These are fast-changing times in the restaurant industry. For the first half of this year, drive-thrus were one of the few ways for consumers to order food from their favorite QSRs. Consequently, more restaurant operators are focusing on collecting drive-thru data to improve speed of service and other metrics.
Below, you’ll find two key ways to take a data-driven approach to your drive-thru so you can continue to innovate no matter what the future holds.
Data that Goes Beyond Demographics
By purchasing Dynamic Yield in 2019, McDonald’s is working on personalizing the ordering experience through collecting data on the weather, time of day, trending menu items, and restaurant traffic. This marks an important change over the past; when deciding which information to capture, restaurants now need to think beyond demographics to compete.
After collecting a large amount of customer data, Dynamic Yield uses multi-armed contextual bandits instead of A/B testing in order to determine which menu items to recommend when customers pull up to a McDonald’s drive-thru. This new approach allows McDonald’s to test multiple combinations of suggestions on its digital menuboards and maximize the types of offerings that customers actually choose. For example, if several drive-thru customers who arrive for lunch during the summer do not order the McFlurry suggested on the order screen, Dynamic Yield’s software will automatically scrap this suggestion for customers who fit this criteria and recommend other menu items until it makes a successful upsell.
Through using AI to learn, revise, test, and re-learn, Dynamic Yield creates customer profiles in real-time. This allows McDonald’s to change its menuboard messaging to fit countless variations of customers until the software identifies the right categories to target.
Segmenting QSR Customers
Viant, an advertising technology company, published a report that separates QSR customers into 5 categories. These are not one-size-fits-all, so make sure you back up each category you generate by sales and ordering data that can help you improve the drive-thru experience for each segment. Viant, for instance, uses the following criteria, among other metrics:
- Breakfast Buyers – 15% of visits occur during breakfast hours
- Lunchtime Loyalists – 90% of visits occur from 12pm to 1pm
- Primetime Patrons – 95% of visits occur during dinner hours
- Weekenders – 35% of visits occur on Saturday or Sunday
- Devoted Diners – make 300% more visits in a 110-day period than the second most loyal group
Remember to make the segments relevant to your target market, meaning the names and criteria for each may vary from one QSR concept to another. Viant’s model is from a study of 1,889,441 customers who visited a 1,000-store national sandwich chain a total of 4,880,438 times. The company used a machine learning algorithm called k-means clustering, which is a method of randomly selecting customers from the sample dataset, averaging their visits for each hour of operation to create clusters, and grouping the rest of the customers based on how closely their data matches each cluster. The process repeats until the average of each cluster, which in this case would be the average number of visits, stays the same.
You can also outsource data collection and analysis, using the insights to improve the drive-thru experience for the different customer segments that result. For example, if you owned the sandwich shop that is a part of Viant’s model, you could highlight Coca-Cola as a popular soft drink option on the digital menuboard during lunch hours, since Viant found that Lunchtime Loyalists are more likely to opt for this beverage than other groups. With the vast majority of lunch visits occuring from 12pm to 1pm for this example, you could also send an employee outside to collect and confirm orders with a tablet POS system to speed up drive-thru service during this peak hour.
For more articles on drive-thru technology, check out our posts on improving drive-thru efficiency and how artificial intelligence is transforming the drive-thru.