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Social Media Analytics: Importance, Steps, Use Cases in 2024

Social media is one of the five sources from which vendors mine alternative data from, a new type of data used in investing, e-commerce, market trend analysis, and other areas. We’ve already talked about the analysis of alternative data, the steps involved, and the tools needed for the process. You can start by reading that to catch up with the topic for today’s discussion.

In this article, we discuss how sentiment data is collected on social media, the steps that go into analyzing it, and share some case studies and use cases of companies that have successfully used social media data to better cater to their audience.

What is social media sentiment?

People discussing their feelings about a product, brand, or service on social media is known as social media sentiment.

Analysts use comments, shares, likes, interactions, and posts to get a sense of how people feel about a company’s offerings.

What is social media analytics?

The term “social media analytics” refers to the collection and analysis of data from social networks such as Facebook, Instagram, LinkedIn, and Twitter. Analytics is typically used by marketers to track online conversations about products and businesses.

Why is social media analytics important?

Social media platforms allow companies to learn directly from their target audience regarding the product, service, or project that they are thinking of investing in.

“Lack of a market” for a product was the 2nd most important reason for the demise of start-ups, behind only “failing to raise new capital.” That’s why it’s now more important than ever for companies to gain new insights into the market they’re thinking of expanding into. Social media analytics can help them do just that.

What are the steps in social media analytics?

There are three main steps in social media analytics:

1. Data identification

Data identification refers to the process of identifying and filtering out the data that is useful to the process from a sea of available data on the Internet. The analysts’ job is to distinguish the irrelevant from the useful, so the data can be presentable to their clients, be it investors, marketers, or retailers. In an attempt to filter the data, they go through the following steps:

  • Type and structure: Structured data is data in a machine-readable format, such as TXT, while unstructured data includes images or videos.
  • Language: This refers to the language in which the content is written in. If the data is in Swahili, it is of no use to analysts or end users unless it has at least been translated into a language that can be used by all stakeholders.
  • Sentiment: Sentiment is the perceptible tone of the audience when they discuss a product, service, or company on social media.
  • Region: The data should relate to areas covered by the project in which the investment is to be made.
    • Recommendation: In order to obtain regional-specific data, users can leverage web scrapers with location proxies to target region-specific websites.
  • Venue: The platform on which the data is at (Facebook, Twitter, LinkedIn, etc…)
  • Time: The data should be within the analyzed time frame.
  • Ownership: Copyright laws have to be adhered.

2. Data analysis

Data analytics is the set of activities that transform raw data into actionable insights. In terms of social media data, analytics can take the form of sentiment analysis (e.g., anger, happiness, disappointment), sentiment factors (e.g., social issues, environmental factors, product pricing), geography, demographics, and more.

Case study:

An example of how sentiment analysis can play an important role in B2C retail concerns an American mattress and pillow manufacturer, MyPillow. MyPillow’s current CEO, Mike Lindell, is a Republican and a longtime supporter of Donald Trump. In 2016, retailers such as Bed Bath & Beyond, Kohl’s, H-E-B, Today’s Shopping Choice and Wayfair all pulled MyPillow products from their stores, following online pressure from a Twitter handle called Sleeping Giants with a growing support base.

3. Information interpretation

Interpretation of data is about answering the question of, “how could this data make sense efficiently so it could be used in data-driven decision making?”. A common method of interpreting the data is through visualizing it, through charts, graphs, tables, maps, infographics, and dashboards.

How to collect alternative data from social media?

Web scraping is used to automatically extract data from targeted social media websites by downloading the relevant information using a set of word processing functions. Subsequently, the structured data can be saved onto a spreadsheet or into an interpretable format.

What are the use cases of social media analytics?

The following are some of the use cases of social media analytics:

  • Targeted advertisement: Companies can use the insights they gain from social media analytics to run targeted advertising campaigns that present their products to the most specific audiences in terms of age, gender, social/political views, etc.
  • Lead generation: Somewhat related to targeted advertising is the concept of lead generation. Marketers can use web scraping features to find out how many users on the internet are interested in a product similar to the one they want to launch (see Figure 1).
Bored Elon Musk Tweets
Figure 1: A satirical example of a Twitter handle, and its followers, showing affirmations to a product an inventor might create.
  • E-commerce: Overall, social media analytics can offer a variety of benefits to e-commerce. One example is sentiment analysis, which can help a company build a better brand by listening to its customers. Starbucks, for example, decided to switch to “Sippy” cups, which don’t require a straw, after environmental concerns from its customers about its plastic straw offering.

For more on alternative data

If you are curious about learning more about alternative data, we have written extensively about it, and its use cases, in the past:

If you are interested in leveraging alternative data for your business, we have a list of 24 vendors prepared.

Go through them and reach out to us to help you find the right vendor:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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