Social Media Analytics Cycle A six-step procedure called social media analytics must be followed to extract the needed business insights from social media data. The six-step process involves the science and the art of deriving business insights from social media data. Interestingly, the social media analytics cycle elements resemble management techniques employed in businesses, such as establishing goals and objectives consistent with the company’s mission. 200 Visualization Business Objectives Interpretation Analyzing Identification Extraction Cleaning Figure 3. The Social Media Analytics Cycle Source: Digital Analytics for Marketing, 2018, p. 176. Step 1: Identification. The art part of Social Media Analytics is the identification stage, which focuses on finding the right source of information for analysis. The information (such as text, conversation, and networks) accessible through social media platforms is enormous, diverse, multilingual, and noisy. Therefore, it is extremely important to formulate the appropriate question and comprehend the data to be analyzed to obtain valuable business insights. The data’s type and source that will be analyzed should be in line with the business’s goals. Step 2: Extraction. The best extraction method and platform tools will be determined by the type (such as text, numerical, or network) and size of the data. For instance, you can manually extract small amounts of numerical data by going to your Facebook fan page, counting likes, and copying comments. The ability to build apps, widgets, websites, and other tools based on open social media data for other entities (such as customers, programmers, and other organizations) is the most significant advantage of using an application programming interface (API). Step 3: Cleaning. This step involves removing unwanted data from the automatically extracted data. Some data may need cleaning, while others can go directly into analysis. In the case of text analytics, cleaning, coding, clustering, and filtering text data may be needed to get rid of unrelated text using natural language processing (NLP).