The Next Wave: Improving Content Marketing with AI

There’s a trending topic getting the growing attention of tech marketers – Artificial Intelligence (AI). The concept has been around for decades and most of us use it everyday without thinking about it. However, AI represents the next wave in marketing innovation for tech marketers and will become more prevalent in 2017 and beyond.

AI is a collection of technologies and algorithms that do things that require human intelligence such as learning, understanding natural language, image recognition and problem solving. A simple example is when someone calls your iPhone an algorithm searches your phone data for a possible match to identify the caller. Another common use case is bidding/optimization tools on programmatic ad platforms; and ad A/B testing is conducted by machine learning and AI tools.

AI can be used to make marketing more impactful by building stronger customer relationships; and to increase the efficiency of prospecting with better segmentation and content execution. One of the strengths of AI is to analyze huge datasets and make sense of them by looking for patterns, creating models and producing recommended outcomes. Marketing has become data intensive and we’ve become very good at collecting and storing vast amounts of “big data” from dozens of MarTech platforms. However, we have a limited ability to process it and generate meaningful and actionable insights while much of this data sits unused in vast databases. This is where AI comes in with an infinite ability to sift though data and make predictions and recommendations.

When applied to a customer database, AI can create smarter segmentation based on several criteria that binds together groups of customers by discerning relationships in the data. Those insights can build predictive models and lookalike audiences for better targeting.

The biggest upside from AI will be improving the development and execution of content marketing.  Content has become a primary vehicle to attract and engage key IT decision-makers, but there are difficulties in execution that can hold back getting a big return on your content investment. We know that just publishing general purpose content on a website is no longer adequate to drive prospects to conversion. The stakes have risen and tech buyers are looking for the right content at the right time. They want information that speaks to their needs and their current place in their buyer’s journey. As a result, every marketers’ goal is to deliver personalized content to prospects at the right moment in the customer journey – true contextual marketing. The challenge is how to do that at scale and automate it so it is invisible to the prospect. Marketing automation platforms make this promise, but it is a manual and often laborious process. AI can do this better and automatically in real time as the data changes.

AI based content learning and recommendation tools can do better job creating segments and predicting what type of content will perform better depending on the context of the situation. Machine learning content distribution platforms can serve the right content at the right time to the right person. These recommendation platforms can execute at scale in a way that humans can’t. Contextually served content will drive stronger engagement and actions taken to produce measurable impact.

There are other important uses of AI and machine learning for marketers such as natural language learning and sentiment analysis. When used in social listening, this deep learning looks for patterns and connections and assigns sentiment such as positive and negative feeling. This can be used to get insights about a marketing campaign that can be paired with traditional analytics (e.g. web site visits, ad impressions, social engagements) to derive a more three dimensional understanding of the results.

There are dozens of companies now developing and offering AI solutions though it is very early in the game. Now is the time to think about your content strategy and how the addition of machine learning and a recommendation engine can help you create personalization at scale to drive greater engagement and results.

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