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The Importance of Analytics on Mobile for Travel Brands

The Importance of Analytics for Travel Brands
It is often said that “Data is the new oil”. Before the industrial revolution oil lay underground in vast, untapped reservoirs, until ways were invented to extract and exploit this resource. So it is today with data. Often it is stored in multiple databases, unused and unexploited. Data analytics is concerned with examining this data and looking for ways to turn that into valuable information that an airline or a travel management company can use to improve its service to customers.

What is Mobile Analytics?

Mobile Analytics in particular is concerned with capturing and analysing the data being generated by travelers as they use travel applications or websites. Travelers are the quintessential mobile person, increasingly relying on their mobile devices for their information and service needs. Examples of the types of data that could be garnered include information about bookings and purchases, trips and their various segments and types, and the interactions that the traveler has with the application (which screens they view and buttons they tap on).

Mobile analytics can be used to:
  • Report On The Past This is the role of traditional Business Intelligence and its goal is to answer questions about what happened in the past. It also gives a business indications about its performance against its Key Performance Indicators.
  • Investigate – Delve into why things happened the way that they did. Airlines and travel companies must take those performance metrics and break them out to identify areas that are under-performing, whether that is a particular region, market segment or product line. Investigation can also highlight surprising results, such as travel products that are always bought together or people in certain regions that have difficulty completing transactions.
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  • Predict – Using past trends, extrapolate into the future to give an indication about what may happen in the future. Each airline and travel company has goals and by developing models on the data, it is possible to estimate the impact on the KPIs of decisions and actions taken.
  • Optimize – The ultimate refinement of analytics is to automate the decisions and actions to deliver the best possible outcome at each user interaction. Using Machine Learning and Artificial Intelligence, the applications will become much more dynamic and deliver what the traveler wants and needs, when they need it.

What can be measured by travel brands and why?
Just because we can measure lots of different things does not mean that all measurements are of equal value to the business. For example, measuring mobile app downloads is useful to give a general sense of the rate of uptake of new users. However, if the mobile application is targeted at a closed set of users and requires registration, then that would be the more useful metric.

"One of the ways to decide what to measure is to look at the particular business goal and then work out how that should be evaluated”

Some areas to consider include:
  • Acquire Relationships – The first step is to acquire new users. For example, when we acquire a new mobile traveler, we need to know which of our marketing channels was responsible. We can then build a picture of how effective the channels are in relation to one another, how much is spent in each channel, and estimate a cost of acquisition.
  • Understand Behaviour – Once the travelers are using the applications and interacting with their data, we want to understand what they aredoing, what they need and identify any friction points along that interaction. For example, we would like to understand what products the travelers are purchasing and when during the trip lifecycle they are purchasing them. We can then investigate the right time to present products for different customer segments.
  • Grow Value – When the travelers are using the applications, we would like to grow the value that they get from our services, which in turn grows the value of our business. For example, we would like to assess which features are used most heavily and figure out ways to make them as easy to use as possible. We can also investigate the effects of new features and services.
Examples of KPIs and Measures
One of the key measures for any application is the number of people that are using it. Clearly not all applications are going to be used by hundreds of millions of people, as social media apps like Facebook and Twitter are.

So it’s important to establish what the target metric is.
  • Airline app For example, consider an airline app. It offers two features, the ability for a traveler to book a flight and the ability to check in. We can estimate the total possible number of users on a given day by adding the number of people flying (who may use the app for check-in) to the number of people buying a ticket. For a large airline, this may indeed be in the millions, but for a small airline, it may be in the thousands. If the actual user count is a significant percentage of that total, that is a success, regardless of the absolute number of users.
  • Travel Management Company app - For a TMC app, the same is true. There is an upper bound on the number of users that we can expect on a given day and the target metric should be considered within that constraint.

“Analytics can also help determine how the travelers
are using the application and identifying problem areas”

In the airline app above, there is a flight booking capability. This feature has a few steps in it, such as searching for the flight, entering the passenger details, perhaps purchasing an additional bag, and finally paying for the flight. This sequence of steps represents a funnel, which the traveler moves through in order to make the purchase. It is highly unlikely that everyone who starts the purchase funnel will end up at the final payment step. So by analysing the percentage of people who make it from step to step, we can also detect where a relatively high number of people drop out. This may indicate a problem with the flow, or an unforeseen issue with payment and so on.

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We can also apply this funnel analysis to any feature that has a few steps in it. Check-in is another part of the application that we could analyse, especially if this particular app allows ancillaries to be purchased during the flow. Using the data, we may discover new opportunities to drive upsell and revenue. In addition, mobile electronic check-in is much less expensive than check-in at a desk in the airport for the airline. By analysing the flow we can encourage more travelers to adopt this method and reduce service costs.Other features in apps should also be measured to see if there is the expected level of engagement. Over time, apps tend to accumulate more and more features so it’s important to weed out those features that are no longer being used by travelers, by perhaps replacing them or even removing them from the app altogether.

If data really is the new oil, then airline and travel companies must figure out ways to exploit it to be able to survive in the 21st century. Mobile applications offer a unique way to harvest data about travelers. This data should be analysed to help drive adoption of the services, reduce costs and ultimately help deliver better products and services to customers.

Stephen Oman

Stephen is the Director of Data Analytics in Travelport Digital, helping our customers get the most from their mobile products through the application of data analytics and machine learning. Prior to joining Travelport, Stephen spent many years in the telecommunications sector, delivering personalization-based products to Mobile Network Operators around the world. He holds a research Master’s Degree in Artificial Intelligence from Trinity College Dublin.

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