Problem:
In a highly competitive airline market, a major U.S. carrier was evaluating additional fees as a new revenue source. The key question was how this move might influence customer loyalty, brand reputation, and long-term market share.
Solution:
PwC built an agent-based consumer behavior model in AnyLogic. The model simulated how travelers make choices, how they influence each other, and how policy changes could affect the airline’s market position.
Results:
- Simulated how customers react to pricing and policy changes.
- Showed how word-of-mouth and past experience influence demand.
- Forecasted long-term impact on market share and revenue.
- Revealed that extra fees would hurt the airline more than competitors.
- Helped convince stakeholders to avoid the fee strategy and explore other revenue options.
The U.S. commercial airline industry is one of the most diverse, dynamic and perplexing in the world. It is fast-evolving, labor intensive, capital intensive, hyper-competitive and very vulnerable to the ebb and flow of business cycles as well as being among the most regulated of deregulated companies.
Airline management is required to make long-term decisions regarding fleet sizes, market fluctuations, and fuel prices while discovering ways to increase profit in an extremely competitive environment.
Problem
A major U.S. airline was facing a situation where opportunities to extend the existing strategy were limited, coupled with an increasing cost structure due to competition, commodity prices, and acquisition integration activities. The airline began to explore several options to generate new profits through ancillary products or changes to existing policies and was under intense pressure from board members, Wall Street and various analysts to do so.
Although the revenue generation through charging additional fees was apparent in the short term, prior to implementing a policy change, the Airline opted to evaluate the long term perceived impact on brand equity, market share and customer loyalty.
Solution
PwC, the world’s second largest professional services network, was employed by the Airline to model the predicted impact of the client’s ticket market share and company brand sentiment after introducing new products or policy changes.
PwC found traditional marketing mix models to be limited and unable to analyze the airline’s challenges. First, because they are aggregate, all customers are represented in a single regression equation which disregards the fact that not all consumers behave the same. Secondly, these types of models do not show interaction between consumers, when in contrary, customers share stories, attitudes, and memories, known as emergent behavior. Emergence is used to describe the behavior a group exhibits because individuals make different choices than what they would if they were not part of a group, as in the market more often behaving as a whole versus a collection of individuals. A third limitation when using typical market models is the lack of explicit representation of the process of consumer decision making. Analysts would be unable to see consumers gathering information, making informed decisions, and forming consideration sets as they do in the real world. Lastly, in traditional regression models, nonlinear relationships are not accounted for, data is limited to time series data, and there is a relatively short time horizon. In the end, this type of model is inappropriate for most consumer behavior analytics.
in the Model
These restrictions, and an increased likelihood of inaccurate results prompted PwC to explore other modeling options. They chose AnyLogic Multimethod Modeling and Simulation Software due to its flexibility, scalability and capability to handle sophisticated, computationally intensive techniques that model behavior of agents (e.g., consumers) in the market.
Utilizing AnyLogic software, PwC built the Experience Navigator, an agent-based consumer behavior model of multiple airline markets which included client competition, the process of consumers making choices and a relatively complete representation of the ecosystem in each market. The project used historical industry data, behavioral economics principles, and measurable experiences to create a behavioral model to help understand the impact on customers’ purchase behavior and the Airline’s social contract.
The information used during model building and calibration included:
- Time series of airline market shares (i.e. volume, prices)
- Cross section of individual travel behavior
- Market research from PwC Experience Radar (customer experience survey completed by PwC)
- Market research from client discrete choice models
- Process and theory from consumer choice literature
- Qualitative knowledge of the airline industry
PwC and the Airline are now able to understand how the interaction of different factors (i.e. fare utility, past experience, loyalty and word of mouth) may produce behavior, influence market share and modify markets overall.
Outcome
PwC’s Experience Navigator is used to:
- Analyze the Sales funnel at a particular segment and individual competitor level
- Visualize consumers changing beliefs over time
- Dive into different agents to understand their positive and negative experiences
- Forecast revenue impact of change in consumer experience
- Set price and marketing influence levels of the Airline
Ultimately, the model results showed that losses in market share and revenue over the long term would significantly offset any gains from charging additional fees. In addition, the model proved that if the company would set the fees the same as the competition, their loss of market share would be considerably greater than the competition, because the choice behavior for this particular Airline was due to positive brand equity and positive perception.
The model provided substantial evidence to convince stakeholders and Wall Street that the Airline should not implement the charging of additional fees, but should cultivate an alternate strategy to increase revenue.
Watch Mark Paich from PwC presenting this project at the AnyLogic Conference 2013:
