How Timing Increases the Booking Value and Improves Customer Relationships

From Search to Booking

Regardless of how far in advance travelers plan their vacation, the customer journey encompasses the same seven stages for every traveler. At each of these stages, there are various opportunities to personalise the journey and enhance customer experience.

This might involve tailoring recommendations based on recent search queries, delivering inspiring content rooted in past bookings, or offering insights into ancillaries customised to the travel party. However, as crucial as adapting your communication's content is, timing is equally vital when engaging with your (potential) customers.

Reaching out to customers when vacation plans are not yet on their radar means you might not be the first brand they consider when they begin their search. On the other hand, if the information arrives too late, it loses its relevance, or they may have already made bookings with a competitor. Poor timing can also result in frustration, which can have adverse effects on your brand's image or lead to newsletter opt-outs.

In essence, to captivate customers from the very beginning, it's imperative to personalise both the content and timing of your communication.

Personalise Timing? Use Data!

As with personalising offers and other communications, timing is best personalised based on data. This includes historical booking data, recent search behaviour on your website, and external factors such as holiday periods or weather. Of course, manually analysing all this information is impractical, but with an AI-driven decisioning engine, it’s a piece of cake.

A decisioning engine analyses data from various sources, past bookings, and data from similar customers, and uses these insights to personalise both content and timing on an individual level.

If data shows that a customer always searches for vacations around the same period each year, the decisioning engine automatically sends the most suitable inspiring content during this period. If a customer always reserves a rental car during the booking process, the decisioning engine shows the best rental car recommendations to the customer during this process. By taking into account both timing and the customer’s preferences and travel type, you achieve maximum relevance, which increases the chances of conversion.

If a customer prefers to book all the extras at the last minute, the decisioning engine saves ancillary recommendations for the moment when the chance of conversion is highest.

Use your knowledge and Experience Wisely

With some decisioning engines, it is also possible to set up business rules that the algorithm takes into account in its calculations. An example of this is consciously delaying recommendations for ancillaries. Many providers know from experience that this increases the chance of conversion, as with spread-out purchases, you feel like you’re paying less for your vacation than if you were to pay for everything at once.

Another example is determining the optimal timing of an incentive. Perhaps you know from experience that offering a discount to certain target groups works to stimulate bookings, while other target groups are more sensitive to discounts on upgrades. By combining such knowledge and experiencing with data, you achieve optimal relevance for your target groups and maximise the chance of conversion.

Optimal Relevance for Every (Potential) Customer

Of course you encounter customers with varying search and holiday patterns, as well as new travellers for whom you have limited or no data. This diversity can make predicting their needs more challenging, but it's by no means an insurmountable task. To ensure a relevant experience for these target groups, our decisioning engine harnesses data from peers, coupled with recent behavioural insights. These serve as dependable indicators for tailoring content and timing, all of which can be employed in real-time.

If a traveler's response differs from the predicted content or if a booking doesn't materialise, our self-learning algorithm autonomously fine-tunes and customises both content and the channel, adjusting sending times based on newfound insights.