Google Price-matching

Google Price-matching: Are Hotel Bookings Next? - PhocusWire

Excerpt from PhocusWire

Recently, Google announced a test of a new brand promise for its Flights product: a 'lowest fare' price guarantee that offers cash refunds to travelers for overpaying on select itineraries.

If the algorithm recommends “buy now,” and the fare actually drops prior to travel, Google will issue a cash refund. In its initial trial, the guarantee only applied on travel booked between August 13 and September 2.

The feature is either a bold product move or a savvy marketing effort - or both, as the guarantee emphasizes confidence in the accuracy of its predictive algorithm.

Either way, it’s captured our attention.

Given how Google’s product suite is closely interwoven, it’s not that much of a leap to wonder if such a guarantee could soon extend to hotels. After all, the company has millions of data points at its disposal via its hotels product. Once you consider the insights flowing through Google Hotel Ads (which originates 65% of metasearch bookings, by one measure), the company may even have more rich data on hotels than flights.

So what are the implications for hotels?

The price guarantee was packaged with another key product evolution: personalized hotel recommendations within the google.com/travel planning interface.

Google said in its blog post: “When you’re booking a hotel, you may need intel on neighborhoods to stay in and what you’ll expect to pay. ... Let’s say you’re planning a trip to Paris. Once you’ve searched for hotels for your destination and dates, click on 'Where to stay' at the top of your hotel results. For each neighborhood, you’ll see a description, the average price and more. You’ll be able to filter your results for hotels in a specific neighborhood.”

You can see how this will look in the image below. For travelers at the top of the funnel, it’s an entirely new starting point when planning a trip. Google's personalized hotel results will also include hotels that travelers have both searched for and stayed at before, as well as recommendations based on a user’s searches for local points of interest.

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