Same, Same, but Different

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Seasonality
To get the correct seasonality pricing, we use advanced decomposition logic to separate out seasonal trends and fluctuations that look back over the past 6+ years in most markets. Before you ask, we are obviously excluding data that breaks away from “normal” trends, like what we saw over the last eighteen months with the pandemic (i.e. just because April 2020 was awful doesn’t mean we will price April 2022 lower).

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We factor in this seasonal trend for every hyper local area, which determines, based on demand, when and to what extent we increase prices for high, shoulder and low seasons. This is especially important as the distinction between these seasons isn’t black and white, but fluid. In most US summer b2b email listb2b email list markets, for example, the beginning and end of June are wildly different in their occupancy levels and rates—your prices need to ebb and flow along with that seasonality.

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While this method works great for long standing historic norms, we understand that not every year is the same. Differences need to be not only expected but monitored and acted upon seamlessly. We also track “seasonal pacing” or certain times of the year that get higher or lower demand than previous years, of which Summer 2021 may be a prime example, given the pent-up demand we’ve seen.

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In these cases, the algo tracks when and how much the demand differs and adjusts prices based on these pacing speed differences. Below is an example of pacing ahead in the Maine Coast.

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Source: Beyond Data
Day of the Week
In addition to seasonality, the algorithm spends a lot of time making sure to get the proper day of the week (DoW) right, which can vary dramatically even in nearby areas. In a city, for example:

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The downtown area may have high DoW pricing on weekends near the bars and clubs,
The financial district may actually have higher demand for Monday - Thursday,
And the suburbs may be somewhere in between.
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All of these factors need to be taken into consideration to get the right price.

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Our algo runs subsequent decomposition to determine the relationship between the days of the week in order to set the right price. As anyone familiar with pricing will tell you, this relationship between days of the week is not constant and depends on the time of year. It must, therefore, be analyzed on a seasonal basis, not a static one.

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The example below shows the DoW factor in South Lake Tahoe, where the deviation between the days of the week get significantly higher in winter with the arrival of weekend ski guests, and then shrinks in shoulder and summer seasons when guests stay longer on the lake.

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