This video talks about content analysis and why it’s essential to uncover the topics and themes that can inform audience relevance and engagement.

According to the 2021 report from the Content Marketing Institute and MarketingProfs, 42% of marketers have a content marketing strategy, up from 33% the previous year. But, on the flip side, 58% of marketers do not have a plan, which scares me.

What is a Content Analysis?

Content analysis is a research method used to understand the words being used within a specific channel, the meaning of the text, and how they are related. A more advanced research technique uses a statistical analysis that can cluster certain words together, analyze the relationships, and categorize them into meaningful themes and categories.

Using Qualitative Data For Content Analysis

You can use several research methods to unearth insights about your content, but qualitative data is a great place to start. This is information gathered through interviews, focus groups, and primary research or surveys. Any research question you ask should be written to perform qualitative content analysis efficiently. Asking open-ended questions may take you down a rabbit hole that you don’t want to go down. In addition, a qualitative content analysis must have a coding process that’s easy to segment based on the research question. Otherwise, it will be difficult to pull any actionable insights, thus, making your data analysis less valuable.

When looking at your qualitative data, you want to look for recurring topics, themes, narratives, and the relationships between each group. Once you’ve identified these, you can start developing a communication strategy grounded in data analysis.

One of the most important things to remember about a content analysis is that it’s not a one-time thing. Instead, you need to continuously analyze your content to ensure that it’s still relevant and engaging for your audience.

Using Quantitative Data For Content Analysis

There are a few ways to think about using quantitative content analysis. The first is to look at engagement data. This would include page views, likes, shares, and comments. In this case, it’s essential to analyze the words or phrases and specific words on each web page you are researching. Then, using content analysis, you can start to piece together the topics of content that your audience prefers to read on your website.

When you’re looking at your engagement data, you want to ask yourself questions like:

• What topics are resonating with our audience?
• What content is driving the most engagement?
• Are there any patterns in the engagement data that we can exploit?

The second way to use quantitative content analysis is to look at conversion data. This would include email open rates, click-through rates, and conversion rates. In this case, you want to look at the words or phrases driving people to take the desired action, whether that’s subscribing to your email list or buying a product.

When you’re looking at your conversion data, you want to ask yourself questions like:

• What specific words, phrases, or concepts drive our audience to convert?
• What content is most effective in getting people to take the desired action?
• Are there any patterns in the conversion data that we can exploit?

Another way to use quantitative data for your content analysis is to look at demographic data. This would include things like age, sex, location, and income. In this case, you want to look at the words or phrases resonating with different demographic groups. Once the analysis begins, you can start to segment the research into categories of content and analyze the relationships and patterns of each method.

When you’re looking at your demographic data, you want to ask yourself questions like:

• What specific words or phrases are resonating with our audience members?
• What content is driving the most engagement with different demographic groups?
• Are there any patterns in the engagement data that we can exploit?

The last way to use quantitative data is to look at performance data. This could include conversion rates, click-through rates, and other marketing metrics. In this case, you want to look for correlations between the different marketing channels that you’re using.

When you’re looking at your performance data, you want to ask yourself questions like:

1. What content is driving the most engagement on our social media channels?
2. What’s the click-through rate for our paid search ads?
3. Are there any patterns in our marketing data that we can use to identify an opportunity?

When it comes to any statistical analysis, there are many different ways to use the data. It’s important to understand what questions you’re trying to answer so that you can use the proper research method. Once you have your data, you can start developing a content strategy grounded in research.

How Does Content Analysis Drive Brand Strategy?

Content analysis can help drive the brand strategy by understanding the topics, trends, and narratives that are resonating with your audience.

When you know what content is resonating, you can continue to produce content on those topics to keep your audience engaged and wanting more. Additionally, content analysis can help you understand the emotions that your audience feels when they interact with your content. This information can help you develop marketing programs that appeal emotionally to your audience.

Finally, content analysis can help you understand the relationships between different pieces of content. When you know what content is working best together, you can create a content strategy that compliments 2-3 different types of content pillars.

Content analysis is a powerful tool for understanding your audience and driving your brand strategy. By using the correct research methods, you can start to see the patterns and trends impacting your business.

Using Data Analysis for B2B Brands

There are two types of content that B2B brands publish on social media. Lead generation and everything else. Consumer brands are similar. They are trying to entertain and create relevancy with new audiences or sell products. But there is a big difference.

B2B brands are different in that they are not selling products the same way as consumer brands. Instead, they are selling solutions to business problems. And this is where content analysis comes into play. You have to analyze what kinds of solutions your target market is looking for and then produce content that creates action.

I won’t spend a lot of time talking about demand generation marketing because most of you watching already know that type of content is and its purpose.

So the question remains, “what is everything else?”

Well, I like to refer to it as lazy content or content that’s 100% self-serving. It’s the press releases, product launches, links to product pages, awards, industry recognition, and all of the other content that no one in the world cares about.

I have a hard time even saying it because it’s 2020, and still today, I see companies doing this on social media. They think they are making an impact, but they aren’t. They are just publishing noise, and it’s a waste of time and money.

Of course, if you are using LinkedIn to attract talent and share the cultural significance of your brand, well, then your content will be all about you.

But outside of that and generating leads, people don’t care about the content all about you. So I want to suggest a new way of creating branded content on social media, and it’s not branded at all. It starts with quantitative and qualitative content analysis.

Content analysis is a research approach often used to inform data-driven storytelling. Simply put, it’s content that is informed based on what’s top of mind and relevant for a specific audience based on the words, themes, text, and language they are using in social media.

There are three different ways I like to categorize the data using audience analytics:

• Specialized audiences
• Affinity-based audiences
• Micro-audiences

Specialized audiences are built using a combination of bio and content search. This approach is excellent for finding specific audiences like the C-Suite, millennials in Oregon, or physicians.

Affinity-based audiences are built based on finding individuals that have similar affinities. This could be as simple as your brand’s followers on social media, wine lovers, or travel enthusiasts.

Micro-audiences are smaller and more influential audiences. Examples could be the top 100 technology journalists or the top 500 AI influencers.

One audience that I don’t talk much about is the media – not necessarily journalists, but the content published on media publications like Forbes, Fortune, and Business Insider.

These are all data sources that would feed into a content analysis, with the intention that you will uncover topics to inform an audience content strategy.

An Example When a Content Analysis is Used

You have a content analysis of everything that a brand publishes on social media channels on the right side. The data is broken down into different topics and themes based on the words and text used. Finally, the information is collected and clustered into this chart to show the relationship between keywords and phrases used the most in branded content.

An analysis based on conversations from a specific social media audience is on the left side. Again, it’s segmented the same way as above based on the volume of keywords and phrases these individuals use when sharing on social media.

When looking at these two examples, you will notice that the gap seems to be broad. The brand talks a lot about RPA and is more specific and technical. On the left, you have more prominent industry-related themes and topics.

This is probably not the best content analysis example because when we do this, the branded content shows topics and themes about the business, products, or services. It’s all self-serving. Most research methods will reveal insights from a quantitative and qualitative content analysis. In this case, we’re looking just at the quantitative data.

Also, if you think about the research model mentioned above, the way to relate to an audience is to speak the way they speak. This means that brands must create and publish content that matches and replicates what is top of mind for the audience.

It’s not just about the messaging and narrative either. The style of the creative content is also essential. I explain all this because content informed by one or more of these audiences is the type of content that all B2B brands need to invest in.

Content Analysis Can Provide Immediate Business Value

Technical audiences want value. They don’t want content with your fonts, colors, creative assets, and logo in the bottom right corner. They don’t want stock photography either.

The below is an example to consider. On the left, you have RPA vendor UIPath. They are a SaaS provider of Robotic Process Automation Software. This is a classic example of the types of branded content that you will see when you scroll through your LinkedIn feed. The content looks great. Its on-message, brand colors and identity is on point, The logo is front and center, and they even created branded hashtags. It’s not bad at all. It’s just not what the audience is craving. The data will prove that this type of content does not perform.

On the right, you have an example of unbranded content. This is not the greatest example because I’m not a fan of flooding the post copy with hashtags. Still, the creative piece isn’t bad, and it looks like they are sharing influencer content which is an excellent example of influencer engagement.

B2B brands need to create content that a) is creative, b) adds value to the audience, c) provides your perspective on how to solve technology challenges or technology in general, and d) isn’t filled with marketing jargon.

Using content analysis and real-time audience listening as a research method allows you to see what’s top of mind for your audience right now. Where are they in the B2B sales funnel? What’s keeping them up at night? What newspaper articles, white papers, ebooks, blog posts, and other content they are reading and sharing with each other?

And then, it’s your job to use the data to determine the opportunity and build a content strategy that breaks through the noise. From there, you can create data-driven content quickly that is aligned to your narrative but relevant to what your audience is talking about. That is audience-informed content marketing.

The outcome of the content analysis will help brands identify the gap between what their target market wants and what they are currently providing. Armed with this information, brands can begin filling this gap with valuable content that will help them build brand relevance.

FAQs

Q: How often should I conduct a content analysis?

A: The frequency of your content analysis will depend on the needs of your business. However, it is generally recommended that you analyze your content at least quarterly to provide the most relevant and valuable information to your audience.

Q: What types of content should I include in my content analysis?

A: The content you include in your content analysis will vary depending on your business. However, it is generally recommended that you analyze the content of your website, blog, social media, email marketing, and the conversations among a specific audience. More advanced content analysis will include a text analysis of particular media coverage from newspaper articles, including headlines and the words within an article or set of articles.

Q: What should I do if I find that my target market is not interested in the content I am providing?

A: This is an excellent research question. If you find that your target market is not interested in the content you are providing, you may need to reconsider your content strategy. One option is to increase the frequency and quality of your content marketing to attract more attention from your target market.