Finnair's strategy is built on precise analytics

Airline grows its Asia routes with demand optimization solution

Location, location, location. Geography puts Finland in an enviable position. Helsinki offers the shortest path between Europe and Asia – the only west European city where a plane can make a roundtrip between the two continents in 24 hours. National carrier Finnair wants to capitalize on its home airport’s geographic advantage – and the growing Asia-Europe flight demand – with help from analytics.

The Finnish national carrier has a bold plan to double its revenue from Europe/Asia flights during this decade and uses analytics to better understand how to acquire market share, price seats correctly and compete effectively.

If you want to analyze the market share and profitability of even one route, you have to understand the huge number of possible trips the passengers can make on the flight in question.
Jukka Lahtinen, Finnair

Jukka Lahtinen
Manager, Route Planning

“The analysis must be accurate, because the airline industry operates under tight margins. Bad analysis shows up quickly as losses, particularly on long-haul flights,” explains Jukka Lahtinen, the manager responsible for route planning at Finnair. “Precise data-driven planning is indispensable to Finnair in order for us to be able to implement our strategy.”

Finnair derives nearly half its revenue from Asian traffic with service to 15 major Asian cities. From those cities, passengers connect to 40 further destinations, along with over 60 final destinations in Europe from Helsinki. Understanding those 1,600 possible flight combinations is where analytics plays its key role. “You have to understand the huge number of potential end-to-end trips the passengers can make on the flight in question, and we must understand the passengers' preferences and be able to compete with all other paths and connections,” Lahtinen says.

The first problem to tackle was the fragmented nature of the market information. This includes passenger volumes, route and demand information coming from Finnair’s own data stores, and publically available data on competitors, which provides itineraries and transfer connections for every passenger on a plane in a given geographic area. “It presents a lot of challenges,” Lahtinen says, noting that outside data is typically in a different format. “Different data sources can have very different definitions of the same thing, and often information obtained from external sources is incomplete. In those cases, one has to gauge the need for additional information and establish where to find that information.”

With analytics, the airline models all of the potential routes, factoring in capacity, flight frequency, time tables and equipment. Automated forecasts are produced estimating likely customers for a particular flight. If there is a difference between the forecasts and the realized passenger volumes, the company analyzes down to the individual route or market level where it succeeded and where it fell short of its objectives. From there it assesses what sales, marketing or pricing strategy might improve passenger numbers or profits.

Finnair's investment in analytics has led to a change in company culture. “The change in the way of operating has required a change in the whole way of thinking,” Lahtinen says. The airline is now merging its data warehouses to improve usability and support the wider use of business analytics throughout Finnair, affecting areas from decision making to operations.

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Challenge

Effectively price and plan routes to meet the goal of doubling revenue from long-haul flights between Helsinki and major Asian cities.

Solution

SAS® for Demand-Driven Planning and Optimization

Benefits

Finnair can model its network, anticipate future passenger flow, predict market share by route, and analyze route-specific and market-specific performance in comparison to competitors. Analytics also helps the company price and market routes and services.

The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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