Aviation Analytics

What is Aviation Analytics & its challenges?

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:

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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

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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.

Business Challenges

  • 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

Business Solutions

  • 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

Business Impact

  • 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 disruptive world.

Business Challenges

  • 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

Business Solutions

  • 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

Business Impact

  • 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.

Business Challenges

  • 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

Business Solutions

  • 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

Business Impact

  • Increased occupancy rate
  • Increased profits
  • 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.

Business Challenges

  • 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

Business Solutions

  • 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

Business Impact

  • 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

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