Semantic content analysis will disrupt marketing – for the better
We know great content is what makes a brand. We also know that analyzing our data with very specific target audiences is crucial for a great return on investment. But we rarely put the two together and use the available data to actually analyze what content works – and why.
Yet knowing exactly why content works can give us that advantage. And, thankfully, the ability to see what unquestionably resonates most with our audience – and determines our bottom line – is already in our hands.
The state of play
In today’s ‘data boom’ climate, audience targeting naturally takes priority, with the majority (55%) of marketers stating that a ‘better use of data’ for audience targeting is a priority. their priority in 2019, according to Econsultancy.
It makes sense. On a daily basis, we are faced with countless blogs, podcasts, speakers and everything in between promising that if we optimize our targeting perfectly, our messaging will beat the intimidating odds of the 0.9% CTR quoted by WordStream. And so, we spend hours and hours every week building personas, guessing audiences, segmenting users, and running long A / B tests to find the content our audiences love. We’re adding tools to our already complex marketing stacks that tell us which message was most successful, so we can leverage it.
But when we find that winner, do we know why it works? Do we know exactly which features caused the CTR increase? Do we know how we’re 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 provide 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 we need to truly understand content preferences in an actionable way.
Semantic content analysis
To open the black box, we need to start doing a deep semantic analysis of our content. Only then can we really begin to understand why some content resonates and some does not.
As seasoned marketers, we have a deep understanding – and fascination with – psychology and our audience, which means we already have the on-paper skills to analyze our content. It’s just a matter of breaking it down into several parts. We will see this in terms of images and text.
If you want to analyze your images, you can take all of the image elements that you have already created and note the particular elements that you have used in each, then check if there are any templates that relate those choices to the performance of your ad. .
- Have you used a photo of your product outdoors? Or in the showroom?
- Were people visible in the photo?
- 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 arouse, 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 want the ease of use of your product?
- Do you focus on your innovative references?
- Do you use particularly casual – or formal – language?
With this process, we can see which types of content are getting 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 increases.
Large-scale content analysis
If we only have a few campaigns running, content analytics is easier, but it gets harder as we scale. It’s no longer practical to expect humans to spend days, weeks, or even months tagging 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 cut down on time-consuming and costly A / B testing and get rid of the guesswork once and for all – a vision we are working towards at Datasine. Our IA Connect platform (formerly Pomegranate) automatically identifies the most effective content for your audience.
By embracing semantic content analysis and working collaboratively with AI, we can be sure we understand exactly what content is going to work before we hit send.