-r01.indd 176 5/30/2017 5:04:00 PM Analytics An Introduction to Social Media 8 177 Step 1: Identification The identification stage is the art part of Social Media Analytics and is concerned with searching and identifying the right source of information for analytical purposes. The numbers and types of users and information (such as text, conversation, and networks) available over social media are huge, diverse, multilingual, and noisy. Thus, framing the right question and knowing what data to analyze is extremely crucial in gaining useful business insights. The source and type of data to be analyzed should be aligned with business objectives. Most of the data for analytics will come from business-owned social media platforms, such as an official Twitter account, Facebook fan pages, blogs, and YouTube channels. Some data for analytics, however, will also be harvested from nonofficial social media platforms, such as Google search engine trends data or Twitter search stream data. The business objectives that need to be achieved will play a major role in identifying the sources and type of data to be mined. Aligning social media analytics with business objectives is discussed in a later chapter. Step 2: Extractio...
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