How to optimize travel search results with an analytics framework
Brand marketer could optimize search results and lift conversion rate by fully understanding search behaviors on your site and organizing the data for the development of insights to drive action and to understand your baseline for improvement.
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.
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:
1. Total number of sessions that included the search criteria
2. Total number of properties and pages of results returned by search criteria
3. The position of the property search result (i.e. 1 of 380, or 380 of 380)
4. The property ID for each result, along with other key descriptive attributes (e.g. guest score, star rating, average nightly rate, etc.)
5. The number of times a given property option was selected
6. The number of times a given property option was booked
7. 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.
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:
1. Properties that are significantly uncompetitive
2. 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.
Either will surely be attractive in grabbing a supplier’s attention!
STEP 3: Review what the data is telling you and apply to your search results presentation rules.
Using the updated data collected in Step 2, the results of adjustments can then be reviewed and compared according to a 30-day pre- and post-view of hotel chain, property select and conversion results for San Diego’s market using a basic pre-constructed analysis framework.
These views look at the data across several key dimensions such as brand, property and position to evaluate the effectiveness of changes and also reveal additional recommendations to drive better results and monetization opportunities.
One sample view can be found below:
STEP 4: Monitor the application of changes and its net effect on results.
Next, you’ll want to continue monitoring San Diego results and identify other properties where additional positive changes can be made, as well as evaluate other markets with similar situations. This includes:
1. Identifying top opportunity hotels that appear in the top 10, bottom 10, etc. of search results
2. Determining what the potential conversion and margin improvement would be in order to make the hotel more attractive to users in the market
3. Making additional changes as needed
4. Monitoring room night, booking charges and margin changes from pre- to post-periods to highlight areas of opportunity
STEP 5: Take action using your findings and take the analysis to the next level
Be sure to investigate opportunities to incorporate this type of feedback loop of learning, testing and adjustment. Using predictive analytics along with machine-learning algorithms can provide continuous, automated improvements for greater monetization and conversion lift.
All in all, with the right analytics framework and a data-driven approach, you can create better experiences for your visitors by optimizing their search results and open the door to new revenue opportunities in the process.
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