How AI Levels the Marketing Playing Field

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By Glenn Schmelzle. Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio is streamed directly from their servers. Hit the Subscribe button to track updates in Player FM, or paste the feed URL into other podcast apps.

If every part of your customer acquisition can be measured, you'll figure out how to do it profitably. That premise has driven why digital marketing, and especially Pay-per-click (PPC) is managed by experienced humans. These professionals scour through data for the relationship between a company's ads and the buyers actions; once found, budgets get shifted to achieve that optimal effect.

A wrench has been thrown into our acquisition dreams by the ad platform titans: Google, Facebook and Microsoft (who own LinkedIn). Thanks to major AI investments they have made in the last five years, they've been able to automate much of the work that marketing professionals have done. In tandem with implementing their 'smart' software that runs autonomously, they have been restricting a marketer's ability to manually control campaigns.

The platforms believe their AI is smart enough to run marketing, so we can either be passive, letting them spend our money as they see fit, or we can choose to give them navigational assistance while they drive. The point is, you should have a game plan that works with the platforms' AI. One that, over time, will generate the leads you need at the best possible acquisition cost.

I believe listening to this episode will give you that plan. It covers:

  • How marketing has become more computationally complex than humans can handle
  • What was in it for the platforms to automate PPC marketing
  • Stages of maturity for dealing with data, ending with predictive analytics
  • Why you shouldn't fight ad platform automation, but instead use your business data to train algorithms how to market you more effectively
  • How you should integrate your in-house systems and apply data science to uncover insights

People/Products/Concepts Mentioned in Show

Paper estimating how much data optimized advertising requires, authored by Randall A. Lewis of Google; Justin M. Rao of Microsoft: "A calibrated statistical argument shows that the required sample size for an experiment to generate informative confidence intervals is typically in excess of ten million person-weeks"

Quote by Chuck Heamann & Ken Burbary in "Digital Marketing Analytics": “If you think about all the tools we have talked about...you see that there is one common denominator: You do not own any of the data. Herein lies what we think is the biggest revolution coming to digital analytics..companies will be building internal repositories for this data.”

Episode Reboot

Go talk to a coworker who uses statistical measurement, to understand how the efficiency it achieves in other fields can be applied to marketing.

Go here for full Shownotes

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