AI Dynamic Pricing: A Secret Weapon for Independent Hotel Revenue Managers

But what about hoteliers who are looking to gain a competitive advantage by incorporating AI technologies into their tech stack while also boosting revenues and optimizing their pricing strategies?

For independent hoteliers seeking a competitive edge while boosting revenues and optimizing pricing strategies, one solution stands out: AI-powered dynamic pricing tools.

A refresher on dynamic pricing

If you haven't reviewed dynamic pricing models and their benefits recently, here's a quick refresher: A dynamic pricing model is a hotel room pricing strategy where rates are not fixed.

Instead, they are adjusted multiple times daily based on real-time data. This data encompasses market demand, trends, competition, occupancy, booking times, customer behavior, and other factors influencing booking patterns.

AI Dynamic Pricing: How does it work?

The hospitality industry has embraced a wide array of AI-driven tools.

These include:

• Guest-facing customer satisfaction platforms

• Outbound marketing and e-commerce tools

• Pricing algorithms

If you're using any automated tool in your hotel operations, it's likely that artificial intelligence is already at work behind the scenes. However, when it comes to dynamic pricing, it's crucial to understand the specifics of how these systems function and set prices.

Software collects data for the AI to consider

At the core of any effective AI system lies quality data. The principle is simple: the more high-quality data available, the more context the system has, leading to better decision-making capabilities. A well-designed dynamic pricing solution is looking at a vast array of these inputs, including, but certainly not limited to: pace, competitor pricing, pickup, segmentation, lead time, and external demand data.

The data powering AI-driven dynamic pricing systems comes from a variety of sources. Internal sources, such as Property Management System (PMS) data, are particularly valuable. By extracting guest arrival dates, departure dates, and reservation creation date, we can determine important metrics such as length of stay (departure date minus arrival date) and booking lead time (arrival date minus creation date) from this historical data.

Other internal data that can power AI models includes: customer data such as customer segments (market segments), out of service rooms, hotel seasonality, etc.

External data sources including GDS and OTA search volume, and competitive pricing and availability are also key external factors that AI tools can use to develop dynamic pricing strategies and better navigate market conditions.

AI algorithms also ingest additional data sources to enhance their pricing decisions. These include forecasts, historical price points, and pricing fluctuations.

But whatever data is collected and ingested by AI models it is important that the data have 5 attributes:

Breadth - The benefits of having vast amounts of data are the multiple dimensions for the software to make data-driven decisions

Depth - Fine-grained detail necessary to form specific conclusions and provide actionable recommendations. Ideally data ranges from the top-most market conditions to the individual customer level

Accuracy - The data must represent reality well enough to deliver correct and reliable data-driven recommendations

Timeliness - The data must be updated often enough to deliver relevant outcomes, especially important for a system that is expected to make data-driven decisions in real-time.

Impartiality - Avoids biases that can skew conclusions

AI relies on machine learning algorithms

While Generative AI is all the rage, an effective AI solution must incorporate aspects of Machine Learning to further refine its recommendations and reach its full potential. Machine learning models can be used to train AI dynamic pricing tools. For example, by monitoring acceptance rates, and outcomes when recommendations are accepted/declined, an AI enabled Dynamic Pricing tool can learn which recommendations are most effective, impactful, and actionable.

By constantly monitoring outcomes and comparing these with the ‘signals’ leading up to the outcome, AI models can learn which variables should be most closely monitored.

Consider this scenario: An AI model observes that abnormally early bookings for a premium room type on Saturday nights often precede a sell-out. This trend is particularly pronounced when the closest competitor adds a minimum length of stay restriction and raises rates.

The AI model can learn to recognize this leading indicator and assign it greater importance, alerting the commercial team as soon as it detects this pattern.

While it may sound simple, keep in mind that in the example above, the system looked at a variety of different factors: booking lead time, room type, historical occupancy trends, day of week, and market conditions (competitor inventory management and competitor price changes).

Constant monitoring enables real-time updates

Now, imagine this type of AI analysis is happening for dozens more factors all of the time, and you’ll begin to understand why these price-optimization systems are so crucial.Even when the revenue management department has gone home for the day, the AI continues to work, analyzing data and adjusting prices as needed.The power of AI lies in its ability to be hyper vigilant.

Why independent hotels rely on AI-powered dynamic pricing

Independent hotels increasingly turn to AI-Powered dynamic pricing because it acts as that ‘second set of eyes’ when a human isn’t able to monitor all of the various fluctuations in their data. Imagine the benefits of having an automated system that is already spotting market trends and making strategy changes before you’ve arrived at the office.

While these advantages can be hard to quantify in simple ROI terms, many hoteliers express the value and benefits of incorporating of such systems in terms of time saved.

Ensuring independent hotel rates are always competitive

When considering competitive pricing, it’s crucial to remember just how much time goes into analyzing the vast number of data points necessary for effective strategy decisions. You must analyze rate shops, a market demand tool, your own PMS data, overnight pickup reports, and other inputs to look for the latest trends. If you’re lucky enough to have all of these tools in one convenient platform you’re already far ahead of the competition, but still it requires a large amount of effort to do this every working day without fail.

For example, an independent hotelier starting their day might pull an overnight reservation report, and get a quick glance at their latest rate shop in the morning, and make a few quick pricing adjustments. However, they may soon get pulled away into the day-to-day operations of the hotel, potentially losing the opportunity to resume their data analysis later in the day.

While hoteliers tend to the daily tasks of the hotel, the competition doesn’t take a break - rates are constantly changing, guests are booking, and events are being announced. An automated dynamic pricing system serves as a crucial defense mechanism in this fast-paced environment. It allows hoteliers to focus on immediate operational needs with the assurance that their rooms are consistently priced optimally, maximizing revenue potential around the clock.

Consider the run-up to the Olympic games this year as an example. Many hoteliers were caught by surprise in the final weeks before the start of the games, and were forced to drop rates significantly to sell rooms due to the unexpected customer behavior. Had more hoteliers utilized an AI dynamic pricing solution, the outcome might have been different. Hoteliers leveraging predictive analytics could have identified the lagging demand earlier in the booking curve. This would likely have enabled these hoteliers to adjust their strategies gradually, potentially avoiding the last-minute price drops.

Saving time on pricing calculations

Even the most skilled revenue managers dedicate substantial time to analyzing data and implementing solutions. For independent hoteliers, this responsibility can be particularly daunting. This is where an AI-powered dynamic pricing solution with automation proves invaluable.

Now, the small amount of time that hoteliers do have to dedicate to pricing and data analysis can be spent making minor adjustments to the automated system which is handling the far more tedious day-to-day pricing and strategy decisions. Without an AI solution that ingests a variety of quality data,and continuously improves through machine learning, it can be extremely difficult to juggle all of the tasks of a revenue manager in addition to other responsibilities

Improving Decision Making

As hoteliers, we must acknowledge that time constraints can sometimes lead to hasty decision-making. In our efforts to be decisive and efficient with our time, we make strategic decisions without weighing all of the inputs that we should, leading to suboptimal outcomes. This is where an AI Powered Dynamic Pricing solution, proves invaluable, ensuring pricing decisions are always being made with a consistently multi-variate methodology that ensures high quality every time.

Consider this scenario:

You notice that two competitors show "no availability" for an upcoming summer weekend. Your instinct might be to interpret this as a sign of high market demand, prompting you to close inventory to protect rooms.

However, your AI-powered dynamic pricing solution has been monitoring these dates for months and has a more comprehensive view:

It's aware that search volume for this weekend is unusually low. The two competitors have had availability for months, suggesting these changes might be anomalous. Rather than closing inventory, the system continues to sell at the optimal price.

Later, you discover that one competitor was holding rooms for a tentative wedding block, while the other hotel was only temporarily closed. The AI system didn't overreact to this single factor (competitor availability), demonstrating the value of its holistic approach.

This example illustrates how AI-powered pricing systems can:

• Provide a more balanced perspective on market conditions

• Avoid knee-jerk reactions to isolated data points

• Maintain optimal pricing strategies even in confusing market situations like this example

By leveraging such a system, independent hoteliers can level up their decision-making process, ensuring that pricing strategies are based on a comprehensive analysis of all relevant factors.For example, we may check our rate shop and notice that 2 of our competitors show “no availability” for a certain upcoming weekend in the Summer. You may instinctively think that this is a signal that there is huge market demand and that you should close inventory right away to protect rooms!

AI dynamic pricing tools allow you to maximize profits effortlessly

An effective dynamic pricing tool should allow you to gain market share and maximize your hotel’s profit margin effortlessly. Once you’ve set up your preferred parameters, and periodic review, your pricing system should always work for you, ultimately making your life as a hotelier easier.

That said, not all pricing systems that incorporate AI are built equally. If you are curious to learn more about a platform that checks all of the boxes, and incorporates best-in-class data quality into every pricing decision, consider a demo of Lighthouse’s Pricing Manager solution.

Pricing Manager is powered by all of the best possible data inputs at every step, whether it be pricing data from rate insight, or external market demand from market insight, so you can be assured that Pricing Manager is consistently generating the most optimal pricing decisions for your property.

The Auto-pilot feature offers a high degree of customization, allowing independent hoteliers to fine-tune the level of automation to their specific preferences.

This flexibility enables you to leverage the system's capabilities as extensively or minimally as you desire, tailoring the automated pricing process to align perfectly with your operational needs and management style.