How many triangles are there?

Can you count the number of triangles in under five seconds?

Can you predict the next shape in the sequence?

At its core, machine learning is a way to quickly label and analyze huge data sets.

At its core, machine learning is a way to quickly label and analyze huge data sets.

People can do this on their own, but a machine helps do it faster and on an infinitely larger scale.

A common mistake businesses make is to assume machine learning is magic, so it’s OK to skip thinking about what it means to do the task well."

Cassie Kozyrkov

Chief Decision Scientist, Google


As people come to expect more personalized, relevant, and assistive experiences, machine learning has become an invaluable tool. We’ve outlined three considerations every marketer should make to prepare their organization for machine learning.

Define your machine learning marketing goal upfront.

Much like us, machines work best when they are given clearly defined goals. Quantifiable, measurable goals help a data scientist build your machine learning models and identify the right data to use when training them. Define what success looks like so you can measure it later.

An algorithm is only as good as its data.

You must have the right data for the problem you’re trying to solve, and lots of it — think hundreds of thousands of data points. These will need to be formatted, cleaned, and organized for your algorithm, and you will need two data sets: one to train the model and one to evaluate it.

Assemble a diverse team with the right mindset.

Marketers can identify opportunities to use machine learning, but only data scientists and analysts can implement it. A cross-functional team is essential to the success of any machine learning program, as is an organizational mindset that prioritizes and rewards experimentation, measurement, and testing.


There are countless ways that machine learning can help your business.

These four brands used it to optimize their campaigns and boost their marketing efforts. Here’s what they learned.


Identify your most valuable customers.

Imagine your team has launched an app, but early results show users who download it don’t open it often. This is a common problem: Only 37% of app installs remain in use after seven days.

74% decrease in cost per registration

51% increase in registrations

The team behind GM’s car rental app needed to reach high-value customers likely to engage with services on the app. By integrating machine learning into their campaign strategy, the team was able to spend less while growing their customer base, freeing up budget for more strategic initiatives.


Develop custom creative.

Machine learning is helping marketers deliver unique and tailored creative to customers. Responsive search ads mix and match multiple headlines and descriptions to find the best possible combination for a user, simplifying the ad creation process and delivering stronger results. after using responsive search ads

10% lift in clicks

When, a leading resource for renters, wanted to optimize creative for its growing audience, it turned to Google responsive search ads. By customizing ads based on criteria such as key moments in a user’s unique rental process, the company was able to boost clicks across its websites.


Make the right bid.

Searches are getting more frequent and specific. For marketers, this means it’s more important, but also more difficult, to land the right bid at search auctions. The deluge of data creates more complexity, obscuring the signals that matter.

67% conversion rate increase

14% decrease in cost per click

33% decrease in cost per qualified visit

Smart Bidding uses machine learning to analyze millions of signals and make adjustments in real time.

When Nissan’s partner agency OMD wanted to boost qualified visits to the Nissan website, it used automated bidding algorithms alongside its own custom settings to reach key customer segments.


Unlock consumer intent.

When people research a product, they often click multiple ads. The last ad clicked usually takes credit for the conversion, but that doesn’t mean it was the most valuable. Data-driven attribution uses algorithms to identify patterns leading to conversions, including the most important touchpoints.

Planning a trip can take months, as people perform hundreds of interactions online. To better understand which touchpoints drive long-term growth, vacation rental marketplace HomeAway used data-driven attribution to locate signals of customer intent representing behaviors correlated with conversion.

Machine learning fairness for marketers

Now that you know how machine learning can help you reach receptive audiences, explore these fairness principles to build an inclusive strategy.

Abridged by Marianna Nash; Designer: Kelly Sullan; Production Lead: Fifer Garbesi; Product Lead: Casey Fictum.
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