Web Analytics - Chapter 6: Social Media for Digital Marketing PDF

**Chapter 6: Social Media for Digital Marketing** **The Rise of Social Media:** Social media usage worldwide has dramatically increased, with 4.9 billion users as of March 2023, expected to reach 5.85 billion by 2027. Facebook dominates with 2.958 billion users. Eastern Asia has the most social m...

**Chapter 6: Social Media for Digital Marketing** **The Rise of Social Media:** Social media usage worldwide has dramatically increased, with 4.9 billion users as of March 2023, expected to reach 5.85 billion by 2027. Facebook dominates with 2.958 billion users. Eastern Asia has the most social media users, and 85% of mobile phone users are active on social media. Millennials and Gen Z are the most frequent users, and Tiktok has experienced significant growth with a 105% increase in the US over the last two years. **The Rise of Social Media in Marketing:** Social media is such a big part of the fabric of society today that it is almost impossible to imagine our lives without it. To succeed on social media and accomplish your goals, there are several **effective strategies and opportunities** one can follow that are given in the following slides. First and foremost, establish your objectives and determine exactly what you aim to achieve through social media. Whether it\'s to increase brand awareness, drive website traffic, generate leads, or boost sales, having a clear idea of your goals will guide your entire social media strategy. Conduct thorough research to really understand your target audience\'s demographics, preferences, and behaviors. Armed with this knowledge, you can tailor your content and engage with them in a more meaningful way. Identify the social media platforms where your target audience is most active and focus your efforts there. It\'s better to excel on a few platforms than to spread yourself too thin across all of them. Produce high-quality content that is both relevant and appealing to your target audience. Utilize a variety of mediums such as text, images, videos, and interactive elements to capture attention and encourage sharing. Partner with influencers or micro-influencers with a large following that aligns with your target audience to promote your products or services, expanding your reach and credibility. Create and share videos that offer value, entertainment, or education to your audience, as video continues to dominate social media. Consider using live streaming, stories, and short-form videos to engage with your followers in real-time. Motivate your audience to create content related to your brand and products. User-generated content helps build trust, increases engagement, and expands your reach. Repost and give credit to users who create relevant content. Use chatbots to automate tasks such as responding to common inquiries or sending personalized messages. A chatbot is a type of conversational AI that automates customer support in a friendly, familiar way, providing 24/7 service. They can enhance customer service and streamline interactions, saving time and effort. Stay up-to-date and experiment with new features and formats like stories, reels, polls, and interactive stickers. These additions often receive priority in platform algorithms and can help increase visibility. **Emergence of Social Media Analytics:** 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 2025. 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. **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 **The Complex and Fragmented Ecosystem of Social Media Analytics:** Measuring and analyzing social media data there are numerous tools, platforms, and metrics available. To simplify social media analytics, define clear objectives, focus on essential metrics, choose the right tools, automate and report, use data visualization, regularly analyze and adjust, and stay informed and adapt. By implementing these strategies, you can focus on the metrics that matter most to your goals and achieve optimal results. **Strategies:** To successfully analyze social media data, it is essential to establish your objectives and key performance indicators (KPIs) upfront (which we cover in other chapters of this book). Identify the metrics that are most relevant to your goals and focus on tracking and analyzing those specific data points. It is crucial to prioritize the social media platforms that are most pertinent to your target audience and business objectives. By doing so, you can concentrate your efforts and resources on these platforms to gain deeper insights. Streamlining the process with social media management tools or analytics platforms can consolidate your data sources and provide a unified view of your social media performance. Furthermore, automated reporting systems that gather and present key metrics clearly and concisely can save you time and deliver timely and relevant data. Rather than being overwhelmed by excessive data, look for patterns, trends, and correlations to extract actionable insights that can inform your social media strategy and decision-making process. Investing in training or hiring individuals with expertise in social media analytics can simplify the process and maximize its value. Continuously reviewing your analytics strategy, based on new insights or changes in your business goals, is also crucial. By implementing these strategies, you can simplify the complex ecosystem of social media analytics and focus on the data that truly matters for your business. This allows you to gain meaningful insights and make informed decisions to optimize your social media performance and achieve your goals. **The Seven Layers of Social Media Analytics:** 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 for digital marketing. 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. 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 (see Figure 6.1), some are visible or easily identifiable (e.g., text and actions), and others are mostly invisible (e.g., social media and hyperlink networks). 1. **Text: Layer one** 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. 2. **Networks: Layer two** Social media network analytics extract, analyze, and interpret personal and professional social networks, for example, Facebook, and Twitter. Network analytics seeks to identify influential nodes (e.g., people and organizations) and their position in the network. 3. **Actions (referred to as intermediate metrics elsewhere in this book): Layer three** 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. 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. 4. **Hyperlinks: Layer four** 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. 5. **Mobile: Layer five** 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). 6. **Location: Layer six** 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. 7. **Search engines: Layer seven** 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.). **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. 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? - The main purpose of Social Media Analytics is to enable informed and insightful decision-making by leveraging social media data. - 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 6.1). Aligning Business Goals & KPIs (usually Intermediate Metrics, in the case of Social Media) for Business Success. **Social Media vs. Traditional Business Analytics:**  The value of socialized data is determined by the extent to which it is shared with other social media accounts (people or organizations): the more it that is shared (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 information can be considered an intermediate metric and is measured by the growth of followers (on Twitter or Facebook). **Types of Social Media Analytics:** Unlike the traditional business analytics of structured and historical data, social media analytics involves the collection, analysis, and interpretation of semi-structured and unstructured social media data to gain an insight into the contemporary issues while supporting effective decision-making. **Like any business analytics, social media analytics can take 3 forms:** 1. **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 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. 2. **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 3. **Prescriptive analytics:** While predictive analytics help to predict the future, prescriptive analytics suggest the best action to take when handling a situation or scenario. 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. **Prescriptive** involves telling people what they should do, rather than simply giving suggestions or describing what is done. **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. 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. **6 steps of Social Media Analytics Cycle:** 1. **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 non-official social media platforms, such as Google search engine trends data or Twitter search stream data. 2. **Step 2: Extraction** The type (text, numerical, or network) and size of data will determine the best method and platform tools that are suitable for the extraction. Small-size numerical information, for example, can be extracted manually (e.g., going to your Facebook fan page and counting likes and copying comments), and a large-scale automated extraction is done through an application programming interface (API). APIs, in simple words, are sets of routines/protocols that social media platforms (Twitter and Facebook) have developed to allow users to access small portions of data hosted in their databases. 3. **Step 3: Cleaning** This step involves removing the unwanted data from the automatically extracted data. Some data may need cleaning, while other data can go directly into analysis. In the case of the text analytics cleaning, coding, clustering, and filtering of the text data may be needed to get rid of unrelated text using natural language processing (NLP). Note that coding and filtering can be done automatically using machines or manually done by humans. 4. **Step 4: Analyzing** At this stage, the clean data is analyzed for business insights. Depending on the layer of social media analytics under consideration and the tools and algorithm employed, the steps and approach to take will vary greatly. For example, nodes in a social media network can be clustered and visualized in a variety of ways depending on the algorithm employed. The overall objective at this stage is to extract meaningful insights without the data losing its integrity. 5. **Step 5: Visualization** In addition to numerical results, most of the seven layers of social media analytics will also result in visual outcomes. The science of effective visualization known as visual analytics is becoming an important part of interactive decision-making facilitated by visualization. Effective visualization is particularly helpful with complex and large datasets because it can reveal hidden patterns, relationships, and trends. It is the effective visualization of the results that will demonstrate the value of social media data to top management. Depending on the layer, the analysis part will lead to relevant visualizations for effective communication of results. Text analytics, for instance, can result in a word co-occurrence cloud; hyperlink analytics will provide visual hyperlink networks, and location analytics can produce interactive maps. Depending on the type of data, different types of visualization are possible, including the following: - Network Data (with whom) - Topical Data (what) - Temporal Data (when) - Geospatial Data - Other forms of visualizations include trees, hierarchical, multidimensional (chart, graphs, tag clouds), 3D (dimension), computer simulation, infographics, flows, tables, heat maps, plots, (where). 6. **Step 6: Interpretation** This step relies on human judgments to interpret valuable knowledge from the visual data. The data should be presented in the right form for the person who is going to read it. It can be as dashboards, for example. Meaningful interpretation is of particular importance when we are dealing with social media data that leave room for different interpretations. Having domain knowledge and expertise are crucial in consuming the obtained results correctly. Two strategies or approaches used here can be summarized: 1. Producing easily understandable analytical results 2. Improving analytics analysis and insights capabilities **Challenges to Social Media Analytics:** 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. Managing and processing social media data can be a daunting task due to its vast volume, diverse range of formats, and real-time nature. The sheer amount of data generated every second makes it challenging to analyze effectively. Capturing and analyzing millions of records that appear every second is a real challenge. Capturing all this information may not be feasible. Knowing what to focus on is crucial for narrowing down the scope and size of the data. In addition, the dynamic and real-time nature of social media data presents challenges for timely data collection, analysis, and response. Ensuring data quality and filtering out irrelevant or misleading information is essential for accurate analysis. Furthermore, organizations must address privacy and ethics concerns to conform to regulations and ensure the ethical use of data while extracting valuable insights. **Summary:** Social media is such a big part of the fabric of society today that it is almost impossible to imagine our lives without it. social media is a constantly evolving landscape, so stay informed about trends, algorithm changes, and emerging platforms. Adapt your strategy as needed and continue to connect with your audience in an authentic and engaging way. 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 for digital marketing. 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.