How big data analytics is helping airlines streamline their future operations

The way organizations interact with their customers, make business decisions, and create workflows is significantly altering because of technological advancements. And without a doubt, the world of aviation has been impacted as well: data is revolutionizing airline operations from pre-flight to post-flight, including ticketing, seat selection, boarding, ground transportation, and so on. As a result, the data needed for hundreds of use cases is recorded as a passenger travel through the various stages of their journey.

We no longer book flights over the phone, and we no longer visit travel agents in quest of the greatest deals. Instead, we now have real-time access to data, which enables businesses to make more educated decisions about operational efficiency and customer experience. Today's airline sector is fiercely competitive, generating billions of euros each year while maintaining a profit margin of less than 1%.

The industry's extremely complex landscape and the fact that modern airlines have many pending business issues, such as a globally uneven playing field, revenue vulnerability, an extremely variable planning horizon, high cyclicality and seasonality, fierce competition, excessive government intervention, and high fixed and low marginal cost, are the main reasons for this.

What are the benefits of big data analytics?

Big data analytics' ultimate benefits include timely reactions to present and future market demands, improved planning and strategically aligned decision making, and crystal clear comprehension and monitoring of all key performance drivers pertinent to the airline business.

Lower operating expenses, greater customer service, market-leading competitiveness, and increased profit margin and shareholder value are all beneficial results of the above-mentioned benefits when correctly implemented. So, what are exactly the ways data analytics is transforming airlines?

1. Increased income for airlines

Data analytics aids the sector in gaining a better understanding of client preferences as well as other maintenance difficulties. For example, utilizing predictive analysis tools, the sector can target clients with customized offers while simultaneously optimizing the price in real-time. As a result of collecting useful data, airlines can obtain more bookings in the period allotted.

Customers perceive product value differently, hence the price they are willing to pay for it varies depending on the target groups they belong to and the moment of purchase. As a result, revenue management experts use AI to define destinations and change rates for specific markets, locate effective distribution channels, and manage seats in order to keep the airline customer friendly and competitive.

2. Smart maintenance

Passengers will escape many baggage tracking concerns thanks to the application of sophisticated data analytics. While radio-frequency identification helps to avoid luggage mishandling, predictive analysis helps to improve fleet dependability predictability. With airport traffic growing by the day, big data analytics will allow airlines to continue working on airspace management, particularly when it comes to runway bandwidth, flight routes, and aircraft types.

Airlines, for example, bear hefty expenditures because of delays and cancellations, which include maintenance costs and compensation for passengers detained in airports. Predictive analytics applied to fleet technical assistance is a suitable approach, with unscheduled maintenance accounting for approximately 30% of overall delay time.

To effectively manage data from aircraft health monitoring sensors, carriers use predictive maintenance technologies. These systems are typically interoperable with both desktop and mobile devices, allowing personnel to access real-time and historical data from anywhere. Employees can notice concerns pointing to possible malfunction and replace parts proactively if they have access to an aircraft's current technical state via alerts, notifications, and reports.

3. Cost reduction

In the event of baggage loss, the introduction of analytics into the aviation industry will result in cost savings. In most cases, the industry pays the losses, but data from real-time luggage tracking can help avoid losing, destroying, or delaying bags. On the other hand, by collecting and analyzing real-time fuel consumption data, one can reach a higher level of fuel efficiency.

Airlines collect and analyse flight data such as route distances and altitudes, aircraft type and weight, weather, and more using AI systems with built-in machine learning algorithms. Systems evaluate the appropriate quantity of fuel required for a flight based on data results.

4. Customer satisfaction

The airline sector keeps its consumers up to date in real time with predictive analysis, sentiment research, and travel trip analysis, presenting specific offers based on their needs, habits, and unique experiences. Airlines can better understand passengers' preferences and behavior by collecting and analyzing data about them. This allows them to give them transportation alternatives they prefer and, more importantly, are willing to pay for.

Similarly, when a flight is delayed or luggage is lost, people become anxious. Customers are less likely to choose this airline for their future journey if they do not receive a fast response or explanation of an issue from an airline agent. The speed with which you respond to client inquiries is just as important as the actions you take to resolve an issue.

As a result, the travel experience is becoming increasingly personalized, and customer focused.

5. Digital transformation

Big data and analytics are transforming the commercial aviation business in order to provide higher-quality services to passengers. Some solutions have been developed to provide the ideal platform for custom-made technology vendors to demonstrate their products and services to airlines and airports in order to provide passengers with a better-connected travel experience.

Emerging technologies are propelling the aviation industry to new heights by assisting them in meeting client expectations, real-time performance dashboards, and predictive maintenance in every manner conceivable.

6. Performance measurements

Airlines often operate in a globally competitive environment, necessitating timely and precise corporate performance assessments. Furthermore, because airlines are volume-driven, slight deviations (passengers enjoying services, fuel used, cargo carried) can compound into significant effects, prompting necessary and timely action. They also have a lot of trouble producing reliable daily/weekly performance measurements.

Big data analytics can automate the production of daily activity reports, such as the number of passengers flown per flight/sector and the distance travelled, which can be used to provide estimated performance measurements, such as daily or weekly revenues for specific routes.

7. Risk management

The truth is that the global airline sector has been hit by big disasters in recent years. As a result, it's critical for airlines to adopt various risk management models and tactics to protect themselves from the harmful consequences of such catastrophes. This is precisely where data analytics can be useful.

Some crew management solutions address the fatigue risk that pilots face because of repeated time zone shifts, long duty days, scheduling changes, and other factors. Developers of Jeppesen's Crew Rostering system, for example, have begun incorporating bio-mathematical tiredness models into flight crew scheduling software. Their goal is to enable schedulers to rely on data regarding anticipated weariness to decrease risks.

8. Control and verification

To be able to control expenses coming from various operational operations, airline carriers require a variety of control and verification methods. Airlines urgently require a complete and integrated collection of flight information data acquired from all of their separate business segments to do this. This will allow for the calculation of various efficiency analytics, such as planned versus actual fuel usage per aircraft and personnel utilization. Aside from that, these issues could be completely resolved by combining and analyzing pertinent flight and aircraft data. As a result of the establishment of a 360-degree picture of each flight/aircraft, airlines will be able to significantly improve their control and verification systems.

9. Load forecasting

Airlines must establish a good forecasting model to check the effects of alternatives such as increasing available aircraft seats, changing pricing, launching new air routes, and so on on a regular basis. Actual statistical trends and results should be factored into forecasts.