Machine learning algorithms used to enable more efficient ad bids over real-time bidding (RTB) networks will generate around $42 billion in annual ad spend by 2021, up from an estimated $3.5 billion in 2016.
This is according to a recent Juniper report tiled AI & Machine Learning: Media Dynamics, Disruption & Future Opportunities 2016-2021, which reveals machine learning; a subset of AI; has arrived at a point where it is both accessible as well as affordable to a wide range of stakeholders. Juniper anticipates the technology will eventually permeate into nearly all industries in the next five years.
In the case of the media industry, machine learning is being used to develop so-called 'bots' and digital assistants, as well as maximise returns on digital advertising, says Juniper. Companies such as Facebook and Google are leading the drive, with the likes of Rocket Fuel and Datacratic developing innovative solutions for digital assistant cases.
Machine learning promises to transform this segment of the digital advertising market in that algorithms are able to predict the success outcome of an impression, and thus adjust bid amounts dynamically, says the report.
According to research from IDC, applications incorporating advanced and predictive analytics, including machine learning, will grow 65% faster than apps without predictive functionality. The same report estimated that by 2018 half of all consumers will interact with services based on cognitive computing on a regular basis.
Visual data discovery tools will grow 2.5 times faster than rest of the business intelligence market. By 2018, investing in this enabler of end-user self-service will become a requirement for all enterprises, says the report.
According to a report by Dataversity, machine learning isn't necessarily a new trend. But, with the growth of data volumes over the past few years, the algorithms employed by machine learning technologies have become ever more important.
"Machine learning starts with data - the more you have, the better your results are likely to be," said David Chappell, in a white paper on Microsoft's Azure Machine Learning solution.
"Because we live in the big data era, machine learning has become much more popular in the last few years. Having lots of data to work with in many different areas lets the techniques of machine learning be applied to a broader set of problems. Since the process starts with data, choosing the right data to work with is critically important," concluded Chappell.
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