The travel booking journey is non-linear and complex. According to a recent report from an EyeforTravel conference, travelers spend upwards of 30 hours on multiple sites before booking.

Recommendations are an essential part of this search. While there isn’t a typical “travel booking journey”, a traveler might go through one or all of the following steps before booking.

Current Traveler Search Behavior

Let´s use a practical example: Kate is a Millennial, part of the largest demographic customer target group with the highest buying power in travel right now. She’s thinking about taking a trip and asks her friends for ideas, just like the 76% of Millennials that decide on a destination based on recommendations from friends.

She gets a few ideas, but doesn’t immediately book. Instead, she looks for inspiration on one of the destinations, Spain. She performs a search like, “holidays in Spain.” According to tools like Google Keyword Planner, 10 million searches a month are conducted globally for terms related to “holidays” and “Spain”.

She now has a few ideas of where to visit and starts to search on a travel booking site. She’s on a modest budget, so starts to filter the results manually by price. That doesn’t work, as too many hotels with poor reviews show up, so she filters by review next. Of all travelers, 81% believe user reviews are important.

Now all of the top results are too expensive for her, creating an unpleasant and frustrating user experience. She might decide to search another site for a “better deal” or for more user-friendly filter options. Among Millennials, 85% will search multiple sites to make sure they get the ‘best deal.’

She still searches for recommendations on where to stay or what destination to choose. She’s already gone to a few sites and not found what she is looking for. This is a clear opportunity for travel booking sites. Personalised recommendations can improve her customer experience and keep the booking on their site.

Personalize Throughout the Inspiration Phase

Kate, like many travel site visitors, didn’t start with a specific destination, hotel or even date of travel in mind. While on the site, she begins to put out ‘signals’ about her interests. From the moment she came to the site, she immediately began filtering her results.

Some of this is obvious filtering, such as by price. She’s also using less obvious filters, such as not clicking on hotels that don’t list a gym.

After a few pages, machine learning algorithms show she wants modestly priced accommodation with great reviews. She likes to be active on vacation, so would like a hotel with serious fitness facilities. And these are more important to her than which particular destination in Spain.

Now is the time to serve a recommendation for hotels in several Spanish destinations with at least 4 out of 5 stars and modest rates. Hotels with fitness facilities would appear higher among the recommendations and their offers for that are highlighted.

After she browses a few more result-pages, predictive algorithms notice she only wants to stay in the heart of a city if the hotel offers a spacious garden to relax in the green. Otherwise, she wants a hotel away from the urban turbulence.

A good travel agent speaking to someone face-to-face will start to understand these sometimes ‘hidden desires,’ but it requires empathy and quite some time. Online and with the right technology, a recommendation can be tailored to each individual user from the moment they land on a travel booking site and after only a short moment of browsing.

Higher Value with Personalisation

It doesn’t take long on a site before personalised recommendations can make a difference. Kate quickly became frustrated and left a travel site after spending more than twenty minutes on the site. She might have clicked on one of your Google ads or sponsored social media content to arrive at your site. If she leaves and never returns after this kind of investment, that’s a huge loss.

Once someone begins to spend significant time on our site, you want to continue to keep them engaged with your offering. In a recent implementation of the bd4travel recommender, up to 34% of users on the site for more than ten minutes interacted with a personalised bd4travel recommender. When a user stayed on the site for longer than 20 minutes, the engagement increased up to 45%.

Kate wants to be inspired, that’s why she spent so long on the site. When she begins to receive personalised recommendations, it’s a chance for you to increase engagement. The more she engages on the site, the more likely she is to return to your site to buy. Of course, the level of improvement can vary depending on the intensity and type of the personalization.

Increased engagement not only improves your chances of a visitor booking on your site, it can also increase the value of their bookings. In cases where a user interacted with a personalised recommendation, their average booking value saw an uplift of 7% to 27%. Clearly a more personalised experience during the inspiration phase results in higher engagement, and ultimately higher revenue.

Travelers want recommendations when they research a trip. They may not know what kind of trip or holiday they are looking for, and this can lead to frustration and site abandonment.

You can personalize the travel journey already during the inspiration phase and enhance those recommendations depending on the progress of the user in their booking journey. Users who receive more personalised recommendations have a higher rate of engagement and ultimately a higher value.

You can see what these more personalised recommendations look like in a demo. Sign up for one here and we’re happy to discuss your options.