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How PriceLabs helped The Vermont A-Frame to increase its occupancy to 85%

Last Updated on 1 month by Vinay Sahu
How does Vermont A-Frame use PriceLabs?
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Introducing the second blog in our series of listening to our team member’s honest experience of using PriceLabs to manage their rental portfolio. We firmly believe that real-life accounts from those who have seen considerable benefits from our product make for the best reviews. 

We chat with Monique DeLorenzo-Pomeroy and get her candid insights about using PriceLabs to optimize revenue for her cabin in Vermont, in the United States of America.

About Monique DeLorenzo-Pomeroy 

Monique has been with PriceLabs for over 6 months as a Solutions Consultant. She is based out of Red Bank, New Jersey. Before joining PriceLabs, she worked within the insurance industry. 

Besides helping various PriceLabs users utilize the dynamic-pricing tool efficiently, she owns a vacation rental in Manchester, VT, with her husband, Jason Pomeroy. The Vermont A-Frame is a two-bedroom, one-and-a-half-bath 1965 A-frame cabin which sits on the edge of the Green Mountain National Forest in southern Vermont. The A-Frame is a place for travelers to gather, recharge, and become inspired by the great outdoors. She has been using PriceLabs to manage revenue for her cabin.

You can find them on Instagram as – @thevermontaframe . Their website – Vermont A-Frame.

Vermont A-Frame Cabin Image
Vermont A-Frame Cabin Image
Vermont A-Frame Cabin Image

Tips from an expert host, Monique

Monique goes into her PriceLabs account once in two weeks to check and optimize pricing whenever necessary. 

She recommends PriceLabs users set up cascading minimum stays and keep the minimum length of stay high further out to attract far-out bookings. As the dates get closer, the minimum length of stay should be shorter to avoid orphan gaps and book properties according to your guests’ booking patterns. In seasonal markets like Vermont, short bookings are higher in demand. 

Features that Monique swears by

Minimum Stay Recommendation Engine

Minimum Stay Recommendation Engine provides data-driven recommendations for your Minimum Stay settings. You can accept the suggested minimum length of stay restrictions or customize them.

Minimum Stay Recommendation Engine

Dynamic Minimum Stays

Minimum Stay Restrictions are used to control how long a booking should be on certain dates. These settings should be used with a knowledge of your market and the kind of bookings you want to get. Like any other customizations, the Minimum Stay Restrictions can be applied to the Listing, Group, or Account level. The minimum stay options can be accessed from the “Edit” button on the Customizations panel when you review prices for a listing. Read more about minimum stay restrictions here.

Base Price

Base price is the average rate a property manager charges. All market factors, demand patterns, and customizations are applied to the base price. This rate will fluctuate based on the quality of your listing.

Base price help

Custom Seasonal Profile

Custom Seasonal Profiles let you set different minimum, base, and maximum prices for different seasons throughout the year, giving you full control over each listing’s seasonality.

Check-in and Check-out Settings

If you don’t want the cleaners to be at work on Sundays, or you want your guests to enjoy the weekend stay and take the option to check out off the table, you can use the check-in and check-out settings.

PriceLabs saved a lot of time in a highly seasonal market

Vermont is a highly seasonal market. The prices fluctuate during peak season and off-season. Each season, Monique had to go and alter prices manually. This, along with managing operational tasks, was tedious. 

That’s where PriceLabs was helpful for Monique, and she finds custom seasonal profiles useful. She could create seasonal profiles for each season and just turn them on during that season.

PriceLabs Dynamic Pricing leverages real-time data to provide pricing recommendations by analyzing historical and current booking data, market supply and demand, seasonal and day-of-week trends, special events and holiday predictions, and days left to book a property, 

Airbnb hosting tips and attention to detail helped Monique enhance guests’ experience and create lasting memories.

Start free trial of PriceLabs

Better integration with Vrbo

The revenue management software Monique used before PriceLabs did not integrate with Vrbo. In her search for another revenue management software that integrated with both Airbnb and Vrbo, helping optimize and increase revenue, she came across PriceLabs. What stood out was; PriceLabs integrating easily and directly with over 90 Channel Managers and PMSs. 

PriceLabs increased their occupancy to 85%

Shifting to PriceLabs from their previous revenue management software was a learning curve. PriceLabs has a lot of customizations and features that require a bit of training.

The Basic Onboarding, Advanced Onboarding, and the other training sessions led by PriceLabs helped Monique craft a pricing strategy that worked. Before PriceLabs, she was not introduced to the concept of Dynamic Minimum Stays. Being able to control the occupancy and the pricing was a neat feature for the seasonal Vermont market. 

She slowly started seeing her major KPIs increase, such as occupancy, RevPAR, and length of stay. Her occupancy rate increased to 85%, RevPAR increased to $140, and Length of Stay increased to 3.2 nights. 

The Knowledge Base provided her with a wealth of information about the dynamic pricing software to set her up for success.

About PriceLabs

Established in 2014 and headquartered in Chicago, PriceLabs is a leading provider of innovative and easy-to-use Dynamic Pricing and revenue management solutions for the accommodation industry. PriceLabs provides a data-driven approach, automation rules, and customizations to manage pricing and stay restrictions that help property owners and managers increase revenues efficiently and easily. PriceLabs’ Market Dashboard and Neighborhood Data provides valuable market & property pricing insights and a deep dive into your potential guests’ booking patterns. Property managers can use this data to make informed decisions on where to invest their money and evaluate the effectiveness of their past investments. Property managers can also use this information to support their strategies and demonstrate the performance of the properties they are managing to their clients, the property owners.

Conclusion

How did PriceLabs help Monique as a host:

  1. PriceLabs saved a lot of time in a highly seasonal market in Vermont
  2. Better integration with Vrbo
  3. PriceLabs increased their occupancy to 85%

Features that Monique swears by:

  1. Dynamic Minimum Stay
  2. Minimum Stay Recommendation Engine
  3. Base Price and Base Price Help
  4. Custom Seasonal Profile
  5. Check-in and Check-out settings
Dynamic pricing in Airbnb refers to the practice of adjusting rental rates in real time based on various factors such as demand, seasonality, local events, and market conditions. This approach allows hosts to optimize their earnings by automatically increasing or decreasing prices to match supply and demand fluctuations. By utilizing data and algorithms, dynamic pricing aims to find the optimal balance between attracting guests and maximizing revenue, ensuring that prices reflect the current market dynamics.
To implement dynamic pricing for vacation rentals, collect relevant data, identify key factors, set pricing rules, use dynamic pricing software, monitor performance, and adjust as needed to optimize revenue.
The aim of dynamic pricing is to optimize revenue and occupancy rates. It is done by adjusting prices in real time based on factors such as demand, market conditions, competition, and other variables. Dynamic pricing softwares seeks to find the optimal balance between attracting guests and maximizing profitability by dynamically setting prices that reflect current market dynamics. The goal is to capture the highest possible value for each booking while ensuring competitiveness in the market.
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