White Paper – Transactional vs. Experiential Loyaltyin the Era of AI

Foreword

Finding a balance between AI-enabled marketing efficiency and high-touch guest experience is one of today’s biggest marketing challenges for restaurants. As an original pioneer of loyalty programs, TapMango’s goal is to continue to provide you with contemporary, practical resources which help you generate revenue and strengthen retention.

TapMango has partnered with global loyalty expert Phil Rubin to develop this whitepaper, a charter Certified Loyalty Marketing Professional (CLMP). Rubin has worked with clients including Adidas, American Express, Citigroup, Disney, Estee Lauder, Kimpton Hotels & Restaurants (now IHG), Nordstrom, PGA of America, Pittsburgh Steelers, sweetgreen, and UPS. We’re delighted to bring his expertise to you.

🥰

PHIL RUBIN,

LOYALTY EXPERT

Introduction

Among the top priorities in loyalty today is delivering more experiential value to complement the ubiquitous transactional value delivered by most programs. This comes after years of research and evidence that customers are willing to pay more or go out of their way for a better experience. That willingness, by the way, is how consumers define loyalty.

At the same time, the broader business priority is AI, serving to add more speed, efficiency and effectiveness while reducing (or more productive) resources. Within this broader priority is an opportunity to leverage your loyalty program more effectively with generative and predictive AI.

Some Context Matters

That illustration is and has been one of the best explanations of loyalty marketing, not loyalty programs, per se.

And it’s as applicable to restaurants as it is to merchants. James Beard used to be constantly bombarded with the question of what his favorite restaurant was. His answer was always the same: “It’s the same as yours. It’s the one that loves me the most.”

And ultimately, that’s where AI comes in to move loyalty so that it’s more experiential, more emotional, than transactional. AI can help an establishment show its love to customers by recognizing them, saving them time, keeping them relevantly informed, and yes, rewarding them.

Loyalty’s Evolution, Brought by Growth and Automation

As the corner stores and neighborhood restaurants became chains, technology brought automation intended to scale loyalty efforts by capturing more customer data to be used for marketing purposes. But this technology was not highly customizable or configurable, serving to reduce loyalty marketing to homogenous programs.

Most of the tech platforms didn’t help sales associates in stores or servers in restaurants. Nor did they connect to planning, merchandising and purchasing systems.

Digital communications – think email, SMS and in-app notifications – became low cost, low barrier, more-is-better spam. Or more generously, not so relevant to the mass of customers that are the “targets” or better, the victims, of “spray and pray” communications. Yes, it’s work to create various audience cells with similarly variable offers, messaging and creative, and that is exactly where AI helps a lot.

Maybe, Just Do It

Loyalty programs enable you to capture a lot of customer data but the value is only unlocked through insights. This is what matters most, if you want to practice loyalty like Nike, who classically refers to loyalty, without using the actual word, as “know to serve”.

Know your customers, what they want and like, and you can figure out what will bring them back if you serve, aka communicate with and service them, accordingly. A perfect and simple definition of loyalty marketing.

“Know” translates into customer segments, tiers and behavioral segmentation (this includes buying behavior, the best predictor of future buying behavior!). In a better world, that segmentation incorporates attitudinal data, such as from surveys and NPS (net promoter) scores. Knowing attitudes are critical in knowing what kind of experiences customers value, allowing you to “serve” them optimally.

Enter AI

Communicating effectively starts with customer segmentation, which means not necessarily saying the same thing to the entire customer base. AI helps with predictive analytics to organize data and customers into segments and match them to the business needs. For example, you might not promote an item to a group that’s never bought an item in a particular category. A restaurant need not promote a new burger LTO to someone who only orders salads, much as a retailer might never promote wool pants to someone who only buys denim.

On an ongoing basis – and with testing — AI can dynamically create and revise these segments based on business or other conditions as well as newly collected customer data. AI can also help you build a campaign grid, mapping segments to the right offers, messaging and creative.

The full value of loyalty programs, and part of their original purpose, comes from recognizing that not all customers are the same, nor are they all of the same value. They don’t behave the same way, have the same attitudes or the same likes and dislikes. They cannot and should not be given the same experience, whether in their email inbox or on premise. The latter of course is more difficult unless you put the insights into the hands of your team, or better yet, use a kiosk, which is usually more efficient and effective.

Beyond communications, AI can use customer data to plan for demand, down to items whether you’re a retailer or a restaurant. The ability to connect customer data to demand works for the customer and for the store or restaurant. Most retailers buy for a store, or for a “door”, not for customers. But who does a store represent but its customers?

Similarly, AI can help a restaurant operator know, or “remember”, that certain items sell better on certain days or day parts. Or based on the weather, or the stock market, or whatever variable you can ask AI to analyze and compare.

If those activities aren’t enough to overwhelm you, how about the measurement and reporting? Again, AI is a huge advantage in automating reporting, providing custom views and even customizing graphics to show performance.

Think of AI as the new enabler to figure out, run tests and optimize, measure and refine. Not for the program, but for what to do with the program to communicate, promote and enable a better customer experience.