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Is Metasearch to Blame for Hospitality’s 5-Digit Look to Book?

Overloading API with trafficThe Look to Book ratio is a figure used in the travel industry that shows the percentage of the number of requests to your booking engine per reservation made. This ratio is important to online travel vendors for determining the ROI of their investment strategies to secure those all-important conversions.

Back in the good old days, perhaps five years ago, the look to book ratios for the best managed hotel distributors was probably the order of 500 searches for every booking. Nowadays, whilst online bookings and revenues have reached all-time highs, so has the look to book ratio. Sadly this is now the order of 20,000:1.  So, let’s get this straight – you and I, when we go searching for that dream hotel or may be that simple overnight stop, we are on average doing 20,000 searches before we book?  I don’t know about others but I suspect they don’t behave dramatically differently from me, and probably do around 20 searches maximum before booking.  So why the discrepancy?

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 maybe 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.

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.

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