Machine Learning: Increasing Engagement Within Loyalty Programs

May 16, 2018

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Machine learning is a hot topic in the world of customer loyalty and it is important to understand the emerging technology’s relevance to marketing, as well as how it could impact your business moving forward. Find out how machine learning can be used to increase customer engagement, build loyalty and power lifetime customer connections.

Machine learning is a highly discussed topic. Amidst all the noise, it’s important to understand the meaning and relevance of machine learning to marketing and how it helps enhance customer engagement at scale. Machine learning can be defined as an application of artificial intelligence that provides systems the ability to automatically learn and improve from the experience (without explicitly being programmed to do so). The benefit to marketers is that it helps them collect and process massive amounts of data, enabling them to better get to know their customers in real time and act in milliseconds to provide relevant offers, creating a personalized and engaging experience. And more marketers are adapting machine learning into their marketing programs.
But how can machine learning be used to power lifetime customer connections? Let’s take a look at some applications.

The monitoring of fraudulent behavior within loyalty programs is an example of how machine learning can be applied. For example, Epsilon’s Agility Loyalty fraud-management solution helps protect loyalty programs against fraud. With Agility Loyalty, you can set up configurable, action-based scoring rules to evaluate the risk of loyalty redemption fraud in real time. If a high-risk redemption order is identified, it’s suspended so it can be reviewed or even canceled. The fraud-detection capability also provides reporting to actively monitor and analyze orders by risk status, and make modifications to scoring algorithms as patterns of fraud change.

Another example of machine learning is how we’ve incorporated it into our VAP (value, attrition, potential) offering. As part of the Epsilon VAP solution, an advanced statistical model is created to segment a given customer base. The model determines how likely customers are to leave, how valuable customers are, and what kind of potential they have in the future. With machine learning, marketers can automate the collection of data, and via the machine learning capabilities, get much more detailed segmentation. This means our data scientists spend time evaluating outcomes and creating strategies, versus compiling data and processing it. The implications of this are huge. We’re able to make our decision scientists happier as they’re doing more interesting work, and our clients benefit from deeper insights delivered more quickly. The machines do the heavy lifting in creating customer scores which in turn get delivered as profile attributes to the loyalty platform. Then strategies are created to determine how to engage customers.

Brands are all at different stages when it comes to machine learning. It’s important to develop a strategy about how to deploy machine learning that’s going to work best for your marketing objectives to optimize marketing performance and efficiency. Begin with evaluating your current technology infrastructure to see if and how it can support machine learning. Make sure your employees and processes are aligned, and remember: Communication is essential. Your loyalty program can serve as the foundation of your machine learning initiatives.
 

Loyalty

Digital & Mobile

Retail

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COLLOQUY Speaks is a part of LoyaltyOne’s thought leadership platform that offers research and insights on loyalty and customer experience industry trends, as well as best-in-class content from LoyaltyOne and other industry-leading experts. 

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Machine Learning: Increasing Engagement Within Loyalty Programs

Aug 28, 2018, 08:59 AM
Machine learning is a hot topic in the world of customer loyalty and it is important to understand the emerging technology’s relevance to...
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Publication date : May 16, 2018, 00:00 AM
Machine learning is a hot topic in the world of customer loyalty and it is important to understand the emerging technology’s relevance to marketing, as well as how it could impact your business moving forward. Find out how machine learning can be used to increase customer engagement, build loyalty and power lifetime customer connections.

Machine learning is a highly discussed topic. Amidst all the noise, it’s important to understand the meaning and relevance of machine learning to marketing and how it helps enhance customer engagement at scale. Machine learning can be defined as an application of artificial intelligence that provides systems the ability to automatically learn and improve from the experience (without explicitly being programmed to do so). The benefit to marketers is that it helps them collect and process massive amounts of data, enabling them to better get to know their customers in real time and act in milliseconds to provide relevant offers, creating a personalized and engaging experience. And more marketers are adapting machine learning into their marketing programs.
But how can machine learning be used to power lifetime customer connections? Let’s take a look at some applications.

The monitoring of fraudulent behavior within loyalty programs is an example of how machine learning can be applied. For example, Epsilon’s Agility Loyalty fraud-management solution helps protect loyalty programs against fraud. With Agility Loyalty, you can set up configurable, action-based scoring rules to evaluate the risk of loyalty redemption fraud in real time. If a high-risk redemption order is identified, it’s suspended so it can be reviewed or even canceled. The fraud-detection capability also provides reporting to actively monitor and analyze orders by risk status, and make modifications to scoring algorithms as patterns of fraud change.

Another example of machine learning is how we’ve incorporated it into our VAP (value, attrition, potential) offering. As part of the Epsilon VAP solution, an advanced statistical model is created to segment a given customer base. The model determines how likely customers are to leave, how valuable customers are, and what kind of potential they have in the future. With machine learning, marketers can automate the collection of data, and via the machine learning capabilities, get much more detailed segmentation. This means our data scientists spend time evaluating outcomes and creating strategies, versus compiling data and processing it. The implications of this are huge. We’re able to make our decision scientists happier as they’re doing more interesting work, and our clients benefit from deeper insights delivered more quickly. The machines do the heavy lifting in creating customer scores which in turn get delivered as profile attributes to the loyalty platform. Then strategies are created to determine how to engage customers.

Brands are all at different stages when it comes to machine learning. It’s important to develop a strategy about how to deploy machine learning that’s going to work best for your marketing objectives to optimize marketing performance and efficiency. Begin with evaluating your current technology infrastructure to see if and how it can support machine learning. Make sure your employees and processes are aligned, and remember: Communication is essential. Your loyalty program can serve as the foundation of your machine learning initiatives.
 
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  • Loyalty
  • Retail
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