Content analysis

Semantic Content Analysis Will Disrupt Marketing – For The Better

We know that great content is what makes a brand. We also know that analyzing our data to target audiences very precisely is crucial for an excellent return on investment. But we rarely combine the two and use the available data to really analyze what content is working – and why.

Yet knowing exactly why content works can give us that winning edge. And, thankfully, the ability to see what unquestionably resonates most with our audience – and drives our results – is already in our hands.

The state of play

In today’s “data boom” climate, audience targeting is understandably a priority, with the majority (55%) of marketers saying that “better use of data” for audience targeting is their priority in 2019, according to Econsultancy.

It makes sense. On a daily basis, we’re faced with countless blogs, podcasts, speakers, and everything in between promising that if we optimize our targeting perfectly, our messaging will beat the dreaded odds of the 0.9% CTR quoted by WordStream. And so, we spend hours upon hours every week building personas, hypothesizing audiences, segmenting users, and running lengthy A/B tests to find the piece of content that our audience loves. We’re adding tools to our already complex marketing stacks that tell us which message has been most effective, so we can optimize it.

But when we find that winner, do we know why it works? Do we know exactly which features caused the increase in CTR? Do we know how we are going to recreate it in our next campaign, to make it even better?

This lack of knowledge – despite all the tools and techniques we use to offer insight – is what we at Datasine call the “black box”, because when it comes to understanding why, we are left in the dark. Just looking at the results doesn’t give us the information needed to truly understand content preferences in an actionable way.

Semantic content analysis

To open the black box, we need to start performing a deep semantic analysis of our content. Only then can we really begin to understand why some content resonates and some doesn’t.

As experienced marketers, we come pre-packaged with a deep understanding of – and fascination with – psychology and our audience, which means we already have the skills on paper to analyze our content. It’s just a matter of breaking it down into several parts. We’ll look at this in terms of images and text.

If you want to analyze your images, you can take all of the image assets you created and note the particular elements you used in each one, then check to see if there are any patterns that relate those choices to your ad performance.

For example:

  • Did you use a photo of your product outside? Or in the showroom?
  • Were people visible on the plan?
  • What was the text size and color of the overlays or CTAs?

It may even be worth inviting a jury to judge your images on the emotions they evoke, or photographers to assess the quality and composition of the shot.

You can do the same for textual content, approaching this by categorizing how you describe your product or service. For example:

  • Do you appeal to the ease of use of your product?
  • Do you emphasize your innovative credentials?
  • Do you use particularly informal or formal language?

With this process, we can see which types of content receive the most engagement. And we can use these features to continue to create great campaigns that we further optimize as our understanding of customer content preferences grows.

Scaling Content Analytics

If we only have a few campaigns running, content analysis is easier, but it gets harder as we scale. It is no longer practical to expect humans to spend days, weeks, or even months labeling what goes into each piece of content. This is where machine learning and artificial intelligence (AI) come to the rescue.

AI models can extract all of these in seconds by semantically analyzing the image or text to examine the content like humans do. This way we can reduce time-consuming and costly A/B testing and get rid of the guesswork once and for all – a vision Datasine is working towards. Our AI Connect platform (formerly Pomegranate) automatically identifies the most effective content for your audience.

By embracing semantic content analysis and working in conjunction with AI, we can be sure we understand exactly what content is going to perform before hitting send.