Research/Blog
Guide to How Machine Learning Works in Content Marketing
- November 6, 2017
- Posted by: CellStrat Editor
- Category: Artificial Intelligence Bots Machine Learning
AI is currently dominated by increased media attention and business hype, the way consumers and brands interact is quietly poised to make a disruptive shift over the next few years through marketing solutions built with AI in their core DNA.
Media and content industry like most others is facing disruption by Artificial Intelligence and lot of companies have started pondering on areas where they can improve with AI like:
- Text, image and video compression with AI to save on storage and bandwidth.
- Text summarisation.
- Text classification.
- Image optimisation.
- Content creation.
- Revenue optimisation with better content personalisation etc.
But how does AI work in content marketing in a problem area like Revenue optimisation with better content personalisation? I have tried to give two approaches some companies take to solve this problem through Machine Learning.
Approach-1
Language databases are built using natural language processing techniques, encompassing millions of words and phrases. Company breaks language into five fields/ variables: emotional (words and phrases that have an emotional impact), descriptions (diverse ways of describing the offer and its specifics, or the product and its features), calls-to-action (functional), along with formatting (stylistic or structural elements, including icons, :)) and positioning (order, the placement of all the difference components in the message).
Approach-2
Company segregates content types like news content as one product, advertised brand as second product, customer and interaction data (interaction between customer and products (content pieces and advertised products) on site).
After categorizing the content types, company uses machine learning to create an unimaginably large combination of options, and then proceeds to rapidly test the effectiveness of each option. The number of variations a computer can generate and test surpasses human limitations. Company can construct millions of potential combinations, and with machine learning, determine which will be most effective for an audience segment.
“This goes way beyond the A/B testing a human copywriter would write.” The automated approach allows marketers to try out more versions of a message than they’d get with regular A/B testing.
It will create variants that a human might never even think of, thus serving right kind of messaging to right person and hence increasing personalisation, engagement and revenues.
Reader’s thoughts and sharing of other approaches are welcome and would be appreciable…
(Image Source: Youtube)