This byline originally appeared in MarketingTech.
Adoption of machine learning is moving up a gear. From diagnosing diseases, to driving cars, to stopping crime, the ability of machines to learn from data and apply those learnings to solve real problems is being leveraged all around us at an accelerating pace.
As data volumes continue to grow, along with advances in computational science, machine learning is poised to become the next great technological revolution.
Digital marketing represents one of the most exciting arenas where machine learning is being applied. Across websites, mobile apps, and other digital media, there are hundreds of billions of opportunities for advertisers to deliver ads to consumers every single day.
Typically, those opportunities each come with a wealth of data attached and are made available in a real-time auction where potential buyers (i.e., advertisers or the agencies working on their behalf) have mere milliseconds to analyse the opportunity and respond with a bid to serve their ad to a particular consumer on a particular device at the perfect time.
The speeds and scales involved dwarf any other real-time transactional medium from financial exchanges to credit cards.
A lot of brainpower has therefore gone into defining what machine learning algorithms do when it comes to digital marketing. In plain English, it consists of things like identifying the most attractive consumers for a given brand or product, determining how much to bid on opportunities to show ads to those consumers in different contexts and on different devices, personalising the ads delivered to ensure relevance, and accurately measuring the impact of those ads on bottom-line sales.
As the pace of innovation accelerates, it’s important for brands to define guiding principles that ensure this amazing technology yields maximum impact
Understanding how to best leverage the vast amounts of data about digital audiences and the media they consume can be the difference between success and failure for the world’s largest brands.
That is why smart marketers are investing heavily in Data Science and machine learning to drive competitive advantage and are increasingly seeking out partners with this expertise.
With so much hanging in the balance, it’s instructive to consider how marketers should approach machine learning.
There are several important factors marketers should bear in mind when implementing machine learning technology to help ensure success:
Start with business goals
When adopting machine learning technology, marketers should begin at the end. Define the specific, measurable business outcomes you want to achieve and gear the machine learning around that.
Avoid ‘shallow’ objectives like page visits or clicks and use deep metrics like incremental sales or ROI. The deeper the metric, the better the results.
Don’t be fooled into the traditional approach of buying media based on indexed demographics or other coarse proxies. The beauty of digital marketing is that it can be optimised to measure success using the same metrics that matter in the board room, achieving the best possible results.
Select a robust platform
When it comes to machine learning, there’s a huge difference between theory and practice. Solutions that work in a small-scale testing environment may fail spectacularly in large-scale production.
It’s critical the machine learning runs on a platform proven to handle the required scales and speeds with the performance, reliability, and security demanded by enterprise-class marketers.
Furthermore, look for platforms that are flexible enough to enable easy customisation, both of the data and the models, to meet the unique needs of your business.
But a word to the wise: platforms cannot serve two masters. Some marketing platforms aimed at advertisers are actually operated by companies who make their money selling media. That conflict of interest should make you think twice.
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