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AI analysis for content and marketing performance

This article by David Rawlings explores using AI analysis for content and marketing performance

Analysing a brand’s content and marketing performance is also a key part of using AI beyond the creative. AI can help you quickly identify trends, patterns, or even non-performers in your content, a task that would usually take a significant amount of time.

As always, this type of use of AI is highly personalised to play brands, own marketing and communication, based on variables, such as the types of platforms used, the way the content is being produced, and how it is connecting with the audience, or even the resources that are available to that brand in their marketing and communications budget.

Having said that, there are a number of key principles to be aware of, as you explore the idea of using AI to improve the performance of your marketing. 

Let’s look at them in detail.

The types of data AI can collect for content analysis

Once you’ve set your goals for analysis, and established the parameters of your measurement, AI can collect a wide range of data types:

  1. Text data. AI will draw together everything from your blog posts, social media posts and other text-based data to analyse the performance of your text. Analysis here includes identifying topics, keywords, content length and sentiment.
  2. Visual data. By analysing the imagery you use in your content marketing, AI can determine which type of visuals perform best and which types of visual content score higher on engagement.
  3. Customer behaviour data. Turning our attention to customer interaction with your content, AI can collect data on page views, engagement rates (such as Likes or Shares), click-through rates to specific landing pages or web sites. It can also determine which content has a higher conversion rate. This then extends into sentiment analysis, where your content can be analysed to determine the sentiment or emotional tone expressed in a customer review, or news article. The goal of sentiment analysis is to classify the text as positive, negative, or neutral and to quantify the degree of sentiment.
  4. Demographic data. The last type of data is demographic-based – AI can collect basic demographic data (age, gender, income level, location) and offer suggestions for further personalisation.

Five key steps in AI and content analysis

AI-powered content analysis provides insights into the performance of content across different channels by aggregating them into a central source.

  1. Step one involves aggregating data from different sources, such as social media platforms, websites, and email campaigns.
  2. Next is data processing. Your data is now processed for analysis, being checked against consistency criteria or rules set down in your analysis planning.
  3. The AI then analyses your data, detecting pattern based on the goals you have established for it.
  4. Visualisation is the key to this process. It’s one thing to have data analysed, it’s another to have the analysis make sense. The best AI analysis can present your data in a palatable way, and in the way you prefer.
  5. Lastly, the AI will make recommendations based on the insights generated. This could include everything from the way the content is written or designed through to the timing or frequency of distribution.

Three things the AI looking for in terms content analysis and improvement

Looking at analysis is just a part of the story. The key to understanding from marketing communication point of you is what is the eye looking for in terms of improvement. Going through this process to analyse your content is just a process until it actually provides you with something that you can add to your marketing mix.

There are three things that most AI platforms are looking for when they analyse your content. 

  1. The quality of your content

    Quality here speaks more to the connection and the response it generates rather than simply “it is well written or well designed”. Your content needs to provide the customers with value – of their time, of their spend or of their relationship with your brand.

    You can determine the parameters by which you measure “value” from the point of view of the customer. It could be about a quick sales process or education in the service process. It could be about time saving with your new product.

    AREAS FOR IMPROVEMENT: Recommendations that AI can make on your content include ways to rewrite introductions and headings so they capture customers more quickly. It could be that your design needs a review because in your visual hierarchy it’s not clear what your main message is.

    In terms of visual content, it could mean a shift or a tweak in the photos that you use or even the opening frames of your video. 

  2. Was it found easily?

    The goal of all content and a marketing program is to be found, and so AI can work with your content to ensure that it is appearing high in search engines, according to the search terms that you know your customers use.

    AREAS FOR IMPROVEMENT: recommendations here could involve everything from streamlining your keywords based on the AI analysis of other industry content that also goes to your customers.

  3. Did it lead the customer to the right place?

    The last, and possibly most valuable part of your content analysis using AI, is that it achieved the goal that you set out to achieve. If the goal of your content is to engage with your target audience and lead them into a sales funnel that provides them with a clear entry into purchasing your product, then the clear analysis here is – did it lead the customer to the right place?

    What is AI will be looking for here? Click-throughs and trails from content to destination. As the marketer, you get to articulate what these are so you requires your audience knowledge as well put the key here is to look at conversions for generation of inquiries.

    AREAS FOR IMPROVEMENT: AI could recommend anything from changing or A/B split testing your call to action at the end of your content through to whether or not you are sending people to the right place to continue their journey on purchasing your product. 

Three key tips in terms of what to look for

  1. Establish (or re-establish) the key criteria by which you’re measuring your content and marketing performance. Articulate how your work translates into sales success, and find the best AI solution that can deliver analysis based on that.
  2. Look for options that also use predictive analysis that helps you analyse content, but also resource spending. These platforms study your historical spend and factor in other elements such as market forces, trends or even seasonal data. You can access forecasts on the expected performance of your current advertising budget and even make suggestions on improvements.
  3. Consider how much value you will get out of sentiment analysis – if it’s something that will deepen your connection with customers, it really does need to be a part of your analysis here.

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What aspects of your business performance would like to analyse with support from AI?
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Harnessing AI in Marketing and Communication

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