How AI-driven performance marketing can drive growth for travel businesses – PhocusWire


While travel might have taken a back seat over the last few years, the industry looks set 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 are eased 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 almost $9.2 trillion for the global economy.
Travel is digitizing at an unprecedented rate, with 82% of all bookings today being made online or on mobile, meaning digital marketing plays an increasingly important role in demand generation.
Accordingly, ad spend growth by travel companies is forecast to increase significantly from 2021 to 2023, with a 36% increase projected in 2022. Well-executed campaigns have the potential to generate significant ROI for brands in the travel sector, meaning now more than ever, travel marketers need to be harnessing the potential of AI and machine learning in their campaigns.
With travel poised to recover to pre-pandemic levels, now is the perfect time for travel businesses to focus on gaining market share. However, the impending demise of third-party cookies is about to trigger a fundamental shift in the digital marketing landscape.
While those travel companies reliant on third-party cookies can expect to see their return on advertising spend (ROAS) plummet, this change also presents a huge opportunity for those that have a robust strategy in place to activate their first-party data.
The legitimacy of third-party tracking cookies is increasingly – and validly – being called into question. Internet users want their privacy respected, and we’re seeing a shift towards technology providers like browsers and mobile platforms that offer people a greater degree of anonymity online.
In 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 lion’s share of the online market, being the preferred browser of 67% of internet users globally. Keen to position themselves as defenders of consumer privacy, other major players such as Apple have already eliminated third-party ad tracking.
Unlike third-party, first-party data is collected directly from a brand’s audiences – comprising customers, website visitors and social media followers. It is data the brand has collected with an opportunity to have gained consent and to have explained how it will be used. First-party data is gathered with explicit consent and is made up of data points from online and offline interactions, and can include information such as demography, purchase history and interests. First-party data is then stored using a technology such as a Customer Data Platform (CDP).
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Not only is first-party data free of privacy issues as customers have consented to its use, but it’s also the most valuable type of data, as it’s accumulated directly from your target audience. This drives better personalization and allows it to forecast behaviors and predict consumer responses more reliably. First-party data tells you exactly what you want to know: which destinations are popular with specific travelers, when a person is booking, which methods of travel people prefer.
However, the reality of most first-party data sets is that they can be difficult to scale. It’s no good simply collecting vast amounts of first-party data, it’s what you then do with it that matters – which is where AI and machine learning come in.
AI and machine learning allow brands to scale their first-party data and create authentic personalized campaigns, making them ideally suited to unlocking the potential of proprietary data. Making best use of this data requires building tailored machine learning models, which can be a serious challenge for all but the largest digital-first brands that have dedicated in-house data scientists.
Luckily, plug-and-play technologies now exist which take the heavy lifting out of machine learning. No-code AI infrastructures simply slot into a brand’s existing systems and analyze historic marketing data to determine how to best allocate digital spend.
Using data sourced from a variety of systems, such as social media channels, traditional performance advertising partners and first-party data from CDPs and CRM systems, the AI identifies different cohorts of users matching specific profiles, automatically personalizing ad creatives and messaging for these prospects. For example, young adults aged 18 to 24 might be more receptive to communication that highlights a discount on flights, while other micro-cohorts might respond better to a visual emphasizing luxury add-ons. Travel retailers need the capability to learn from their audiences over time so offers, messages and creatives can be truly tailored to a customer.
AI constantly learns from these variables, and takes what works to build relevant, accurate campaigns that are highly tailored to each micro- cohort. When used correctly, AI and machine learning allow marketers to build 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 rapidly drawing to a close and being replaced by real-time decisioning using deep-learning AI models. The travel brands that gain an edge in this new environment will be able to invest their marketing dollars based on a comprehensive understanding of where the correct traveler is to be found and the message they’d most like to see, be it through social or traditional performance media outlets.

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