Why Do You Need Dynamic Pricing Optimization? Machine Learning in Novel Pricing Strategies

Have you ever heard of the Amazon effect? It’s when pricing changes so rapidly that consumers can’t keep up. That’s the online retailer’s price optimization strategy in a nutshell: pricing that constantly adjusts in order to stay ahead of the competition and maximize profits.

When done right, dynamic pricing can be a game-changer for businesses. It can help them increase profits while still providing good value to customers. And it’s not just Amazon that is benefitting from this pricing strategy - there are plenty of other examples out there.

A novel approach to dynamic pricing optimization utilizes machine learning to analyze data and make pricing decisions in real time. This allows businesses to be more agile with their prices and react quickly to changes in the market. In this blog post, we will discuss why dynamic pricing optimization is so important for modern businesses and how machine learning can be used in innovative pricing strategies.

Price Optimization in the Modern World

In a world where the consumer is always changing, and new technologies are emerging, businesses must continuously adapt to stay ahead of the competition. One of the most important ways for businesses to stay competitive is through pricing.

What is Price Optimization?

As the name suggests, price optimization is a strategy allowing sellers to find the optimal price range or price points that will maximize their chances of profit. With the help of data analytics, they can estimate the potential customer response to different prices. The simulation allows them to check the impact the prices have on sales without risking profit drops or affecting their reputation.

The algorithms can simulate it, using the existing data instead of forcing the seller to experiment in practice. Of course - the more information is available, the better accuracy, so trying out different strategies and gathering insights is more than recommended. However, with analytics, it is never just plain guessing - the estimations originate in existing data.

To understand how this mechanism works, we should start by defining the aspects that impact the probability of sales for a particular price. The target group is a significant indicator - its habits, expectations, weak points, and no-gos.

Then, there are the circumstantial factors - such as the state of the customer’s wallet, time (date, time of the day), market conditions, or even public sentiment. For instance, the inflation that escalated in the second half of 2022 in Europe impacts customers’ attitudes, forcing the companies to rethink their pricing strategies.

What Are the Strategies for Setting Optimal Prices?

There are different ways you may approach price optimization. Some involve machine learning techniques, while others rely on traditional analytics. The difference between them is the factors they take into account and the way they impact the optimization suggestions. They can be value, time, or competition-oriented. You can also try to combine different strategies if they do not exclude each other. Here are some strategies you may try out.

Multi-Objective Pricing Problem

When the decision-making process is supposed to involve two or more interdependent objectives, you can reach out for a multi-objective pricing problem. What does it mean in practice? In terms of pricing, you could try to make the highest acceptable margin while keeping a regular frequency and volume of sales. This mathematical method will allow you to make an optimized decision that takes this dependency into account.

Product Cost Cross-Subsidization

If, at some point, you lose profit because of selling a particular product or service below its market value, you can reach out for product cost cross-subsidization. The aim of this pricing strategy is to sell another product with a price above its market value to make up for this loss.

Dynamic Pricing Optimization

Dynamic pricing aims to find the highest price point a particular customer is ready to pay at the moment. The algorithm estimates the price that will leave you with maximum profit while maximizing the probability of purchase. Considering the dynamic changes that the market is undergoing recently, this strategy seems the most suitable for businesses to adopt, particularly if they use marketplaces.

Real-Time Pricing Optimization

A real-time pricing optimization strategy may be an element of dynamic pricing, although dynamic pricing does not always rely on the time-based opportunity - sometimes, the prices change according to the target group. Then, they are estimated based on historical data.

In the case of real-time pricing, the optimization suggestion relies on real-time data, as the name of the strategy suggests. The algorithm analyzes it and estimates the price that is the most suitable at the particular moment, considering the changing conditions. This way, companies can adjust their prices to the changing demand, but also the costs of fuel, shipping, and other variables that impact their margin.

Including External Market Trends

The prices cannot be estimated in limbo - it’s a chain of dependencies. The business owners depend on the prices their providers, contractors, or partners establish, while they depend on the market conditions that impact costs. So do the customer behaviors. Macroeconomic factors are crucial to the success of the pricing strategy. Thus, it is essential to monitor them constantly.

Promotions Optimization

Discounts and sales events are a great tool to stimulate sales and gain a competitive edge, particularly during periods of low demand. However, if not used thoughtfully, they can easily start generating losses instead of increasing profit margins. With the help of algorithms, the retailers - which are the main group using promotions optimization - can find prices that get the sales going while securing their profit. Extracting insights from past promotions data can help them make their estimations more accurate.

Machine Learning-Based Pricing Strategy

Not all pricing optimization strategies involve machine learning. Some rely on traditional programming, in which the rules are determined manually. In the case of machine learning, on the other hand, the model uses the historical data as an input, predicting In the case of pricing, such an approach brings impressive results due to the variety of variables that can impact the purchase probability and nuances that, if paired together, can have surprising results.

It’s hard to establish suitable rules for such a multi-conditioned outcome with the non-ML methods. Since the rules of the market game change constantly, the traditional algorithms would require constant reprogramming. On the other hand, machine learning models establish their own logic, getting more accurate with every other decision made. That makes businesses’ life easier as the whole process becomes fully automated.

Machine Learning Methods for Price Optimization

Machine learning techniques can fuel different pricing strategies, helping companies find optimized price points that drive sales and profit and, as a result, fulfill their goals. Sometimes, it may be worth pairing them or putting them all together to achieve maximum accuracy.

In order to explore buying behaviors, you can reach out for a data mining technique. It’s helpful, particularly in the strategies that focus on the target group instead of time opportunity. It allows the company to understand their customer better and adjust the pricing to their customers, maximizing the purchase probability. To apply these insights, they need to extract them from large datasets using one of the data mining techniques, like clustering, classification, decision tree, or neural network.

One type of available neural network-based method for price optimization is reinforcement learning. 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 pricing strategy leads to better outcomes in terms of, for example, profit margin, consumer loyalty, churn, and long-term revenue.

This type of model is useful when you need to optimize a complex process with many variables that are difficult to track (demand forecasting in volatile markets, multiple pricing factors, internal and external data, or tracking competitor prices).

That leads us to another technique that maximizes the accuracy of price estimations - customer segmentation. Throughout the data mining process for customer behaviors, the algorithm may identify patterns among users who share particular characteristics. Based on that, the companies can adjust their prices, maximizing their margins on every particular segment. The customers may be grouped based on the demographical criteria, the channels they use, etc.

Predictive analytics is another ML technique that is essential in machine learning price optimization, allowing companies to predict the market conditions which have a crucial impact on the prices they establish. At the same time, they can use it to predict how the established prices impact sales. As a tool, it prevents them from taking risky steps that could, in the worst case, compromise their financial safety. Using predictive analytics, they can validate their pricing decisions before introducing them in practice.

Using time-series data analysis, the companies can analyze the data points over a period of time, identifying trends in pricing, their impact, and development. For instance, they may observe how the customers reacted to dynamic pricing and whether the reactions were getting more positive over the course of time. Time-series analysis can help them identify risks and predicting future tendencies.

Regarding the types of models, no one silver bullet would outperform the others in every situation. The selection of a suitable model depends on each use case’s specifics and the available data. Thorough data analysis is required to understand which models best fit your problem and data. Usually, companies create a custom mixture of various machine learning models to create a complex, competitive pricing strategy to maximize revenue.

How Does Machine Learning Based Pricing Work?

There is no better way to demonstrate the mechanisms behind a particular strategy than through a practical example. So here we are, listing use cases that will help you understand the potential of machine learning-based pricing for various niches! Take a look and get inspired by real-life examples of dynamic pricing strategies.

Airline Tickets

The airline industry was among the first to adopt a dynamic pricing strategy and got customers accustomed to flexible prices. Today, it’s obvious to us that the price of a plane ticket depends on various factors. While time is the most significant one (usually the tickets get cheapest in the 60-20 days window), the airlines and the flight engines do not limit to this variable, taking demand into account and introducing customer segmentation.

Similar to the other niches of the tourism sector, the airlines go very dynamic with their prices, which often change for one user in the span of a few minutes. That strategy, although still a little controversial, keeps their sales going smoothly.

Dynamic Pricing for Hotels and Accommodation

The hospitality industry has also been using machine learning price optimization for quite some time already, particularly when it comes to larger hotel chains. In a way, the booking platforms have imposed this strategy on them. Dynamically changing prices have been a core of their politics since the beginning, and customers got accustomed to that quickly.

Hospitality pricing strategies have always strongly depended on the seasons and demand surges (when there are big events in the area, the prices may double or triple), but the booking platforms have strengthened that trend since they automatize the estimation. Using them, accommodation providers can rely entirely on machine learning algorithms, avoiding tons of manual work and the possibility of errors that are tightly related to it.

There are chains that decide to invest in their own machine-learning engines that are more sophisticated and take their target’s specifics into account. For instance, Hilton and Intercontinental have introduced their dynamic award pricing in their loyalty programs.

Dynamic Pricing in Shipping and Logistics

Logistics is another sector conquered by dynamic pricing. No wonder this strategy is gaining popularity among shippers and carriers - the market’s dynamics force them to evolve. Changing geopolitical situation, rising costs of fossil fuels and deepening fluctuations in demand- all these aspects are calling for a more flexible and comprising strategy that takes real-time conditions into account.

Described changes also contribute to the popularity of the digital freight platforms that connect shippers and carriers. They use dynamic pricing, automating the quoting process and maximizing the logistics companies’ margins. For the FTL (full truck load), companies like Sennder can be a good example of such a use case.

Convoy, on the other hand, is a digital freight marketplace that focuses on sustainability and carbon emission reduction, allowing companies to avoid empty miles and helping them sell their spot last minute.

In this blog post, we describe how exactly such platforms function. You can also delve into the benefits of dynamic pricing for carriers with another of our logistics-oriented articles. And here, we tackle the freight bidding process that dynamic pricing can streamline.

Dynamic Pricing Strategy for Ridesharing Solutions

We would not exaggerate in stating that the ridesharing applications contributed to the popularization of dynamic pricing among customers more than any other industry. Since the beginning, it was the core of their business model - the user would know how much they would pay before the start of the ride, and the prices would change constantly. The algorithm would estimate it for every particular search based on different variables.

The dynamic pricing models used by the ridesharing apps focus on the time opportunity. During peak hours which imply increased demand, the prices rise significantly. Uber is quite transparent in this regard.

eCommerce Retail Price Optimization

Dynamic pricing has revolutionized retail, mainly due to the contribution of the marketplaces, which have made it an essence of their business strategy. At the beginning of this article, we have mentioned Amazon and its “Amazon Effect.”

This phenomenon has a few different sides. First of all, it refers to an impressive surge in online shopping observed during the covid-19 pandemic. The trend didn’t end with the restriction, ensuring the retailers that they need to adapt their business models to the new reality. The focus on the customer experience that we could notice in traditional retail was an effect of that transformation.

However, dynamic pricing remains the main challenge to tackle. For the retail chains, implementing it in their stores in a form the marketplaces use would likely not be a favorable business move. However, they compensate for it by introducing price optimization in a different form.

Let’s take the example of Zara, which has been using price localization in its online and physical stores for a few years already. As a result, Zara’s prices in South Korea or the US would be almost double that in Spain, where its umbrella company, Inditex, is originally from. This article takes a deeper look at that.

Zara also uses AI to determine its entry-level prices and adjust them further depending on customer reactions and trends. Ralph Lauren and a few other brands from this sector follow this example, taking advantage of ML’s potential to improve their inventory management through price optimization. Read more about using ML in ecommerce here.

Benefits of Using Machine Learning for Price Optimization

The main goal of ML-fuelled price optimization is to maximize profit. Dynamic pricing and other machine learning-based strategies can do wonders in that sense. However, businesses across different sectors can achieve much more with it. Above all, machine learning helps them:

• fuel sales in the low-demand period - the algorithms can help them find the optimal price points to increase the sales frequency in low seasons while still maximizing the margin
• regulate supply and demand - some customers put time over price, and others have the opposite priorities. With price elasticity, everyone can get what they want. This way, the companies can handle the supply, as some customers will choose to wait.
• optimize their inventory - by manipulating the prices with the optimization algorithms, the retailers are capable of getting rid of the surplus assortment, becoming more cost-effective while fueling sales.
• avoid empty miles - in logistics, the carriers can become more sustainable and cost-efficient by selling out their spots last minute and consolidating shipments. Optimized pricing fuels this process.
• access new customer groups - with customer segmentation, the companies are capable of making the price more adjusted to the new target group preferences. That’s helpful when entering new markets, for example.

Tips for Implementing a Machine Learning-Based Pricing Optimization Solution

A lot depends on the type of your industry and the type of your business activity. The first essential step is to understand your customers deeply through data analysis to see which strategy could bring the best results. In order to make your strategy as efficient as possible, you should gather as much historical sales data as possible - it’s precious!

You also need to determine your goals - as you can see from the paragraph above, the spectrum of benefits you may aim for is really broad. Do you want to increase low-season sales, maximize revenue or optimize the supply-demand balance? Your strategy will depend on what you aim to achieve. You can mix the different price optimization techniques we described above.

That, however, requires in-depth pricing expertise and machine learning skills. If not implemented thoughtfully and in accordance with your client’s needs, price changes may be counterproductive. Thus, it is worth teaming up with a specialized team that can help you create a relevant strategy and implement it with an ML model that will fully automate the price optimization process from now on. Let us know if you have an idea in mind!

References

Getting the price right in logistics - McKinsey & Company

What really matters in B2B dynamic pricing - McKinsey & Company