How 1 Single Piece Of Data Helped Shift € 43726 Of Direct Bookings Monthly.

Hotel Revenue Analytics

This was the very first time I made use of RevANALYTICS to help detect an issue and support a hotel to boost their direct bookings.

It was almost 2 years ago and after spending sleepless nights and weekends coding and theorizing, for months, finally the real case came on the way.

A hotel realized that the booking engine conversion dropped, from 8.5% to around 5% on average.

How come? How to fix it? But first, how to determine where exactly the issue was?

Conversion Rate is usually associated to either distribution or revenue-related issues. In other words, is it a cross-channel drop, meaning other 3rd-party channels are experiencing the same? Then we should be focusing on the revenue area and how appealing our rates are. Instead, if the drop is happening on the booking engine only, then it’s distribution.

In this particular case, the drop was happening on the booking engine only.

Knowing the hotel and the fact that, in the period in exam, nothing changed in terms of usability, features and available options, neither on the website nor on the booking engine, I was quite sure I had to look into specific revenue-related data, that would have revealed the root cause of the drop.

First of all, when did the issue start? A quick check on the timeline by selecting a long period immediately revealed it: at some point end of March.

drop-timeline

With that in mind, I wanted to check the booking funnel. More specifically, I wanted to see what the exit rate was, in the Rooms&Rates booking step, respectively before and after the drop:

booking-funnel

Booking Funnel

This information is pretty useful because it tells how much this drop may be related to the decreasing appealing of rooms&rates: the higher the exit rate in the rooms&rates booking step, the less appealing the hotel seems to be.

In fact:

Exit Rate PRE issue: 86%

Exit Rate POST issue: 91%

Avg.: 88%

Translated: the hotel is losing appeal. 5% higher exit rate in this booking step is quite a lot.

Now I could go into all other revenue-related metrics. Lucky me, first shot: bingo! It’s the Length of Stay.

length-of-stay

Report by Length of Stay

 

Whilst searches 1 up to 4 night-LoS returned similar conversion rates for all LoS values before March, searches for 3 and 4 nights dropped as of April.

Spot check on Booking and Expedia: same search, 3 nights, same dates and number of adults, as compared to the same search on the booking engine:

AHA…

Whilst for searches for 1 or 2 nights, all channels looked to be in perfect parity rate, the booking engine gets more expensive for searches of 3 nights.

booking-engine-price

booking-com-price

expedia-price

Considering that 478 EUR was not the price the hotel was expecting to see, the next and last step was now to compare the above drop by LoS with some of the statistics and data both Booking and Expedia make available in their extranets.

2 years ago the best way to benchmark statistics by LoS was by looking into rate production. More specifically I was looking after LoS-based rates, like the classic “Stay 3 Nights and Get X% OFF”.

In fact, both B and E reported higher sales for those rates, as compared to the period pre issue.

As it turned out, something was wrong in the settings of the channel manager, as a result of which it was basically feeding those LoS-based rates with slightly wrong value that were causing the above disparity.

CONCLUSION

The fix was a 2-min job. After 1 month we checked again the performance and the conversion got back from 5.02% to 8.89%, even a bit higher than it used to be.

In Revenue, although comparing two different periods, meaning different seasonality, 8.89% CVR resulted in € 43276 in more direct bookings.

results-pre-revanalytics

Results PRE RevANALYTICS

 

results-post-revanalytics

Results POST RevANALYTICS