Predictive Analytics for the Masses

https://www.mercator.com/blog/how-predictive-analytics-is-reshaping-the-airline-industry

Mercator Blog

From the latest aviation advancements to executive viewpoints and customer successes, we have the information you need to keep up with the best that Mercator has to offer.

How Predictive Analytics is Reshaping the Airline Industry

By Michele Drummond on 27 August 2015

Imagine being able to offer the most profitable package for any given traveler – one that they are most likely to buy and most likely to come back because of. The catch is, this traveler has never flown with you before.

When People Express launched in 1981, it grew quickly, and on a simple premise: The same fare for every flight, and every traveler. After purchasing several smaller carriers in 1985 – including Frontier Airlines – People Express had swelled to 41 cities, 4,000 employees, and 72 airplanes. 

Still, with the simplified fare structure at the core of its business, People Express had not considered what the larger carriers were just beginning to discover: A new customer base loyal to an airline with a completely different marketing scheme may not respond well to your own marketing scheme.

Ultimately, People Express, burdened with debt and shadowed by major legacy carriers, went to Continental in 1987. A well-known caveat continues to drive the evolution of the travel industry today – that what’s right for some, may not be right for all.

Predictive Analytics for the Masses

Most major airlines have been using customer segments for 30 years. By grouping customers into specific cohorts, these companies can predict their future needs and offer strategic pricing designed to earn the most profit from the most travelers. 

In order to deliver, predictive analytics needs something to analyze – data, and lots of it. Third-party and cloud-based solutions gather, store, analyze, and report on the terabytes of data going through a carrier in any given day. And to be able to effectively apply predictive analytics to your marketing and operations, it’s important to understand how it’s different from other types of analytics – namely, historical.

While predictive schemes also use historical data, historical analytics bases assumptions and conclusions on carrier’s past performance with existing business, in existing markets. Predictive analytics, on the other hand, relies on insights about a group or market as a whole in order to help a carrier deliver the best possible experience to all travelers.

Predictive analytics can be used throughout a carrier to drive profit, minimize revenue leakage, increase efficiency, and boost loyalty.

Revenue Accounting: Predictive analytics in an airline's revenue accounting department relies on financial data and past trends to reduce loss, prevent fraud, reconcile transactions, and reduce remittance cycles.

Air Cargo: With air cargo predictive modeling, airlines can plan more efficient and cost-effective routes, appropriately profile risk, and save money on fuel, personnel, trucking, equipment, and maintenance.

Customer Experience: From age-group and travel patterns to cohort characteristics and tendency to purchase ancillary services, there are dozens upon dozens of behaviors and traits that can be used to predict the future behavior of a customer. And while this effort is most effectively concentrated on existing customers – as it’s far more efficient to attract current customers than brand-new customers – it can go a long way toward enticing new customers to become lifelong fans.

Once you acknowledge the possibilities of predictive analysis, it’s time to assess what you have – and what you need. 

Data: First and foremost, as mentioned earlier, you need data to successfully use predictive analysis. It needs to be the right kind of data, in the right format, and should support your organization’s goal. You’ll also need a place to store this data.

A Goal: Before a question can be answered, it must be asked. Without a goal in mind, it can be difficult to compile and sort the necessary type of data. What’s more, the process can be prolonged without an end in sight.

Buy-in: It’s essential that everybody responsible for your analysis and its consequences be on board. They should all share a goal as well.

Communication: Once the data has been stored and mind, analysis run, and questions answered, a carrier can move onto predicting future events with recommendations. The findings must be shared throughout the organization, however – to ensure that the recommendations reach their full potential.

Customization and segmentation is and always has been the name of the game in the airline industry. Today, these age-old strategies are gaining power with corollary developments in data mining and extraction. The airline that leverages data using both historical and predictive approaches will be well poised to capitalize on the advent of this analytical trend.

Topics: Aviation, Revenue