Wednesday, May 22, 2024

How could metasearch evolve from price comparison to product comparison?

A recent article by Tnooz suggested it’s time for travel metasearch to evolve.

But what should this evolution look like? On the one hand, the industry has made big strides recently.

An increasing number of companies now allow customers to complete a booking on their own site, rather than sending them to the travel agent or hotel’s website to complete the transaction. A slicker service, for sure.

But in other ways, the metasearch model has remained static. As more and more companies jump on the bandwagon, customers are faced with a huge array of sources to research their travel arrangements.

In principle, more choice sounds good. In reality, more companies offering a similar service makes decision-making tougher than ever.

Harder to compare

Hotel MetasearchIn the hotel sector, major players such as Kayak and Trivago have continued to offer a virtually uniform experience of comparing properties.

This experience generally involves hundreds of hotels spilling out onto a page, requiring customers to hack their way through the options with a broad range of filters as their tool.

Invariably, the most clearly visible filter is one that encourages and funnels people into making a booking based on one overriding factor:


This isn’t always helpful. After all, not everyone wants to book accommodation based on the best deal or the lowest rates. With little distinction between competitors and limited choice for consumers, change is clearly needed.

A new model for metasearch

There are murmurs in the industry that a more nuanced form of comparison is needed—a model that moves away from price comparison and embraces product comparison. In this new model, choosing a hotel would involve consumers having far more control.

This makes sense. Given the growing demand for personalized travel experiences, the evolution of metasearch should arguably follow suit.

And slowly, it is.

Orbitz now offers a service that prompts customers to type in the names of previous hotels they enjoyed staying at. Armed with this information, their search engine can then suggest new properties that offer similar features and facilities.

Modern metasearch is also beginning to delve into the subtleties of human psychology to help people make decisions.

Currently, every conceivable option is presented to a customer all at once. Room type, upgrades and special deals all have to be considered at the beginning of the reservation process.

But studies have shown that information overload can actually create a form of buyers paralysis, which can stop people buying altogether.

One solution is to stagger the range of options over the course of a booking. At each stage, a select range of choices would be presented to make the purchasing process a lot more simple. This more user-friendly model could help hotels boost conversion rates.

But there’s still even greater room for evolution.

Machine Learning

To help customize the booking process even further, travel sites are beginning to harness the power of machine learning.

Travel search engine WayBlazer is helping travelers make decisions by using the computing power of IBM’s Watson, a natural language-based cognitive service.

Watson is able to trawl through huge volumes of fragmented data and present back the most relevant information, ultimately helping people make more informed choices.

A major benefit of Watson is its ability to understand queries that use everyday, natural language and respond with highly specific answers.

Applied to the travel sector, this technology has huge implications.

For example, when someone types in a request for a boutique hotel in Barcelona that’s great for families, close to the best tourist attractions and has in-room Wi-Fi, Watson can scour the net and offer a perfect match that meets these preferences.

That’s a huge leap forward.

An integrated engine

The future of metasearch may well involve an integration of personalization and machine learning, helping to shift the basis of comparison away from price and towards the products themselves.

Machine learning could present users with a carefully curated list based upon their previous choice of accommodation and specific personal preferences.

Customers could then systematically filter through this list to find a hotel that not only hits their budget, but also offers all the bells and whistles they’re looking for from a property.

By replacing largely unorganized data with tailored recommendations in this way, there seem to be two obvious benefits.

Firstly, decision-making fatigue would be eliminated. Rather than being frazzled by choice, users would be given a faster route to booking, reducing the risk of them abandoning a reservation altogether.

But secondly, hotels would be more carefully selected and booked based on the overall experience they offer, rather than low rates or latest offers. This could mean they ultimately enjoy more repeat business from consistently happier guests.

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