January 17, 2020 CPG, Customer Data & Analytics, Customer Engagement, Customer Experience, Financial Institutions, Loyalty Trends, Retail, Telco, Travel & Hospitality

The Future of Loyalty – Artificial Intelligence (AI) & Machine Learning

Loyalty Machine Learning Data


In 5 to 10 years, the loyalty industry will look very different than it does today due to massive disruption.  Industries are always changing, so perhaps the prediction that “loyalty will be revolutionized” won’t be a shock to many, but accurately identifying the drivers and timing of significant change tends to be much more elusive. Disruptions can come from a variety of different directions and trying to keep track of the trends from so many angles to effectively “see around the corner” can be dizzying. Sometimes industry disruption comes from new enabling technology. Other times it comes from innovative business models.  Occasionally it occurs as a result of a fundamental shift in the competitive landscape or industry value chain.

So, what disruptions will shape the future of the loyalty industry? They won’t be faddish technology like QR codes or blockchain that will be used in only a handful of applications, but game-changing trends that will shake loyalty to its core.

This series examines the top loyalty trends and changes that will take us through this new decade.


Artificial Intelligence in Loyalty

The past couple of decades have seen AI grow by leaps and bounds. Followers were amazed when Deep Blue (the IBM supercomputer) beat chess world champion Garry Kasparov in 1997. And while this was impressive, chess does have a limited rule set and options to play. That’s why it was significant that Google’s Alpha Zero not only taught itself how to play chess like a human, but it also conquered the more complex Chinese boardgame GO, much faster than experts had anticipated.

Five or ten years ago, companies started applying artificial intelligence to solve discreet business challenges. Google was one of the first players in this field, having statisticians build out models to predict the best search results for different types of searches, and then optimizing those results over time. In 2014, they decided to test how well their machine learning models could optimize this data. What they found was that, with the amount of data Google processes each second, not only was the machine-led process much more scalable, but it also produced better results than their most sophisticated human-developed models. Today, Google continues to push the boundaries of machine learning, even pushing its AI to be so human, actual humans may not realize they’re talking to machines.

It’s not just the tech giants that are using machine learning.  Now these tools and methods have become so readily available that many companies that are not AI experts are implementing machine learning into their businesses, whether it’s figuring out the best temperature to cook food, optimizing supply chain or building marketing programs. We’re still in the early stages of reaching the full potential of this technology, and we’ll eventually move into a world where tasks we once considered only possible for humans will be easily handled by machine learning; even highly creative processes like developing ad copy or recruiting talent for businesses.

Tasks historically performed by smart loyalty marketers such as analyzing customer data, determining breakage estimates and developing promotional campaigns will be largely automated in the near future. Even though statistical models will be determining the marketing activity, if done properly, consumers will feel a personalized relationship with brands. Ultimately even highly complex and creative endeavors such as rewards management, points pricing and loyalty platform development will be performed using machine learning. The loyalty industry will shift away from gut feeling and experience – where marketing has historically played – to brute force computing and experimentation that will enhance loyalty program effectiveness forever.

Today, our programs are already seeing the impact of machine learning. Predictive churn modeling has proven a reduced campaign cost of 700%, and an ROI of more than 11% by identifying lapsing customers and running realignment campaigns. Further tools such as customer lifetime value and next best experience models not only identify the biggest opportunities for businesses but create personalized journeys at a fraction of the effort traditional marketing teams are able to expend.

This trend is no longer in the realm of contemplation for brands, but one they should be capitalizing on now.


The complete whitepaper is available for download here.


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