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Predictive Analytics in Aviation Industry


Predictive Analytics in Aviation Industry


This festive season, I am sure all of you have noticed that e-commerce companies such as Amazon, Flipkart and Snapdeal are asking potential buyers to download their apps. Cab aggregators like Ola and Uber are completely app-based services. Travel, flight, Indian railways apps have also become very popular. Having apps on your handheld device, like a smartphone, no doubt gives you freedom, but why are businesses so interested in giving you this convenience, unless having you using the apps make the businesses understand you in a better way?

Booking a Journey? Analytics at work.

Did you know that Airlines have been using analytics for at least 3 decades — even before the term ‘analytics’ was coined? Airlines are segmenting their customers for a long time. They are looking into travel pattern, passenger preferences, ability and willingness to pay and many other facets of airline travel to provide a better customer experience as well as generate maximum revenue.



American Airlines was one of the first airlines to appreciate the value of revenue management and invested in a central reservation system to track customer data. The revenue management team of American Airlines used customer information, churned the data and looked for customer behavior insight. Their target was to categorize passengers into multiple segments and charge them differently for the same ticket.

This enabled the airlines to offer tickets at a lower cost to a segment of the fliers, whereas for the same sector certain other segment got charged higher. This differentiation was not only dependent on the flying class, eg economy, business or first-class. Variations like even within the same class, higher prices were charged the closer to the day of departure. At the same time, for business class passengers, corporate agreements for certain total flying mileage a year saved corporates thousands of dollars. American Airlines perfected their analytical ability so much so that after 9/11 it was the only airline which did not file for bankruptcy in the USA.

In India the brightest example is Indigo. With 100 aircrafts and more than 600 flights a day, Indigo controls more than a third of Indian market share. At the root of Indigo’s success lies the facts that the organization recognized early on that an aircraft is profitable as long as it stays in the sky and aviation fuel is the most expensive variable to be managed. Indigo analyzes fuel efficiency on ground, and detailed cost analytics at every step of its operation to track profitability. Indigo also uses only one type of aircraft Airbus A320 so that inventory of spare parts is optimised and crew management is ensured.

How do Airlines use Predictive Analytics tools?

For all important managerial decisions whether it is merger and route code share, or how many seats in each flight in each route to be at Rs. 1000 or how many seats will be left till the last moment to be sold at ten times that price- all such decisions are based on intensive forecasting mechanism and revenue management systems riding on Data.

Each and every flight between a new pair of origin and destination is introduced after an intensive study of the size of the market. To get an idea of flight load factor number of passengers flying between a pair of origin and destination, by season and by day of week is easily available from historical data and can be projected for future. For optimum allocation of manpower, passenger arrival distribution is studied for each flight along with the number of bags and an estimated number of bags for hot and cold connections.

Accuracy in estimated taxi-out time at different airports may have an impact on the fuel consumption. Now that improved air traffic control systems are in place at several airports to allow flights to climb up to a higher altitude and squeeze more aircrafts into a smaller space than before, aviation is going to grow faster. Add to it DGCA’s vision of making short-hop flying affordable to everybody. Even the taxi-out and taxi-in time distributions are optimized so that each gate can be used to full capacity and no prime resource is left unutilized.

Scenario based projections for long term manpower planning and for short-term planning based on tickets are constantly churned to arrive at revenue optimization and customer satisfaction targets. It is now imperative for all airlines to assimilate analytics in their DNA and completely intertwine it with the management strategy.

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In summary

The question business stakeholders need to ask is “Are key business decisions assisted and honed to precision by predictive analytics? “ Big data captures a lot of information. At the same time it contains a lot of noise. Elimination of the noise and recognition of the pattern and finally making a decision regarding which of these insights are actionable, is the target of business analytics. The Airlines industry has shown us how if the wealth of data available across all areas of business operations if used with the right predictive Analytic tools, will drive business metrics and profitability.

The sheer volume and variety of data all commercial organizations get when their clients use their apps are enormous. The organizations are at a stage where they are drowning in data and realize that its potential is unlimited, but are not necessarily able to exploit the vast treasures of knowledge by mining the data. Without going into any technicalities, Analytics may be defined as an intelligent process of converting collected data and information into knowledge for improved decision making.

The knowledge, like priceless gems, will lead the organization in its future endeavors. Instead of depending on a few stakeholders’ opinions and sentiments about the market and possibly biased thought process, trained Analytics practitioners help guide a business through the path shown in the data and provide the knowledge required for decision making.


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