How Loyalty Program Data and Machine Learning Drive More Personalized Customer Experiences

Data can be a tricky thing for loyalty marketers. If used and refined in the right manner, data can be the golden key to success for loyalty marketers.

Machine learning is also becoming a more prominent ingredient for successful loyalty programs.

Loyalty360 caught up with Jennifer Bontas, vice president of data science at CrowdTwist, to find out more about this burgeoning trend.

How are marketers leveraging loyalty program data/machine learning to drive more personalized customer experiences?

Bontas: Today’s multichannel loyalty programs reward customers for activities across many channels, whether it’s making online and in-store purchases, checking into a partner location, engaging on social media, reading an email, or checking out a brand video on YouTube. Plus, brands are gathering information about loyalty program members through surveys and registration forms. The ability to track all of these actions and tie them back to a single consumer offers a plethora of rich, first-party data about consumer behaviors and preferences.

Machine learning is a class of algorithms that “learns” from data over time and can make increasingly accurate predictions about the data sets. The ultimate goal is to go beyond what is possible for humans to detect, and find patterns and insights that were previously unknown. Marketers are applying this tactic to loyalty program data to make relevant offers and targeted promotions.

For example, brands are using machine learning to optimize customers’ shopping cart experiences based on previous behaviors, map customer journeys that predict new shopper behaviors and create look-alike audiences that resemble their best customer segments. The possibilities for applications of these insights are virtually limitless.

How are these experiences converting more customers?

Bontas: According to Harvard Business Review, personalized offers have five to eight times higher ROI than traditional offers, and lift sales 10 percent or more. Today’s consumers expect value and relevancy from brands and retailers. In a world of increasing retail competition, it’s critical to deliver the right offer at the right time to acquire and retain customers.

What are loyalty marketers doing well in this regard and where do the challenges lie?

Bontas: The high volume and dimensionality of customer data can be overwhelming, and many marketers get stuck in the “Big Data” rut. Challenges include interpreting the data and patterns incorrectly and making generalizations based on one data set that doesn’t apply to others. Another pitfall is failing to deal with errors gracefully. There will inevitably be errors from bad data or an edge case that wasn’t anticipated. It’s important to think about what is best for the brand and customer should one of those errors materialize.

Many brands are hiring marketers with deep data analysis expertise, and turning to data and loyalty partners to help them apply machine learning techniques effectively and make recommendations for the best way to leverage analysis.

What is your advice for marketers when it comes to driving more personalized customer experiences by leveraging loyalty program data/machine learning?

Bontas: Loyalty programs have the key ingredient that few industries have: The volume and quality of data required for machine learning. Armed with this data about their consumers, marketers have an opportunity to foster change across their organizations, making a positive impact on everything from ad creative to inventory to customer service. Marketers should come up with thoughtful ways to action on data-driven insights and scale the impact of machine learning by measuring outcomes, evaluating results, and iterating. In the next few years, brands that personalize their customer experiences will gain a competitive advantage. Now is the time to invest in these strategies.

For more information, check out CrowdTwist’s new whitepaper, How Machine Learning is Driving Personalized Marketing.

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