Update pricing with machine learning-driven dynamic pricing. How to leverage it to earn customer trust.

In recent years, the adoption of "dynamic pricing" utilizing AI technology has been advancing in areas such as hotel room rates and sports event tickets. Dynamic pricing refers to a method of frequently adjusting the prices of goods and services based on supply and demand. While this approach has existed for some time, it has recently gained renewed attention due to the ability to achieve highly accurate pricing through AI-driven big data analysis.

This time, for those wondering, "Is our current pricing truly appropriate?" or "We want to know the optimal price that balances customer satisfaction and maximizes revenue," we spotlight the essence of dynamic pricing and key points to prioritize in pricing decisions.We go beyond the simplistic notion of "dynamic pricing = flexible pricing" and consider the perspective: "Can dynamic pricing not only maximize profits but also strengthen trust with customers?" We explore the "optimization of value and price" that dynamic pricing can deliver.

Dynamic Pricing: Bringing Fresh Air to Product and Service Pricing

Traditionally, the price of goods and services was determined by factors like cost, profit margin, and the balance of supply and demand. Popular items saw their prices rise in proportion to demand—a seemingly logical standard. Prices set this way were typically presented as a uniform value, like a fixed price or manufacturer's suggested retail price (MSRP), in conventional commerce.

However, a different mechanism called "dynamic pricing" is gaining traction. Dynamic pricing utilizes the latest AI technology to fluctuate prices based on demand levels. By raising prices when demand is high and lowering them when demand is low, it aims to maximize revenue.

You're probably familiar with this dynamic pricing in areas like hotel rates and airline tickets. For example, when booking a hotel, rates are higher during peak travel times like New Year's holidays or Golden Week. A major domestic hotel chain boldly adopted this approach, creating a price range of 10,000 to 30,000 yen between off-peak and peak seasons, achieving one of the highest profit margins in the industry.In Europe and the US, dynamic pricing has long been adopted across a wide range of areas, including tour prices, highway tolls, electricity rates, and restaurant meals. In Japan too, its introduction is beginning for tickets to sports, music, and theater events.

Among these, the professional sports industry, including baseball and soccer, is seeing particularly rapid adoption.It goes beyond simply changing prices between weekdays and weekends. By calculating the optimal price in real time based on various factors—such as past sales performance, match results, and weather changes—it prevents unsold tickets and improves team revenues. Recently, this trend is spreading to retail and theme park ticket pricing. It's fair to say momentum for dynamic pricing expansion is building in Japan right now.

Dynamic pricing, when implemented effectively, can benefit both businesses and customers.

So how exactly does dynamic pricing set prices? Let's explore the methodology by unraveling the algorithms.

Competitor-Following Type

This method involves collecting competitors' price data and setting prices to match their levels. It tends to be adopted in industries with intense price competition, such as electronics retailers setting prices close to the lowest available.

Data Analysis Type

This method collects and analyzes historical data, then presents prices based on rules derived from that analysis. For example, if current sales volume is lower than the historical average, it assumes competitors have set lower prices and automatically reduces the price.

Demand Forecasting Type

A method that analyzes demand trends based on historical data, predicts future demand using statistics or machine learning, and derives the price that maximizes revenue.

Reinforcement Learning Type

Reinforcement learning is a type of machine learning where the system itself guesses and selects optimal prices through trial and error. Its key feature is that accuracy improves the more it is used.

Among these, the third type, "Demand Forecasting Type," is currently the mainstream approach in marketing. For sports event tickets, AI analyzes various data points—including past sales performance, the match's popularity, weather conditions on the day, seat location, competitor event prices, and sales trends after ticket release—to dynamically adjust prices in real time.

While dynamic pricing enables this advanced, data-driven pricing, it's crucial to understand that it has drawbacks as well as benefits. Let's examine these from both the company's and the customer's perspectives.

Among the drawbacks mentioned above, the second one from the company's perspective—"Excessive price fluctuations may cause customers to hold back on purchases or develop resentment"—requires particularly careful consideration. If implemented poorly, it can lead to negative customer reactions like: "I checked social media and saw lots of empty seats, yet the ticket price was high," or "I bought at a high price, only for it to drop sharply right after, making me feel like I lost out."To avoid fostering customer distrust, it is essential to make a concerted effort to communicate, in a way that customers find convincing, that "dynamic pricing offers benefits for both customers and the company."

Optimizing product value and pricing for universally acceptable pricing

As mentioned earlier, when companies introduce dynamic pricing, the key point is whether customers can understand and empathize with the reasons for its implementation and the resulting price fluctuations. Let's now delve deeper into dynamic pricing from the perspective of "benefits customers can enjoy" and "value provided to customers."

While dynamic pricing has gained significant attention in recent years, similar concepts have existed around us long before technological advancements. Take, for example, the "market price" at sushi restaurants. The practice of "offering specially sourced ingredients at the optimal price for that day" is recognized as a traditional form of dynamic pricing in Japan.

Modern dynamic pricing using AI technology is an extension of this concept. In today's world where customer behavior and needs change rapidly, isn't it natural for pricing to become more flexible and change more instantly to meet diversifying customer demands?For instance, implementing dynamic pricing can offer thrifty shoppers opportunities to purchase at lower prices. Simultaneously, it allows us to provide premium experiences—like enjoying a theme park without crowds or spending a luxurious moment in a hotel room with guaranteed spectacular views—to those prioritizing timing or quality, even at higher prices.

The ultimate goal is to achieve pricing that ensures everyone feels fair and satisfied at all times. By establishing a system that sets precise, appropriate prices in real time, taking current conditions into account, we can capture customer loyalty, boost conversion rates, and strengthen trust.

The essence of machine learning-driven dynamic pricing lies in responding to increasingly diverse and complex customer needs through "optimizing value and price." This approach, leveraging real-time technology for marketing, seems applicable beyond pricing—much like personalization features in e-commerce. How can we master cutting-edge technology to enhance customer satisfaction? Examining technological evolution through this lens may reveal insights for business improvement.

Dynamic pricing uses machine learning algorithms to calculate optimal prices based on supply and demand balance. This approach can maximize revenue in industries where costs remain relatively stable despite fluctuating demand, such as airlines, hotels, sports events, concerts, and amusement facilities. Furthermore, if a company can offer customers unique experiences or commensurate value through its products or services, dynamic pricing may also contribute to enhancing customer engagement.