How to optimize travel search results with an analytics framework


NB: This is a viewpoint by Tony Gray, senior director of North American Consulting Services at Webtrends.

Recent news about Expedia’s Accelerator program for hotel listings has opinions swirling about the monetization of search results in the travel industry.

In the case of the Expedia program, hotel brands can increase the amount of commission paid to Expedia in return for higher placement in search results, although Expedia insists it’s not as simple as paying to show up in a more obvious location on a shopper’s page.

Expedia uses an algorithm that prioritises offer strength and hotel quality, with a smaller weight on the compensation component.

Whether you agree with the pay-for-play plan or not, the process of optimizing search results can be highly beneficial for all types of travel and retail sites – both for the brands who optimize and for the consumers who are seeking the results.

That’s because wading through a sea of options can be frustrating and tiresome for consumers.

If you can bring forward specific results that will resonate, it’s a win-win. So how do you as a brand marketer and optimizer get started?

The answer: apply an analytics framework using your site’s digital data to understand your search results and to apply your learnings.

STEP 1: Review areas of your website where visitors can search and view a list of results, ensuring that the appropriate level of data collection is in place to fully understand search behaviors on your site.

Let’s walk through an example looking at search results for San Diego hotels on a travel aggregator website.

Typical hotel search results for an online travel provider (first and last result shown)

From a search results perspective, at a minimum, the following items should be collected through your analytics solution and available for a time series analysis:

  • Total number of sessions that included the search criteria
  • Total number of properties and pages of results returned by search criteria
  • The position of the property search result  (i.e. 1 of 380, or 380 of 380)
  • The property ID for each result, along with other key descriptive attributes (e.g. guest score, star rating, average nightly rate, etc.)
  • The number of times a given property option was selected
  • The number of times a given property option was booked
  • A lookup table for property IDs to be able to classify by brand, hotel type, geography, etc. to support deeper dimensional analysis

In practice, additional data for feature usage, sort selection, etc. would also be captured and included for broader analysis.

STEP 2: Organize the data for the development of insights to drive action and to understand your baseline for improvement.

In this example, the data is first used to determine a baseline for performance for each hotel chain, then to view baseline results at the hotel property level. Most analytics providers allow for the easy retrieval of data via a web service, simplifying retrieval and compilation.

Simple analysis framework using collected search results data (sample data for illustrative purposes only)

Based on these benchmarks, it’s easy (and a great idea!) to delve into some A/B and multivariate testing to see how properties perform based on certain factors. You’ll get a clear understanding of your results including:

  • Properties that are significantly uncompetitive
  • Higher conversion properties that are buried deeper in results pages that are candidates for preferential placements

Your goal is to have data that shows how changes to search results will affect visitor behavior. Then, in the case of an OTA marketer, you can better monetize and realize premiums for brand placements in top merchandising spots, such as the top three (or top “n”) results on each search results page.

In a pay-for-placement program, you can reach out to hotel suppliers and provide tested data about what a boost in their specific search position will do. It might be a significant conversion lift, such as 25 percent higher conversion from moving from position 15 to 3, or a 30 percent improvement in margin contribution, etc.

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