Is This America’s Most Data-Driven Private Club?

Sean Palmer knows who is coming to dinner.

On his screen is a member of The Union League of Philadelphia with a 5:15 reservation. There is his photo, his profession and where he went to college. Palmer can see how often he visits, which club he typically spends the most time, what he orders and what he drinks. He can see whether he plays golf, stays overnight or buys apparel in the golf shop.

Palmer, PGA, Chief Operating Officer of The Union League, calls it a “scouting report.”

Sean Palmer, PGA, Chief Operating Officer, The Union League of Philadelphia

“If I have 60 covers coming into our high-end dining room tonight, we should know something about every one of those members,” he says. “The hostess and servers should know who’s coming in so they can recognize them and have their favorite drink ready.”

For years, The Union League already had much of that information. The problem was that it lived in different places.

A dining reservation might be in one system, a tee time in another. Hotel stays, purchases, event registrations and account activity could all sit somewhere else.

Over roughly a decade, The Union League has worked to connect those pieces across seven geographically separate properties. It built dashboards capable of pulling data from multiple systems and answering questions that once required hours of exporting, cross-referencing and spreadsheet work.

Now the organization is layering AI and custom applications over that foundation.

The result is beginning to change how The Union League staffs banquets, connects members and even greets someone pulling through the gate.

Building the Foundation

Palmer started thinking seriously about data while working at Merion Golf Club (Ardmore, Pa.), where he spent nine years before joining The Union League in 2014.

Questions would arise about golf-shop sales or rounds compared with the previous year. Palmer wanted context behind the numbers.

“Public operators and resorts are really good at using data to understand performance,” he says. “They’ll look at the year and say, ‘We had six fewer playable golf days, so we had six fewer days to sell the golf course.’”

He began building spreadsheets.

“The data doesn’t lie,” Palmer says. “It can kind of tell a story.”

That mindset became more important as The Union League expanded. Growth brought more properties, more members and more software.

“The same member that books a tee time and then buys golf balls is using two different pieces of software,” Palmer says. An overnight stay and dining reservation could each involve another.

About a decade ago, The Union League began bringing those disparate sources together and building dashboards that could examine spending, demographics, events and member behavior.

“Every electronic interaction with the club creates data,” Palmer says. “If a member buys something or checks in somewhere, we have a record of it.”

By the time generative AI arrived, The Union League had already done the hard part. It had organized its data.

The dashboards, however, still required someone to know how to use them. Palmer did. A handful of other people did. But the Union League employs approximately 1,200 people.

“We had built these powerful tools, but they still required some expertise to use,” Palmer says. “I was using them, along with maybe a handful of other people at the club.”

AI offered another way to distribute that intelligence.

“A lot of people think of AI as, ‘Help me write an email or a paper, or organize my calendar,’” Palmer says. “What we’re doing takes it to the next level.”

The organization has an on-site IT team, including a programmer who uses AI tools to build custom applications over the club’s existing data.

“Getting to this point was really hard because the member’s picture might be in one database, golf information in another, overnight stays in another and reservations somewhere else,” Palmer says. “We had all of these different pieces sitting in 15 different buckets.”

“AI is pulling all of that information together and putting it into a format that’s actually easy to use,” Palmer says.

Looking Forward

One of The Union League’s most ambitious applications looks at something clubs already know a great deal about—upcoming banquet business.

Palmer pulls up the Banquet Intelligence Report.

“This is pretty wild,” he says.

The system can look 30 or 60 days ahead at upcoming events, guest counts, meal periods and locations. Palmer selects a day with 259 banquet covers and can see how that business breaks down.

The Union League can then layer operating assumptions onto it.

“For a plated event, we can set staffing parameters based on the number of guests and determine how many servers we’ll need,” Palmer says.

The system can begin recommending staffing levels. Eventually, Palmer believes, it could help draft schedules.

That moves decision-making ahead of the expense. Traditional financial reports may not show Palmer a labor problem until well after the month closes.

“If I don’t get my financials until 12 or 15 days after the month closes, I’m already halfway through the next month before I can correct a problem from the month before,” Palmer says.

Banquet business is already booked. The data can help managers anticipate the labor and purchasing it will require.

Palmer thinks it could eventually change the workflow inside the kitchen.

“Eventually, this could replace the traditional BEO meeting and the clipboards on the wall,” Palmer says. “It could completely change how we manage events.”

He imagines chefs, cooks and events managers seeing live information tailored to their roles. When something changes in the underlying event system, it updates for everyone.

“People worry that AI is going to replace jobs,” Palmer says. “I see it making people more efficient by taking away the hours they spend entering information into spreadsheets.”

The harder transition may be human.

“The hardest part is changing how people think about the work,” Palmer says. “We have teams who have been running BEO meetings the same way for years.”

The Club as Matchmaker

Other applications are aimed directly at member engagement.

“Our job is to connect people,” Palmer says. “We’re matchmakers. Those relationships are what create the culture and sense of community within a club.”

The Union League has information members provide about their interests, professions and families, along with behavioral data showing how they actually use the club. The organization has built a tool that brings those signals together and identifies members who may have something in common.

The value of those connections becomes particularly clear when members leave. When The Union League reviews resignations, Palmer says, the membership team often sees a pattern.

“It’s usually the members who haven’t found a group,” Palmer says. “They’re not part of the men’s golf group or the fishing club. They don’t have those relationships pulling them back to the club.”

The club is beginning to use the technology to identify prospects for affinity groups. Palmer sees similar potential in new-member onboarding, where the club could recommend groups and experiences based on an individual member rather than handing everyone the same information.

Two Minutes Ahead

The gatehouse application Palmer’s team is finishing may be the clearest example of how all of this can translate into hospitality.

At several Union League properties, members might arrive for golf, a lesson, dinner, an event or a meeting. The organization already knows about those reservations.

The new application puts that information in front of the person at the gate.

If Mr. Jones arrives for a lesson with Billy, the employee can see his photo and know why he is there.

“The employee can greet him by name and say, ‘Mr. Jones, have a great lesson with Billy today,’” Palmer says.

The employee clicks his picture. At the bag drop, another employee gets an alert.

“The bag drop gets an alert that Mr. Jones is about two minutes away, along with his photo, vehicle and reason for visiting,” Palmer says. “They can have his clubs ready before he arrives.”

By the time he arrives, his clubs can be waiting.

The technology itself largely disappears from the member experience.

What Comes Next

Some of what Palmer demonstrates is already in use. Other applications are only months old or remain in development. Security and access remain considerations as The Union League determines how widely to deploy them.

But the work has already changed Palmer’s thinking about club technology.

For years, he says, The Union League searched for a single platform capable of handling everything.

“For years, we chased the unicorn,” Palmer says. “We kept looking for one software platform that could do everything and replace all the others.”

He no longer believes it exists. Instead, Palmer wants the strongest technology for each function and a way to connect the information underneath it all.

“You need the best technology for each part of the operation,” Palmer says. “The future is finding a way to connect all of it.”