Growth is often a marker of success—but it can also introduce unexpected strain behind the scenes. That was the case for one of Stratfield’s clients, a national retail brand with thousands of locations and over $20 billion in annual sales. The company is on a strong growth trajectory, opening more than 100 new locations per year across the U.S. and internationally. However, with each new opening, operational complexity increased—and so did support volume. What began as a manageable rise in support tickets became a rapidly compounding issue. By 2023, the client’s internal help desk fielded nearly 300,000 support tickets in a single year. Ticket volume was not the only concern. The issues ranged from urgent (“a vehicle just crashed into the front of the store”) to highly repetitive but disruptive (“a credit card reader has failed”). At the same time, their legacy ticketing system provided little in the way of meaningful data analysis or automation. Support costs were escalating, response times were deteriorating, and both store management and customer satisfaction was suffering. 

The Ask: Can We Find a Better Way to Manage Support? 

Having worked with Stratfield across several practice areas over the past decade, the client asked us to step in with fresh eyes. What began as a program management engagement evolved into a broader discovery effort. The client’s leadership team had a clear goal:  better understand their support burden and uncover ways to make it more efficient. Our team proposed a focused engagement to determine whether AI/Machine Learning and data science could make sense of the unstructured ticket data and provide actionable insight that would lead to faster issue resolution, lower support costs, and a better experience for their store managers in the field. 

The Approach: A High-Impact Consultant 

Stratfield deployed an experienced consultant: a Solution Architect / Data Engineer who also had deep experience from a business perspective. Working over a span of just a few weeks, Stratfield analyzed a year’s worth of historical ticket data using open-source AI and ML tools, along with Python and a SQL-based environment to re-categorize, analyze, and organize the data for analysis. The first objective was to make sense of the raw, unstructured ticket data. Once organized, we were able to categorize and rank issues by frequency, cost impact, and severity, into new ways of looking at the data. One clear insight emerged quickly: a substantial volume—approximately 12%—of total tickets were due to failed credit card readers. These types of repeat issues offered an ideal opportunity for attacking root causes, as well as automation and process redesign. 

The Solution: Smart Categorization and Automation 

Using our new, flexible approach to re-structuring the data, Stratfield worked with the client to design an improved support model tailored to high-volume, repeatable issues. For example, when a credit card reader failed at a location, store managers and team members previously had to submit a support ticket and go through diagnostic troubleshooting steps. Now, the new system allows them to use a web app to receive a replacement unit within 24 hours. No diagnostics, no follow-ups, and no delays. For other categories of issues, AI models were able to provide prioritization logic, pattern recognition, and resolution routing that made triage faster and more accurate. 

The Results: Meaningful Impact in a Short Period 

This wasn’t just a technology play. It was a strategic improvement to an essential business process. Within 90 days, the client realized measurable impact across several areas: 

• A 15% reduction in internal support costs through process efficiencies and automation 

• A 20% increase in store manager and Customer satisfaction scores due to faster resolutions and less friction 

In addition to the performance improvements, this project laid a strong foundation for broader AI integration across other operational areas—from supply chain to preventative maintenance and employee support. 

Lessons Learned: Why AI Works in High-Volume Support Environments 

This engagement demonstrated that the right mix of data, domain understanding, and focused execution can drive results. A few key insights stood out: 

• Unstructured or badly structured data is often the biggest obstacle. Once addressed, the path forward becomes clearer. 

• Not every issue needs human attention. Some just need a streamlined response plan. 

• Smaller, experienced, multi-disciplinary teams can move faster and deliver more targeted solutions than large, generalized project teams. 

• AI isn’t just for innovative roadmaps. When applied pragmatically, it solves everyday operational problems. 

What’s Next: Scaling Smart Support 

With early wins in place, the client is now evaluating opportunities to extend this model into other areas of their business. Many of the same tools and principles—data structuring, automation, and intelligent triage—can be applied to location performance, workforce planning, training, and inventory systems. AI may not solve every problem, but with the right approach, it can create immediate, material impact—especially for large-scale operations with repeatable service challenges. Interested in learning how Stratfield can help optimize support for your locations? Let’s talk. We can share what worked—and what to look out for—before you make your next move.