5 property management systems consolidated into one

Challenge

The company managed five properties, each on a different property management system. Without a unified player view, they couldn’t track high-value customers across locations or coordinate loyalty programs. Maintaining these separate systems cost $1.9 million annually.

Approach

We created a player data platform on Snowflake that pulled data from five property management systems. We used probabilistic matching to link player identities across properties and created a single view for each player. Then, we built a Tableau analytics layer that let the marketing team spot players active at multiple properties and calculate their lifetime value.

Outcome

We launched the unified player platform in week 18, cutting annual tech costs from $1.9M to $420K. By analyzing player behavior across properties, we identified 14,200 multi-site players, unlocking $2.4M in potential marketing revenue. The initial cross-property loyalty campaign delivered $680K in new revenue within 90 days.

How We Pulled Together Player IDs from Five Different Systems

Alright, here’s the deal. We had five different property management systems, and each one had its own way of ID’ing players, no common thread to link them up. So, we rolled up our sleeves and built a probabilistic matching method. We matched players based on name, birthdate, address, and phone number. This let us stitch together player records across all those systems. In the end, we uncovered 14,200 players active at more than one property. For the marketing team, this was pure gold, they’d never had this kind of insight before.

How We Built Lifetime Value Models for a Few Key Business Units

Here’s what we did: built a lifetime value model tracking each player across every platform they used. The interesting bit? Players hopping between multiple sites brought in 2.8 times more revenue than the ones sticking to just one. So, the marketing team flipped their retention budget to focus on these multi-property players who actually make a difference.

We jumped right in and launched the first campaign on a handful of key sites.

About a month in, the marketing team dove in and used visit history to send personalized offers to 14,200 players who played on different properties. That simple move brought in an extra $680K over the next three months.

Results

• $1.9M Annual technology cost saved
• 14,200 Cross-property players identified
• $2.4M Incremental marketing opportunity
• 18 wks Unified platform delivered

In the client's terms

The question

What breaks when these systems become one?

The situation

Five properties on five different property management systems. No unified view of a player, so high-value customers could not be tracked across locations and loyalty could not be coordinated. The five systems cost $1.9M a year.

What we found

The cost of the five systems was the visible problem. The expensive one was invisible: players active at more than one property were being treated as separate people by every campaign the business ran.

What it produced

Live in week 18. Technology cost fell from $1.9M a year to $420K. 14,200 cross-property players were identified, representing $2.4M in marketing opportunity, and the first cross-property loyalty campaign returned $680K in 90 days.

The roadmap

A player data platform on Snowflake drawing from all five systems, with probabilistic matching to resolve identity across properties into a single view per player. A Tableau layer on top so marketing can see multi-property players and their lifetime value without asking for an extract.

The work behind this result

• Our systems need people in between them, the problem this engagement resolved.
• AI Workflow Automation Consulting
• AI Agent Consulting for Business Workflows
• AI Automation Consulting
• CRM Data Access and Rollback Checklist, for whoever has to approve the spend.
• The 30 day Corporate Drag and Risk Diagnostic, findings yours either way.

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