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Enhancing Customer Experiences in Hospitality: Data Strategies Beyond Generative AI

Only when a business’ enterprise data strategy is optimized can AI be empowered to work for the brand.
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The hospitality/travel industry is no stranger to the profound impact of technological advancements on customer experiences. Recent innovations have prompted investment into more meaningful touchpoints offered by brands, reimagining everything from the hotel check-in experience to an array of in-flight experiences. One of the buzziest new offerings is generative AI, which has emerged as a promising tool for creating hyper-personalization by analyzing vast amounts of data -- including travel history, preferences, online behavior, and social media activity. Though this opportunity is no doubt exciting, hospitality companies must remember to put fundamentals before technology. Only when a business’ enterprise data strategy is optimized can AI be empowered to work for the brand.

That’s not to say there’s no place for generative AI today. The technology can be impactful in low-risk use cases and shows great promise for changing the way consumers interact with brands, their applications, websites, and much more. Expedia and Kayak, Priceline and Airbnb are among the early adopters beginning to explore this future state. But, with data privacy regulations becoming more complex and dying signals like third-party cookies leaving the ecosystem, no company can afford to overlook getting their data house in order. The two fundamentals for a successful AI program are a high-quality data asset and the availability of compute power to execute algorithms capable of recognizing patterns and connections not discernible by the human eye.

Rather than waiting for generative AI to evolve and mature, travel and hospitality brands can get a head start by enhancing the first-party data strategy for their enterprise in parallel with adopting these technologies. By leveraging the many data collaboration strategies available today, they can securely combine data sets to unlock new insights, transform the customer experience, and power AI with the most robust data possible.

Build a Strong Enterprise Data Foundation

To architect a data strategy that allows AI to flourish, enterprises must build on a bedrock equipped to navigate the complex forces driving today’s ecosystem, unpinned by a solid identity strategy.

With rising travel costs, consumers are more likely to prioritize convenience and price over loyalty to a particular airline or hotel. Personalized customer experiences are nothing short of critical when it comes to retaining consumer loyalty. Increasingly stringent privacy regulations, third-party cookie deprecation, and the diminishing availability of device identifiers compound the problem. It's critical to be able to identify customers accurately at every touchpoint without ever compromising their privacy.

This requires building a strong, enterprise-wide identity foundation. This data infrastructure should use a consistent framework with clear rules that protect privacy and align with business priorities as the company grows. With enterprise identity, brands can create an accurate, connected view of customers across channels, partners and platforms, break down data silos, and reveal hidden insights that can transform every customer experience and interaction.

Build on What you Know with Data Collaboration

A strong identity foundation empowers travel and hospitality brands to take advantage of data collaboration to gain unique access to otherwise inaccessible datasets in a privacy-centric way.

Data collaboration opens countless, valuable use cases spanning top-of-funnel advertising and cross-screen measurement to product innovation and internal collaboration across business units.

Travel and hospitality brands are especially well-positioned for data collaboration thanks to their rich and unique customer datasets that often include behavioral insights. Businesses can combine the power of first-party and loyalty data to improve personalization, paid media effectiveness, and addressability at scale. Deeper collaboration with partners can take co-branding partnerships between airlines, credit card companies, rideshare services, and food and beverage brands to the next level. It can also transform what data can achieve within an enterprise’s own four walls.

Take a large hotel conglomerate with a global portfolio of brands, for example, which can use data collaboration to consolidate data from companies it has acquired. Connecting dots across lines of business and brands can reveal powerful insights about consumer preferences and complex customer journeys, transform customer service, and fill in data gaps to make investments in AI and other technology perform better across business partners and applications.

Data collaboration works in sensitive scenarios too. With privacy-enhancing technologies, businesses like airlines, which collect significant amounts of travelers’ personal information due to TSA regulation, can use data collaboration to unlock new insights without exposing customers’ personally identifiable information.

Understand the Right Role for Generative AI Today

It’s no question that laws regulating the use of generative AI will be passed. Until then, it’s best suited for testing low-risk scenarios such as customer recommendations and co-branding offers. For applications beyond this, travel and hospitality companies must be careful to mitigate for biases in the data, avoid exposure of intellectual property, and of course, adhere to the strictest level of privacy protection to avoid damaging brand trust or violating data privacy regulations.

Maximizing the outputs of AI to improve customer experiences also requires collaboration across the enterprise. Marketing is not the only team to touch customer data. CDOs will be most interested in maximizing the effectiveness of their data by making it available to all applicable parties in their organization, and using AI to accelerate product development. They can also empower their CMO counterparts to glean better insights from data across the customer journey, from website visits to actual sales. This is where internal data collaboration really shines -- joining insights and equipping teams to reimagine the limits of what technology can accomplish.

As generative AI becomes more mature, and its use cases and privacy implications are tested, companies can focus on strengthening the quality of data they’ll feed into AI models. Much like the “garbage in, garbage out” analogy, only high quality data can ensure AI delivers the customer insights travel and hospitality brands are looking for, rather than misinformation. Before long, generative AI will become a valuable tool for making customer interactions faster and more seamless and anticipating customer needs.

In many ways, the innovation of data and AI are a parallel path progressed through a test-and-learn approach. While travel and hospitality companies eagerly contribute to the maturation of generative AI technology, they can build enterprise data strategies that empower AI to do more. By adopting a privacy-first identity strategy and unlocking the power of internal and external data collaboration, brands will be well-equipped to build stronger, deeper connections with their customers, wherever they are.

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