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Machine learning in digital marketing: 4 use cases and future outlook | by Andrey Koptelov | Aug, 2022

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Photo: Pixabay: https://www.pexels.com/ru-ru/photo/ipad-35550/

While there is no shortage of machine learning solutions applicable to a company’s operations, marketing is poised to reap the most benefits from ML. Today’s companies collect a myriad of data points on customer behavior, and everything from the number of likes on a brand’s social media page to the amount of time it takes for a customer to click ‘order’ can be recorded and analyzed.

In this article, we discuss how organizations can use machine learning in digital marketing to find new customers, streamline consumer segmentation, and make customer experiences more personalized.

The vast amounts of data that people generate online on a daily basis are every marketer’s goldmine. All types of content that people post, like, share, search and interact with can help marketers identify consumers who are most likely to purchase a specific type of product or service in the nearest future.

In the meantime, a natural language processing engine coupled with a computer vision solution can automatically detect what topics interest a particular customer the most.

In fact, this is precisely how Google’s App Campaigns work. A proprietary machine learning model analyzes a myriad of actions that a user performs online and shows a brand’s ad to users that are most likely to make a purchase. What’s more, Google’s ML model also takes care of app ad designs and formats. Companies only need to provide relevant texts, videos, images, and HTML5 assets and ML will figure out the best combination for an ad. Google does this by automatically testing different ad designs across Google Play, Google Search, Discover, and YouTube to reveal which particular ad designs perform best.

Customer segmentation is one of the oldest and most effective practices that allow marketers to decrease cost per acquisition (CPA) and increase ROI. Conventionally, marketers group customers primarily on age, location, income, lifestyle choices, interests, and other intuitively justifiable characteristics. But ML can help marketers group customers’ attributes that don’t make sense at the first sight.

This is done with the help of unsupervised ML algorithms, which can find patterns in untagged data. In very rough terms, you can throw a huge dataset at an unsupervised ML algorithm and it will automatically find similarities between different customers and cluster them in segments. First, the solution can significantly decrease the time needed for customer segmentation. Second, it allows revealing hidden relationships between seemingly unrelated customers and customer groups.

Personalization has been a hot topic in the marketing context for the past decade. Marketers have had varying degrees of success with a rule-based approach to personalization, however, as target audiences expand along with a range of product offerings, personalization becomes increasingly more difficult to achieve. Most importantly, customers’ needs and wants are constantly fluctuating, which makes manual approaches to personalization ineffective in the long term.

ML partly eliminates the need for manual configuration of personalization strategies and allows companies to achieve personalization at scale. With the help of collaborative and content-based filtering, organizations can save huge amounts of time on figuring out what products to offer in their next email newsletter or after a customer made a purchase of a product in a particular category. From content creation to recommendation engines, ML allows marketers to increase brand loyalty and foster long-term customer engagement.

Using ML, companies can predict demand and identify reasons for customer churn. While demand forecasting is often associated with financial management, such insights are proven to be beneficial for improving marketing campaigns. Knowing what products will be of high interest at a specific period can help marketers to drive more conversions.

ML models can also detect patterns in customer behavior and help businesses understand the reasons behind churn. This way, marketers can use ML to understand what customers are most likely to unsubscribe from a newsletter or cancel a subscription and target them with special offers and promotions.

What’s more, for the majority of companies, acquiring a new customer costs much more than retaining an existing customer. ML enables the prediction of customer lifetime value, which allows marketers to ensure that the most valuable customers have the least churn probability in the long term.

The abovementioned examples of ML applications in digital marketing are only scratching the surface of the technology’s true potential. In a nutshell, ML allows marketers to continuously analyze a huge stream of data constantly generated by its past, present and future consumers.

At this point in time, overlooking ML-based marketing will inevitably lead to a competitive disadvantage in the future. When a customer realizes that he or she needs to buy a specific product, marketers that use ML can identify that prospect much faster than those who don’t. From there, different brands will compete with each other based on the timing and design of their ads, which is also rapidly becoming one of the ML’s expertise. This way, as the technology continues to advance, marketing competitive advantage becomes more dependent on the company’s proficiency to harness ML.

Realistically, such deep integration of ML into marketing operations is years ahead, since there is still room for technology to improve and for regulations to catch up. However, those who are already exploring ML-driven strategies will have a clear advantage when this technology will become ubiquitous in the marketing context.


Photo: Pixabay: https://www.pexels.com/ru-ru/photo/ipad-35550/

While there is no shortage of machine learning solutions applicable to a company’s operations, marketing is poised to reap the most benefits from ML. Today’s companies collect a myriad of data points on customer behavior, and everything from the number of likes on a brand’s social media page to the amount of time it takes for a customer to click ‘order’ can be recorded and analyzed.

In this article, we discuss how organizations can use machine learning in digital marketing to find new customers, streamline consumer segmentation, and make customer experiences more personalized.

The vast amounts of data that people generate online on a daily basis are every marketer’s goldmine. All types of content that people post, like, share, search and interact with can help marketers identify consumers who are most likely to purchase a specific type of product or service in the nearest future.

In the meantime, a natural language processing engine coupled with a computer vision solution can automatically detect what topics interest a particular customer the most.

In fact, this is precisely how Google’s App Campaigns work. A proprietary machine learning model analyzes a myriad of actions that a user performs online and shows a brand’s ad to users that are most likely to make a purchase. What’s more, Google’s ML model also takes care of app ad designs and formats. Companies only need to provide relevant texts, videos, images, and HTML5 assets and ML will figure out the best combination for an ad. Google does this by automatically testing different ad designs across Google Play, Google Search, Discover, and YouTube to reveal which particular ad designs perform best.

Customer segmentation is one of the oldest and most effective practices that allow marketers to decrease cost per acquisition (CPA) and increase ROI. Conventionally, marketers group customers primarily on age, location, income, lifestyle choices, interests, and other intuitively justifiable characteristics. But ML can help marketers group customers’ attributes that don’t make sense at the first sight.

This is done with the help of unsupervised ML algorithms, which can find patterns in untagged data. In very rough terms, you can throw a huge dataset at an unsupervised ML algorithm and it will automatically find similarities between different customers and cluster them in segments. First, the solution can significantly decrease the time needed for customer segmentation. Second, it allows revealing hidden relationships between seemingly unrelated customers and customer groups.

Personalization has been a hot topic in the marketing context for the past decade. Marketers have had varying degrees of success with a rule-based approach to personalization, however, as target audiences expand along with a range of product offerings, personalization becomes increasingly more difficult to achieve. Most importantly, customers’ needs and wants are constantly fluctuating, which makes manual approaches to personalization ineffective in the long term.

ML partly eliminates the need for manual configuration of personalization strategies and allows companies to achieve personalization at scale. With the help of collaborative and content-based filtering, organizations can save huge amounts of time on figuring out what products to offer in their next email newsletter or after a customer made a purchase of a product in a particular category. From content creation to recommendation engines, ML allows marketers to increase brand loyalty and foster long-term customer engagement.

Using ML, companies can predict demand and identify reasons for customer churn. While demand forecasting is often associated with financial management, such insights are proven to be beneficial for improving marketing campaigns. Knowing what products will be of high interest at a specific period can help marketers to drive more conversions.

ML models can also detect patterns in customer behavior and help businesses understand the reasons behind churn. This way, marketers can use ML to understand what customers are most likely to unsubscribe from a newsletter or cancel a subscription and target them with special offers and promotions.

What’s more, for the majority of companies, acquiring a new customer costs much more than retaining an existing customer. ML enables the prediction of customer lifetime value, which allows marketers to ensure that the most valuable customers have the least churn probability in the long term.

The abovementioned examples of ML applications in digital marketing are only scratching the surface of the technology’s true potential. In a nutshell, ML allows marketers to continuously analyze a huge stream of data constantly generated by its past, present and future consumers.

At this point in time, overlooking ML-based marketing will inevitably lead to a competitive disadvantage in the future. When a customer realizes that he or she needs to buy a specific product, marketers that use ML can identify that prospect much faster than those who don’t. From there, different brands will compete with each other based on the timing and design of their ads, which is also rapidly becoming one of the ML’s expertise. This way, as the technology continues to advance, marketing competitive advantage becomes more dependent on the company’s proficiency to harness ML.

Realistically, such deep integration of ML into marketing operations is years ahead, since there is still room for technology to improve and for regulations to catch up. However, those who are already exploring ML-driven strategies will have a clear advantage when this technology will become ubiquitous in the marketing context.

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