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. JU 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).
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. JU 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).
Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment
Section: Chapter Questions
Problem 1CE
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Please explain and give REALISTIC examples of steps 1 to 3 ONLY.
Transcribed Image Text: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).
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