Pricing & Revenue Management Blog

Are airlines making the most of Big Data?

Apr 6, 2016 1:44:44 PM
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Are airlines making the most of Big Data?

The current airfare management model has become outdated regarding data analysis. While other industries have taken advantage of big data analytics, airline pricing is still behind regarding market insight, reducing its flexibility and capacity to respond to market trends, competitive moves and customer demands.

Information Technologies have opened up a range of opportunities to collect, manage and analyze an exponential amount of information for different industries. All businesses that work with dynamic parameters and inputs must make complex decisions in uncertain scenarios. When calculating supply and demand, market trends, competitors' reactions and customer behavior, Revenue Management managers must make a calculated bid. 

Big Data analytics helps make this leap with a more informed confidence. The new range of applications available can analyze large amounts of disseminated information and turn them into trends, projections and competitive insight for strategic, technical and operational decisions.

By leveraging the power of Big Data analysis techniques, managers can find patterns and correlations. This activity allows them to go beyond historical sales analysis and link previously non-disclosed information through a range of external data sources. It is estimated that up to 85% of the data is unstructured, as it is spread out in social media information, competitive actions, web files, presentations, and marketing material.

Rajendra Akerkar, M Sc. in applied mathematics and a Ph.D. in computer science, claims that Big Data is economical and inexpensive to process massive amounts of information, as well as democratic. It is no longer a silo for database administrators and developers, but managers can use it and provide input, as much more actors can be involved in generating, processing and consuming data.

The capacity to get trends can grow exponentially regarding:

  • Volume: data sizes ranging from terabytes to zettabytes.
  • Variety: it can come in a diversity of formats of structured and unstructured information, harder to search and analyze by conventional means.
  • Velocity: data continuously arrives at very high frequencies and at high-speed data streams.

An IBM report states that airlines can use customer analytics to:

  • Improve yield management and pricing
  • Determine which customer segments are price sensitive and which ones are not
  • Calculate each customer segment's willingness to pay for a given route

Hence, advanced analytics can help drive pricing strategies, and improve fare fence optimization and yield management.  "By using value-based customer segmentation to help make decisions regarding these basic operational issues, airlines can find opportunities to reduce costs associated with specific routes, while increasing customer revenues," the report states.  

Akerkar affirms that “tracking the time and place a customer booked from, fares offered from other websites, and customers IP addresses would allow airlines a greater insight into its customer needs.” However, he asserts that “until now most airlines have analyzed partial datasets which are thinned subsets of the Big Data analytics. Thus, the airline companies haven’t been able to extract the broad understanding it was looking for.”

One of the biggest problems the industry has in its implementation is the switching cost. As airline pricing works with an integrated system, they only track transactional data, "missing out a huge chunk of possible marketing opportunities,” Professor Akerkar declares.

The International Air Transport Association (IATA) admits that the change “has been surprisingly slow,” as the sector is “not fully utilizing the benefits that this technology brings to the table. While some usage has been seen in areas of revenue optimization and customer service, there has been a lack of concerted effort throughout other airline modules. One obvious reason has been a lack of data connectivity between modules.”

Competitive scenario

An Accenture Industrial Internet Insights Report analyzed the importance and priorities of Big Data analysis for CEOS of many companies:

61% of airline executives regarded it as a top priority while 20% considered it as one of the top three priorities.


61% of airline officials consider profitability through improved resource management as a priority, and 58% prioritize insights in consumer behavior and competitive edge. 


Other industries have grown with the use of this information. Ernst & Young reports that companies such as Google, Yahoo and Twitter “have been able to create new business and revenue streams from selling data or delivering services that are based on data analytics.” They have moved forward by “inventing and implementing the technology needed to solve the problems related to data handling and analysis in their own operations.”

Industries whose supply and demand business intelligence is crucial, such as retail, have moved forward in the use of Big Data. A 2012 Aberdeen Group study analyzed trends in the use of Big Data analytics in retail companies in the United States. Executives stated that it provided:

  • Agile business execution value, with easily available information
  • Improved product and service innovation
  • Enhanced predicting capabilities for product and customer problems
  • Detailed performance data available to rectify errors
  • Assistance with the development of one view of product information

The most complex scenario is that consumers have already stepped up to the plate with pricing intelligence analysis, with applications such as Expedia or Computer scientists Oren Etzioni, Craig A. Knoblock, Rattapoom Tuchinda and Alexander Yates, gathered airfare data from Orbitz. They illustrate that “it is feasible to predict price changes for flights based on historical fare data, despite the complex algorithms used by the airlines, and the absence of  information on key variables, such as the number of seats available on a flight.”

Their data mining method achieved 88.6% of possible savings by appropriately timing ticket purchases, as its predictive model saved 697 simulated passengers US$283,904, just by advising them when to buy and postpone purchases”. Hence, data mining can save customers substantial sums of money a year, “at least until corporations begin to fight back.”



For the airlines, the capacity to undertake future predictions and start catching up with Big Data technology becomes more vital as online travel agencies and Social Networks are giving more information power to the passenger, enabling them to exploit any mistake or arbitrage opportunity and making them much more sensitive to price differences. But the problem, Accenture, states is that "many airlines and airports cannot analyze and process the amount of data they receive, but such data could be used to revolutionize the passenger experience. The vast amount of data produced related to passenger flights cost reduction, and revenue enhancement is too much to handle for most airline IT departments,” it concludes.


Big Data analysis has enormous -even uncovered- opportunities for the aviation industry. The first actor to make the decision to absorb switching costs and implement Big Data solutions will be the one to seize most of these opportunities.

Have you used Big Data analytics? What challenges do you see in its implementation? What practices from other industries would you like to replicate in the airline industry?

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Tags: Big Data
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