Elder Research partnered with a fast-food restaurant chain to develop a minimum viable product solution that leveraged data to help the client better manage customer demand.
The Challenge
Customer service is at the forefront of the restaurant industry. And specifically for fast food restaurants, ease of ordering and quick order preparation are critical. As the overall demand at a restaurant fluctuates throughout the day—from breakfast to lunch to dinner—so does the demand across specific ordering channels, whether drive-through, in-store, or mobile ordering.
One of our clients, a nationwide restaurant chain, sought to explore the feasibility of incorporating demand-management strategies via their mobile ordering platforms. Could distributing demand across their various ordering channels make the fast-food experience better for customers and franchisees?
To answer that question, Elder Research created and tested a methodology for assessing (1) the level of demand a restaurant is experiencing at a given time and (2) how the current demand relates to their typical pattern.
We delivered a minimum viable product (MVP) that explored whether implementing a “busyness” metric was useful to customers and if it was able to reduce ordering congestion in their more popular channels.
The Solution
We knew we had to base our solution on real-time data streams to guarantee timeliness, so we put significant effort into identifying busyness indicators we could derive from these data. Our solution successfully identified and measured these real-time busyness indicators, focusing on our client’s most customer-facing ordering channels to provide alerts when these channels appeared busier than usual.
To determine whether a channel really was busier than usual, our team analyzed real-time ordering data and compared recent values against historical data. We established threshold busyness values at scale, determining the criteria for being “busier than usual” across every location and channel.
To establish the best threshold values for alerting, Elder Research scientists worked closely with client subject-matter experts and stakeholders.
Our work provided new insights into the real-time data, and we demonstrated several methods for establishing and quantifying busyness. This collaboration with business experts helped us establish alerting thresholds that accounted for key internal business factors. This partnership ensured our thresholds were both technically accurate and practically useful.
Results
Our work provided an MVP solution to help the client better manage customer demand, demonstrating a new way their data could provide real-world value for both customers and staff.
Busyness alerts are now being tested in the client’s mobile app, shown to customers during the ordering process. And these updates are well on their way to general adoption. If implemented, this demand management system will help customers to be more informed when deciding where and how to place their orders. It will also help mitigate channel congestion, benefiting both customers and employees.
To support the client’s future data efforts, Elder Research provided recommendations as to where the client ought to invest resources to make these kinds of applications even more beneficial.