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Five examples of data science in travel

Five examples of data science in the travel industry

Data science allows you to extract practical knowledge from data at your disposal. Knowing where your customers want to go next, what kind of experiences they prefer and what prices they are ready to pay are just a few things achievable through strategic data analysis.

The travel industry leaves a long trail of data – indicated customer preferences, reservations, enquiries, additional services purchased. Most travel businesses already have valuable data at their fingertips. Travel companies already have a clear end-goal for their big data initiatives:

What is the main driver for your data program? Graph

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However, most businesses struggle with choosing the right path towards the established goals. Learn by example, and take a look at how progressive players are harnessing the power of data analysis.

1. Conversion optimisation in real-time

By leveraging internal and external data hospitality businesses can achieve razor sharp targeting, reduce customer acquisition costs and increase customer lifetime value. The best part – all of this can happen in nearly real-time.

According to Skift, companies leveraging traveller data tend to:

  • Reduce customer acquisition costs by 21%.
  • Generate a 17% increase in hotel/vehicle reservations.

By pairing internal marketing data with external sources, hotels can improve their operations even further. The simplest example would be leveraging weather data to adjust the current offerings and estimate the possible bookings. A skiing-based property can proactively outreach to customers with personalised offers whenever more snow is expected. Or, on the contrary, pitch additional leisure activities and offers to guests when not much powder is available on the slopes.

Brands with more advanced setups can also capitalise on micro-moments happening in nearly real-time. Red Roof Inn management estimated that around 90,000 passengers in the US were stranded every day due to flight cancellations. Their marketing team developed an algorithm to track flight disruptions in real-time and trigger targeted mobile search ads for nearby property locations. This “micro-moment” based campaign generated a 60% increase in bookings.

2. Optimised disruption management

Speaking further of cancelled flights, data science is a powerful tool to deliver instant help and response to affected travellers.

The algorithms can be trained to monitor and predict travel disruptions based on the information at hand – weather, airport service data, on-ground events such as strikes and so on. Your system can be trained to alert your staff about the possible disruptions and to create a contingency action plan in response.

As a travel agency, you could automatically assign personal assistants to affected travellers and help them adjust their travel plans e.g. organise a new transfer or check available hotels if they are stranded halfway to their destination. According to a PSFK Travel Debrief survey, 83% of industry experts say that control over their own travel experience through real-time assistance will be very important for travellers.

Airlines can also ramp up their disruption management plans with the help of data analytics. For example, Amadeus has recently introduced a “smart” Schedule Recovery system – a robust recommendation engine that predicts possible operational disruptions and helps airlines handle those in a timely manner.

Screenshot from the Amadeus Schedule Recovery system - courtesy of Amadeus.com
Screenshot from the Amadeus Schedule Recovery system – courtesy of Amadeus.com

Qantas, an Australian carrier, was among the early adopters. The company further reported that the Amadeus solution helped them reduce the number of delayed/cancelled flights due to bad weather conditions. Their competitor, who had used a manual system to manage disruptions, cancelled 22% of flights, whereas Qantas reduced the number to 3.4%.

3. Niche targeting & unique selling propositions

Data science isn’t reserved for the big brands only. Smaller travel companies can leverage their existing data sets to become the best within their niche, instead of competing for every/any kind of customer.

Citizen M, a boutique hotel chain with properties in seven locations, leveraged their existing guest data to hone their unique value proposition. Instead of pursuing a large pool of target clients, the chain’s COO decided to focus on pursuing a micro level niche demographic – solo travellers with a business-level budget.

The hotel chain aggregates on-site data into a central dashboard system that allows managers at all locations to review every guest interaction, and obtain further insights on how to improve customer experience.

The company specifically focuses on attracting the one particular type of customer and builds strong matches between customers and properties. The accumulated data is used to make better decisions about services and craft more targeted marketing campaigns targeting ‘lookalike’ prospects.

Small businesses in the travel industry can follow Citizen M’s lead and maximise their internal data to pursue the right customer, instead of wasting budgets on ineffective marketing to a broader segment.

4. “Smart” social media listening & sentiment analysis

Deloitte survey data indicates that 33% of people name social media as their primary source for travel ideas. Another survey of UK consumers aged 18-33 found that 40% of holidaymakers prioritise how ‘Instagrammable’ their experience will be.

Most travel companies have accounts registered on popular networks, yet they view still view ‘social media’ as a separate entity, rather than an organic extension for their business growth.

Social media is a two-way communication alley. Sharing updates aren’t enough to succeed. You have to communicate and listen to your customers (and prospects). Applying data science to social listening can help marketers consolidate large amounts of scattered data and turn it into specific market research campaigns.

Travel brands can follow Citizen M’s cue and leverage social media to create highly targeted buyer personas and scout for ‘lookalike’ prospects. All the conversations happening around your brand can be studied, analysed and refined to the key points customers are expressing. The new data can then be applied to create better copy for all channels.  In fact, thanks to Facebook’s new ‘Trip Consideration’ feature you may be able to target your ideal prospects, who haven’t considered your destination (yet).

Customers may not be enticed to fill in on-premises surveys, but they are more willing to rate their stays semi-anonymously online. Advanced ML-tools now allow brands to collect review data from different sources (e.g. TripAdvisor) and distil valuable business insights from those. For example, Hotel Aspect allowed one popular hotel chain to come to the conclusion that London properties tend to score the worst – with food, wi-fi and cleanliness being the top concerns.

5. Dynamic pricing strategies

Dynamic pricing is no stranger in the travel industry. This year though, more airlines plan to deploy highly personalised airfare prices.

According to the revenue management provider PROS, some 80 companies are having a major price overhaul. By using advanced predictive analytics tools, airlines are now capable of proposing:

  • Unique fare structures for each market and each departure day.
  • Continuous pricing – airlines can become more flexible with prices and set a unique price for an individual ticket. This is comparable to how Uber individually computes custom rates for each ride.
  • Bundled pricing – the airline can now create custom fares and price bundles for itineraries and additional services. For example, the carrier knows that passengers flying from Paris to London tend to check in 1.5 bags on average at £25 apiece. The company can now offer them a lower ticket price compared to passengers flying from London to Paris as they do not have checked-in baggage.
  • Dynamic price engines that would adjust the prices depending on the customer’s story with a brand (past purchases, loyalty status, typical routes etc).

The hotel industry has also largely benefitted from data-driven pricing. For example, El Cortez Hotel & Casino in Las Vegas has been using predictive analytics tools to adjust their pricing strategy in the following manner:

  • Reduce the number of discounts on dates that don’t need extra promotion.
  • Decrease mid-week rates, while increasing rates on weekends and holidays.
  • Maintain better rate parity between different channels – hotel website, third-party sellers and walk-in guests.

These changes have resulted in a 30% increase in revenues, 109% increase in direct room nights and a 4.5% increase in occupancy rate – all thanks to data-driven decision making.

Data helps businesses understand what their customers want, when they want it and how the business can meet them halfway. Data science has unlocked an array of new opportunities in the industry. It’s your call how you will put your data into action.

Need advice?

If you’d like to learn more about how you could be using data science to get the edge over your competitors, talk to us today. Simply call 023 9283 0281 or submit your details here and we’ll call you.

Duncan is a PPC specialist at Vertical Leap.

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