How the travel sector can better utilize AI for data and SEO

The below article has been jointly written by SALT’s Head of Data & Insights, Thierry Ngutegure, and Head of Technical SEO, Dan Taylor.

The travel and tourism sector is on the brink of a significant transformation with the emergence of AI.

AI technology streamlines booking, offers personalised recommendations, and creates immersive experiences. In addition, AI’s impact is expected to boost global economic activity by ￡1 trillion by 2030 according to a PwC study.

Travel is a sector that relies on communicating the experience of the few to leverage the future experience of the many. With SGE pushing for “snackable, personal and trustworthy” content, it’s really opened the playing field for challenger brands to flex their rich and tailored content.

One great example of where AI can supercharge the travel user experience is in AI-driven smart price predictions. This allows a business to use its flight database to provide personalised optimal hotel and flight prices, answering the age-old question — “Should I buy now or wait for a better price?” We believe this will have the most significant impact on last-minute holiday bookings, allowing customers to be proactively spontaneous — something business reporting will need to consider when communicating success to stakeholders.

We’re living in a world where we have more data than ever before, yet struggle to derive the insight we need to fuel business decisions.

Here are three ways AI can help us focus our attention and improve our activities.

Understanding our customers

Travel companies can use AI to categorise and understand vast amounts of customer surveys, feedback and reviews. Inevitably, this will help brands better understand what their customers think of them and how this compares to their industry competitors.

Segmentation involves dividing guests into groups based on various criteria or exhibited behaviour, such as marital status, purpose of travel, age, or whether they are new or returning clients. Since price sensitivity/elastcicity can vary among different customer groups, precise segmentation can significantly influence audience pricing strategies. Machine learning allows for meticulous categorisation and the crafting of personalised services or offers, enabling them to be better tailored to your customer’s needs and price sensitivity.

Another popular application is clustering, which is AI-powered segmentation based on hidden correlations between multiple data variables. Essentially, you can utilise clustering techniques to refine your segmentation and supplement it with extensive customer insight.

A well-organised forecast captures a substantial portion of market segmentation, acknowledging each customer segment may exhibit different preferences, booking patterns, and purchasing intentions.

For example, a family on a leisurely holiday will have unique requirements compared to a business traveller attending a conference.

Anticipating the demands and conditions of each customer segment can aid you in better targeting your marketing efforts, achieving an optimal business mix, and controlling your operational costs. In addition, you’ll have a greater understanding of when to accept or decline certain business at specific times.

Personalised AI-driven smart price & occupancy predictions

AI helps customers understand when they should book to get back for their buck while also helping the business understand the seasonality and intent of its audience more than ever before.

AI plays a crucial role in the travel and tourism sector by predicting occupancy rates. Hotel managers utilise historical data and AI models to accurately forecast future occupancy trends. Factors like time of year, seasonality, traveller type, pricing, location, and amenities are considered.

With AI and machine learning algorithms, hotels can forecast occupancy rates more accurately and anticipate customer needs.

This enables them to adjust pricing policies and optimise bookings for maximum profitability. Advanced analytics help determine profitable rates for specific seasons and understands customer preferences for tailored services.

Accurate occupancy prediction is vital for efficient supply chain management, allowing hotels to stock necessary supplies based on anticipated customer demand. This reduces costs and maximises profits.

The use of AI for occupancy prediction is increasingly popular, as hotels aim to optimise operations and enhance efficiency. It has helped the hospitality industry lower costs, improve customer experiences, and increase profitability. As this technology continues to evolve, it is expected to revolutionise hotel business practices in the future.

Personalised travel suggestions

The practice of travel personalisation based on static personas (e.g., persona A prefers destination X) is becoming obsolete. Unique customer experiences are now shaping the intent behind travel decisions.

AI is transforming the travel industry by interpreting intent according to individual customer journeys.

Providing customised end-to-end travel suggestions, everything from safe travel zones, avoiding weather implications, considering dietary restrictions, accounting for accessibility, and budget predictions. All of this helps us understand what truly matters to our audience and where we should focus our energy.

The conventional customer journey has deviated from its linear path.

Nowadays, a customer navigates erratically among these various stages, advancing and revisiting steps based on the information they encounter. For example, a customer might be interested in a major airline’s weekend package to Paris, including flights and accommodation, only to abandon the idea due to a negative review.

Instead, they might choose a similar package from a travel agency. In another scenario, a customer who enjoys meticulously researching their trips can be deterred by relentless emails that fail to offer the desired information.

Given this context, it’s hardly surprising that Google, based on its clickstream data analysis, posits that no two customer journeys are precisely the same, and that the shape of the marketing funnel is continuously adapting to customer intent. This transformation in the customer journey suggests travel companies can no longer force customers into a traditional marketing funnel modell Instead, they must concentrate on customer intent.

The individual nature of each customer’s journey implies that it’s no longer feasible for travel companies to implement a generic approach to B2C personalisation using a finite set of static personas. Each customer makes decisions uniquely, and hence, the responses from travel companies should reflect the distinct journey of each customer.