Google is increasingly relying on machine learning to order search results, increasing concerns among travel brands about how they’ll continue to be ranked.
Sure, Google’s use of machine learning might not be anything new, but understanding how it’s using this technology is vital for the sector which can sometimes struggle to satisfy its notoriously fickle and savvy customer base.
NB: This is an analysis by Daniel Bensley, travel industry lead at Qubit.
To me, this report highlights a wider challenge for the industry around our approach to AI and machine learning techniques.
Whether it’s Google changing its search ranking metrics or technology’s impact on jobs in the sector, headlines around AI are often more likely to spark fear rather than excitement.
The fact of the matter is that everyone, across all sectors, needs to better understand how to harness this technology to identify, reach and engage with customers.
For the travel industry, in particular, the benefits can be enormous if the technology is understood.
Supercharge segmentation
The travel sector has regularly outspent all other industries on pay-per-click ads, yet with sometimes as many as 80% of customers abandoning their shopping carts on travel websites there is clearly a wide gap between acquisition and satisfaction.
Put simply, travel brands have spent too much time and money trying to attract eyes to their websites instead of thinking about how they can delight customers when they arrive on the site.
Naturally travel’s sale cycles tend to be longer and involve a greater degree of research than other sectors, but there is still plenty of room for improvement for operators looking to provide a more relevant, satisfying experience for customers - this can only be achieved by building a better understanding of who your customers are and what they need.
Segmentation in travel is nothing new - the sector is in a fantastic position since customers are only too happy to hand over information about who they are and what they are searching for.
But many operators are still only scratching the surface, building basic, core segments, using limited data such as "First-time customers vs Repeat customers" or varied stages of the purchase journey.
Machine learning can add to and analyse data about a visitor’s behaviour and needs far quicker than any human can, in order to create a hyper-detailed picture of that customer and build deeper, more sophisticated segments.
For example, a customer may let you know they’re based in the UK and searching for a family holiday for two adults and two children.
A deeper analysis of previous purchase behaviour, combined with real-time customer feedback could tell you that this customer is in the ‘intent to purchase’ phase and likely to be interested in short haul flights, within the school holidays and would respond best to speaking to a call centre adviser.
Armed with these insights, a travel brand can then choose how to best engage with this customer in a much more meaningful way than before.
With so many variables to factor into travel marketing, machine learning represents a tremendous opportunity for businesses to become far more accurate and targeted with their segmentation efforts.
Uncover new opportunities
Those headlines that examine the opportunities of AI tend to focus on the end of the funnel - a host of brands have rolled out chatbots to assist customers in finding low-cost flights, while VisitOrlando attracted plaudits for its bot that went a step further and could point interested travellers in the direction of popular brunch spots and craft beer bars.
This is obviously the sexier end of the AI spectrum, but in my opinion, the real opportunity is far less visible and much more powerful.
AI and machine learning can not only supercharge travel brands’ existing segmentation practices by answering questions about who your customers are - it can help spot entirely new opportunities by identifying more nuanced patterns in this data that would otherwise go unnoticed by a human analyst.
Brands can then choose what they want to serve up to these new segments.
As we approach St Valentine's Day you may see customers searching for city breaks during the evening of February 13 are open to spending more than those browsing during for couples getaways in late January.
You can make up your own mind on whether these are forgetful last-minute spouses desperately searching for a last-minute gift - machine learning’s role here is to flag the new pattern.
You can then decide how you can better serve these desperate customers and point them in the direction of temporary offers for romantic getaways that suit their needs and boost your average order value at the same time.
AI shouldn’t be approached as a panacea for the travel sector, but it shouldn’t be feared either.
As it stands, machine learning is an incredibly useful tool that allows travel operators to seize opportunities they don’t know exist yet, but like any tool it’s important to not jump in and start using it before having a clear understanding of how you want to use it and the outcomes you want to see.
You also need to ensure you’re feeding your AI platform the right data - provide useless information, expect useless results.
Ultimately we do need to be careful in developing AI, but not for the reasons many believe.
The danger isn’t that we lose control, lose our jobs (it will free us up to do more meaningful tasks) or that Google decides your website isn’t important enough.
The true danger is that we let this tech revolution pass us by without tapping into its true potential beyond headline-grabbing bots.
AI’s untapped opportunities are legion - if it screws up, then we’re to blame.
NB: This is an analysis by Daniel Bensley, travel industry lead at Qubit.
NB2: Travel mobile selfie image via Pixabay.