One minute with... Nicolas Darveau-Garneau, Chief Search Evangelist at Google

Nicolas Darveau-Garneau is Google’s Chief Search Evangelist. He took some time out of his busy schedule to speak to us about his three step plan for businesses wanting to use machine learning to automate their marketing and boost profitability.

Machine Learning (ML) is an entirely new way of thinking about automation. Just like a regular student, it takes time for an ML system to learn new things, but once those skills have been acquired, they quickly accelerate beyond human capabilities. And while a lot of attention has been generated by game-playing algorithms like AlphaGo and AlphaZero, elsewhere at Google, machine learning is powering automated marketing systems being used by some of the world’s most successful advertisers.

One minute with... Nicolas Darveau-Garneau

Whether your focus is sales, app downloads, branding or lead-generation, in almost all instances we have ML-powered tools and features that can really make a difference. From algorithmically generated Dynamic Search Ads to auto-optimised bidding with Target CPA and Target ROAS, intelligent systems are available that can handle the large scale, time consuming work of managing campaign fundamentals, leaving you free to concentrate on higher value strategic and creative work.

If you’re trying to understand the potential of machine learning and what you need to do to make the most of it, there are three key rules to follow:

  1. Automate, automate, automate! Across a variety of marketing tasks, ML systems are already vastly quicker, more accurate and more efficient than people could ever be. Starting automating as much of your marketing as possible, or risk being left behind.
  2. Start using and measuring the full marketing funnel, rather than just the lower funnel. Measuring video, display and search holistically will enable you to expand your reach, stretch your budget and see the integrated value of all your campaigns.
  3. Make sure you tell the algorithms to optimise for the right metric. If you constrain yourself by managing to a fixed budget, you limit the system’s ability to find value and risk leaving money on the table. If you’re able to forecast lifetime value better than your competitors, you’ll have the edge, even if you’re optimising using the same ML platform.

Machine learning excels at solving complex business problems, and its capabilities are increasing all the time. If you haven’t already started to explore what it can do for you, now is the time to start!

 

 

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