How Machine Learning Can Help Quick-Service Restaurants Personalize Content

June 20, 2019 Vibes Marketing

How Machine Learning Can Help Quick-Service Restaurants Personalize Content

While every brand benefits from having a better understanding of its customers so that they can personalize marketing content, quick-service restaurants (QSRs) in particular can really benefit from personalization. 

Thanks to machine learning and artificial intelligence, QSRs have more capacity than ever to deliver exactly the marketing offers their customers want. 

Take a Note from McDonald’s 

Whenever this grandaddy of QSR does something, you better perk up and pay attention. Recently, McDonald’s announced plans to acquire Dynamic Yield, a startup that processes algorithmically-driven "decision logic" technology to help brands understand what customers want. 

So how will Mickey D’s leverage the data it uncovers from its 77 million daily customers? It plans to start with the drive-thru. Using the digital displays the restaurants already have stationed at the drive-thru, the brand can use machine learning to make certain assumptions about customers based on their orders. 

Let’s say a McDonald’s is located near a school and a customer orders two Happy Meals at 4 pm. The assumption is that it’s a parent who just picked up his or her children from school. The software could then display a special offer for a pick-me-up Iced Caramel Mocha for Mom or Dad. 

The software could get even more personal, scanning customers’ license plates to identify them by past orders to make recommendations. 

Stay tuned to see how this works out...and to see other brands follow McDonald’s lead. 

Loyalty Programs Create Upsell Opportunities 

Quick-service restaurants are among the best candidates for digital loyalty programs because of the frequency that customers buy from them. Just consider your friend, Jane, who has a daily Starbucks addiction.  

Maybe she has a few favorite drinks, and when she opens the Starbucks app (which is tied to her loyalty account), she sees an opportunity to earn extra stars for buying a grande flat white two days in a row.  Even if she normally buys a tall size, she’s willing to buy bigger to earn more points toward a reward. 

Loyalty programs do an excellent job of tracking purchases and making recommendations that spur more sales. They can also be used to give members exclusive access to new products, which can spur great word-of-mouth campaigns. 

Geolocation Push Notifications Spur Mobile Orders 

Mobile orders have taken off for many QSR brands. But the purpose of allowing customers to order from their phones is to make grabbing dinner easy. This service, until recently, didn’t guarantee the food would be ready upon arrival. 

Cook it when a customer is five miles away, and it will be cold. Delay the prep too long, and customers are waiting at the restaurant. Brands like Panera and Chik-fil-A use geolocation to make sure the food they prepare for mobile orders is ready just as the customer is in the area. They can also let customers know if the order won’t be ready by the time they get there, based on their location. 

Leverage Redemption Data Across All Mobile Marketing Channels 

You may use mobile wallet, SMS, and push notification to send offers to your QSR customers, but it can take a lot of human hours to analyze the redemption data to better understand how to customize future offers for best results. 

But when you leverage data like what time of day more people redeem offers, which promotions see the best redemption rate, and where people are when they view your message - you have the potential to step up your personalization game and really deliver exactly what your audience wants. 

Our recent integration with Sparkfly allows you to do just that. Whether the promo code comes via text, email, app, or social media, your point-of-sale system tracks redemptions and analyzes the data so you don’t have to. 

Machine learning and analytics are moving at lightning speed in terms of their capabilities. They’re reducing the time brands have to spend digging into the data and determining what it means. We’re looking forward to more innovations in this technology in coming months. 

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