Algorithms in Advertising: How Machine Learning & AI Help Marketers Sell

Algorithms in Advertising: How Machine Learning & AI Help Marketers Sell

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We’re not mind readers or magicians, but the algorithms can sometimes make it seem that way. Love them or hate them, algorithms simply come back to the basics of computer science. They work on a set of “if-this-then-that” rules, but have developed to offer marketers a whole host of ways to sell to their customers.

Using knowledge about specific customers, demographics, previous and predicted behaviour, and data about similar customers, companies have information at their fingertips that better equips them to sell, upsell, cross-sell, promote, and so on.

Algorithms are everywhere. They play a part in what we buy, what we watch, and even what we believe. Estimates say that 35% of what we buy on Amazon, and 75% of what we watch on Netflix can be put down to algorithms. For our purposes, we’re going to look at the core algorithms we as digital marketers use: Google and Facebook.

Machine learning in Google Ads

Google messes around with its algorithms all the time. It’s impossible for advertisers to predict what’s going to happen next. As of 2021, Google has more than 200 ranking factors in its organic search portfolio, and uses machine learning in services including Gmail, Google Search and Google Maps. But we’re going to look at how Google Ads uses machine learning and artificial intelligence in PPC advertising.

Google uses a Smart Bidding feature that automatically optimises your PPC campaign for you. Once you’ve completed the initial setup, this leverages Google’s user data metrics to optimise bids and maximise conversions. Machine learning is based on patterns, so Google uses stored information from years of PPC ads to automatically boost new campaigns.

This helps advertisers in a few ways:

  • Google is a mega name in the space, with access to massive amounts of data. It uses this to find your ideal audience and to understand when they’re most likely to convert.
  • You can save time and leave most of the optimising to the machines.
  • There’s little manual maintenance needed; Google Ads campaigns need tweaks every now and again, but machine learning takes care of actually showing your ads at the most optimal times.
  • Your audience gets a more personalised experience that’s essential to appeal to modern consumers.

Machine learning & Artificial Intelligence in Facebook

Artificial Intelligence relies on a lot of data – something Facebook isn’t short of. Facebook uses the technology in a way that focuses on emulating human intelligence, with Mark Zuckerberg saying he wants to “enable computers to understand language more like humans would”.

Following the Cambridge Analytica scandal, the company pledged to use AI to solve issues across 7 key categories:

  1. Hate speech
  2. Terrorism
  3. Nudity
  4. Graphic violence
  5. Spam
  6. Suicides
  7. Fake accounts

But of course, advertisers provide the biggest chunk of the company’s income (98% of its revenue comes from selling ads), so Facebook is also using machine learning and AI to help marketers connect with their customers.

The way users communicate and interact on Facebook is a huge source of its data. What they like, don’t like, their lifestyle, the devices they use, what they buy and watch, and who they speak to. It’s all collected and quantified by AI, to give structure to the messy information and generate valuable insights from it.

Some (slightly futuristic) ways AI benefits marketers on Facebook include:

  • Facebook uses a tool called Deeptext, which deciphers the meaning of textual content and then shows users relevant ads based on the conversations they’re having.
  • Machine learning models predict how likely a person is to take an advertiser’s desired action. It uses information like the person’s behaviour on and off Facebook, time of day, and the ad’s content.
  • Facebook revealed that it uses multiple layers of machine learning (ML) models to manage what people see in their News Feed. ML predicts what users are likely to be interested in or engage in, based on who/what they follow, and their previous engagements. When ranking ads in the News Feed, this information helps machines to work out which users are most likely to take action when they see particular ads.

When the machines & humans work together

Relying on ML and AI is only half of the story. We believe that effective advertising needs that personal touch. The specialists at Abstract Digital have an intimate understanding of core advertising algorithms, and can use this to boost your digital marketing and ensure it reaches the right users at the point of intent.

For more information about how we strengthen data-backed ads with creativity, get in touch.

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