Hotel websites could be working a lot smarter. Frank Reeves explains
Many hotel websites remain mere online versions of brochures to which a booking engine has been attached. Potential guests are shown a lot of static, yet gorgeous, content before they eventually click the ‘reserve' button, at which point they are taken off to a second system which does the heavy lifting.
Online travel agents (OTAs) have long since dropped these tactics. They tailor everything based on what their system thinks you want to see next. While a hotel website can't compete on all fronts, it should, at least, be able to tailor the experience around their potential guests' needs. To assist hoteliers and accommodation providers everywhere in building personalisation into their websites, we've condensed our efforts into six key principles.
The six principles of personalisation1 You cannot force conversion
Guests are smart. They will find the 'book now' button if they have decided to book your hotel.
If users took the time to visit brand.com instead of sticking with the information found on OTAs, review sites and metasearch engines, then hotels should reward them, rather than punish them by trying to manipulate their behaviour. A good hotel website should not be a giant red 'Book Now!' button with some information around it, but it should give users the information they are looking for when they are looking for it.
Most bookings don't happen on the first visit to the hotel's website, so trying to force 'best rate' as an entry-level communication will not help the guest in their discovery and research of the hotel.
2 What users do is more important than what users 'like'
A user's intent gives much more actionable data than their social media behaviour. Intent happens in the present and is best analysed through clicks and sessions.
Looking at personal data tends to increase the risk of stereotyping and oversimplifying users' behaviour based on who they are rather than what their intention is. Analysing search and click patterns on your hotel website gives accurate and instantly actionable information on what to personalise.
3 Too many options is like no options
A good AI system needs a lot of options and choice to offer to the guest, but limit the amount being presented at any given time.
This is where AI can help - by having multiple packages, rates and room types available for various scenarios, a personalised website will present a limited choice based on the intent patterns that have been analysed. Rooms and rates choices, together with packages and upgrades, should exist in abundance, but they should be presented with scarcity. Having multiple choices affects the level of satisfaction that people experience after making a decision.
4 Everything you think you know is (probably) not true
We all suffer from what psychologists call confirmation bias, as we have the tendency to focus selectively on the information confirming our pre-existing beliefs. Just because we haven't seen a black swan, it doesn't mean that there are no black swans.
In data analysis, therefore, there are two intrinsic risks: over-trusting the data, or filtering out what does not confirm our hypothesis. In the emotional and guest-centric world of hospitality, we need to find the right balance between what the data says and what our experience says.
In the end, it needs to tell a story. Just because a guest acts one way on a visit, it doesn't mean they will act the same on the next. Do not over-trust data and avoid confirmation bias.
5 Average is always wrong
When analysing your website, you can easily be fooled by an average visitor path to booking. You may be tempted to assume that the pages that count the most are the home page, rates and location, since this is what the average booking journey looks like. But this assumption ignores the actual path that Mrs Jane Smith took that led her to book on an OTA. Why? Because her questions weren't answered on the brand.com site. Every hotel is different and changes over time; just like guests change, so do patterns.
6 If results are inconclusive, increase the sample size
If you aren't able to clearly distinguish which ideas work best with your guests, you need to increase the sample size you are testing on.
If you're trying to determine whether you should be talking about your Mandarin-speaking staff or the fact that you accept WeChat payments to visitors coming from China, but the results show that interest is almost the same for both options, your first step would be to let the test run for longer in order to have a larger sample size. Don't jump to conclusions based on inconclusive data, no matter their size.
Frank Reeves is co-founder and CEO of Avvio
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