The use of AI techniques is now a reality in the media industry, and will soon become a necessity. Here we look at how AI can be used to tailor content, products and services to the benefit of both brands and consumers.

The number of customer touchpoints and the variety of signals that influence a purchase decision require AI techniques – and particularly machine learning – to understand and optimise. The customer journey will soon encompass virtual-reality purchasing, dynamic pricing, automated voice recognition and digital attribution, managed within a single system and eventually through a natural-language interface.

The optimisation of content using AI applications represents a significant opportunity for marketers. This starts by using image-recognition APIs to understand what components exist in a piece of content, including photographs, video and sound. Faces are particularly useful components to analyse: facial-recognition technology can now quantify the emotional state of each individual in a piece of content. This allows us to classify and segment each piece of content in real time, and in the case of video, frame by frame. Once we have quantified our content, we can feed this data back into the creative production process and optimise it. This means automatically varying the images, backgrounds and soundtracks of our content so that it achieves its campaign goals more effectively, and ultimately maximises content ROI.

Beyond the optimisation of individual pieces of content, the real opportunity lies in linking together all the content features – each generated by these AI techniques – across a user journey. We can then use machine-learning techniques to ensure that the right content appears at each stage of the journey, and that each piece of content is as effective as possible at producing the desired consumer response at that stage. These techniques promise to revolutionise e-commerce. Machine-learned recommendation engines will be able to tailor the products and services on offer to individual users, based on each individual’s user journey, and the content that they have responded to the best. These recommendation engines will even be able to adapt their own look and feel, and the way users navigate between their pages, according to individual users’ actions and responses.

This will be particularly important for brands whose consumers face a wide and potentially confusing number of product choices, product variations, or variations in product features. Machine-learning and image-recognition techniques that control and optimise consumer choice have already been used to increase product purchase by up to 15%. As the initial experience becomes more streamlined and efficient, these techniques will be used more frequently to improve
customer loyalty and repurchase. The objective is not to replace the creative development or production process, but to use AI to generate data that allows us to better serve a given consumer, and to ensure that their experience improves steadily over time.

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