AI to serve human needs
Machine learning is famous for its use in finding hidden inner relations between small parts of a complex system. What is the most complex system on the Earth? A human, of course. Thus, machine learning helps us to understand ourselves better!
Think about where machine learning can be employed: medical care, voice/image recognition, bank credit system etc. Everything is tightly connected with a human, his health, appearance, behavior. What about advertising? A main task of such business is to recognize this or that segment of audience and match a definite advertisement with a most responsive personality, with a human who is likely to be interested in the given advertising and act in a way an advertiser wants him to.
Ok, so how it works? Due to the recent computer computational capabilities burst, it became possible to process large amounts of raw data and transform it into useful information in milliseconds. Historical data of people’s, let’s say, reactions to this or that advertising is stored and then fed to machine learning algorithms. Scientists tune these algorithms in such a way that they divide all the processes and actors to small “bricks” or features and try to find inner reciprocity between those features that led to a desirable result, e.g. purchase of goods after looking at or hearing an advertising. Well-fed and trained algorithms help advertisers in the following:
Predictions. A user is browsing a website or using an application. We, as an advertiser, want to show him a banner. We have a thousand of available banners and would like to choose one that has the highest probability of being clicked. Algorithm calculates a probability of each banner to be clicked, so we choose the one with highest chance.
Recommendations. The mechanism is close to Predictions, but its aim is not to calculate a definite number (possibility chance), but to rank my banners in such an order that the most attractive banners for that user are at the top of that ranking list.
Visualization. To understand the most convertible audience segment is a primary task for an advertiser. Statistics for the period of time is enough to be capable to build 2D or 3D graphs of particular informational slices and realize audience segments you should target your advertising on. Machine learning processes hundreds of features at once, it operates with 4D, 5D, 100D space and using its techniques it became possible to automatize a search process of a mostly responsive audience segment and further its visualization. Finally, it brings lots of insights for advertisers.
Machine learning algorithms for online advertising have online nature. It means that they should constantly learn on upcoming data and update their predictions. Online algorithms are known to be less precise, but the same time a minimum human intrusion is needed.
Online advertising is a type of business. For every kind of business a driving power is profit. Here comes a necessity to transfer machine learning mathematical predictions to an understanding of how much profit it will bring to advertiser. Usually advertiser has no time and opportunity to make decisions each time an algorithm makes a prediction. He needs some kind of a decision making system that acts with an only aim — to maximize profit for advertiser by targeting the right audience and showing relevant ads to an engaged user. Such self-learning, self-updating, smart, online system is called a trade robot.
To cap it all, online advertising is the only industry that has a true business necessity to use a fully functional Artificial Intelligence aimed to maximize profit and engagement of all parties. This fact will turbo boost its development the next years.
Key evolution trends of online advertisement in next years will be reviewed in Part 3.
P.S. OranGear SaaS solution has several integrated trade robots, well-fed machine learning algorithms, employed to maximize client’s profit and automate 95% of routine processes.
