Beyond Black Friday: Why Price Alone Won't Win in Travel and Hospitality

With Black Friday around the corner, businesses are offering enticing sales to drive consumer spending. Travel and hospitality companies are no exception, rolling out discounts to fill seats and rooms. It’s a lucrative time for businesses, and one that consumers also relish as an opportunity to buy their Christmas presents without breaking the bank or book that holiday break they have been planning for months. But are pricing promotions the only tool that travel and hospitality companies should rely on?

Pricing has always been an effective lever to drive conversions in the travel sector, and dynamic pricing in particular - the practice of continually adjusting prices in response to changing market conditions such as demand, supply, time, competition and customer behaviour - has become a standard tool. Most people know that travelling to a beach destination in the summer is significantly more expensive than doing so in winter, or that last-minute flights often cost a premium compared to those booked months in advance.

The use of adaptive pricing strategies is understandable, especially for companies that operate on razor-thin margins such as airlines and hotels, but it can be a blunt instrument that frustrates customers and risks company reputation. Delta Airlines recently came under investigation from the US government for the use of AI-personalised pricing. Several hotel chains in London faced similar scrutiny for increasing room prices more than fivefold when Taylor Swift performed at Wembley Stadium. No travel company wants to make headlines in this way. So what is the best strategy to balance the risks and rewards of dynamic pricing?

The power of customer understanding

There is growing evidence that travel companies that invest in collecting and analysing customer data - such as travel history, preferences and feedback - and use it to create personalised offers see significantly higher conversion, in some cases generating ~40% more revenue from those activities than their competitors. The smartest strategies combine compelling, personalised customer propositions with adaptive and competitor-aware pricing. Central to this is recognising that some customers are price sensitive, while others are not: they may value benefits such as flexibility, comfort or safety instead.

Understanding which customers fall into which category, and what they truly value, is where the real opportunity lies. Sometimes this means rethinking your product offering, how you manage your inventory, or even your customer service practices. To be clear, this doesn’t mean hitting customers who are less price sensitive with inflated prices - that would be exploitative. But it does mean that dynamic pricing is a conversion lever that works best when combined with deep customer insights.

At Datasparq, we have seen companies investing in expensive revenue management software that is too generic and cannot accommodate the volume, variety or product and ancillary relationships in their inventory. Others have invested in bespoke dynamic pricing solutions only to find little evidence in their data that customer demand reacts significantly to changes in price.

On the other hand, we’re seeing companies we work with, such as airlines, blending the best human insight with machine power to combine intelligent revenue management with an increasingly nuanced understanding of what drives customer satisfaction and repeat conversions. Elsewhere in the market, hotel chains such as Marriott and Hilton have been doing this for several years through personalised guest experiences, and are achieving tangible results. Hilton, for example, has seen a 15% increase in ancillary revenue from up-selling services such as spa treatments and room upgrades by gathering guest data from past stays to anticipate preferences.

The reality: it's harder than it sounds

However, establishing a comprehensive view that transforms customer understanding into action is far from simple. It usually involves significant operational, technical and organisational complexities, and the reality is that the ability to navigate these challenges can vary substantially depending on the organisation.

In terms of data acquisition, low-cost carriers (LCCs) often hold a structural advantage over full-service carriers, driven by the inherent agility of their business models. Their digital-first strategy, combined with the high purchase frequency associated with lower price points, creates a naturally data-rich environment. This continuous stream of online interactions provides the ideal foundation for deploying AI-driven personalisation. We experienced this first-hand while working with easyJet, when we were able to go from concept to pilot in just under 3 months for most of our projects, including personalised travel recommendations that materially increased flight conversions and a propensity model that recommended changes in ancillary bundles, maximising upsell.

Contrast this with a luxury river cruise operator we collaborate with, whose customer base leans towards an older demographic. Here, nearly ~70% of transactions are conducted offline, often via phone. This creates a challenge due to the lack of a standardised data collection process. Without capturing these offline interactions and integrating them with online sources, the adoption of data-driven approaches remains limited.

Legacy systems and ways of working: the hidden barrier

Even when a company manages to bridge these data gaps, turning insights into personalised action can be hampered by internal constraints, such as those generated by legacy systems and entrenched ways of working. For example, many established hotel chains and airlines still heavily depend on Global Distribution Systems (GDS), the historical backbone of travel distribution. These platforms were engineered with a specific objective: to connect providers with agents for inventory control. Consequently, while legacy GDS platforms excelled at managing static pricing and availability, they struggled with the real-time, unstructured data required for modern personalisation.

While legacy providers are certainly evolving as they introduce smarter, more integrated solutions that bridge the gap between traditional bookings and digital tech, the reality is that these systems are deeply embedded in the core operations of many organisations. Replacing them would incur major costs, risks, time and resources. Travel companies will most likely need a combination of systems, fully integrated, to really reap the benefits of data-driven revenue management and customer experience strategies. This ought to be a key consideration when scoping and implementing any change initiative. 

The consequences of outdated, siloed systems are tangible. They create the “Revenue Lag”, forcing your teams to rely on decisions based on outdated information and miss out on opportunities for personalised offers. Traditional Revenue Management Systems (RMS), for example, often use simplistic demand forecasts that rely heavily on past booking patterns, assuming demand repeats predictably. This breaks down during external shocks (pandemics, weather events, strikes). They operate under an “inventory and booking curve” mindset, built for direct bookings and travel agents, ignoring rich data like multi-channel browsing behaviour, loyalty profile, trip purpose, or willingness-to-pay indicators. In short, they don’t align with how travellers actually search and book today. As a result they miss opportunities for personalised offers or micro-segmentation and end up leaving revenue on the table, or over-correcting with manual adjustments.

Taking the path forward

To embark on these initiatives that require both technology and organisational change is a significant undertaking, the duration and complexity of which depend heavily on an organisation's digital maturity. However, such investment is vital to remain competitive. Increasingly we’re seeing examples of siloed, off-the-shelf dynamic pricing solutions failing to deliver the promised returns and clients asking how to make the expected revenue improvement real. Robust customer understanding acts as an augmentation tool, reinforcing revenue management strategies by filling the blind spots that pricing algorithms alone cannot see.

Ultimately, while pricing strategies remain essential, a balanced approach that prioritises understanding and serving the customer better will enable you to master your market and stop leaving money on the table. 

For more on how that holistic approach to data and AI use are delivering tangible impact for travel and hospitality clients take a look at our in depth case studies, or get in touch with us.

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