Data analytics services case study

McDonald’s Optimizes Food Delivery Performance With Data Analytics

Overview: As food delivery surges, McDonald’s improves operations ahead of the curve

McDonald’s, the leading global food service retailer, partnered with Wavicle Data Solutions to optimize the performance of its third-party food delivery providers in 40 markets across five continents. McDonald’s needed to measure and track the performance of its growing third-party food delivery function and its effect on the business to ensure: sufficient inventory for popular menu items, adequate staffing, on-time deliveries, and ongoing customer satisfaction. Without these assurances, the company was concerned about losing control of revenue and consumer brand loyalty. An intelligence solution was critical for financial success in a highly competitive market.

Solution: Disparate data transforms into valuable visualized metrics

Wavicle implemented a data model and process to load source data from multiple third-party food delivery partners into a single Amazon Redshift cloud data warehouse, transforming disparate data into valuable operational intelligence.

On-demand customizable reports gave McDonald’s management insight into sales, delivery, customer feedback, and fees, with the ability to “slice and dice” these metrics by restaurant location, food delivery partner, date range, and time of day. With access to visualized metrics, they could continuously measure and improve food delivery operations.

Outcome: Serving up the right food delivery analytics

This solution provides McDonald’s operators with key analytics at a macro-level, giving them the ability to dive into details, resolve specific issues, and optimize food delivery performance. Since implementation, McDonald’s has increased total food delivery orders, which now exceed more than ten percent of sales in locations that offer delivery. Additionally, the average dollar value per transaction is significantly higher for food delivery vs. non-food delivery. They now attribute food delivery as a key driver of global growth.

This solution combines existing corporate data such as point of sale (POS) with third-party food delivery data to track, measure, and visualize key metrics:

Sales metrics by channel (in-store, drive-thru, and food delivery)

• Total sales: in dollars, number of orders, percent by channel
• Average check size: in dollars, number of items by channel
• Refunded items, based on customer complaints: in dollars, number of orders by channel
• Uptime by restaurant location: hours per day each location accepted food delivery orders

Food Delivery metrics

• Average route time (length of time for the driver to arrive at the restaurant)
• Average restaurant wait time (length of time driver waits at the restaurant to receive the order)
• Average fulfillment time (length of time for the driver to deliver to the customer after leaving the restaurant)

Customer analytics

• Total sales in dollars, number of orders (new customers vs. existing food delivery provider customers)
• Market basket analysis to understand what items are selling the most via food delivery, vs. differences from in-store purchase behavior
• Customer satisfaction scores and feedback by food delivery order (delivered via food delivery apps)

Fees metrics

• Fees paid by restaurants to food delivery providers
• Fees paid by customers to food delivery providers