Why it’s just the beginning for AI in marketing

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By Shady Francis, Regional Country Manager, MEA at RTB House

AI growth has dominated most of this year’s technology news, from algorithms learning how to play Go without human input, through Elon Musk and Mark Zuckerberg disputing the dangers and benefits of AI to humanity, down to Russia and China declaring artificial intelligence a top priority.

 

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Looking ahead, 2018 will be a year of developing and deepening all available AI technologies. Deep learning, one of the most important subfields of research on artificial intelligence, will be especially promising. Shady Francis, Regional Country Manager, MEA at RTB House, and an expert in retargeting solutions based on AI, talks about ending the year in context of AI and its upcoming developments in marketing sector.

A must-have technology

Generally speaking, the goal of artificial intelligence is to make computers as smart, or even smarter than human beings, by giving them human-like thinking and reasoning abilities.

This is especially true when it comes to deep learning—an innovative branch of machine learning that closely imitates the work of the human brain in processing data and creating patterns of decision making.

Last year it has become a must-have technology in many areas (like healthcare or car automation). From a marketer’s perspective, deep learning has made a huge impact on the entire advertising industry.

Inspired by the biological neurons in our brains, deep learning made it possible to get more reliable, richer, machine-interpretable user descriptions of customer’s buying potential without any human expertise.

 

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For example, recently at RTB House we have analyzed massive data sets to show that an AI-based approach can lead to a 35% better conversion rate than the marketer’s natural instincts.

But deep learning algorithms can achieve even more. This technology is able to predict a user’s unique habits and desires for the advertising industry.

It is simplifying our everyday user experience by bringing deeply targeted ads that contain not only products we are more likely to buy, but also those which we haven’t seen or products we haven’t even thought about.

Many brands see the benefit in implementing AI and deep learning solutions into their products or tools. In 2018, we fully expect to see leading companies focus on developing their deep learning AI potential.

Supervised learning to new areas

In 2017 we saw a departure from so-called ‘supervised learning’, a standard approach used by machine learning. Its premise is based on human giving instructions for a computer to learn, taking into account patterns of pre-existing examples, datasets, and answers.

In 2018, in AI research will delve into more sophisticated areas, like ‘transfer learning.’ This is a form of deep learning, where teaching a machine is based on various simulations.

The machine learns to make decisions using the knowledge gained from many simulations, instead of data from reality. Using this method, a machine learns to make decisions with logical conclusions, analogy, or deduction by itself.

For example, in older machine learning models, a self-driving car would carry a human and drive for millions of kilometers while recording data. This data would be fed to a machine, which learns how to drive based on driver decisions.

 

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But thanks to transfer learning, there’s no need for a physical driver. By simulating millions of driving hours, a machine learns by itself how to drive and it can transfer that knowledge into the real world.

The second approach is referred to as reinforced learning. Its purpose is to have a machine make the best decisions, based on the feedback it receives from the environment and its actions.

For example, it applies to advertisers when bidding to buy ads. Auction systems are very complicated.

Even specialists often have problems with determining the optimal rate that will allow them to achieve their desired results at the lowest cost. A machine will also meet similar problems at the beginning of its journey.

However, unlike a human being, it can work and bid 24 hours a day in a simulation environment. It can also learn much, much faster than a human. Based on the results of their auction simulations, it can learn how to bet the most effectively, and thus, how to win the auction.

New jobs and new tasks

Of course, deep learning algorithms learn the same way people do. But unlike them, the machine learns incomparably faster and is able to analyze unimaginable amounts of data.

They also don’t get sleepy and don’t make too many mistakes. Does it mean that machines while outperforming humans will also take their jobs? Not exactly!

According to the World Economic Forum, 65% of children entering primary school today will end up in jobs that currently don’t exist. The current rate of AI development enables more companies to look for more IT specialists, data analysts, and programmers.

Next year we will probably have a boom of new job offers for data scientists – a position that has not been very popular so far.

 

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By now we can observe how AI based solutions go hand in hand with human specialists. For example, IBM’s Watson uses the cloud-based supercomputer to analyze huge amounts of data (notes, studies, pictures) to recommend best possible treatment for cancer.

Other example is Stanford’s algorithm, which can diagnose lung condition based on X-ray pictures.

2017 innovations will be enhanced in 2018

The ultimate goal for deep learning is to make our lives easier and our work more effective. Therefore, the use of AI is no longer a standard, but a necessity for companies that want to be competitive on a global market.

It is not about the ability to personalize or improve the capabilities of a final product, but also about a range of other indirect product activities – such as the collection and analysis of data. Companies now have such a large amount of data to analyze that they do not keep up with processing.

This directly affects the decisions made by their employees, and consequently financial results. Companies that are specialized in collecting and analyzing data for various businesses will play an increasing role.

 

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Companies with larger budgets will use AI in turn to suggest what to offer customers, recommend terms to give suppliers, and instruct employees on what to say and do – in real time.

It must also be assumed that many new startups will arise soon, offering solutions based on self-learning algorithms, as this technology is going to spread.

AI had already become a part of our lives in 2017 while the coming years will focus on developing AI-based technologies that will replace humans in many difficult tasks, ultimately making our lives a lot easier.

But there is still a lot of work ahead of us.

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