This article is authored by IPMI Technology Committee members Andrew Sachs, President @ Gateway Parking Services; Chris Perry, SVP of National Sales & Operations @ Parking Base; Danyell Little, Director of Client Success @ Metropolis; Andrew LaMothe, VP of Business Development @ IPS Group; & Sarah Becherer, VP of Marketing @ Ocra

It was first published in the November 2024 editions of Parking & Mobility magazine by the International Parking & Mobility Institute. To read the original article, click here.

Data has been driving decision-making within parking for some time, and new opportunities continue to emerge as we develop new foundations and scaffolding for data collection and exchange.

However, even for the most tech-enabled and data-savvy parking operators or asset owners, instances remain where our assets are underutilized, underperforming, or producing undesirable customer experiences.

We can’t foresee every interaction between people and people, people and equipment, or people and technology that takes place within our microenvironments.

But we can use data to systematically create, prevent, or prepare for these instances.

In this exploration, we’ll refer to the practice of leveraging data to accomplish this as the art of “Seeing, Understanding, and Communicating.”

• See: Meticulously access the data and surface it
• Understand: Apply analysis and business intelligence
• Communicate: Show it to the right platforms and people

This comprehensive approach to data transforms parking into a strategic asset.

Let’s get down to brass tacks with some specific examples of how “See, Understand, and Communicate” can be applied: operations, commercial, municipalities, and hospitality.

Seeing, Understanding, and Communicating: Operations

In parking operations, levers pulled using data on customer habits immediately affect a location’s performance.

Metrics such as average dwell time and occupancy highlight the trends around existing demand and focus on maximizing results based on a known customer population.

Using data gleaned from a known population to achieve optimized revenue results from that population is certainly logical.

What about the customers outside of that population who aren’t parking at the location due to a poor customer experience?

Trends that negatively impact customer service lead to customer loss.

Therefore, we need to examine the data from the opposite side to learn how to generate new revenue and prevent customer churn.

The data required for this analysis in a staffed or valet facility is already available in existing operational reports:

• Staff schedules.
• Parking transaction data.
• Session data.
• Heat maps of open and closed ticket times.

Overlayed with each other, these datasets yield insight into demand’s ebbs and flows, which can be directly correlated with tariff adjustments, staffing schedules, and unnecessary operational expenses.

For example, understaffing could lead to a poor parking experience and potential loss of customer revenue, while overstaffing could lead to unnecessary operational expenses.

Understanding the data can help determine the optimal balance.

Here’s a use case with LPR that provides insight into the customer experience:

• In a valet-staffed facility equipped with LPR, a monthly parker arrives. LPR immediately recognizes them as recurring customers and pre-populates the required information with the digital ticket.

• All that is left is for the attendant to finish the check-in process. This check-in is required as it tracks the vehicle transfer from the customer to the operator.

• If the LPR entry timestamp reads 8:00 a.m. and the claim check start time is 8:07 a.m., then that gap represents the time the customer waited before being fully checked in by the valet and free to go about their business.

• The summary of these time gaps could demonstrate staffing or service shortfalls that could negatively impact on the customer experience.

A manager who sees this data point can understand the customer service issue and then take actionable steps to improve the quality of customer service.

The key is overlaying the datasets to see how the trends impact one another.

The timing of the trends is as important as the trends themselves.

Seeing, Understanding, and Communicating: Commercial

In the past, when the cost of generating actionable research was cost-prohibitive, Burger King adopted an approach known as the Spillover Effect to leverage McDonald’s market presence to optimize its store locations.

The approach was simple yet effective: strategically position Burger King outlets within sightline of McDonald’s outlets.

This “spillover” tactic allowed Burger King to expand its footprint without the high costs of conducting its own market research.

It relied instead on the assumption that McDonald’s had already invested in extensive research to identify prime locations.

Commercial operators have simplified the spillover approach to a quick rate survey and adjusted location pricing to match or slightly undercut the clustered competition.

However, this “spillover light” approach constitutes a significant opportunity cost in today’s data-rich environment, where operators can make informed decisions quickly and accurately using readily available data instead of guesswork based on competitors’ prices.

Modern PARCS (Parking Access and Revenue Control Systems) allows operators to adjust rates in real-time, often with just a few keystrokes. Rates, from regular pricing to event rates, can be adjusted based on real-time demand.

For instance, if a lot has a $25 rate for a baseball game but demand is low, the operator can swiftly reduce the rate to $20.

Conversely, if demand surges at the $25 rate, it can be increased to $35 just as easily.

For such a responsive strategy to work, there must be a seamless data flow between attendants and management.

With cloud-based PARCS, the manager can be anywhere, even across the country, and still make real-time adjustments.

When aggregators are part of the equation, pricing strategies can become even more potent.

Rates can be tiered based on occupancy, meaning that as the facility fills up, the price increases, maximizing revenue as spaces become scarcer.

This automation reduces the need for constant monitoring.

Dynamic pricing engines further simplify the process by automatically setting rates based on real-time demand data.

These systems lower prices during slow periods to attract customers and increase rates during busy times to maximize profits, constantly adjusting to find the optimal price point.

This use of big data and sophisticated algorithms by dynamic pricing engines allows operators to fine-tune pricing with precision, ensuring the most efficient and profitable use of their assets without the need for constant oversight.

Seeing, Understanding, and Communicating: Municipalities

In municipal parking environments, data drives policy.

Cities plan for residents’ future needs, and data is one of the most valuable assets in this planning process.

Technology providers and operation specialists in municipalities are responsible for understanding the sensitivity and complexity of the ecosystems in which we operate and for creating agile systems for data examination and communication.

Beyond seeing the data, we aim to ensure it is digestible for each relevant subgroup.

We must prioritize creating transparent systems that can quickly identify the client’s needs and policy changes, which provides reassurance and confidence in our processes.

When communicating with municipal clients regarding data, it’s essential to review:

• How do current policies impact how technology is implemented and deployed?
• How do deployment and curb management needs differ based on details like city and state ordinances and laws?
• What are the current needs of the city?
• What are the current and future goals of the city?

These questions will guide municipal parking professionals in creating mobility synergy within the city’s ecosystem, use cases, and seasonality of operations.

Seeing, Understanding, and Communicating: Hospitality

As data and the scaffolding and mechanisms for seeing, understanding, and communicating parking data evolve, we see more opportunities for engagement with adjacent industries, such as hospitality.

The opportunity for parking that data cracks open in the hotel industry is in terms of helping hotels understand that their parking assets can be utilized and monetized not just for guests of their hotel but for non-guests, as well.

Non-guest parking represents a staggeringly large ancillary revenue stream for hotels and strongly impacts net operating income (NOI) and bottom-line profitability.

However, many hotels aren’t tapping it as they perceive it as a distraction from their core business: the guest experience.

We need data to wake hotels up to this possibility and overcome the ubiquitous assumption that a parking space will never be as valuable as a room or food and beverage.

(Even though, with a little back-of-the-napkin math, it’s arguable that there are few products for a hotel that are more lucrative than self-parking!)

Some data points that we can surface to shift this mindset:

• Inventory (# parking spaces)
• Parking spaces/room (how much parking is allocated for guests)
• Average hotel occupancy (how often are they full, AKA they will have the least amount of “extra” parking inventory to sell to non-guests?)
• Points of interest in microclimate (physical proximity to airports, convention centers, hotels, event venues, and commercial districts)
• Online demand for parking (via aggregators)

The key is to leverage data to show hotels that they do, in fact, have excess parking inventory (more parking than they need for their guests) and that the demand for that parking inventory from non-guests exists for their location.

The good news is that the landscape is ripe for transformation, and data moves the needle.

The hospitality industry is five to ten years ahead of parking in terms of data standardization, system integration, and technology adoption.

They have already widely adopted tech that parallels much of what exists or is being developed in parking today.

Online travel agents (or OTAs) are branded ad-driven consumer marketplaces that enable the pre-booking of hotel rooms that parallel aggregators in their functionality (driving massive consumer demand).

And hotel revenue management systems (HRMS) allow hotels to centrally manage room pricing across channels at the room type and rate code level for optimal yield.

Hotels also have a mature revenue management function: maximizing revenue based on the principle that demand varies over time and that businesses can use data and analytics to predict customer behavior and price accordingly.

To start, operators can use data from aggregators to prove that online demand exists and show how much customers are willing to pay.

Then, they can implement a relatively low-cost handheld POS system for redemption and access. That’s a low enough barrier to entry to overcome any hotel’s reluctance to make hefty up-front investments.

Then, when parking revenue starts accelerating, use that data to justify the implementation of more sophisticated equipment, such as gates, PARCS, and sensors, that can collect even more data about the length of stay, ingress, and egress, etc., to make even more informed decisions about pricing and inventory management.

Conclusion

We’ve explored examples of how the Seeing, Understanding, and Communicating approach can be used to gather, connect, and apply data.

Its applications go far beyond improving customer experience and pricing strategies.

In today’s big data landscape, even a single data point can provide valuable insights—if you’re paying attention and ready to act.

For instance, while one negative customer review might seem like an isolated incident, it often signals a deeper, systemic issue that needs management’s attention.

As parking professionals, we have the opportunity – and responsibility – to leverage data to strengthen connections within our industry and enhance the value we offer to related industries.

By doing so, we can drive higher revenue and create a better experience for our customers.