Is Metasearch to blame for Hospitality’s 5-digit Look-to-Book?
Look to Book ratios continue to climb,but the reasons are not that clear
Let's assume that my supply chain consists of a metasearch site, which connects to a reasonable number of hotel distributors, so we can do some back of the envelope numbers:
20 meta-searches x 20 hotel distribution API calls = 400 searches at hotel distributors
So according to my reckoning, we can probably account for a grand total of 400 searches out of the 20,000 we are seeing. I can hear someone saying "but they may city searches translated into individual hotel property searches" rather than the city I started with. Most hotel distributors run APIs that offer city and hotel level searches and if one organisation maps the city into a list of preferred hotels, then they send the list of hotels in single search so it doesn't change our maths!
So the question remains – which of us out there is generating the other 19,600 searches? My presumption is that there is no way on earth this is user driven. How can I say that? The reality is that 19,600 searches at say an average completion time of around 10 seconds and we spent a further 30 seconds (very short) examining the results, we would be each be spending somewhere in the region of 217 hours or 9 days just looking and reviewing hotel listings. I can't speak for everyone, but I have infinitely better things to do with my life.
In a nutshell, these have to be machine generated searches and the explanation is most likely scraping of pricing data for analysis. Imagine thousands of servers generating requests to populate two dimensional grids of pricing data – hotel property by check in date. I remember one of my clients comparing it to the children's' game of 'battleships' where you keep crossing off squares in the grid until you get a hit.
Of course another simple reason could be the sheer volume of requests between intermediaries. The same request on behalf of the same client for the same travel products could be being sent by several different bedbanks, wholesalers or aggregators to the same supply source.
All this traffic has a major impact on infrastructure and resources. So with look-to-book ratios soaring, it is understandable why travel supplier and OTA websites are frustrated by machine driven scraping for competitive data. There is a cost associated with each one of the 20,000 searches required to bring home a booking.
In the good old days, the challenge was to develop methods to detect the scraping requests and try to eliminate or frustrate them in some way. Nowadays, I guess the smart money is probably on detecting the genuine requests and prioritising them. Know what your channel partners are requesting and receiving is part of the picture, and there we have an analytics solution that can help.
It is essential to get under the skin of the changing dynamics of your distribution and supply chain. With analytics is is possible to differentiate channel partners and the return they bring (or not). This dynamic landscape is starting to accentuate the leaders from the laggards and it has never been truer that not all channels, or partners in the same channel, are equal.