AI and analytics help a quick-service restaurant get even quicker
Machine learning helps the help desk at a global quick-service restaurant
BETTER HELP DESK ANALYTICS
= WORKING BETTER
(NOT HARDER)
The Situation
We’ve all been there—maybe you’ve even been there on both sides of the counter. Customers are stacked up six deep at a quick-service restaurant. Orders are being taken, paid for, cooked, assembled, and delivered through a truly remarkable coordination of whip-smart process, human skill, and technological sophistication.
And then . . . the tech breaks down. The touch screen won’t place that chicken order. The timer on the fryer won’t start timing. The automatic drink dispenser won’t stop dispensing.
Help!!
For one global quick-service restaurant, the help desk (or, rather, the loose network of disconnected help desks and help providers scattered around the world) that fielded these often-frantic calls for help from thousands of restaurants wasn’t operating nearly as smoothly as it needed to. For example, because ticket resolution was geared toward just “solving the immediate problem,” and solutions rarely addressed the roots of those problems, the same issues were being raised over and over again in ticket after ticket. And the trickle-down effect of all of those stacked tickets, redundant tickets, delays in helpful responses, and redundancy in systems and approaches to answering questions was starting to affect the restaurants themselves—and, more to the point, the experience of the customers in them.
The trick was, the company did not have the tools, insights, resources, or time to perform the complicated and strategic analysis needed to diagnose—and then fix—the problem.
But Deloitte did.
THE SOLVE
Powered by Deloitte’s SFL Scientific business, the Deloitte team went to work. Step one was to gather and standardize ticket data (to the tune of more than 500,000 individual tickets from more than 20 countries) into a single data warehouse so that the team could analyze patterns across common issues and their root causes. Specifically, Deloitte brought to the project a sophisticated, and proprietary, ticket analytics engine to ingest consolidated ticket data, run analyses on those tickets, and output information on common issues and root causes. Through this effort, the team quickly surfaced a number of challenges that were contributing to the lack of smooth help desk ticket resolutions. Among them were an excess of ticket categories, mis-categorization of tickets, an inability to identify and track trends across tickets globally (especially given the number of languages in play), and rules that were inconsistent from geography to geography.
For example, when the team looked at information related to the touch screen ordering registers used by cashiers across those 20+ countries, they realized that items and functions had different names and labels around the world (a complexity that went beyond mere matters of translation). And moreover, some regions were collecting more detail than others. This made it nearly impossible for the company to understand common pain points and identify opportunities for solutions that would fix problems in every country.
Our team was quickly able to suggest solutions. For example, it was clear that a multilingual approach needed to be installed across the enterprise. A much more data-driven and specific hierarchy and ticket schema (that is, approach to organizing tickets) would need to be developed. And from there, a cross-market prioritization prediction tool could be created to enhance the responsiveness of the help desk.
As part of the work, we also turned to artificial intelligence (AI) capabilities that would allow users to ask questions about service ticket data using a simple chat interface—making the help desk experience more natural and simpler for harried workers the world over. And, more broadly speaking, this work was one piece of a larger Service Management transformation, in which the company was investing in things like how it standardizes processes, measures success, and performs various service activities.
AUTOMATING THE
EASY SO THAT
HUMANS CAN
FOCUS ON THE HARD
The Impact
Before, the company had been applying redundant investments to solve the same problems in multiple markets in the same way, over and over. Now, by bringing global standardization and efficiencies to the new help desk model, our services have given service desk workers time back to focus on the tickets that truly need their attention and expertise. And, as help desk operations start to run more smoothly, the revenue loss associated with system downtime and system inefficiencies should begin to reverse direction.
The end result? A better overall experience for customers—and workers.
Ticket Analytics is just the starting point to unlocking advanced levels of service management maturity and AI operations. Applying machine learning capabilities, implementing causal analysis, and introducing AI operations and predictive analytics can give restaurant owners faster response times; local markets better data on their restaurant issues; and corporate IT greater ability to identify, prioritize, prevent, and fix issues.
Additional benefits:
- Now that analysis can be conducted across geographies, topics can be addressed broadly—and once—as opposed to each time they arise, time and time again.
- Trends can now be rapidly, and visually, identified and quickly addressed by either simple go/no-go decisions or via further investigation.
- Users now have access to real-time ticket data from which to pull information and create graphs simply and quickly.
-
Michael Caplan
Principal
Deloitte Consulting LLP
mcaplan@deloitte.com
+1 973 738 9862 -
Mike Luk, PhD
Managing Director
Deloitte Consulting LLP
miluk@deloitte.com
+1 401 678 8939 -
Tomiko Partington
Senior Manager
Deloitte Consulting LLP
tpartington@deloitte.com
+1 612 806 1252 -
Celia Ludwinski
Specialist Leader
Deloitte Consulting LLP
cludwinski@deloitte.com
+1 415 783 5441 -
Kristin Ruffe
Manager
Deloitte Consulting LLP
kruffe@deloitte.com
+1 872 336 1272
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