Due to the rise of social media, communication and interaction has changed between people. Instead of untraceable direct communication from one person to another, social media technologies allow people to simultaneously communicate with many people while leaving a digital record. From a business perspective, these communications provide almost endless insights. These insights can be used to learn more about customers, find behavioral patterns and trends, help understand what different customers want, and in turn how to help businesses grow. In this blog we will discuss they key aspects of social media data analysis.
1. Collect social media data
Although this may seem the most obvious task of data analysis, it is the foundation to social media data analysis. Collect data from various social media such as internet forums, blogs, social networks, micro-blogs, podcasts, or even videos. The goal is to study the opinions and behaviors to gain insights of consumer behavior, marketing insights and product sentiment. Context accuracy is an important aspect of the data collected to be analyzed. Good Natural Language Processing (NLP) is therefore a critical requirement of tools used to gather the social media data.
2. Extract and track the flow of relevant information
Some of the basic units of information are pieces of information that propagate between users, media sites, phrases, quotes, messages, links, and tags. The goal is to extract and track units of information that correspond to each other or that have relation to what your consumers are talking about. Think about brand names, product categories, industry jargon words, etc. While collecting data, make sure to collect the right information and to collect it properly, because it is vital to a sound social media data analysis that one collects relevant information.
3. Correct for incomplete or missing data
It is important to look at the big picture when it comes to social media, but complete social media data is nearly impossible to collect and the data that is missing will bias the results. Social media data analysis cannot just be computational output, it needs carefully analysis to track sources, add content, and pull together trends.
4. Predict and model the flow of information
Some questions to ask:
How do messages spread through social networks?
How can the spread of information be predicted?
What “network effects” are crucial to spreading the word?
Identifying patterns in the flow of information and using graphs and charts will help visualize information to help strategic decision makers get a better understanding of the insights. Providing an online portal or “Dashboarding” of these insights and trends helps to disseminate social media data analysis insights to entire organizations, and can help automate insight reporting.
5. Combine the social data with other data sources-triangulate!
Triangulating the social data with other forms of data provides significant additional power to the actionability of the data. Other data forms including behavioral data, qualitative and quantitative primary research insights and syndicated data. These combinations further inform how if and how the social activity generated an ROI.
6. Harness the insights and engage social media
The true value of a social media data analysis comes in the execution of the strategy that the analysis informs. Revising social media campaigns, improving customer experiences, responding directly to customer dissatisfaction, and much more can be easily accomplished through social media data analysis
Social media has changed the game. If your business needs help with social media data analysis or you would like more information, please contact Silicon Valley Research Group or leave your comments below.