Airline Decision Analytics

Airline operators face complex challenges and are making fundamental changes to business models in an effort to sustain competitive advantage.

New strategies, and tactics are aiming to simultaneously boost revenues, contain costs, improve operational efficiency, and manage assets effectively.

With everchanging markets, meeting the needs of more informed, more diverse consumers is getting more difficult. Competition from niche players is placing pressure of pricing at a time where other factors such as rising fuel prices and lower utlization rates from congestion at airports extending turn around times is making decision making very much a daily event right across the airline.

New technology is driving more efficient services, many of which are seen as competitive customer strategies as much as operational efficiency drives. Global e-booking and ticketing, more competitive alliance programs, more hotel and rental car locations, and increased domestic and global routes all add layers to profitability calculations.

Airlines are seeking new ways to:

• Differentiate themselves to customers
• Leverage new opportunities that arise
• Reduce operating costs whilst maintaining and improving service levels
• Retain customers through loyalty programs and new services
• Improve customer insight to focus marketing campaigns and support EBM

All this means that airlines need to be ablet to access massive amounts of valuable
operational data for quick, decisive, fact-based action. Current, actionable, integrated
operational information must be delivered directly to the front line.

Airline industry studies have shown that improving revenue management processes by just 0.1 percent can add millions of dollars on the bottom line. This can be gained through insights into:

• Show Rates- knowing when to open or close inventory on existing flights to avoid overbooking
• Customer Behavior - how your customers fly on your airline

Using advanced analytics to identify and analyze:

• Overbooking and passenger no-show data captured in the Integrated Passenger Name Record (iPNR) data warehouse and flight data, revealing the impact of customer behavior on demand and show-rate trends
• The effect of decisions made throughout the revenue chain
• Market or flight conditions - leading to improved inventory control and productivity
• The impact on revenue and forward bookings at the customer level
• Ticketing data to uncover fraudulent activity, leading to recovery of revenue and commission

Some business intelligence solution providers have developed airline industry specific data models that act as a fundation for enterprise-wide views of data. For instance, the Teradata Travel Industry Logical Data Model [LDM]

Travel Industry Logical Data Model (LDM)

The LDM is a flexible blueprint of how data are organized within a Teradata system. It provides a ready to use structure for addressing key issues experienced by all airlines, across multiple business areas.

Specifically, the Teradata Travel LDM includes:

Customer

• Customer Analysis – Revenue generated vs. miles flown, customer profitability, lifetime value, usage trends.
• Web Activity - understand behavior of customers online, and how to increase engagement and conversion rates.
• Loyalty Program Analysis – realize which promotions generate the greatest activity X customer tier X markets.
• Partner/ Alliance Member Analysis – confirm whether customers are utilizing your business partners and understand the impact of promotions on use of Alliance members
• PNR Analysis – understand the impact of the booking date to ticketing date on the show rate X PNR X flight. Assure group bookings are supported by contracts.

Marketing

• Product Affinity – identify any correlation between online booking rates and promotions such as bonus miles / points offered
• Promotional Analysis – advertising impact on booking activity by market or offer. Relationships between promotions and customer behavior, such as Web usage can be readily identified.
• Channel Analysis – understand correlations between online sales and those made by travel agents. Reveal what impact the channel volumes have on average ticket price.
• Customer Activity – track end to end customer behavior to identify impact on operations.

Financial

• Revenue Management Analysis – maximizing revenue for each seat X booking class X flight. Inventory management can be optimized through knowing exact overbooking levels should be assigned for flights to special events.
• Financial Analysis – uncover drivers of spending trends. Identify target areas for cost reduction, such as assets and inventory levels. Financial reporting cycles can be shortened
• Fraud Detection - identify travel agencies abusing special incentives and which customers are not completing multiple leg flights

Operations

• Load Analysis – impact of variables such as price and time of departure on loading X flight X time period.

Decision analytics can reveal insights that overturn long held, incorrect assumptions and uncover outside the box opportunities to increase revenue and reduce costs.

Next: Customer Relationship Management [CRM]

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