Prediction, anticipation and influence: The importance of AI and machine learning in loyalty programs

Data is the foundation of every modern marketing plan. What distinguishes some marketing approaches from others is the way that data is collected, how it’s leveraged and how effectively it can drive desired outcomes.

The ability to nail these competencies, which can be optimized by the right technologies, can mean the difference between success and failure.

Loyalty data as a driver

Loyalty programs can be robust sources of customer information and are particularly suited for collecting data at the top of the sales funnel. It is a mistake to view loyalty and promotion programs as simply a vehicle for driving incremental purchases.

If you acknowledge them as effective data-collection mechanisms, you will be more effective at targeting, segmenting and influencing future shopper behaviors.

But just considering the loyalty program from a data-driven perspective isn’t enough; brands and retailers need the right technology to:

  1. Accumulate that customer data.
  2. Link it with data from other touch points (transactional/POS, CRM systems).
  3. And then leverage it to generate results.

The tools for loyalty program data collection are well-known and widely utilized; nearly every loyalty program will gather at least some information from its members.

Many of the most successful brands and retailers have sophisticated systems for integrating their various customer data channels, enabling them to create a single customer view that facilitates effective marketing tactics.

It’s in the third area, however, that some of today’s “buzziest” emerging technologies — AI and machine learning — may hold the key to more effectively predicting and shaping customer behavior.

Leveraging customer data the right way

Typically, loyalty program data is used as a historical guide, delivering insights on what a program member (or customer) has done in the past, so that marketers can make an educated guess as to what they might do in the future.

For many marketing aims, such as identifying customers’ position in the purchasing cycle or tracking habits and preferences, this is sufficient. But for optimizing processes like behavioral targeting, influencing the next buying decision and predicting future customer actions, AI and machine learning can be indispensable.

Many industries are already using artificial intelligence and machine learning for a variety of applications, but only recently are these technologies making their way into loyalty and marketing programs (beyond those of the earliest adopters).

Consumers, to an extent, are also becoming familiar and more comfortable with using these technologies: Research shows they are increasingly willing to rely on algorithms and smart devices for certain retail experiences. This, in turn, fosters an expectation for similarly convenient and low-friction experiences with their loyalty programs and brand interactions.

AI and machine learning may be helpful in streamlining the customer experience, but they are most promising in managing and interpreting customer data captured by loyalty activities and customer interactions. A marketing strategy that is infused with integrated AI and machine-learning technology will be able to create a single customer view dynamically, in real time.

With this, brands and retailers with large footprints, a large number of locations and/or multiple operating banners can understand their customers faster and react to changes in the market more effectively.

Predict accurately and influence effectively

Perhaps the most intriguing application of AI and machine learning is the ability to more accurately predict customer behavior and the agility to influence that behavior within the shortest time frame possible. Loyalty program data can provide an initial embarkation point for the sales funnel and customer life cycle, and robust cross-system integration can facilitate the single-customer view.

But AI and machine learning can take the customer identity, combine it with real-time analysis of customer activity and forecast what the next customer action might be. The result is dynamic delivery of messaging, offers or incentives to influence that activity.

In short, these technologies transform the data-driven marketing approach from an inherently backward-looking process to a predictive one. AI and machine learning can minimize the guesswork that often comes with identifying and shaping the next customer encounter. Used effectively, they are incredibly valuable tools for optimizing loyalty marketing strategies.

Of course, enthusiasm for these technologies is at a high point, and there are many varied predictions about the impact AI will have on the world at large. In the loyalty and marketing worlds, however, adoption will be more incremental, as brands and retailers determine how AI and machine learning fit into their strategies and how much investment they are worth.

For marketers who want to jump-start the results of their loyalty and marketing programs, being early to embrace the AI revolution will help them do a lot more with the data they’re already collecting by taking more effective action, more immediately. Any marketing approach that can leverage these advantages will pay off in terms of improved customer engagements and increased revenues.


Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.


About The Author

Jose Cebrian is Vice President and General Manager of Email and Mobile Messaging for Merkle. He came to Merkle after spending nine years at Acxiom, where he grew to Managing Director, Global Client Services for Digital Impact, Acxiom’s email and SMS division. Jose led a global team responsible for optimizing interactive direct marketing campaigns across the web, email, and mobile.

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