How Google Uses Machine Learning for Solving Operations Issues and What it Means for Marketing

The recent TechCrunch article about Google’s AutoML that lets developers train custom machine learning (ML) models without having to code is just one of many big deals that are changing how ML will be used in Marketing and other fields. While operational ML tech like AutoML and Google’s RankBrain program for intelligent query searches of billions of pages show ML’s power under the hood, they are more the leading edge of solutions that are becoming ubiquitous marketing tools today and tomorrow.

To understand exactly what these and other ML-based tools mean for marketing and advertising executives, it starts with understanding the basics of ML and what it does. In simple terms, machine learning uses algorithms to learn information from provided data sets so that programs can learn in a similar way that humans do and make accurate decisions. Today, machine learning is being used all around us and Google in particular has taken its use to new heights both operationally and by providing solutions for marketers that make it possible to accomplish jobs too big for any group of humans.

How Google Uses Machine Learning

Today Google technology is predominantly defined by the use of AI and machine learning through the reinvention of products like Maps and YouTube among many others. For example, the Google Assistant uses speech recognition and natural language understanding to help people accomplish countless tasks. But when it comes to how Google is using ML to help marketers the list is getting longer every day and includes:

  • A new Doubleclick tool called Custom Algorithm uses machine learning to increase the number of viewable impressions bought on premium placements. By making sense of historical data, it increases the likelihood that ads are served to the most relevant audience.
  • The Google Analytics 360 Suite use machine learning to provide marketers with tools like Smart Goals that bring in different metrics like session length, page views, geographic location, device and others to enable finding signals that impact those metrics and make higher value decisions on specific user groups.
  • Google’s use of machine learning for YouTube video classification including flagging and disabling ads placed on them.
  • The use of Google Smart Bidding, Smart Display Campaigns, and In-Market Audience to help businesses maximize conversions in PPC campaigns via machine learning.

What ML Means for Digital Marketing

Machine learning is emerging as the perfect complement to digital marketing operations where Multi-channel, multi-touch, multi-path customer journeys are the new marketing funnel. Machine learning can gather data from a virtually unlimited number of sources. With ML tools and platforms developed by or inspired by Google and other innovators in the ML space, marketers can adjust brand messaging based on the most recent consumer behaviors.

This enables the automation and personalization of certain marketing processes by letting ML tools make decisions on content, call-to-actions, and even design, in real-time. Moreover, it can analyze consumer profiles from countless interactions across every disparate data source to build a completely comprehensive profile.

Machine Learning can be used to identify various segments of a target market and create micro-segments based on hidden behavioral patterns to provide predictions on where and how users enter the sales journey and guide them through. This melding of context marketing and machine learning goes beyond just completing a sale as in the traditional sales funnel. It helps marketers to track interactions and accurately calculate customer lifetime value to reach them where they are and in the manner that they prefer in order to ultimately optimize future interactions.

Machine Learning can be used to by marketers to identify trending content that gets the highest to lowest user response over countless pieces of content and a wide range of user interactions provide you with valuable insights. This can be seen in content strategies where machine learning via SEO algorithms focuses on providing each user with content that will serve a specific purpose as opposed to just keyword density.

By enabling the collection of deeper data history over time, machine learning will be able to recommend more strategy choices with a higher degree of accuracy. That can include past responses based on content type, images, layout and other factors that enable marketers to adapt designs and strategies to meet different needs and audience segments.

Email Marketing supported by machine learning tools and platforms can help marketing operations to pick the right images, optimize and personalize subject line and body copy, predict customer churn, and optimize delivery time among others. Personalization technologies that use machine learning can make social media strategies more executable and effective at scale.

As marketers get beyond the myths of AI and machine learning, they will be able to do much more in complex campaigns that could never be accomplished by even the largest marketing department. With deeper insight into the who, what, where and when of customers, marketers can make proactive changes to their digital marketing strategies. Leveraging this data to work for a brand in the smartest way possible can help increase engagement rates and win business.

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