A recent2020 Demand Generation Benchmark Study, stated that 51% of content marketers are using datato inform their campaigns. This means that 49% of marketers aren’t.

The opportunity cost of not using data informed content is a loss in revenue, time, resources and ultimately relevance. At the end of the day, it really is a waste of money when brands are creating content without understanding whether it is relevant to their customers or not.

While a content analysis might seem like a heavy lift, it really doesn’t require that much time and resources to generate insights based on language.

Content is language. It is a culmination of words that matter. People say things both publicly and privately with some type of intent in their mind. Perhaps they want to influence a certain someone to get them a special gift for their birthday. Orperhaps it’s using words to persuade a family member to cosign on a student loan.And sadly, sometimes words are said purposely to hurt the others.

This is the same for audiences on social media. They say things in public forums with intent. They talk about the products that they love. They talk about the productsthat theydon’t love. They share their experiences with brands every single day, whether good, bad, or mediocre. And we’ve certainly the influx ofpolitical discourse all over social platforms like Twitter because people don’t necessarily think alike.

A strategic content analysis gives insights into the meaning behind words; and this type of meaning shows intent.

Here are the three steps to take when doing a content analysis:

  1. Identify the data source.This is a critical piece of the process. If you get the data source wrong, the entire program, your entire strategy and all your content marketing efforts will be a waste. A data source can include social media conversations from certain audiences. It can include website data based on content that audience is sharing and pointing to. It can include analyzing owned content based on engagement data with the blog post.
  2. Cluster the content.Content clustering involves categorizing keywords and phrases based on volume.For example, if you are looking at social media conversations of an engineering audience and through the cluster analysis you find that they are using the word scale, scaling, and scaled at a high volume, you might begin to hypothesize that they are talking about the importance of scaling software in an organization, as an example.
  3. Mine for insights. Based on the above example of engineers. It would be very difficult to contextualize their conversations without mining through the data. Datamining simply means that you were spending time reading and filtering through data to understand the context of the conversation. This part of the process takes the longest but it’s just as important than the others.

The fact that 78% of Internet users said that relevant content from brands increases their purchase intent tells me that a content analysis is no waste of time. It also tells me that if brands can invest in analytics and build agile teams, they can produce relevant content quickly and efficiently.

Unfortunately, the Internet isn’t slowing down. More and more people are using social media every single day. This means that it’s getting more difficult to reach individuals without interrupting their daily journey online. The best way to interrupt a consumer is doing so with content that adds value to what it is they are doing in the first place. And that value comes from a content analysis.

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