Starting in 2021, PDQ accelerated its investment in technology and data, adding new capabilities across third-party delivery, first-party delivery, loyalty, and other digital channels. But as each new system added another disconnected data source, any decision that touched sales, cost, or the guest experience required assembling the relevant information by hand.
That work largely fell to Bryan Groc, Chief Strategy Officer for PDQ Chicken and Glory Days Grill. His lean team, spanning marketing, digital analytics, and third-party delivery, turns raw numbers into proactive decisions, from evaluating campaign performance to understanding menu results and informing pricing.
The company’s reporting setup was built around a traditional Power BI environment, but Bryan still had to manually gather data from the point-of-sale system, third-party platforms, and marketing sources. Before analysis could even begin, he spent hours cleaning, combining, and transforming it.
Other functions waited on Bryan and his analyst before they could act. “I was a gatekeeper, and the analytics team was the source of truth for the data,” he explains. Every manual step in between was another chance for error, and another delay before critical decisions could be made.
That delay showed up differently depending on what was at stake. Running a paid or organic campaign meant treating it like a live test: see what's working, refine it, redeploy before the window closes. The slower the data, the less runway Bryan's team had to act on what the campaign performance was telling them.
Product mix decisions carried the same time pressure, plus more complexity. Before changing a menu item or operating approach, the team needed to separate the effects of promotions, ordering channels, and outside conditions. When that comparison took too long to build, PDQ often had to fall back on individual operator observations. Those observations brought important local context, but they could not show Bryan whether the same behavior held across locations and channels.
Bryan knew that decisions needed to move faster than his team’s manual processes. He started evaluating platforms that could shorten the path from data to decision, without asking every user to master a traditional BI tool. Ease of use, browser-based access, efficient onboarding, and pricing that fits the business all factored into his search.
Ingest stood out on those criteria, but the relationship mattered just as much. Bryan spoke directly with its leadership and customer team, giving him a clear picture of how the platform would support PDQ’s day-to-day operations.
“The problem was that everything was fragmented. On the sales side alone, we had to pull data from our main POS and third-party systems, layer it together, clean and transform the tables, and then bring in the marketing data.”
During onboarding, Bryan’s team worked with Ingest to map the sources behind PDQ’s sales, labor, and product mix reporting. Ingest then cleansed, normalized, and transformed the data according to PDQ’s own metric definitions, creating a consistent data set tailored to how the business measures performance.
PDQ supplied the operating context behind those definitions, including which data should feed each metric, how menu items should be grouped, and how promotions or unusual trading days should be treated in comparisons. Ingest built those rules into the platform, so PDQ could apply the same definitions and comparison logic consistently across reports without relying on one person to remember every detail. “It didn’t feel like we signed and were left on our own,” Bryan says. “Ingest directed the onboarding with a lot of clarity to give us the best output for what we needed.”
Today, Bryan, his analyst, and members of PDQ’s training team use Ingest to analyze sales, labor, traffic, and product mix. They compare performance year over year, quarter over quarter, and day over day, omit dates affected by evergreen promotions, and compare one Monday with another without building a complicated series of traditional slicers. That flexibility lets Bryan isolate the conditions behind a change before his team recommends the next action.
Using Ingest’s filters and product mix views, Bryan separates drive-thru, dine-in, and kiosk behavior, then drills into individual items, modifiers, and attachment rates. In one analysis, PDQ found that 30% of sandwich orders were customized, omitting the specialty pickle used only on that item. The finding provided the team with evidence to simplify the ingredient mix, a low-risk change that cut recurring costs across locations. The same views help PDQ assess how menu changes and ordering prompts affect each channel without having to rebuild the comparison across separate systems.
That same foundation frees Bryan and his team from serving as the go-between for every recurring question. “Before Ingest, we were more of an assembly,” he says. “Now, we understand what the information means and use it to drive the next action.”
Now, PDQ’s training team retrieves unit-level scorecards and contest results directly, without waiting for the analytics team to validate the numbers first. Bryan’s team translates broader data changes into clear direction on sales, prime cost, team engagement, and guest satisfaction, freeing operators to stay focused on running their restaurants.
As PDQ’s menu evolves, the team maps routine item changes using the training Ingest provided and works directly with Ingest when a larger update or reporting need comes up. “They’re very transparent about what they can support and where we may need another approach,” Bryan shares. That clarity lets him plan with confidence.
“For our operators, it’s more about direction and action than analytics and dashboards. Ingest helps us deliver that direction more efficiently and effectively.”
By partnering with Ingest, PDQ unified fragmented data, providing the team with a reporting foundation for evidence-based decision-making across all locations. That shift reaches beyond Bryan's team: operators act on clear direction, and product mix analysis catches real costs before they compound.
Looking ahead, Bryan plans to extend the same foundation to sister brand Glory Days Grill, where he also serves as CSO, and use it as the basis for new internal analytics and AI projects. That same data also lets him pursue what he calls precision hospitality: making every guest interaction count, building loyalty, and preserving the human service at the heart of PDQ.
“Ingest is evolving with us, and we now have clean, maintained data we can put toward other internal needs and projects.”
Got questions? Excited about what Ingest can do for your restaurant? We're all ears, and we’re here to help!
TALK DATA TO ME
