Machine Learning: Helping to increase engagement within loyalty programs

Machine learning is a highly talked about topic. Amidst all this noise, it’s important to understand the meaning and relevance of machine learning to marketing and how it helps to enhance customer engagement, and do it 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 to 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. 48 percent of companies plan to use machine learning to gain a greater competitive advantage.

But how can machine learning be used to help you power lifetime customer connections? Let’s take a look at some applications of machine learning.

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 to 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 you with the reporting you need to actively monitor and analyze your orders by risk status, and make modifications to your scoring algorithms as patters of fraud change.

Another example of machine learning is how we’ve incorporated into our VAP offering, which stands for value, attrition, potential. As part of the 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 much happier as they’re doing more interesting work, and our clients benefit from deeper insights delivered more quickly. The machines are doing the heavy lifting and customer scores are created which in turn get delivered as profile attributes to the loyalty platform. Then strategies are created as to determine how to engage customers.

Brands are at all 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.