Content Analysis in Marketing Research: Decode Meaning From Data

Content analysis in marketing research: Big data? No problem! Actionable insights? Absolutely. Consumer behavior? Done. Content analysis in marketing research is crucial for understanding and predicting consumer behavior, identifying trends, and developing a strategy that delivers business outcomes.

Content analysis is not a fad. It’s not a buzzword or a trend. Instead, it’s the foundation of digital marketing and can be used to decode meaning from data. With the explosion of user-generated content across social media, apps, podcasts, and even generative AI for marketing, making sense of large volumes of data is becoming increasingly important.

This is why content analysis in marketing research should not be overlooked.

Content analysis is not just about managing and analyzing data. It’s also about using that data to make better decisions. By understanding customer conversations’ context, sentiment, tone, and vocabulary, you can uncover insights to inform your marketing strategy and drive business growth.

But it’s not only about analyzing conversations. There is a wealth of data and insights that you can get from analyzing media coverage.

Spending an hour reading five articles from the top business media outlets is one thing. I mean, no one does that anyway. But imagine being able to analyze 50K articles in half that time and extract the meaning, sentiment, and context of all that coverage. It’s possible.

Content marketing analysis involves examining text data to identify patterns, themes, and trends that would be difficult to uncover without technology and human analysis.

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. This means that they are leaving actionable insights on the table.

What is a Content Analysis?

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

Digital content analysis can be done with text data, including news coverage, blogs, forum posts, review sites, call center transcripts, or social posts. This is one of many qualitative research methods that can be used to understand the true meaning of words.

Types of Content Analysis

Breaking Down Video Content Analytics

Video content has always been critical in attracting and engaging audiences on social media, especially on apps like TikTok and Instagram. It served as a way to stand out from text-based posts that dominated the early days of the internet. Today, video has become one of the most important forms of content marketing, offering a unique and relatable way to tell a brand’s story. The effectiveness of video content is evident when analyzing Gen Z shopping habits.

Beyond its marketing potential, video content can also be analyzed. Video content analysis is evaluating video content to extract actionable insights. For example, this information can be used to understand customer behavior or how an audience may respond to a keynote presentation at an industry event.

Video content analysis usually begins with the digitization of footage, a task that can be accomplished using video AI software. Once the video data has been digitized, it can be analyzed using various methods, such as frame-by-frame analysis or motion detection. Depending on the objectives of the analysis, different types of information can be extracted, such as object classification or tracking data.

The insights gained from video content analysis can be used to optimize various business processes. For instance, retail stores can use this data to analyze their customers while shopping in-store, optimize store layouts, or enhance merchandising strategies.

Google Cloud’s Video Intelligence AI product is one of the best video analysis tools available. Users can tag and categorize video content, search for relevant snippets, transcribe language and speech, generate subtitles, and identify and rate videos with explicit content.

There are also free tools like YouTube Transcripts, which can extract text from any video. This can be helpful if you want to collect the transcripts from a series of interviews or a panel discussion and then use natural language processing to analyze the content.

Content analysis in marketing research is more critical than ever. Whether it’s text data, video, or audio, understanding the meaning behind the words is essential for making data-driven decisions.

Real-time Content Analysis

Real-time content analysis is a process where information or data is analyzed immediately as it becomes available. This allows for quick understanding and decision-making based on the analyzed data. It is often used when rapid response or updates are needed, such as social media monitoring, real-time monitoring of media coverage, and crisis management.

Real-time content analysis tools can help you understand and respond to emerging trends and potential issues. Social media monitoring tools like Talkwalker track mentions and hashtags, while news aggregators like Google News can keep you informed on current events. Media monitoring software can aggregate media coverage, but I don’t recommend it for real-time content analysis.

Web software like Google Analytics can provide insights into visitor behavior. Visualizing tools like Tableau present complex data visually for easy analysis, enabling efficient decision-making across your organization.

Content Analysis Research Example

Before jumping into the data below and discussing how content analytics can help, we should quickly level set on one thing. I’m a firm believer that people search the way that they talk. Suppose there are specific keywords, phrases, and buzzwords that I use in my vernacular or general social media conversation, most likely.

In that case, I will use those keywords or similar ones when searching Google. However, I consider myself a more sophisticated user, and at times, I will even use Boolean logic in my search queries when I am looking for something particular. I do the same when doing social listening for clients.

No one would argue that software developers are vocal online. They discuss cool technology, brainstorm innovative ideas, and share Spotify playlists. They’re trying to solve complex problems, debating, collaborating, agreeing, and disagreeing on every platform, from Twitter to Reddit to GitHub and everything in between. Of course, they are also Googling these same conversations.

Content analytics can help you understand the technical narratives that audiences are having publicly. Then, you can make data-informed content and storytelling. Over time, that content will appear in the search engines, mainly Google.

To illustrate my point, I wanted to show a quick example of text analytics content optimization. The below social media data represents 51K Tweets mentioning DevOps over the last six months. The tweets were clustered into keywords and phrases using natural language processing and AI.

I also layered in author bios that mention DevOps. So not only can we see an example of the hidden narratives driving engagement with this audience, but we can also see who is having these conversations about the topic.

Content Analysis Example of Media Coverage

The same thinking and methodology of content analysis in marketing research can also be applied to media coverage. Below is an example of analyzing the top 50 business media outlets that have covered digital transformation.

Digital transformation is a complex topic with hundreds of definitions on the internet. This type of content analysis can help you understand the context of that media coverage. More importantly, you can do the same research on how the media is writing about your business, brand, or product.

Using Quantitative Data For Content Analysis

Qualitative Content Analysis Benefit

There are a few ways to think about using quantitative content analysis. The first is to look at engagement data. In this case, you aren’t analyzing the context of the content but how the audience reacts to it. This would include page views, likes, shares, and comments. Here, you would analyze the words or phrases on each web page you are researching. Then, using advanced website content analysis, you can start to piece together the topics of content that your audience prefers to read.

The second way to use quantitative content analysis is to review 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 subscribing to your email list or buying a product.

Reviewing demographic data is another way to use quantitative data for your content marketing analysis. This would include things like age, gender, location, and income. In this case, you want to examine the words or phrases resonating with different demographic groups. Once the analysis begins, you can start segmenting the research into content categories and analyze each method’s relationships and patterns.

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 your marketing channels.

When it comes to any statistical analysis, there are many different ways to use the data. It’s essential 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 develop a content strategy grounded in research.

Drilling Down On Quantitative Data For Analysis

When you embrace qualitative data analysis for content analysis, you’ll be amazed by its ability to understand human behavior. The depth of qualitative data empowers you to uncover hidden patterns and subtle meanings within the content. For marketers seeking to grasp consumer preferences and market trends, content analyses have become invaluable tools.

Understanding the importance of content analysis in market research arms you with the know-how to make well-informed decisions. Qualitative content analysis lets you explore the motivations, emotions, and beliefs that drive consumer actions. This helps you fine-tune your marketing strategies and be more relevant to your marketing.

The qualitative data analysis method provides a unique lens through which you can assess and interpret data with nuance. Brands that harness this approach reap the rewards of a profound understanding of their market landscape. Leveraging a content analysis service for market research incorporating qualitative data analysis can spark growth and innovation within your organization.

Quantitative Data Analysis Example

For example, a Peloton competitor wants to launch a fitness app to inspire users to adopt a healthier lifestyle. They incorporate qualitative data analysis into their strategy to better understand their audience’s needs and preferences.

First, they conduct online surveys and virtual focus groups with their app users, gathering insights on their experiences, challenges, and aspirations related to fitness and wellness. Analyzing the survey data, they identify common themes–the desire for personalized workout plans, a sense of community, and the need for accountability.

Next, they monitor social media platforms and online forums to explore the conversations about the broader health and wellness industry. This allows the company to gain further insights into customer preferences, competitors’ strategies, and emerging trends.

With these insights, they implement the key findings into their product roadmap, develop content, and launch marketing programs with the messaging. They may add several features that enable users to set personalized fitness goals, a place where users can share experiences, and develop an in-app reward system to motivate them to stay on track.

Implementing these improvements and aligning its marketing strategies with the insights from qualitative data analysis can create a more meaningful and relevant user experience.

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