$90 million in savings a year: how accommodation companies use demand intelligence in pricing and staffing strategies

Victor Carapito
Business Value Strategist

Regardless of the rate of recovery in your key markets, accommodation businesses are seeking ways to navigate the uncertainty of recovery by ensuring their business is optimised for efficiency and resilience. We wanted to share how our accommodation clients - including leading hotel chains and accommodation marketplaces - are using our intelligent event data to move the needle in two major ways:

  1. Labor optimization: Efficiently matching staff requirements (both food and beverage and hotel labor)  to predicted demand while providing consistent service quality and guest experience. PredictHQ’s data has been used to drive improvements in guest arrival/departure forecast accuracy by 10% as well as improvements in Food & Beverage forecasting by 25%.

  2. Pricing intelligence: Understanding what events are coming back on the map first and what events will drive bookings well before the booking curve to alter packaging and pricing. A major hotelier customer of PredictHQ identified more than $200,000 in potential revenue improvements for one US city alone.

Accommodation has been hit hard by COVID-19 and the recovery trend for the short to mid-term remains unclear. McKinsey research posits a best case scenario of a revenue per available room (RevPAR) decrease around 53% in 2020 and bouncing back by mid 2021, with a more dire scenario of demand not recovering until 2023, with a RevPAR fall of 60% in 2020 with only limited recovery in 2021. While occupancy rates are still negative year over year, they are up 25% from the lowest point a few months ago. As demand continues to slowly recover, hoteliers need to rethink their pricing and staffing strategies. This will rely on more dynamic and resilient demand forecasting, as monthly booking patterns and seasonal trends are less relevant this year.  As such, they will need help monitoring the market, identifying where demand is picking up, and creating effective pricing initiatives.

The PredictHQ customer success team is working closely with our customers to make their planning more dynamic and resilient by identifying the events that drive their demand both incrementally and decrementally, so they can calibrate their staffing and pricing to available demand.

Labor Optimization: Matching staff rostered to demand months in advance.

Workforce investment takes up around 50% of most accommodation group’s operating expenses, and having more accurate forecasts, means hoteliers can optimally match labor to predicted demand.  This can drive efficiencies across hotel chains' portfolio of properties which can yield a significant impact to the bottom line.

Whether it’s a sports match, a school holiday, severe weather, a major conference, or a college graduation, events cause millions of people to make purchasing decisions every week. While the impact of scheduled attended events such as conferences and sports can feel remote during the pandemic, many countries are beginning to ease restrictions and mass event bans over the coming months. Being ready for these events to return and making the most of the thousands of ongoing events such academic events and observances will ensure you can meet demand while running a lean team.

One of our customers in the accommodation space is one of the largest hotel chains in the world.  Their operations teams incorporated our intelligent event data into their labor optimization models and identified the following benefits:

  • Improvement in guest arrival/departure forecast accuracy by 10%

  • Improvement in Food & Beverage forecasting by 25%

  • Estimated labor savings of up to $90 million per year by better calibrating the amount of staff in front desk, housekeeping, banquet, and kitchen staff according to predicted demand.

How to use real-world events for more accurate labor optimization + better pricing

There are four steps to uncover how events impact your demand and then include them in your models at scale:

  1. Discover which events impact your demand by correlating your anonymized historical transactional data with our verified events data. Most accommodation groups begin by focusing only on events they already know have some impact, such as conferences, expos and sports games. But like the hotel case study mentioned above, many find they are impacted by a far wider range of events across our 19 categories.

  2. Identify the impact of spatial relevance on your demand during your correlation. This tool enables you to identify events that are within a set radius of your properties. Using it will help pinpoint the impact of a 1,000 person event happening one mile away compared to a 30,000 person event happening 15 miles away. You can then use our Radial Search tool to identify events around each property.

  3. Once you know which events impact your demand and by how much, you can use the output of our Aggregated Event Impact tool in your demand modeling. All PredictHQ events are ranked by predicted impact, but identifying the combined impact can be difficult as they are logarithmically scaled and event data is dynamic. We do this for you, so you can take the output of this tool directly into your forecasts.

  4. Once you know which days were most impacted by events retrospectively, you can calculate a new and more accurate baseline. As demand recovers, you will be able to identify key events and peak days coming up, and ensure you have the right amount of staff  as well as prices to ensure great service and yielding without over-investing. You can find out more in our data science guide.

Optimized room pricing by predicting compression or high-vacancy nights in advance

The other most popular use case for using intelligent event data by hotels is identifying your busiest days and weeks many months in advance so you can tailor your packages and pricing to yield more revenue. Being able to do this before bookings begin flowing in ensures that you can set competitive yet profitable packages for the majority of your customers, rather than relying on automated systems that only kick in once many bookings have been secured at the original rate.

Another one of our customers, also one the world’s largest hotel chains, has been using our demand intelligence for this. This enabled their revenue management teams to improve their models to reveal:

  • A consistent pattern of high-impact days where nearby events were driving demand yet they were priced lower than major competitors. So significant was this pricing disparity that it amounted to more than $200,000 in revenue for just one city. All in globally, they were leaving more than $70 million on the table each year.

  • A clear and actionable understanding of their demand causal factors - the events that drive both incremental demand as well as decremental demand.

Decremental demand causal factors are particularly hard to track in the wake of COVID-19, which is hopefully the most impactful decremental causal factor for the accommodation industry of our lifetime. Tracking lockdown and shelter-in-place mandates is an important recovery insight, as is knowing which events can impact future bookings where you may be able to minimize staff and stock waste. 

Correlation will reveal demand levers you can use directly to drive profitability

The foundational step for all breakthrough uses of demand intelligence is correlating your anonymized historical transactional data with verified and intelligent event data. PredictHQ is the only source of this, and we have more event data than any other business - both in terms of sources and years of it. Most ticketing APIs delete their data after 12 to 18 months, which means creating your own body of data to correlate with is difficult. PredictHQ retains seven years of verified historical data from hundreds of sources.

PredictHQ has time series modeling and correlation specialists ready to assist in this often complex process, and then to help you identify which events you need advance notice of so you can prepare. Get in touch with our team to start making targeted, localized decisions at scale.