How much is left to sell for not controlling the price of hotels

Source: Mirai

The price competitiveness of almost 1,500 hotels on its own website has been analyzed against the price that the OTAs offered for that same hotel during the second half of 2022, a fairly healthy period in terms of reservations.

All this data has been extracted from Google Hotel Ads, Google's price comparator and source of a lot of rich information. For each search/query made by a user, Google categorizes the results into four options that it calls Price Buckets:

• Not lowest: elegant way of saying that you have a lower price (with a 2% margin) than other OTAs. The number of these cases with respect to the total is what we call %Lose.
• Tied for lowest: you have the best price in parity with other OTAs with your product.
• Unique lowest: you have the best price and you are the only one (the others have a higher price).
• Only partner shown: you are the only alternative, there are no other options selling your product for those dates (the rest of the channels are closed).

All the queries made for all these hotels (10 million impressions) have been downloaded, aggregating all the information and grouping for each of these four buckets. Thus, it has been decided to analyze only the data related to "Traveller-set dates", which is when the end user interacts with the dates and shows a clear interest (the "Default dates" alternative contains many cases without real interest of the end customer , that perhaps he was looking for something else).

Hotels do not completely control their prices
In 56% of the queries there was one or more OTAs that offered a better price than the official website. Unfortunately, Google doesn't say who, so in this case a third-rate pirate OTA (there are many) is the same as Booking.com or Expedia. The impact is not the same. Still, it's bad news that hotels still aren't quite in control of price.

In 19% of the cases there was real parity, and in 22% the hotel was the one that offered the best price on its website. The last 3% are those brave people who offered rooms on their website when the rest of the OTAs were closed.

At a better price, greater visibility, more visits, more conversion and more reservations
. The most fascinating and clean thing is the obvious correlation that exists between price buckets and the three fundamental variables of great value for the direct channel:

• Up to 22% more impressions (visibility), reflected in the impression share, IS% (percentage of occasions in which the hotel appears in the results that Google paints. It depends largely on the bid and the price).
  It clearly improves with competitiveness, which is logical, since Google's algorithm takes into account the prices of different advertisers when it comes to resolving Hotel Ads auctions.
• Up to almost 3 times more visits, reflected in the CTR% (Click Through Rate or Click Ratio would be the number of clicks that the hotel achieves for every 100 times that it appears visible, which depends on the previous variable).
  Here the improvement is even more remarkable. Users tend to click more often when the price is more competitive. The difference between a scenario with a worse price than the rest, compared to having the best price, is almost double! (7.1% vs. 13.6%). Think that each click on this step means a visit to your website where, if you do it right, you can convert it into a reservation.

Conversion, measured as the percentage of clicks (visits to a website) that convert to a reservation. 
In this case, the parity scenario has the best conversion rate, “Unique lowest” converts the same and, when only the hotel appears, it converts slightly less. This scenario only represents 3% of the cases in our data and, in most cases, is related to periods of high occupancy and very high prices, which logically reduces the conversion rate. Additionally, the fact that no large distributor appears (at any price), can generate certain doubts in the end customer and they need a little more time to decide.

Up to 43% more reserves
Combining the three previous variables, it is possible to infer the reserves that can be achieved in each bucket. For example, the case of "Tied for lowest"

It seems clear that the more competitive the price, the more it will be possible to sell on the web. The main qualitative leap occurs from the unfavorable cases where there is a worse price (“Not lowest”) to a parity scenario (“Tied”), which achieves +87% of reserves than the starting scenario.

If 1,000 searches are simulated with the data from the analysis and 56% of the "Not lowest" cases pass to "Tied" (which had 19%), 75% of the cases are in parity, which would generate 2.4 reservations. These reservations would be added to the 0.8 that come from the 22% of searches with the best price for the hotel (“Unique lowest”) and to the 0.1 from the 3% of cases “Only partner shown”. All of this adds up to 3.3 reservations, compared to 2.4 in the initial scenario, which translates into an increase of +34% in total direct reservations.

Doing this same exercise, but passing all those searches to a scenario in which the web has a better price than the rest of the channels (“Unique lowest”), the increase in reservations reaches 43%.

The relationship between price competitiveness with respect to distributors and web conversion
In the previous analysis, the relationship between these two variables is not very clear, so the summary is: when the % Lose increases (that is, when competitiveness worsens), Low conversion %. If we look at the evolution of both indicators (added weekly to make it easier to read) over time (from week 35 to the end of 2022) for all these hotels as a whole. What happens when competitiveness improves (that is, when the %Lose goes down), and vice versa?

There is an inverse correlation between these two factors. When the % Lose goes down, that is, the competitiveness improves (up to week 48 approx), the % Conversion goes up. And vice versa, when disparities rise (less competitive hotel), conversion falls.

It therefore seems reasonable to say that by reducing unfavorable price disparities with the OTAs that sell your product, you improve the conversion of your direct sales.

How to get your own price competitiveness data?
Accessing the Google Ads platform and entering the Reports section. Then, "Custom" is selected and a data table is created where, at a minimum, the following should be added:

• Variables: Day, User location, Device, Price bucket.
• Metrics: Eligible hotel impressions, impressions, clicks and conversions.
   

With this information, a report can be created to analyze how competitive prices are in different markets, devices, etc.

What can be done to improve your competitiveness and sell more?
– Product and availability

- Conditions

- Price

Conclusion
Every time you manage to reduce disparities and improve prices, you are almost automatically getting more impressions, more clicks (visits) and more reservations in direct sales. A simple simulation with aggregated data reveals up to +43% additional reserves.

There seems to be a clear relationship between direct pricing competitiveness and website conversion: reducing unfavorable price disparities with OTAs selling a product will improve your direct sales conversion.