What is Dynamic Pricing and How Does It Work?

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Barbara Rybicka

Updated Oct 17, 2023 • 15 min read
shopping online

Imagine an online marketplace where price tags change in real time, influenced by multiple factors from the time of day to the actions of competitors.

At a time when technology and economics intertwine with unprecedented complexity, a question emerges: how do businesses navigate the fluctuating tides of supply and demand to set the right price at the right moment? The answer lies in an algorithm-driven concept known as Dynamic Pricing – a phenomenon shaping the future of online shopping and autonomous stores experiences.

In this article, we uncover the multifaceted domain of dynamic pricing, its application across industries, its various types, and its pros, and cons. This guide is particularly relevant for decision makers at start-ups, and enterprises looking to deploy dynamic pricing on their digital platforms.

What is Dynamic Pricing?

Dynamic pricing is a real time pricing strategy that allows businesses to change the prices of their products or services based on current market demands. Rooted in economics, this model responds to fluctuations in supply and demand, competition, and other factors programmed into the pricing strategy.

For instance, during a flash sale, prices may be lowered to attract buyers, and during a product shortage, they may be increased. Platforms like Amazon deploy dynamic pricing to maximize profit margins by offering competitive prices without compromising profitability. These constant adjustments aim to increase sales and respond to competitor actions with agility.

Dynamic pricing employs algorithms and artificial intelligence (AI) to analyze competitor prices, inventory levels, consumer behavior, and market trends, among many other datasets. This is a prime example of automation in retail, where sophisticated systems automatically crunch vast amounts of data to make decisions. Prices are then adjusted frequently, sometimes even minute by minute, to reflect the prevailing market conditions.

The contrasting approach to dynamic pricing is static pricing, a strategy where prices are primarily fixed but may change over some period of time based on specific triggers. Unlike dynamic pricing, static pricing changes are infrequent and not directly responsive to short-term external factors like daily market fluctuations or minor changes in demand.

In reality, even static pricing strategies might involve occasional adjustments. However, the key distinction is that these adjustments are typically not as nimble or responsive to immediate external influences as dynamic pricing strategies would be.

Around one in five (21%) of ecommerce businesses in North America and Europe that took part in a 2021 survey said that they were already using dynamic pricing. Almost two-thirds (63%) of respondents said they are considering dynamic pricing, doing it, or will introduce it in the future. The interest in dynamic pricing is already substantial, indicating that it may be an important strategy for staying competitive in certain areas.

The value of dynamic pricing in specific sectors of retail, such as fashion, electronics, and online marketplaces, along with other industries like travel and hospitality, is significant. It offers the ability to adapt quickly to market changes and optimize pricing to benefit both businesses and consumers. When implemented effectively, dynamic pricing can help businesses align with market realities and achieve tangible results.

Dynamic pricing across industries

To illustrate further how dynamic pricing works, let’s take a look at how companies across different sectors are applying this pricing strategy.

  • Airlines and hospitality: Airlines, car rental companies, hotels, and accommodation marketplaces (e.g., Airbnb) often use dynamic pricing to adjust prices based on factors such as seasonality, demand fluctuations, day of the week, and booking lead time. Prices may increase during peak travel periods or special events and decrease during off-peak periods to attract more customers. By aligning prices with demand, airlines and hospitality providers can maximize revenue during busy periods and incentivize bookings during the off-season.

To illustrate this further, a pricing system vendor for over 400 airlines says that in their industry, dynamic pricing increases conversion rates by 50% and revenue by 10%.

  • Ride-sharing and transportation: Ride-hailing platforms like Uber and Lyft employ dynamic pricing to balance the supply and demand of rides in real-time. Through price optimization based on current demand, traffic conditions, and the number of available drivers, these transport apps ensure that riders can always find a ride while compensating drivers fairly during peak times.
  • Energy and utilities: Some utility companies implement dynamic pricing to align consumer costs with the fluctuating wholesale price of electricity. By offering variable pricing that reflects real-time supply and demand, utility providers can encourage consumers to use energy during off-peak hours, contributing to a more balanced and efficient energy grid.
  • Entertainment, events, and attractions: Ticket prices for concerts, sporting events, theme parks, and theaters may also employ dynamic pricing. Prices can change based on the popularity of the event, seat location, and how quickly tickets are selling. This model allows event organizers to maximize revenue and offer pricing that reflects the actual market value of the ticket.
  • Financial services: Some financial institutions use dynamic pricing for services like loans, mortgages, and insurance premiums. Pricing can be personalized based on an individual's risk profile, creditworthiness, and other relevant factors. Dynamic pricing enables banks, insurers, and other financial services firms to offer tailored financing options to clients while managing risk and profitability.
  • Retail and ecommerce: Online marketplaces, such as Amazon or eBay, often utilize dynamic pricing for their products to adjust prices on-the-fly. By utilizing algorithms and machine learning, these platforms take into account factors like inventory levels, competitor pricing, demand fluctuations, customer behavior, and seasonal trends.

Types of dynamic pricing

Dynamic pricing is not a one-size-fits-all model. It takes various forms to cater to different business needs and market dynamics. By understanding these types, businesses can choose the approach that aligns best with their goals and customer base. Here are some prominent types of dynamic pricing:

  • Time-based pricing: This method adjusts prices based on specific time frames. Food delivery apps might offer early-bird specials during off-peak hours to boost sales during otherwise quiet periods.
  • Peak pricing: Unlike time-based pricing, peak pricing specifically targets periods of high demand, increasing prices to manage scarcity. Ride-sharing companies like Uber often use peak pricing during rush hours or big events, ensuring that drivers are available to meet increased demand.
  • Segmented pricing: By analyzing customer behavior and demographics, businesses can offer different prices to different segments. An app-based service might provide special pricing for students or seniors, allowing tailored offerings that reflect different customer needs and willingness to pay. The same digital service may also have different prices for different countries or locations.
  • Penetration pricing: This approach sets an initial low price to attract customers and then gradually increases it. Streaming services may offer a low introductory price for new subscribers and then increase the monthly fee once the trial period ends.
  • Competitor-based pricing: Here, businesses monitor competitors' prices and adjust theirs accordingly. An online electronics retailer might employ algorithms to match or slightly undercut the prices of major competitors, ensuring competitiveness in a crowded marketplace.
  • Behavior-based pricing: Utilizing data on individual customer behavior, this approach tailors pricing to specific customer actions and profiles. An ecommerce site might offer a personalized discount to a shopper who has visited the site multiple times but not made a purchase, aiming to convert interest into a sale.
  • Bundle pricing: Offering a set of products or services together at a reduced price, bundle pricing incentivizes larger purchases. Ecommerce platforms may offer bundles of related products, such as a smartphone packaged with headphones and a protective case, at a reduced price.

Benefits of dynamic pricing

All these examples paint a clear picture of the many benefits of having a dynamic pricing strategy. To understand this further, let’s take a closer look at the key arguments why companies should consider this approach.

  • Real time market adaptation: Dynamic pricing allows companies to adjust prices instantaneously, rapidly responding to supply and demand fluctuations. Ride-sharing services like Uber increase prices during high demand, balancing supply and demand. This strategy enhances service availability and ensures customer needs are met.
  • Meeting key business and financial metrics: Utilizing intelligent algorithms for product pricing enables businesses to achieve key metrics. This could be gross merchandising value, profitability, or user growth, among many others. For instance, during a certain period, a platform may choose to undercut its profit margins to gain market share.
  • Personalized pricing experience: Offering a tailored approach to individual customers fosters loyalty and engagement. Ecommerce platforms that provide discounts to loyal customers or first-time buyers create a personalized shopping experience, thereby boosting repeat business.
  • Competitive advantage: By adapting to the market swiftly, businesses can stay ahead of competitors who stick to static pricing. For example, an online retailer that dynamically prices products can outperform competitors by offering compelling deals when consumers are most active.

Potential drawbacks of dynamic pricing

While dynamic pricing is a compelling strategy, this pricing model also carries inherent risks and challenges, as follows:

  • Customer perception and trust: Dynamic pricing can risk eroding trust if customers feel they’re paying an unfair price. Constant fluctuations in prices may lead to confusion or resentment, as in the case of an online shopper discovering a higher price than previously seen.
  • Complexity of implementation: Dynamic pricing requires sophisticated technology, data analysis, and continuous monitoring. Small businesses may find the initial setup and ongoing management to be resource-intensive and challenging without the proper expertise.
  • Regulatory and ethical concerns: Certain industries must comply with regulations regarding dynamic pricing, and ethical considerations are paramount. Dynamic pricing in electricity, fuel, or medical supplies may attract legal scrutiny and societal pushback.
  • Potential market alienation: Over-reliance on dynamic pricing might lead to the alienation of segments of the market. For example, a constant surge in pricing during peak hours might deter budget-conscious consumers from using ride-sharing services, reducing the overall customer base.

How to implement dynamic pricing

The implementation of dynamic pricing requires careful planning, a nuanced understanding of the market, and a robust technological infrastructure. Here is a brief, step-by-step guide to adopting dynamic pricing:

  1. Understand your market and customer behavior: Analyze customer behaviors, competitors' pricing strategies, and other market information. For example, a fashion retailer may study seasonal trends to identify when customers are more likely to shop for specific items, determining when to adjust prices.
  2. Choose the appropriate dynamic pricing strategy: Select a dynamic pricing type that aligns with your business objectives and customer needs. An online store selling electronics may adopt competitor-based pricing to rival major online retailers.
  3. Invest in technology and tools: The implementation of dynamic pricing demands a robust technological backbone capable of real-time price alterations. This is driven by a complex matrix of data analytics, machine learning, and market intelligence.
  4. Comply with legal and ethical standards: Ensure that the dynamic pricing strategy adheres to legal regulations and ethical considerations. For instance, a pharmacy employing dynamic pricing must ensure that they comply with regulations regarding the pricing of essential medications.
  5. Determine pricing boundaries: Set minimum and maximum price limits to ensure profitability and maintain customer trust. A luxury brand may set a floor price to protect brand value while using dynamic pricing to offer exclusive discounts to loyal customers.
  6. Integrate with inventory management: Align dynamic pricing with inventory levels to manage stock efficiently. An online bookstore might lower prices for overstocked items while increasing prices for limited stock, ensuring inventory turns over effectively.
  7. Monitor and analyze performance: Implement monitoring tools to track the impact of dynamic pricing on sales, customer satisfaction, and overall performance. A sporting goods store might analyze how dynamic pricing on seasonal items affects overall revenue and adjust its strategy accordingly.
  8. Adapt and evolve: Regularly review the performance, customer feedback, and market changes to continually refine the dynamic pricing strategy. The dynamic pricing strategy isn’t a ‘set and forget’ mechanism but a finely tuned instrument that requires regular calibration.

Technology behind dynamic pricing

The technology behind dynamic pricing represents a fascinating intersection of economics, data science, and business strategy. Dynamic pricing software is a specialized tool that automates the process of adjusting prices in response to the programmed factors and the large amounts of data it ingests in real-time (e.g., demand, competitor pricing, seasonality, and inventory levels). In essence, it serves as a prime example of AI in retail.

Effective dynamic pricing software integrates with existing CRM, ERP, and inventory management systems to provide a holistic view of business operations. Users can set specific rules and parameters to guide the pricing decisions, such as a hotel setting rules to automatically reduce room prices if occupancy falls below a certain threshold. Many dynamic pricing tools also offer intuitive dashboards and reporting features to enable non-technical users to manage and understand pricing strategies.

At the heart of dynamic pricing software lies a series of algorithms that analyze, predict, and decide on optimal pricing strategies. These algorithms are built on various mathematical and statistical models. Advanced dynamic pricing systems often leverage machine learning to predict future price changes based on historical data, like an airline using machine learning to forecast demand for certain routes during holiday seasons.

Competitor-based algorithms consider competitors' pricing and market positioning to set prices strategically, enabling a retailer to keep prices in line with major competitors without sacrificing profit margins. Demand-based models adjust prices based on fluctuations in supply and demand, as exemplified by a ride-sharing service that may employ this algorithm to increase prices during rush hours and reduce them during off-peak times.

Some algorithms work on optimizing multiple objectives, such as maximizing profit while maintaining customer satisfaction and meeting compliance requirements. This ensures that the pricing strategy aligns business goals with customer expectations and regulatory adherence.


Dynamic pricing stands as a testament to the incredible fusion of technology and economics. It’s a tool that goes beyond profit maximization and also targets balancing market demand and improving the customer experience.

As more digital businesses embrace dynamic pricing, the challenge for today's decision-makers isn't merely about technology but understanding its nuanced application to the entirety of the business. The integration of data-informed pricing models into digital platforms demands a careful balance between automation and human oversight.

Adding another layer of complexity, emerging technologies like autonomous stores with scan-and-go systems offer retailers the opportunity to design a more seamless in-store experience that aligns with dynamic pricing strategies.

How will your organization navigate this sophisticated landscape to not only optimize financial results but also uphold brand integrity and customer trust? The answer may well become your competitive advantage in the modern marketplace.

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Barbara Rybicka

Commercial Director | Retail at Netguru
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