Explore the phenomenon of dynamic pricing algorithms explained in plain words. After reading the guide, you will have a foundational understanding of the concept, know how it works, how to select them, and why it is crucial to use technological advances along the way. 

Imagine a scenario where you have a product. It is something new, something that market has never seen before. Immediately, you face a challenge. The key issue is what price to set. You know the manufacturing cost and the margin to get to make profits. However, you want to know what maximum pricing thresholds you can have to get the most of the product. It is the moment when dynamic pricing algorithms come into play. 

In short, dynamic pricing is setting a price based on the input and output of advanced algorithms. These often come with artificial intelligence and machine learning. Dynamic pricing help reach various KPIs. And more importantly, sell the product with a price tag that will bring you maximum profits while bringing satisfaction to consumers. Following is a simple guide to the dynamic pricing phenomenon, focusing on the context, practical applications, and beneficial technologies coming along with it. 

The roots of dynamic pricing algorithms

Dynamic pricing came into the light in the 1980s, the time when manual calculations determined pricing. What does it look like? It was simple yet time-consuming. Analysts got some available information on consumers, industry, and competitors. They managed spreadsheets and analyzed the info there. As a result of all the manual work, they later received an approximation of a good pricing strategy. 

One should add that dynamic pricing, in its traditional sense, correlates to several key pricing approaches. Each one depends on the situation a business faces and an objective suggested. First, there is penetration pricing. It is used when a company enters a new market and wants its product to get traction among consumers. Second, there is premium pricing. It is employed

when a firm wants its product to stand out, which is often done through the idea of value and getting something unique. 


While both approaches remain part of dynamic pricing, companies use more sophisticated instruments. The introduction of algorithms into dynamic pricing opened new horizons and made it possible for tools like dynamic pricing software to emerge on the market. 


How do algorithms in dynamic pricing work?

One can say that algorithms are a set of instructions directed at engaging in particular actions. In the context of dynamic pricing, algorithms work as mechanisms to collect, analyze, and synthesize data. They operate with some input variables and offer pricing recommendations as an output.

Often, dynamic pricing algorithms work with a range of determinants. For instance, they can collect and analyze data on historical sales to determine particular price patterns. In addition, algorithms can be fueled with information concerning shifting price points, price anchors, or price thresholds. They can also work with the demand function and price elasticity function. A myriad of variables dynamic pricing algorithm can operate with, both pricing and non-pricing in terms of the origin. 

Dynamic pricing algorithms create models that suggest optimal pricing strategies based on all the input data. More importantly, the most sophisticated algorithms can alter pricing strategies in real-time and adjust themselves according to the shifting market or company’s objectives.

Scenarios for when to use dynamic pricing algorithms


Dynamic pricing algorithms can be applied in a range of scenarios. However, it is crucial to know in what particular situations the phenomenon is most required. These cases illustrate the best conditions when dynamic pricing algorithms can do the most good for both business and its customers. 


  • The first scenario takes place when a company introduces a product and wants to boost customer satisfaction by meeting white consumers’ expectations. Dynamic pricing software can offer a pricing recommendation meeting such prerequisites in such a situation. 


  • The second scenario includes a situation when a firm has a broad portfolio of products. The thing is that prioritizing one product can lead to the cannibalization of others, for instance, in the case of the iPhone and iPod. Within the scope of the situation, dynamic pricing can help set a price that will bring profits and help avoid the cannibalization of other ones. 


  • The final scenario is when a company enters the market with a lack of information on the demand curve or function. Usually, setting prices in such conditions is extremely difficult. However, equipped with dynamic pricing algorithms, companies can receive sufficient information to determine the demand function and set a price respectively. 


If your company faces one of the aforementioned scenarios, you should definitely consider using dynamic pricing algorithms. 


Real-life examples of dynamic pricing algorithms

Going further, it is important to show how various firms managed to use dynamic pricing algorithms properly. The most notable example is linked to Wiggle Chain Reaction Cycle. It is a company serving many clients – cyclists, runners, swimmers, and adventurers. The value directly depends on the timely delivery of services for the firm. In such a case, the company pursued automation to boost customer satisfaction. Through the utilization of dynamic pricing algorithms, the business managed to reduce the repricing time by a staggering 50%. 


The case of Wiggle shows several key insights. First, repricing can be a challenging task if it is done manually. Besides, the longer it takes to reprice the products or services, the more profits the company losses, not saying about the decrease in customer satisfaction because the consumers need to wait until a product is repriced. Second, using automated means of repricing directly depends on pricing algorithms, the ones companies now have access to.

When advanced software meets dynamic pricing

Seeing the current development of dynamic pricing software, one can anticipate an even greater future for the technology. There is sufficient evidence to point out that dynamic pricing will further be coupled with tools like AI and ML and will enter new markets. Soon enough, people will use dynamic pricing algorithms everywhere, which will enrich the library of different cases and will make pricing even easier. It means that companies of the future will automatically learn from the mistakes of their predecessors without even knowing that. Isn’t it neat?