People can do this on their own, but a machine helps do it faster and on an infinitely larger scale.
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.
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.
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.
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.
These four brands used it to optimize their campaigns and boost their marketing efforts. Here’s what they learned.
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.
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.
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.
When Apartments.com, 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.
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.
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.
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.