To truly enjoy the benefits of automation, one needs to consider various aspects, including, say, the intricate balance of freedom with automation. Let’s start with dynamic pricing.

What are the different dynamic pricing strategies?

By offering an agile approach that adapts to changing market conditions, dynamic pricing strategies lie at the heart of this transformation. While demand-based pricing is classic, let’s also take a look at two other strategies.

• Demand Based: A classic in its own right, demand-based pricing is a strategy commonly seen in transportation services events and sports games. Prices increase during periods of high demand or peak hours, and conversely, decrease during times of lower demand and off-peak hours. Prices may also change based on the time of the day, day of the week, or season (time-based pricing).
• Competitor Based: In some cases, a company may deliberately choose to set lower prices for their offerings even if it means accepting lower profit margins or even operating at a loss for a period of time. But, why? This can be a strategic move to make it challenging or nearly impossible for a competitor to gain a foothold in your market. In some other cases, when a competitor sets significantly higher prices for similar products or services, a company that follows a competitor-based dynamic pricing strategy might act on the opportunity to increase their own prices to achieve a more attractive profit margin.
• Cost Based: The primary objective here is to ensure that the revenue generated per passenger covers or exceeds the cost of accommodating them. In other words, you take into account the marginal cost of every passenger to make sure that you aren’t paying to make them travel. By meticulously managing pricing this way, companies can optimise their profitability and operational efficiency, thereby aligning their pricing with the constantly changing dynamics of supply and demand.

What happens when automation is applied to dynamic pricing? Aspects to consider

There are a few important things to consider while optimising automation for revenue management. For instance, what level of freedom do you allow your automation to take? How do you define what is allowed to the optimiser? How do you define its personality?

The degree of automation you would like to implement

This is one of the first decisions to make while implementing dynamic pricing automation. It depends on the complexity of your pricing strategy as well as the confidence you have in the automated system’s capabilities. From simple rule-based pricing adjustments to more advanced machine learning algorithms, one can choose from varying levels of automation according to specific needs and preferences. For instance, in the realm of passenger ticketing, opting for a low degree of automation might involve the use of simple rule-based pricing adjustments like increasing fares during peak travel seasons, whereas, a higher degree of automation can enable real-time price adjustments based on factors such as demand, seat availability and competitor pricing.

Defining the scope of automated optimisation

Defining parameters allows the automated system to make decisions and optimisations in a controlled and responsive manner. It means setting clear boundaries and guidelines for how it should operate. Consider that you may want to grant the system the authority to optimise pricing, but within a defined price range, or apply discounts, but only when a certain criteria is met, for example excessive inventory levels or low demand for particular offerings. It’s therefore crucial to specify which aspects the system is authorised to enhance or optimise. These parameters encompass pricing strategies, discount application, bundling options or product placement.

Aligning pricing automation with brand identity and values

 Needless to say, the personality of a company ought to be reflected and maintained not only in their offerings but also in their day-to-day operations. It’s important to be mindful of how certain changes or improvements in processes may impact what the brand represents. A brand that offers premium services, for example, should exercise caution when contemplating aggressive discounting, as it could potentially dilute the brand’s perceived value. Consistency in brand identity shouldn’t be compromised while implementing transformative initiatives such as automation, which can be leveraged to further enhance the brand’s image.

Appropriating the right amount of freedom

Freedom in automation can be quite intricate to navigate. How much freedom is too much freedom? Striking a delicate balance between giving the automation enough freedom and retaining human oversight is challenging- give too little freedom, and the optimiser will be hampered; it will always call for human intervention to allow it to do what it has to. Give it too much freedom, and you might observe behaviors that are mathematically logical but may be opposed to your brand’s image. The right degree of freedom will enable the automated systems to adapt and respond to real-time market changes while ensuring that it aligns with the company’s objectives, brand identity and risk tolerance. Tighter control can lead to undesirable rigidity failing to capitalise on opportunities or to react to emerging market trends.

Capacity optimisation through demand forecasting

Consider a situation where a choice needs to be made between assigning a large aircraft or a smaller one to a specific destination. There is a complex process of “allowed exchange of planes” between routes if the demand allows it. This means that if there is high demand for a flight, it might make sense to use a larger plane in order to accommodate more passengers, while on routes with lower demand, a smaller plane might be cost-effective. This is just one instance illustrating how the practical application of forecasting, which involves predicting future demand, can be instrumental in guiding such complex decisions and arriving at solutions that not only optimise revenues but also the costs of operations.

Artificial Intelligence and Machine Learning - shaping the future of revenue management

Tech-enabled market resilience is the key to sustaining and thriving businesses in the contemporary landscape. It would be an understatement to say that Artificial Intelligence and Machine Learning are changing the game for good. According to McKinsey, AI-based pricing and promotion hold the potential to unlock a global market value between $259.1B to $500B. Findings by BCG also demonstrate that automation of pricing rules within revenue management systems using AI can lead to revenue increases of up to 5% in less than nine months.

While ML utilises historical data to predict future behaviors, AI enhances the strategy by refining the settings based on the valuable insights generated by ML. When predictive analytics and optimisation (ML) meet real-time data processing (AI), it is possible to harness the power of data-driven decision making in both planning and immediate response to dynamic situations. This way, businesses are equipped to adjust their pricing dynamically, based on demand, seasonality, competitor pricing, customer preferences and other internal and external factors.

While big changes naturally evoke apprehension, moving ahead requires proactive adaptation to change and the transformative tools that will amplify human efforts. Embracing these advancements is no longer an option that’s nice-to-have, but a necessity for businesses looking to stay competitive in the ever-volatile realm of demand and supply.