Decoding Portfolio Analytics: How KPI & Historic Report Drive Smarter Decisions (A Compilation of Case Uses)

Decoding Portfolio Analytics: How KPI & Historic Report Drive Smarter Decisions (A Compilation of Case Uses)

Understanding how to navigate and reach the graphs in Portfolio Analytics is important. However, knowing about how you can use this information to better set up your listings is equally important. So, let us go over some of the case uses for Portfolio Analytics to help you better understand each chart and its practical applications.

Trends By Stay Date

Revenue


Understanding the historical revenue per month for a listing or portfolio offers crucial insights into its financial performance over time. By analyzing this data, we can pinpoint consistently high revenue months and adjust pricing strategies accordingly to maximize profitability. Conversely, they can devise alternative strategies for months with lower revenue, such as offering discounts or promotions to stimulate demand.
For example, in the graph above, though January and March 2024 might be slightly lower than last year, February 2024 shows a significant increase. Despite these fluctuations, the overall trend indicates a positive performance, with revenue steadily growing year over year.

Use cases:

  1. Can be used as a reference to estimate the subscription costs should they consider moving to percentage billing.
  2. Allows us to compare whether PriceLabs has been effective if revenue has increased since joining. 

RevPAR


RevPAR (Revenue Per Available Room) can effectively predict your ADR’s success at filling available rooms. This, therefore, provides a constructive view of your property’s operational performance. It is the balance between the occupancy rate and ADR that is, it is the occupancy rate multiplied by the average daily rate.
RevPAR allows you to account both for the occupancy rate and ADR simultaneously. This is much more powerful than looking at those metrics alone. You could have a high ADR and low occupancy. Or a low ADR and high occupancy. Both of these signify poor performance. When your RevPAR is trending up, you’ll realize your performance is improving.
For example, in the graph above, through January and April 2024, the RevPar has increased (this can be because either the occupancy or the ADR has increased, too), but from May 2024, we can see it decreasing, which shows that either the occupancy or the ADR of the listing has decreased.

Occupancy


The graph provided shows the Occupancy Rate in % over time from January to December across multiple years indicated. 
The red line shows the recent Occupancy Rate for 2024, while the dotted red line shows the Occupancy rate for 2025. 

Use Cases:

  1. You can see which is your high and low season if there’s a recurring pattern of occupancy rates across past years
  2. You can conduct a comparative analysis between years to highlight which month had higher or lower occupancy rates. 
It will give you an idea of your occupancy rate forecast. You can use this graph to look back on years with similar or different patterns(was there a significant event for that month that boosted or lowered the occupancy rate) so you can plan and predict your future occupancy rate. 

ADR


The graph provided shows the ADR or Average Daily Rate in USD over time from January to December across multiple years indicated. 
The red line shows your recent ADR for each month in 2024, while the dotted red line represents your ADR for 2025. 

Use Cases:

  1. You can look for a seasonal pattern to see which month will be your highest/lowest ADR.
  2. You can compare past years and see what daily rate you should expect for a specific month, which can give you an idea for your future pricing strategies based on historical trends.

Booked Nights


The provided graph displays the number of booked nights for a specific year, broken down by month across multiple years. While the current graph shows data on a monthly basis, you have the option to change the view to weekly, daily, or even day-of-week (red box).
The solid red line represents the current number of booked nights for 2024, while the dotted red line indicates the occupancy rate for 2025. Additionally, the solid black line shows the number of booked nights from the previous year, 2023.

Use Cases:

  1. Seasonal Patterns: You can use this graph to identify months with the highest or lowest number of booked nights, helping you determine high and low seasons if there's a recurring occupancy pattern across past years.
  2. Comparative Analysis: The graph allows you to compare booked nights across different years, highlighting which months had higher or lower bookings. From this, you can develop strategies to improve your listing's performance, such as lowering prices during months with fewer bookings to boost overall occupancy.
  3. Projection and Planning: This graph provides insights into the projection of your booked nights. By analyzing patterns from similar or different years (e.g., significant events that influenced occupancy rates), you can plan, strategize, and make adjustments to maximize your booked nights.

No. of Active Listing


The provided graph shows the number of active listings across the entire account or within selected listings for Portfolio Analytics.
While the number of active listings may not provide as much insight on its own compared to other graphs, it becomes more valuable when paired with other data.
In the example below, we've paired the number of active listings with the number of booked nights. 

You’ll notice that the number of booked nights for 2022 (broken black line) shows a significant difference compared to 2024 (red line). When paired with the active listings graph, this comparison becomes clearer. In 2022, the lower number of booked nights can be explained by the fact that there was only one active listing, which accounts for the difference in booked nights.

Use cases:

  1. You can check this graph to see if it affects market occupancy. If market occupancy drops, then we can see the supply and demand. This indicates bookings in the market did not increase; however, listings increased, dropping market occupancy.
  2. To understand how saturated the market is at the moment with active listings and based on that planning the pricing of your listing and promotions. 

No. of Check-ins and Check-outs

Like the previous one, these graphs must be selected from the drop-down menu.
They illustrate the number of unique Check-ins/outs with the selected frequency. They can be used to estimate the workload related to preparation and cleaning after the stays are over, especially for bigger portfolios. 

Here, when filtering by day of the week, you can understand which days are the most popular for Check-in or -out. 

You likely don't want to see your guests checking out in the middle of the weekend, as it's not optimal for your revenue.

No. of Unique Bookings

This graph is not shown by default and can be selected from a drop-down menu in the Trends by Stay Day section. It shows the number of unique stays with selected frequency(monthly, weekly, daily, or by day of the week). 
However, it does not illustrate the length of said stays, so it can be tricky to use it to estimate performance(especially in the case of longer stays or the mid-term market).

In this example, the graph illustrates how the number of bookings for the your account has grown throughout the years, with the highest spikes in the Spring and summer seasons.
For 2024, you can see the number decreasing further out as future months are yet to be booked.
When viewing the data by Day of the Week, you can easily understand which days are the busiest for a specific listing, a selected group, or the entire portfolio. The below graph illustrates how Friday and Saturday are the most booked days of the week:

Trends By Booking Creation Dates

Revenue


This chart gives us an idea about the estimated revenue the listings selected received from the bookings which were created from 1 Aug to today (31 Aug). The booking date can be any date in future.
According to the chat above, the listings received an estimated revenue of 36.91K in 2024, 97.91K in 2023, 70.88K in 2022, 9.4K in 2021 and 4.6K in 2020 for period 1 Aug to 31 Aug

Use Cases:

  1. To analyze and compare the business's revenue performance over different years.
  2. To project future revenues by examining which month gets more bookings.
  3. To evaluate the effectiveness of marketing campaigns run during a specific period by looking at bookings created during that time.
  4. To understand the impact of seasonality on revenue by comparing how different periods perform across various years.
  5. To allocate budgets and resources effectively by understanding revenue inflows during specific periods.

Bookings


This chart gives us an idea about the number of bookings the listings selected received from 1 Aug to today (31 Aug). Using this graph, we can observe and compare the demand for the particular listings for a specific period between various years.
According to the chart above, the listings received 24 bookings in 2024, 63 bookings in 2023, 50 bookings in 2022, 7 bookings 2021 and 3 bookings in 2020 for period 1 Aug to 31 Aug

Use Cases:

  1. Identify peak and off-peak months.
  2. Time campaigns based on booking trends.
  3. Compare monthly booking growth across years.
  4. Adjust prices based on monthly demand.
  5. Spot unusual booking patterns.
  6. Understand booking preferences by month.
  7. Project revenue based on historical bookings.
  8. Visualize business performance trends.

ADR


This metric helps us to visualize the Average Daily Rate trend for the bookings received in various months mentioned above.
According to the chart above, the average daily rate of the bookings created between 1 Aug till today (8 Aug 2024) is 376.6 USD for 2024, was 312.7 USD in 2023, 412.5 USD in 2022 and 0 USD for both 2021 and 2020. 

Use Cases:

  1. Track and optimize ADR to maximize revenue.
  2. Adjust rates based on historical ADR trends by month.
  3. Identify seasonal pricing patterns and peak rate periods.
  4. Analyze ADR changes to measure growth or decline.
  5. Inform financial planning and revenue projections.
  6. Assess the effect of promotions or discounts on ADR.
  7. Align costs and services with expected ADR.

Booked Nights


This metric gives us an idea about the number of booked nights we received for the bookings created in the months mentioned above. 
As per this chart, the listing received total 5 bookings from 1 Aug to 8 Aug in 2024, 18 booked nights in 2023, 34 booked nights in 2022 and 0 booked nights in 2021 and 2021 for the same period (1 Aug to 8 Aug)

Use Cases: 

  1. Identify trends in booked nights per month to gauge demand.
  2. Understand peak and off-peak booking periods by booked nights.
  3. Assess the impact of campaigns on increasing booked nights.
  4. Project future revenue based on the number of booked nights.
  5. Track growth or decline in booked nights across years.
  6. Optimize property availability based on booked nights data.
  7. Adjust rates based on the volume of booked nights in specific months.
  8. Understand booking duration trends and preferences.

Average Length of Stay


This metric helps us to visualize the Average length of stay for the bookings received in various months mentioned above.
This chart can be used to understand the average length of stay of your listing/portfolio. You can view this for a specific month after hovering over it, and it will give you data on the average Length of stay for past years as well (Based on data we have for your listings).
According to the above chart, the average Length of stay of April for 2024 was 2.850 which is significantly lower than the Length of stay in 2020. 

Use Case:

  1. You can then compare the average Length of stay with the Occupancy and Revenue to understand which Length of stay impacted the performance of your listing/ portfolio.
  2. Compare which Length of stay gives you maximum occupancy to previous years and then set your minimum stays accordingly.

Average Booking Window


This metric gives us an idea about the average booking window (number of nights between when the booking was created and the check in date) of the bookings received/created between 1 Aug to 8 Aug and other months. 
According to this chart, the Average Booking Window is 36.50 nights for bookings received from 1 Aug to 8 Aug in 2024, 32.22 nights for 2023, 34.86 nights for 2022 and 0 for 2021 and 2020 for the same period.

Use Cases:

  1. Understand how far in advance customers book during different months.
  2. Tailor promotions to target early or last-minute bookers.
  3. Adjust pricing based on typical booking lead times.
  4. Optimize availability by anticipating booking windows.
  5. Identify seasonal trends in booking lead times.
  6. Predict future booking behavior based on historical trends.
  7. Identify different customer types (e.g., planners vs. last-minute bookers).

Length of Stay and Booking Window Trends

By default, the chart shows trends for bookings created over the past 30 days. You can choose a desired date range to view data for 4 possible combinations:

Booking Creation Dates vs Total Booked Nights


Graph 1 here shows that of all the bookings created in the last 30 days(in this case), the majority of bookings were created 7-13 days before the stay/check-in date and then for 2-6 days before the stay/check-in date.
Graph 2 here shows that of all the bookings created in the last 30 days, most of the bookings were 7-14 days long and the rest of them were 3-4 days long.

Booking Creation Dates vs Booked Nights by Booking Window and LOS


This view shows a further breakdown of the bookings created in the past 30 days(in this case).
Graph 1 here shows that of the majority bookings created within the 1 month window, most are for a 7-14 night long stay which means that guests prefer to book 7-14 night long stays for the listings.
Graph 2 here shows that of the majority 7-14 LOS stays, the most amount of bookings were created 7-13 days in advance. None of the shorter (3-4) LOS bookings were created in advance.

Inferences:

  1. Here, you are receiving very few bookings for the 2 month+ window so we can review prices and market occupancy 2 months out to look for market trends. 
  2. You can use these trends to formulate Minimum stay settings (last minute/far-out)
  3. You can also reduce discounts 2 months out since majority bookings happen in the last 1 month and a higher revenue can be made for far out bookings
  4. You can also get insights on how to set up their last minute prices based on past booking trends
  5. You can formulate how to set up LOS pricing or Weekly discounts based on the insights in these graphs
  6. You can also review these charts to find the most suitable OBA profile.
  7. You can see here that there aren’t 1-2 night LOS bookings for their listings. This may also be due to the your current minimum stay settings which might require further investigation.

Stay Dates vs Total Booked Nights


Graph 1 here shows that of all the guests that stayed in the last 30 days(in this case), the majority of bookings were received 7-13 days before the stay/check-in date but a lot of bookings were also received 4-6 months before the stay/check-in date.
Graph 2 here shows that of all the guests that stayed in the last 30 days, most of the bookings were 7-14 days long and the rest of them were 5-6 days long.

Stay Dates vs Booked Nights by Booking Window and LOS


Graph 1 here shows that of all the guests that stayed in the last 30 days(in this case), the majority of bookings were received 7-13 days before the stay/check-in date and most were 7-14 night long bookings.
A lot of bookings were also received 4-6 months before the stay/check-in date and all of them were 5-6 nights long.
Graph 2 here shows that of all the guests that stayed in the last 30 days, most of the bookings were 7-14 days long out of which most were received 7-13 days in advance but some were also received more than 6 months in advance.

Inferences:

  1. Here, a majority of the guests that stayed in the past 30 days booked their stay 7-13 days in advance.
  2. But a significant number of bookings were also received 4-6 months in advance, this can signal a special event/high demand dates which were popular and booked well in advance including guests that may have stayed previously and made a repeat booking.
  3. You can use these trends to formulate Minimum stay settings (last minute/far-out)
  4. If you are worried about not getting bookings for an upcoming period, we can look up the Stay Dates trends for the same period in the last year and draw insights about the expected booking window for you.
  5. You can also get insights on how to set up their last minute prices based on past booking trends
  6. You can formulate how to set up LOS pricing or Weekly discounts based on the insights in these graphs
  7. You can also review these charts to find the most suitable OBA profile.
  8. You can see here that there aren’t 1-2 night LOS bookings for their listings. This may also be due to the your current minimum stay settings which might require further investigation.
Viewing the trends for past dates shows the full picture whereas viewing the trends for upcoming dates will lack the data for bookings that will likely happen between present date to future date.



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