TheJakartaPost

Please Update your browser

Your browser is out of date, and may not be compatible with our website. A list of the most popular web browsers can be found below.
Just click on the icons to get to the download page.

Jakarta Post

How AI can drive customer loyalty programs

Brands are at different stages when it comes to making use of artificial intelligence.

Adrian Hoon (The Jakarta Post)
Jakarta
Thu, February 20, 2020

Share This Article

Change Size

How AI can drive customer loyalty programs Illustration of artificial intelligence (Shutterstock/Lightspring)

A

span id="docs-internal-guid-5d579bdf-7fff-0c1c-244a-0081750e722e">Artificial intelligence (AI) 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 AI can be used to increase brand loyalty and customer engagement.

AI technology can be defined as the application of machine learning that affords systems the ability to automatically learn and improve from the customer experience and enhance engagement at scale. The benefit to marketers is that it helps them collect and process massive amounts of customer touchpoint data, enabling them to better know their customers in real time and act in milliseconds to provide relevant offers, creating a personalized and engaging customer experience.

But how can AI be used to power lifetime customer connections or business processes? Let’s take a look at some applications.

AI could be incorporated into the value, attrition and potential (VAP) offering. An advanced statistical model can be 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, achieve much more detailed segmentation. This means your data scientists spend time evaluating outcomes and creating strategies instead of compiling data and performing data processing. The machines do the heavy lifting in creating customer scores, which in turn get delivered as profile attributes to the loyalty or marketing platform. Then strategies are created to determine how to engage customers with the right offerings.

Monitoring of fraudulent behavior within loyalty programs is an example of how machine learning can be applied. For example, the GetPlus Loyalty fraud-management system helps protect loyalty programs against fraud. With GetPlus OCR within the membership mobile app, members can scan their receipts from participating merchants to earn GetPlus reward points at their convenience. We set up configurable, action-based scoring rules to evaluate the risk of loyalty fraud in real time. If a high-risk earn order is identified, it is suspended to be reviewed by human operators, or it will get cancelled. The fraud-detection capability also provides reports to be actively monitored, and orders can be analyzed by risk status. It can also make modifications to scoring algorithms as patterns of fraud change.

Brands are at different stages when it comes to artificial intelligence. It’s important to develop a strategy on how to deploy machine learning that’s going to work best to optimize marketing performance and efficiency. Begin with evaluating your current technology infrastructure to see if and how it can support AI. Also, make sure your employees and processes are aligned. Your loyalty program can serve as the foundation of your AI initiatives. (wng)

***

Adrian Hoon is the chief operating officer of Global Poin Indonesia, the leading operator of the GetPlus coalition loyalty program in Indonesia. He is a seasoned CRM/marketing professional with more than 20 years of experience in marketing and CRM technology in Asia-Pacific.



Your Opinion Matters

Share your experiences, suggestions, and any issues you've encountered on The Jakarta Post. We're here to listen.

Enter at least 30 characters
0 / 30

Thank You

Thank you for sharing your thoughts. We appreciate your feedback.