How AI-powered performance marketing can drive growth for travel businesses

While travel may have taken a backseat in recent years, the industry looks poised to bounce back at a record pace. Travelers are looking to take to the skies again as vaccination rates continue to rise and travel restrictions ease around the world.

The global travel and tourism sector is expected to reach $8.6 trillion this year, just 6.4% below pre-pandemic levels, when travel and tourism generated nearly $9.2 trillion. dollars for the world economy.

Travel is going digital at an unprecedented rate, with 82% of all bookings made online or on mobile, meaning digital marketing plays an increasingly important role in generating demand.

As a result, travel business ad spend growth is projected to increase significantly from 2021 to 2023, with a projected 36% increase in 2022. Well-executed campaigns have the potential to generate significant ROI for brands across the globe. the travel industry, which means now more than ever. , travel marketers need to harness the power of AI and machine learning in their campaigns.

The consequences of the end of cookies.

With travel poised to recover to pre-pandemic levels, now is the perfect time for travel companies to focus on gaining market share. However, the impending demise of third-party cookies is poised to trigger a fundamental shift in the digital marketing landscape.

While travel businesses that rely on third-party cookies can expect to see their return on ad spend (ROAS) plummet, this change also presents a huge opportunity for those with a strong strategy for activating their first-party data.

The legitimacy of third-party tracking cookies is being questioned more and more, and rightly so. Internet users want their privacy respected and we are seeing a shift towards technology providers such as browsers and mobile platforms that offer people a greater degree of anonymity online.

As a sign of the times, by 2024 Google Chrome will no longer support third-party cookies, a move that will have significant implications for targeting. Chrome currently claims the largest share of the online market, being the preferred browser for 67% of internet users worldwide. Keen to position themselves as advocates for consumer privacy, other major players like Apple have already removed third-party ad tracking.

Using proprietary data to understand your audience

Unlike third parties, first party data is collected directly from a brand’s audiences, which include customers, website visitors, and social media followers. It is data that the brand has collected with the opportunity to obtain consent and explain how it will be used. First party data is collected with explicit consent and is made up of data points from online and offline interactions, and may include information such as demographics, purchase history, and interests. The first-party data is then stored using technology such as a customer data platform (CDP).

Source data is not only free from privacy concerns, since customers have consented to its use, but it is also the most valuable type of data, as it is collected directly from your target audience. This drives better personalization and allows you to forecast behaviors and predict consumer responses more reliably. Proprietary data tells you exactly what you want to know: which destinations are popular with specific travelers, when people book, what travel methods people prefer.

However, the reality of most proprietary datasets is that they can be difficult to scale. It’s no good just collecting large amounts of your own data, what matters is what you do with it, which is where AI and machine learning come in.

Navigating a cookie-free future with AI

Artificial intelligence and machine learning allow brands to scale their proprietary data and create authentic personalized campaigns, making them ideal for unlocking the potential of proprietary data. Making the best use of this data requires building custom machine learning models, which can be a serious challenge for all but the largest digital-first brands that have dedicated in-house data scientists.

Fortunately, there are now plug-and-play technologies that take the heavy lifting out of machine learning. No-code AI frameworks simply plug into a brand’s existing systems and analyze historical marketing data to determine how best to allocate digital spend.

Using data from a variety of systems, such as social media channels, traditional performance advertising partners, and proprietary data from CDP and CRM systems, the AI ​​identifies different cohorts of users that match specific profiles, automatically personalizing ad creatives and messaging for these prospects. . For example, young adults ages 18-24 may be more receptive to communication that highlights a discount on flights, while other micro-cohorts may respond better to a visual element that emphasizes luxury add-ons. Travel retailers need the ability to learn from their audiences over time so that offers, messaging, and creative can truly be tailored to a customer.

The AI ​​constantly learns from these variables and takes what works to create relevant and accurate campaigns that are highly tailored to each micro-cohort. When used correctly, AI and machine learning allow marketers to create effective campaigns with high ROAS even without third-party cookies.

In an increasingly competitive and digital travel industry, understanding how to incorporate AI to drive digital demand is becoming increasingly important. The days of manually analyzing only certain data resources are fast coming to an end and are being replaced by real-time decision making using deep learning AI models. Travel brands that gain an advantage in this new environment will be able to spend their marketing dollars based on a comprehensive understanding of where the right traveler is and the message they would most like to see, whether through social or traditional media. performance media.

About the Author…

Neel Pandya is Managing Director of Europe and APAC at Pixis.

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