With programmatic turning 10 this year, Yun Yip (pictured below), Country Manager for Australia and New Zealand at MediaMath, looks at the changing role of machine learning and how intelligent automation is the key to driving marketing outcomes.
Machine Learning (ML) has been a fundamental part of the marketing automation process for some years now and is only going to increase in terms of scope and importance over the next decade.
Deloitte expects ML to be a major trend in Australia, as the technology continues to offer extraordinary opportunities to businesses and their technology partners. But as the amount of available data grows exponentially and the algorithms that we put into machines grow in complexity, how does ML transform this influx of data to increase the insights available to marketers?
In short, ML turns simple marketing automation into intelligent marketing automation.
ML, or the practice of programming computers to “learn” without manual input, has been fundamental to the growth of Real-Time Bidding (RTB), which lies at the very heart of programmatic advertising. Relying on a series of algorithms that allows computers to adapt and learn from observing certain data sets and behaviours, ML can predict a consumer’s likelihood to purchase when exposed to an ad.
Back to the future
Looking back in time, Arthur Samuel wrote the first ML program in 1952, in which a computer learned to play checkers by building up patterns of winning behaviour and strategies that were most likely to secure a victory. ML steadily progressed from there, with events such as the introduction of Explanation Based Learning (EBL), where a computer analyses training data and created a general rule it can follow by discarding unimportant data, as well as the creation of NETalk, where a computer learned to pronounce words the same way a human baby does.
The 1990s is where ML moved away from machines relying on a knowledge-driven approach, shifting instead to a focus on learning with data. At this point, data scientists began creating programs that allowed computers to analyse large amounts of data and draw conclusions from the results.
Machines in marketing
The same core value applies to ML in marketing, with the hope of a machine predicting the strategies that will lead to sales. Historical data is drawn in to a computer from a variety of sources, evaluated by a set of algorithms, and used to determine which ad to serve to a browser or potential customer. For example, an ML program can learn to recognize pictures of cats when shown a sufficiently large number of examples of pictures of “cat” and “not cat”. Or an autonomous driving system learns to navigate roads after being trained by a human on a variety of types of roads. As the program gains “practice” with the task, it gets better over time, much like how we humans learn to get better at tasks with experience.
As programmatic adoption in the region continues to rise, ML will continue to be at the forefront of accurately predicting human behaviour – specifically their ever-changing wants and needs. As algorithms become more refined, machines will be able to predict things more accurately, like who is most likely to purchase, when they are likely to be in the mood to buy, and what they are most likely looking for. Static data rules will no longer be a viable way for marketers to use data, as ML provides much deeper insights in real-time.
With massive amounts of data now feeding into marketing systems, refinements to a campaign can be made as required, reacting almost instantly to a rise or fall in demand, shifts in browsing behaviour, geo-positioning data, and more. Ultimately, ML empowers marketers to optimise their campaigns, therefore absolutely maximising the investment that their company or client has made.
Intelligent marketing automation
With the wealth of data now available to marketers, the question of the quality over quantity of data always arises. No doubt, quality is key and ML has an important role to play in ensuring this, as data continues to flow in from more and more touchpoints. Discerning which data is most useful to a situation or campaign will become an important role for machines to take on, with algorithms driving the outcome. Machines will continue to take more of the guesswork out of data-driven marketing, applying rules for certain data sets, and elevating one above another depending on changing needs and circumstances.
Personalising customer experiences is also at the core of modern marketing and ML allows this, even across multiple platforms. Rather than relying on a small focus group to draw conclusions on customer behaviour, ML can take on board a whole swathe of data across different touchpoints and optimise it to draw a very wide range of conclusions. This in turn allows for a much more precise focus on the individual – their likes, dislikes, browsing behaviour, and more – and that knowledge can be used to offer them better, more tailored online marketing.
Ultimately, ML will allow marketers to do what they do best. By removing a lot of the heavy-lifting from a campaign and providing deeper insights that allow marketers to tailor their approach, ML enables marketers to reach the right customer at the very best time and with the most meaningful messaging. It is a tool to be used to drive better outcomes and make better use of data, making sense of this wealth of information that we now own.