7 Layers of Social Media Analytics (W/ Examples)

The seven layers of social media analytics include text, action, network, and hyperlink analytics. Others are mobile app, search engine, and location analytics. These layers form a system for marketers and data analysts to collect, organize, and analyze social media data. 

Most marketers focus on metrics such as likes, shares, and impressions. But the smart ones dive deep into social media analytics to understand their audiences better. They use the seven layers of social media analytics to understand what consumers like, talk about, or feel. They then turn these insights into action in their content plans and campaigns.

This guide covers the seven layers of social media analytics with examples. Keep reading to know how these layers help you understand users’ online behavior and relationships. Learn how to use them to improve your social media strategy. 

#1: Text analytics layer 

The text analytics layer tells you what people are saying in written content. It’s also known as text mining because it extracts meaningful information from social media text. This layer helps you understand the context behind:

• Written posts (like the tweets on X)
• Comments
• Reviews
• Captions
• Hashtags
• Opinions (on online forums)

Why does text analytics matter?

Text analytics offers insights into consumer behavior, helping marketers think ahead. People share questions, frustrations, and what excites them through written text. These conversations reveal sentiments, opinions, and perceptions that guide future campaigns.

Here are more benefits of text analytics:

• Text analytics reveals the context behind words. A negative comment on a Facebook post could mean that a customer didn’t like your product or service.
• It helps you understand sentiment. A tweet mentioning a brand holds clues about how a user feels about the brand.
• It helps you spot patterns or shifts in consumer behavior. For example, a viral hashtag tells you what topics are trending. It may also tell you what your customers care about in a certain season.
• Text analytics reveals consumers’ opinions. For instance, a LinkedIn post on a company’s page might show that consumers appreciate reliable customer support.

Examples of text analytics

• Sentiment analysis measures the tone in written content. For example, “Is this tweet positive or negative?“
• Intent mining reveals users’ intentions in text. For instance, a review like “Buy at your own risk,” shows a person who’s not willing to be a return customer.
• Concept mining extracts connected ideas or concepts from written text. For example, two customers could write these two comments to explain their experience with body lotion: “My skin feels smooth now.” “Soft, clear skin without irritation!”
• Trends mining reveals patterns from social media data and predicts future events. For example, a spike in Facebook comments mentioning natural hair gel shows a rising demand for these products.

#2. Action analytics layer

The action analytics layer tracks what people are doing online. It also measures and interprets what these actions mean in response to content. 

Examples of user engagement actions include:

• Reactions (e.g., likes, dislikes, support, celebrate)
• Shares, retweets, and pins
• Comments
• Saves
• Clicks
• Views or impressions
• Tags and mentions

Why does action analytics matter?

Action analytics helps marketers understand user intent. What users do online is often more important than what they say. Plus, it allows marketers to measure and prove their social media marketing efforts.

Let’s get into the details.

• Action analytics measures engagement. How users engage with your posts determines if the content suits them. For instance, a high number of likes, shares, and saves on a post shows it resonates with your audience. But a post with zero engagement could mean that it didn’t reach the right people.
• Action analytics reveals users’ perceptions of a brand. How users react towards a brand’s page shows what they feel or think about it. For example, users who unfollow a brand on Facebook may no longer like its services or what it stands for.
• Users’ actions show their sentiments towards a brand or its products. For instance, a spike in positive comments could show that customers are happy with the quality of your products or services. 
• Action analytics helps you measure campaign performance. Social media metrics like reach, impressions, click-through rates, and ROI help you answer campaign questions. “Did our campaign increase sales or web traffic?” Or, “Which posts drive the most shares?”

#3. Network analytics layer 

The network analytics layer focuses on social networks. It analyzes relationships, influence, and interactions online. It also offers helpful insights into consumer behavior on social media.

The most common networks on social media platforms include:

• Follower-following networks. Here, users follow other users, especially if they’re influential, experts, or thought leaders. These networks are on platforms like LinkedIn, Facebook, and X.
• Friendship networks include users who share content with people they closely relate to, for example, on Facebook.
• Group networks are on platforms like Facebook, LinkedIn, Twitter, Reddit, and Quora. Users in such groups have a common agenda or interests. They also share content or hold online events.
• Fan networks consist of users who follow an influential person. The most common ones are on Facebook, where people support musicians, artists, and politicians.
• Content networks consist of users who share a common interest in content formats like videos and images. They’re mostly common on YouTube.
• Professional networks are made up of users who share professional relations. LinkedIn is the best example of a social platform that supports professional networks.
• Dating networks use personal information like age, hobbies, and interests to match up people online.

Why do network analytics matter?

Network analytics helps data analysts and marketers answer real-world questions on social interactions. For instance, they understand:

• Who is following who?
• Who is mentioning who?
• Who are the influencers in a specific niche or industry?
• How do communities form or run on social platforms?
• How does content (or conversations) spread across a network?

Here are other benefits of network analytics:

• They help you spot influencers within your networks. For example: “Who are the key amplifiers of our brand’s message?”
• They help you identify consumers with common traits. For example, if you run a digital marketing agency, subreddits on digital marketing are great places to find clients.
• They help you understand how factors like culture affect social networks and user behavior. For instance, users from countries like Japan may form more group networks on Twitter, unlike those in the USA.

#4. Hyperlink analytics layer 

The hyperlink analytics layer analyzes the URLs people share on social platforms. It tells you where social conversations link to. Hyperlinks also reveal sources of information. For instance, a tweet can link to a blog or a news site. A LinkedIn post may lead you to a landing page or a YouTube video.

Types of hyperlinks include:

• In-links are hyperlinks to a website from other platforms. An example is a link from a Facebook post to a website. They help marketers know the source of their web traffic. 
• Out-links are hyperlinks leaving a website to other platforms. For example, a web page can link to an Instagram or LinkedIn page.
• Co-links are indirect hyperlinks among web pages. A good example is a web page linking to two different web pages. Or, two different web pages linking to another web page.

Why do hyperlink analytics matter?

Hyperlink analytics analyze content distribution channels and external influence on your audience. Marketers and SEO experts track hyperlink patterns to know what sources audiences trust or promote.

Here’s why you should track hyperlinks:

• Hyperlink analytics help you understand how users share your content across platforms. For example, you’ll know popular content sources and your audience’s favorite platforms.
• They help you analyze referral traffic. You’ll identify platforms that refer people to your website or social profiles. That way, you’ll answer questions like, “Which external sites are driving clicks to our blog or Facebook page?”
• Hyperlink analytics help you measure your platform’s authority. In-links (like backlinks) show that other sites trust your website as a source of valuable information. So, a high number of quality backlinks increases your site’s authority.

#5. Mobile app analytics layer 

The mobile app analytics layer measures how mobile apps shape users’ social behavior. For instance, it tells you how users interact with content on apps. For example, most users visit platforms like Facebook and X via their mobile apps. So understanding app usage helps shape platform-specific strategies.

The mobile app analytics layer is in two forms:

• Mobile web analytics deals with the actions, behaviors, or traits of mobile web visitors. For example, you may collect the demographics of users who visit your website via their phones. You can also track their behavior, like how they download content from your platforms.
• App analytics analyzes the behavior, traits, and actions of mobile app users. For instance, you may want to understand how your customers buy products from your company app. “Do they read reviews on your site before making purchases?”

Why does mobile app analytics matter?

Mobile app analytics helps you understand users’ engagement with apps. It also lets you track their attitude towards mobile app features. Plus, it is useful when measuring the performance of app-driven campaigns.

Here are more details:

• Mobile app analytics measure user demographics. You understand who your customers are, their age, gender, and location.
• This analytics layer analyzes user actions and behavior on mobile apps. For instance, you understand how users navigate the app. What features do they like? Which ones don’t they use? How long do they stay on the app?
• Mobile app analytics offers insights into improving the user experience. For example, you might notice that most users exit the app on the checkout page. This could mean that the payment process is slow or confusing. So, simplify the steps or add more payment options to improve the user experience.
• They help you measure campaign performance. Which item do most users buy on your app? How many sales do you make daily on your mobile app? Which app version brings the most sales?

#6. Search engine analytics layer 

The search engine analytics layer analyzes what people search for. It looks at the relationship between social media and search behavior. For example, how do social media posts reflect what people search on Google or YouTube? It also lets you spot trending topics based on what users are looking for on search engines like Google.

Why does search engine analytics matter?

Search engine analytics helps you understand how user behavior is related to social media keywords. For instance, which keywords are trending? What products or services are on the first page of Google’s SERP (search engine results page)?

Here are more benefits of search engine analytics:

• It helps you spot search trends that matter to your business. For example, you might notice that many people are searching for eco-friendly cookers online. As a manufacturer, creating relevant content on your social profiles boosts your reach and sales.
• Search analytics informs your SEO efforts. For instance, knowing which keywords people are searching helps you optimize your web pages, blog posts, and social media posts. Adding the relevant keywords in these places ensures that the right people find your content.
• Improved ranking brings more sales. Websites appearing on the first page of Google get more clicks. So, if you run a commercial site, you’re more likely to get more conversions and sales if your site gets high ranks on Google’s SERPs.
• Search analytics tell you how many people search for your brand. Dig deeper into the profiles of these people to understand their search intent and needs. That way, you’ll know the best way to serve their needs.

#7. Location analytics layer 

The location analytics layer, or geo-analytics, tells you where conversations are happening. It does this using geolocation data from posts, check-ins, and user posts. With this information, you’ll target the right people and customize your content for local audiences

Why does location analytics matter?

Location analytics offers helpful insights that help marketers target the right people. Plus, it’s a great way for businesses to offer or improve location-based services.

Let’s dig deeper:

• Location insights help with local targeting. Once you know where your customers are, you’ll offer localized products or services. For instance, Google tells you the nearest shop, airport, or hospital based on your location. 
• They help you spot geographical trends. Tracking geo-maps that have consumer data allows you to spot patterns. For instance, most people in cities spend their time in malls and movie theaters. They might also be more engaged in online shopping than folks in rural areas. 
• Location analytics improve campaign personalization. When planning campaigns, tailor your content to match the specific needs of your audience based on their location. For example, users in coastal areas will engage with posts mentioning beachwear.
• They help you understand local sentiment. Tracking sentiment by region tells you what people in a specific area feel about your brand. For example, you’ll get more negative comments from rural areas if there are more delivery delays than in major towns.
• They help you benchmark against regional competitors. Tracking competitors by their location reveals what’s working for them locally. You’ll also know your share of voice compared to them in that region.

Use the 7 layers of social media analytics to make smart, data-driven decisions

The seven layers of social media analytics offer different lenses into your audience’s digital life. You may not use all seven at the same time. But each layer helps you understand your audience’s behavior or intent. And the insights they give allow you to make data-backed decisions.

Also, combine two or more layers to plan smarter campaigns or create better content. For instance, use text, action, and network analytics layers to discover:

• What your audience is saying
• How they’re reacting to your content
• Which regions have the highest engagement

Learn more about social media analytics here: Complete Social Media Analytics Guide