06
Nov

Predictive Analytics in the Airline Industry

Long gone are the days when computer systems existed for simply ‘processing data’ and automating manual tasks. Computers are harnessed at far more advanced levels these days. The plethora and complexity of information, ‘big data’, data mining, machine learning and business intelligence have all contributed to the growth of ‘predictive analytics’. Predictive analytics seeks to analyse current data to make predictions about future or unknown events. Such predictions allow organisations to shape business decisions, forecast trends and improve outcomes and performance.

Predictive Analytics is increasingly being harnessed by all types of organisations, but fewer industries have developed more use cases and sophistication with these techniques than the commercial airline industry.

So what exactly is Predictive Analytics?

Predictive analytics is an advanced form of analytics, Predictive Analytics is used to make predictions about unknown future events. Techniques such as data mining, statistics, modelling, machine learning, and artificial intelligence are used to analyse current data to make predictions about the future.

Take for example – using flight scheduling data and scheduled maintenance job data to be able to predict what impact flight delays and early arrivals will have on engineering teams to be able to perform the maximum number of maintenance jobs at every airport layover.

Below are just a few examples of the how these systems are used by airlines.

Predicting demand

Predicting demand is now commonly used by commercial airlines. This allows commercial airlines to vary their prices, including offering large discounts while at the same time maximising yield for every seat they sold.

Predictive maintenance

Many airlines use predictive analytics solutions based on historical maintenance data and machine learning to predict mechanical issues to avoid flight delays or cancellations within the next 24 hours.

Building loyalty

By analysing trends and customer behaviour, airlines can tailor relevant offers, products and services to their customers. This targeted approach enables the airline the ability to offer the highest value products to the most loyal customers, and minimising customer churn by recognising those customers that are at greatest risk of switching their business to another carrier or loyalty program.

Ancillary Products and Pricing

Ancillary revenue is essentially all revenue from non-ticket sources and can include commissions from hotel and car rental bookings, the sale of on board food, beverages and entertainment, luggage fees, optimal seat charges, amongst many others. By analysing historical customer behaviour data, airlines can target offers most likely to be taken up by each customer segment without spamming customers who are unlikely to purchase. According to IdeaWorksCompany, airlines around the world collectively made $40.5 Billion in ancillary revenue in 2015.

These are just a few key areas in which commercial airlines utilise predictive analytics for business success. Many more examples exist, and this area will only continue to grow in the airline industry as more people fly and the competition for their business intensifies.

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