The prosperity of the universe is increasing, thanks to worldwide integration. Globalization and competition come hand in hand. There is a giant race or contest in Aviation Data Analytics. It will also have into the profits of airlines, living cost-losing customers.
Primary challenges faced by the aviation industry are:
Underutilization of staff in the Aviation sector.
Unused cargo space.
The occupancy rate is meagre, which is a challenge.
Maintenance cost has been rapidly increasing.
Ineffective strategies are challenging.
On the other hand, shared insights on connecting flights.
The data is all over the world. Aviation Services can be used by historical and present data and draws the trends and insights related to the industry.
Many solutions that can infer from the aviation industry, which are
Effective routing is analyzed by using Network Analysis.
Analyze booking, can use Forecasting or Time series.
Pricing analysis is used to determine profitability efficiently.
Can obtain feedback from the customers by using Sentiment Analysis to achieve any changes or modifications.
Case Study 1: How to optimally utilize the cargo space by predicting the unused space, pre-assigned to vendors
Business Problem Statement
Surviving in Aviation industry over a long run by generating profits constantly month-on-month is no trivial task. Cargo division of aviation is even more challenging with the cargo space preallocated to the big players in logistics space to ensure that
aviation companies do not run into risk of losing these players. Sometimes, this strategy of retaining big players is costing them dearly. Research says that, in majority of the trips, the entire space pre-allocated to a few customers
is not filled with goods, leading to flying empty cargo flights. This in-turn hits the profits to nadir, risking the bankruptcy of the aviation firms. Pre-allocation of space does not provide opportunity to add new customers even if
the entire space is not filled, leading to slippage of opportunity cost.
Reduced optimum utilization of cargo space allotment
Revenue loss due to unutilized space, pre-allotted to established logistic companies
Difficulty in resource levelling & balancing due to uncertainty in space booking due to pre-allotment
Loss of new businesses due to pre-allotted space not completely used
Predicted the cargo space utilization a week in advance of travel start time
Routinely perform Forecasting / Time-series analysis to forecast the amount of unused space based on daily cumulative booking
Provision of ‘what-if’ analysis for effective resource utilization based on quantity & quality of goods to be transported
Pricing analytic for optimum pricing of free space was predicted
Reduction in % of unused cargo space
Increased space utilization effectiveness
Increased number of new customers
Increased businesses from existing customers by providing them more space to book
Improved customer retention rate
Increased profits by predicting the space utilization so that unused space is released for booking
Case Study 2: How to use Airline reward points to reduce the customer churn
Business Problem Statement
“Acquiring a new customer is extremely costly in comparison to retaining an existing one”. Companies are struggling to retain customers because of cut-throat competition among business rivals in acquiring new customers. In the light of the same, companies
have started the loyalty program of accruing points & redeeming the same for gifts. However, using this information in reducing the customer churn is still naïve for most of the companies. What to do with the data captured? Is segmenting
the customers are devising strategies for each segment the only way out? How can statistics & predictive modelling help in the space of customer analytics? These are a few questions to be addressed in keeping companies afloat in this
Lack of targeted marketing for passive clients who accrue reward points
Lack of proper recommendation for active customers who accrue reward points & search for flight fares on websites
High cost of acquiring new customers and high churn rate of existing customers
Lack of appropriate benefit options for redemption against accumulated reward points
Cluster / Segment based on characteristics of the customers, for targeting segments with specific marketing strategy
Built recommendation system based on user-item utility matrix & by accommodating reward points as another column in the utility matrix
Built prediction model based on various demographic details of customers alongside other domain specific variables to predict the customer churn
Built association rules for identifying the benefit options by establishing tuples of reward points & products
Increased customer satisfaction because of personalized marketing
Improved sales through effective recommendation of products/services based on previous purchase behavior of customers
Increased customer retention rate by predicting customer churn
Increased profits from repeat business by existing customers
Case Study 3: How to effectively change the connecting airports to make airlines profitable
Business Problem Statement
The percentage of occupancy defines the profitability of airlines. Most often than not, flights go empty if there are direct flights connecting places in different continents. Old fashioned strategies of marketing promotional offers to increase occupancy
rate will eat into the profits, leaving cost-cutting as the only option, thereby providing substandard service. This in-turn has ripple effect of losing customers. Another solution to increase the occupancy rate is to have connecting
flights based on forecasting demand over a time period. However, finding the right hop as connecting airport is not often a straightforward identification. Companies globe-over are still trying hard to solve this problem with very
less success rate.
High cost of direct flights from source to destination due to low occupancy rate on some routes
Increase in time taken to reach destination for a few connecting airports because of increased nautical miles
Poor availability of crew on long distance flights
Extreme Climatic conditions of a few geographical regions cause heavy wear & tear of flight components
Network analysis performed on flight & passenger traffic helped in identifying effective connecting routes thereby improving occupancy rates
Measures of centrality used to identify the most cost efficient route by connecting flights with the airports that are most feasible
Performed data simulation with what-if analysis for effective crew management
Analysis on weather changes of various regions helped in effective routing, thereby reducing flight maintenance cost
Increased occupancy rate
Reduction in maintenance cost
Effective crew utilization
Increase in customer satisfaction
Case Study 4: How to predict Fuel Consumption
Business Problem Statement
Almost all airlines are trying hard to stay afloat because of the huge operational costs. Low occupancy rate, high fuel cost, stringent regulatory authority norms, high airport usage cost is adding more fuel to fire. Retaining customers & converting them
to become brand loyal for continued future business is a task for the airline firms. Arriving on-time is one key measure which is driving the passenger’s decision to go with a specific airline.
Approximately 30% of airlines’ operational expense is attributed to fuel cost, leading to direct impact on bottom-line because of increase in crude oil prices
Travelling at a reduced speed is leading to saving fuel cost but at the same time reducing the on-time arrivals of flights, thereby leading to reduced customer satisfaction
Losing out loyal customers to the competitors is causing a huge dent into the profits, leading to bankruptcy of companies
Built a prediction model to predict on the fuel consumption across different stages of flight (taxing stage to landing stage)
Built optimization solution to determine the appropriate speed of flights at all stages of the flight journey to give the best mileage
Predicting the on-time arrival of flights so that appropriate action is devised in case of delay so that cascading delay is not experienced in all the routes of that flight journey
Reduced operational cost because of reduced fuel consumption
Increase in the on-time arrival percentage
Reduction in the customer churn rate
Increase in customer satisfaction & repeat business through loyal customers
Increased sales & also profits because of brand loyalty