Revenue Management

Revenue management is the strategic and systematic process of optimizing revenue and profit for a business by manipulating pricing, inventory, and demand. It is an essential part of any successful business strategy, particularly in industries that have variable demand and capacity, such as airlines, hotels, and car rental companies.

Revenue Management is a data science-driven enterprise that brings together customer segmentation, predictive analytics & machine learning, design of experiments, and mathematical optimization in an orchestrated manner to maximize revenues attained with existing capacity. By understanding the principles of Revenue Management, data scientists can apply their skills to help their firms and clients see substantial increases in revenue.

Analytics for Revenue Management

Revenue Management (RM) is the science of predicting real-time customer demand at the micro-market level and optimizing the price and availability of products or services. RM was pioneered by American Airlines in the 1980’s in response to a threat posed by a new low-fare competitor, People Express. American’s RM system differentiated prices between leisure and business flyers. It also used optimization algorithms to determine how many seats to open to early-booking, low-fare flyers and how many to protect for later-booking, high-fare business flyers. This approach proved highly successful, restoring American’s profitability while ultimately leading to the closure of People Express. As a result, many other industries have likewise begun employing RM.

The goal of RM is to offer the right product or service to the right customer at the right time through the right channel at the right price. Traditionally, RM was developed for businesses operating under a few conditions:

• Fixed Capacity: Airlines have a fixed fleet size, and hotels have a fixed number of rooms, for example.
• Perishable Products or Services: Unsold seats on a flight or hotel rooms on a given date cannot be sold later, for example.
• Customers Reserve Resources Ahead of Time (e.g., booking a flight or hotel room)

Revenue Management Techniques

Today, RM techniques are being adapted beyond these assumptions for new industries such as E-commerce. In general, however, RM involves four interrelated activities:

Activities Process for Revenue Management Techniques

These four activities should be thought of as key ingredients rather than strict steps in a process. Some RM systems focus primarily on capacity allocation, fixing prices ahead of the sale period, while others update prices frequently in response to ever-changing market conditions. These activities need to be coordinated in a tailored way for each individual application to achieve the best return on investment.

1. Segment the Market

The first activity involves identifying different customer segments based on usage patterns or desires. For example, airlines segment customers into leisure flyers who book early and business flyers who book later, while car rental companies segment customers based on whether they want compact, mid-sized, or deluxe vehicles.

2. Determine the Price Response

After segmenting, we must determine the price response and choose the best prices accordingly. If we price a segment’s offering too low, we may have no trouble selling, but we will also leave revenue on the table. By contrast, if we price too high we risk losing revenue through customers balking at our offers. We must therefore determine the best price each segment is willing to pay and the impact price increases will have on demand. This is often done through pricing experiments and can also incorporate competitor and industry data. Experiments should be conducted in real market conditions and repeated periodically, as factors including inflation, wage growth, holidays, and new competitors can all impact the price response.

3. Forecast Demand

To begin applying RM, we need to specify a time horizon and then forecast demand for each segment in each usage slot within the horizon. As RM works at the micro-economic level, the time horizon is typically near-term and short in duration. A usage slot might be a flight leg in the airline industry, or a Friday-Sunday stay in the hospitality industry. So, for example, we forecast the demand for leisure and business flyers on each flight leg, or the demand for small and large hotel rooms for each bookable time interval.

4. Optimize Capacity Allocation

Once we estimate the demands for each segment, we need to determine how to allocate our capacity to maximize revenue over the horizon. This tells us, for example, how many seats to sell to leisure flyers and how many to hold for business flyers, or how many cars to use to meet compact demand and how many to use for deluxe demand in each time slot. This can be mind-bogglingly difficult to do by hand because usage slots can overlap and prevent the same resources from being used for both. Substitutions can also be done when advantageous (for example, offering a large hotel room to someone who requests a small room), further increasing the complexity. For this reason, capacity allocation is typically accomplished via integer programming.