Dynamic Pricing in the Age of Machine Learning: How to Apply Dynamic Pricing Strategy Within Your Company

Dynamic Pricing in the Age of Machine Learning: How to Apply Dynamic Pricing Strategy Within Your Company

Krzysztof Suwada - July 28, 2022

In the age of machine learning, more and more businesses are turning to dynamic pricing models to increase profits. Dynamic pricing is the process of adjusting prices for goods or services in response to changes in supply and demand. This can be done manually, but it’s becoming increasingly common to use machine learning algorithms to develop dynamic pricing models. There are a number of different applications for dynamic pricing, and they can be used in a variety of industries.

In this article, we will discuss what dynamic pricing is, how it works, and how you can apply dynamic pricing methods within your own company.

What Is Dynamic Pricing and How Does It Work?

Traditional pricing strategies are losing their impact in the dynamically changing market landscape, vastly dominated by online sales. Dynamic pricing keeps up with the pace of these changes, using a time-based opportunity to maximize profit. It implies switching from fixed prices to variable prices, determined by different factors.

The aim of this strategy is to calculate the maximum value the particular customer is ready to pay at that exact moment. No wonder every year there are more companies switching to it. The potential profitability of dynamic pricing is undeniable – however, a lot depends on the particular case and the way the strategy is implemented.

There are various types of dynamic pricing, or rather, dynamic pricing can be merged with various strategies (value-based pricing, surge pricing, price discrimination, cost plus pricing, etc.) in order to boost profit.

The factors that determine the final price estimation may differ depending on the industry and the particular company’s specifics. However, the standard set usually involves:

  • seasons
  • demand
  • availability
  • market trends
  • market prices

Based on that, the algorithm estimates a price for each particular case. Would you like to see how it works in practice? Every case is different, but this blog post illustrating a successful dynamic pricing strategy for the FTL transportation market clarifies how such models work in general.

Static Pricing vs. Dynamic Pricing

Static Pricing (Single Price Point) vs. Dynamic Pricing (Multiple Pricing Points)

Machine learning adds a competitive edge to dynamic pricing since, with time, the algorithm gets better at finding a balance between profit and the likelihood of purchase. By analyzing customer behavior and finding price-relevant patterns in previous transactions, artificial intelligence can identify the optimal price range or price point. Usually, the dynamic pricing engine consists of two modules – the first one predicting the impact of the price and the second optimizing price adjustments to make it the most beneficial.

dynamic pricing model how it works

Is Dynamic Pricing Fair?

Dynamic pricing is as fair as the traditional pricing strategies. The product’s or service’s value is always relative – and any price, whether fixed or variable, is contractual. In the end, dynamic pricing offers customers a choice, which is not an option with traditional strategies. With the fixed price, they are entirely dependent on the seller’s decisions. Dynamic pricing enables them to wait for a moment when the product or service becomes more affordable.

Thus, in many cases, the presence of that choice increases inclusivity, enabling customers to afford something they would likely not be able to buy in the fixed price scenario. However, the clients do not always think about it that way. Dynamic pricing is still a relatively new strategy, and its understanding does not always correspond to its actual concepts.

As a result, the customers may feel deceived by the changing prices. It’s actually the most common aspect that companies are advised to consider when switching to this model. That, however, is solvable with education. With time, dynamic pricing will likely become more common across industries, and the lack of trust observed among some groups may fade.

Dynamic Pricing Across Industries - Examples of Different Dynamic Pricing Strategies

Some industries have embraced dynamic pricing much earlier and to a much greater extent than others because of their specifics. Those who use it extensively are usually strongly impacted by seasonality. Many of them rely on non-essential goods that are often purchased in advance. That makes the customers feel the benefits of dynamic pricing much more.

Let’s take a look at the use cases in order to understand a bit better the impact dynamic pricing has on particular industries.

Dynamic Pricing in Airline Ticket Prices

Anyone who ever bought airplane tickets has experienced the dynamic pricing in practice. It can be a frustrating experience at times since the prices often jump high from session to session. However, it also creates opportunities the most determined air ticket hunters can take advantage of once they figure out the mechanism behind the dynamic price estimation.

Air transportation has undergone a full-blown metamorphosis in the last two decades. Before, it used to be regularized, having the prices dictated by the national carriers. Today, in terms of pricing, it’s one of the most diverse and profit-driven industries.

The popularization of affordable tourism has driven that change, stratifying the target groups that would be quite uniform before. Airlines stopped using fixed prices since it simply wouldn’t be profitable for them in the changing market landscape. The stratification of target groups had an influence on the way the dynamic pricing is approached by the airlines.

In their case, the first crucial step is to determine whether they’re dealing with a business or leisure customer. That’s because each of these groups is flexible to a different extent in terms of prices. The factors that influence their decisions also differ. For instance, the business customer is rather focused on the destination, time of the flight, the length of the trip, and stopovers. For the leisure one, these aspects might (but do not have to) be secondary, yielding to price or comfort.

The algorithm analyzes variables like the anticipation of the booking, average and current demand for the route, or remaining capacity, combining them with the customer data extracted from the past bookings in order to determine the maximum price the customer is ready to pay. Artificial intelligence can be incorporated here also for the purpose of prediction. Based on the historical data combined with real-time data, the machine learning model can predict the probability of selling the remaining seats and adjust the price in regard to this parameter.

Dynamic Pricing in the Hotel Industry

Hospitality was one of the first industries to really embrace machine-learning-powered dynamic pricing. The hotel prices have always been strongly influenced by seasonality, but the booking platforms have brought it to a new level. In most cases, they keep dynamic pricing optional. The businesses that offer their rooms via, for instance, Booking or Expedia, can choose to turn on this pricing model to observe its impact on their profit.

Dynamic pricing solves a few problems the hotel managers have to face daily. First – it saves them an immense quantity of monotonous work. Changing the price of each room for each day manually takes a lot of time. It’s also error-prone, and that may have financial implications. The dynamic pricing software does all the job automatically, making decisions based on multi-variable insights instead of relying on manual research.

The prices in the hotel industry are dictated not only by the seasons but also by short trends and particular events. If the hotel is located in a bigger city, it may be hard to monitor all the occasions that impact demand and, thus, the price. Backed with a dynamic pricing algorithm, the hotels can react quickly and automatically.

The machine learning algorithm estimates the price considering all the factors (events, seasons, the occupancy of the hotels in the particular area for the chosen period, the advance of the booking, the time of the booking, etc.) combined with the customer-related information (booked transportation, previous hotel bookings). Just like airlines, hotels may also reach out for target group identification (business vs. leisure), as it helps them identify the acceptable price range for each particular case.

Dynamic Pricing Strategy in Ride-Sharing Services

Ride-sharing is the best example of the regulating impact dynamic pricing can have on the market. Contrary to other industries mentioned in this article, such services are practically only available in this pricing model. That’s because the boom of such services hasn’t started long ago. They got popularized with one pioneer app - Uber - that made dynamic pricing the core of its strategy, setting a path for the next startups with a similar profile.

Both in Uber and other ride-sharing apps such as Bolt or LYFT, the price is estimated for every order you make, based on different variables such as:

  • distance of the route
  • traffic
  • demand for drivers
  • time of the order
  • neighborhood you’re ordering from and going to

Since the beginning, Uber’s main advantage, as opposed to taxi services, was obviously transparency - the users could finally have an idea of the ride’s final price before even getting into the car. But interestingly, the pricing dynamics were also embraced by the users with much more confidence than in other industries. Maybe that’s because most customers use the app frequently (sometimes daily). This way, the benefits of fluctuating prices gain visibility, and the users themselves learn how to maximize their benefits from this model.

The dynamic pricing works in favor of ridesharing platforms because they can manage the current market demand while maximizing profit. The user can either pay more for a valuable ride or wait until the demand reduces and pay less. As a natural consequence, the demand self-regulates what would not happen in the traditional pricing model.

Dynamic Pricing Models in Logistics and Shipping

The last few years were harsh for the logistics and shipping companies. First, the pandemic has put the supply chains to the test and forced the carriers to adjust to movement-limiting regulations. Then, the geopolitical situation made cost predictability even lower. As a result, the industry embraced dynamic pricing with even more enthusiasm.

In such a dynamically changing market landscape, dynamic pricing serves as a shield, helping logistics and shipping companies maximize profit regardless of the unpredictability of the prices. With a dynamic strategy, they can adapt to fluctuating spot rates, rising oil costs, or sudden rises and drops in demand. Depending on the specific conditions of their niche, companies may, for instance, combine the surge pricing approach with a competitive response or focus on other variables than demand and competition to maximize their profit. For the big picture of particular niches within the logistics and shipping sector, we recommend checking our dynamic pricing-themed article.

Dynamic Pricing Strategies in the eCommerce Industry

Compared to other industries, the dynamic pricing in e-commerce is still crawling. Maybe because the retailers are the most vulnerable to customer backlash. While seasonality-driven industries meet with more understanding when implementing such pricing strategies, customers may feel that it is unjustified in the case of e-commerce. However, as dominating marketplaces like Amazon use dynamic pricing extensively, the attitude towards it is changing.

In the case of models built specifically for e-commerce, the variables taken into account during estimation might be a little different. While seasonal factors also matter here (around sales peaks such as Christmas, the demand increases, expanding the acceptable price range), they are not as crucial as the competitor prices and user behaviors in relation to the price (clicks, rates, search data). The estimations should also take the inventory state into account. AI models can also help retailers find the most profitable prices for new niche (long-tail) products for which the competition data is hardly available.

Dynamic Pricing for Ads Placement and Campaigns

Online advertising tools and platforms made the users accustomed to dynamic pricing. Instead of paying for a specific result in advance, they get billed based on achieved results, usually at the end of the month. All the big platforms (Facebook Ads, Google Ads, etc.) rely on this method, and dynamic pricing is its backbone. The campaign issuer can, of course, set up a budget, but the results they will be able to achieve within it will change from month to month.

What are the variables that influence the price? First, it’s what you’re paying for. The cost of the ad will change depending on the mode you pick (PPC, PPV, etc.) and the type of conversion you’re expecting. For instance, if you pick app download as a goal, you will likely pay more than when you want your target to subscribe to the newsletter. Other aspects that have an impact on the price include, for example:

  • the size and specifics of your target group
  • the competitors you have
  • the current demand for advertised products or services
  • the time and place the ad is displayed at

Common Machine Learning Models for Building Dynamic Pricing Algorithms

Dynamic pricing using Machine Learning is the contemporary answer to a successful pricing strategy since it may dynamically adjust and re-optimize based on variables like inventory levels, traffic rates, and product-based sentiment analysis.

Unlike rule-based solutions, machine learning and deep neural networks allow you to understand relationships between the different data fields related to the product and forecast long-term outcomes of your product sales.

Decision Tree Model

Decision tree-based methods are very interpretable and can be used when data is scarce, or you need to explain the model’s decisions to business stakeholders. The algorithms that utilize dynamic pricing in decision trees can help companies figure out which variables have the most influence on prices and which of these price ranges predict the highest sales.

Bayesian Models

Bayesian methods are based on probability theory and can be used when you need to account for uncertainty in the data. They are usually heavily rooted in the historical data available.

In dynamic pricing, Bayesian optimization can help find the best price by balancing different objectives, such as revenue, profit, and consumer demand. The algorithm does this by iteratively testing different prices and updating its beliefs about which price is optimal.

Reinforcement Learning Models

Reinforcement learning is a type of unsupervised learning where the machine learns to make decisions by trial and error. In the case of dynamic pricing, the reinforcement learning algorithm simulates different price changes and learns which ones lead to better outcomes in terms of, for example, profit margin, consumer loyalty, churn, and long-term revenue.

Reinforcement learning model for dynamic pricing algorithms

Reinforcement learning model for dynamic pricing algorithms

This type of model is useful when you need to optimize a complex process with many variables that are difficult to track.

Thompson Sampling Method in Dynamic Pricing Models

Discrete price-demand model based on Thompson sampling for dynamic pricing implementation

When it comes to the types of models, no one silver bullet would outperform the others in every situation. The selection of a suitable model depends on the specifics of each use case and the data available. Thorough data analysis is required to understand which models are the best fit for your problem and data.

The Benefits of Using a Dynamic Pricing Strategy

Even though dynamic pricing may provoke some resistance, its profit-maximizing potential outweighs this minor risk. Considering the pace with which the market is changing, there is no more cost-effective way to price your service or products. Dynamic pricing partially protects the companies against the implications of inflation and helps the exporters or those who sell internationally adjust their prices to the current realities of the particular markets.

Another thing this pricing method can do is keep companies through low seasons. Considering that the demand is becoming increasingly unpredictable, the long-established patterns are becoming irrelevant. Dynamic pricing can overcome that, helping companies boost revenue in downtimes, even if their product or service is seasonally-dependent.

Regardless of the economic landscape, dynamic pricing allows businesses to maximize their profit by finding the highest price point the customer is ready to pay. Even though it uses the time-based opportunity, it’s still a more democratic approach since the clients get to choose whether they will pay more or less. In the case of some industries (like ride-sharing), dynamic pricing also equals an opportunity for the employees to earn more.

How to Implement Dynamic Pricing Strategies in Your Business

The construction of the system, its modules, variables that will be taken into account, and the training dataset all depend on the specifics of your business and industry. A lot depends on whether you use platforms and marketplaces or sell your products/services only in your own shop/application. There is no universal formula, but there are good practices that can maximize your chances of a smooth landing in the dynamic pricing world! We’ll point out some of these below - meanwhile, we encourage you to connect with our AI experts so that we can advise you on your particular case!

Tips for Avoiding Common Pitfalls When Implementing a Dynamic Pricing Strategy

Dynamic pricing can help you make the most out of your business, but before implementing it, it’s worth rethinking these aspects:

#1 Are your customers actually willing to convert to the dynamic pricing model?

You are the one who knows your target group the best. If you feel that variable prices are a no-go for a majority of them, don’t revolutionize your pricing model from day to night. Try the small steps - implement dynamic pricing for a while and inform your customers about it. You can also survey them to get their feedback and show them that their opinion matters to you.

#2 What does the price of your product really depend on?

The wrong choice of variables for the model to base its estimation on can do your business more harm than good. Of course, you can switch back to fixed prices anytime, but such a move could undermine the customers’ trust. Thus, before implementing dynamic pricing, it’s worth analyzing what exactly should determine the prices in your case and consulting your selection with a company experienced in implementing this solution for your industry.

#3 What is the dominating pricing strategy in your industry?

If the majority of your competitors are already using this strategy, it’s about time to join them. Customers, even if hesitant at first, quickly get used to the flexibility of dynamic pricing and may give up on your offer just because you stick to fixed prices. However, if it’s the opposite and you would have to be the pioneer, make sure your transition to dynamic pricing is backed with a marketing plan that will strengthen your trendsetting power.

Pricing Strategies of the Future

Considering all the benefits it provides to businesses, dynamic pricing will likely entirely replace fixed prices in the nearest future. The dynamics of this strategy itself may change, though. We’re already observing a shift in approach to dynamic pricing, with more emphasis being put on understanding customers and the impact the price has on their decisions. That will help at pushing profit to its limit.

With time, more and more companies may reach out to neuroscience to research the correlation between price and the probability of purchase. At the same time, we expect that the companies will become more transparent about the way they estimate their prices due to increasing customer pressure.

About the author

Krzysztof Suwada

Krzysztof Suwada

Data Science Expert

Linkedin profile

Krzysztof is a data scientist who applies machine learning and mathematical methods to solve business problems. He is particularly interested in developing end-to-end solutions for companies in various industries using deep learning and NLP techniques.
Mathematician, software developer, and trainer. Krzysztof's expertise in machine learning earned him a Google Developer Expert title. A fan of Albert's Einstein quote: "If you can't explain it simply, you don't understand it well enough."

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