Dynamic Pricing – Definition, Types, Advantages, Examples and Usage


Dynamic pricing also referred to as surge pricing, demand pricing, or time-based pricing, is a pricing method. In it, firms analyze present market demands to fix flexible prices for services and goods. It is a kind of price discrimination. Its goal is to find the best price at any time.

Firms can set prices with the help of algorithms. They consider aspects like customer willingness to purchase an item at a particular time, demand and supply, rival prices, and various external influences on the market.

Dynamic pricing is quite popular in many industries. These include public transport, electricity, retail, entertainment, tourism, and hospitality.

Types of Dynamic Pricing strategies

Penetration pricing

This is aimed at recent products that have lower prices than the market price. It gets used when firms target a huge market segment to let consumers get used to their products. Prices go up slowly along with rising demand.

Peak pricing

Over time, a firm might learn that sales of a product or service are seasonal or rely on rush hours. Usually, this reduces options for customers as no rivals are selling similar products or services.

Pricing dependant on Time

This is a dynamic pricing approach. It is connected to how old a product is or when it was launched in the market. Once new products are launched in the market, older products’ prices can be reduced to clear out excess stock.

Pricing that is Segmented

This approach involves setting varying prices for similar products, although they have similar distribution and production costs. It is aimed at consumers in various locations, as is clear from the term ‘segmented’.

It is employed to generate income in places where customers are not much affected by price changes.

Dynamic Pricing Advantages & Disadvantages


  • Let the price show the demand
  • Increases demand
  • Optimizes profits
  • Increases sales


  • Consumers do not favor it
  • It loses sales
  • Bucks the system
  • Price conflicts

Dynamic Pricing & Artificial Intelligence

Dynamic Pricing & Artificial Intelligence

“Dynamic pricing uses data to understand and act upon any number of changing market conditions, maximizing the opportunity for revenue,” says Alex Shartsis, founder and CEO of Perfect Price.

Alex says that AI and ML-based dynamic pricing strategies are usually the best used in situations that deal with a lot of daily transactions. The demand keeps changing, and customers are fine with a dynamic price. AI and ML get improved solution functionality through extensive data analysis.

Pricing software that has pricing models using machine learning have these capabilities and features:

1. Using cluster analysis for customer segmentation at a granular level.

2. Multiple variables for the many items are taken into account.  Competitor and attribute-based pricing are some of the influencing factors that must be assessed for a price recommendation: External factors like industry trends, seasonality, weather, location;

3. KPI-based pricing.

Businesses can set up a product to align pricing recommendations with performance metrics of interest, for instance, margin, turnover or profit maximization, inventory optimizations, etc.

4. Assessing market information in real-time minus the complicated rules.  It’s possible to automatically optimize prices to changing demand and market conditions in real-time without specifying complex pricing rules.

5. Measuring price elasticity.  These solutions give users the capability to define price elasticity to predict whether customers will accept a new price before making a pricing decision.

Dynamic Pricing & Hospitality

  • Hotels and other hospitality businesses employ dynamic pricing to set room prices. Packages also rely on supply and demand needs at any given time.
  • Prices go up in peak season or at the time of special events.
  • When it is off-season, hotels might only ask for what is needed to keep the business going. Profits and investments are earned when it is the high season.


  • 2016 onwards, Starwood Hotels are a part of Marriott. They employ data analytics to align room cost with present demand.
  • In 2014, the company launched its Revenue Optimizing System (ROS). It invested upwards of $50 million in it.
  • ROS uses both external and internal information. It uses real-time analytics to predict demand and recommends ideal prices.

Dynamic Pricing & Airlines

Dynamic Pricing & Airlines

Airlines set prices by profiling their consumers. They categorize fliers into a couple of groups: leisure or business. Pricing for each segment differs. Leisure passengers usually book months in advance, so airlines tend to start the prices for these seats relatively high. It then adjusts the prices according to market response.

For typical business routes, airlines will start with low prices to fill a minimum capacity, then increase prices steeply as corporate passengers tend to book last minute. According to him, technology has permitted some airlines to design a ‘basic economy fare’. It has limited amenities to match with low-cost, minimal service carriers. Reduced prices let full-service carriers show up on the initial page of Google flights and other search engines.


Airline Tariff Publishing Company (ATPCO) gathers and gives out information about fares for airlines worldwide. They have invented a novel strategy for dynamic pricing. It says it will increase the airlines’ capacity to give passengers customized prices.

Dynamic pricing is a method that provides personalized prices to individual passengers based on their flight history, etc. A system for airline booking can identify information like an IP address or browser history to calculate exactly how much a consumer is willing to pay.

Dynamic Pricing Example


Dynamic Pricing Example

Uber is aware that a customer can pay the fare on certain occasions. Uber tracks consumer’s phone batteries and can decide whether they can pay. They use dynamic pricing to decide the price.

AI takes into account a lot of data gathered from various sources. This algorithm uses factors like the number of people requesting rides, time of day, events, etc. They can use personal consumer information and historical data to decide what the fare should be. If the online action reflects that a consumer prefers certain places and times, they may ask for more at those times.

“Uber relies extensively on machine learning (ML) to establish a robust and reliable dynamic pricing system,” said Ivan Didur, CTO & Co-Founder at DataRoot Labs.

“With the help of ML, Uber generates a future-aware forecast of multiple conditions of the market and uses a system that is very sensitive to external factors: these factors ultimately include the global news events, weather, historical data, holidays, time, traffic, etc.”

Data lets Uber predict future prices based on LSTM (long short-term memory) networks. It uses deep learning models to make predictions about the future market stance and unaccounted events, even before they take place.

Dynamic Pricing & Controversy


  • The firm’s surge pricing has been very controversial.
  • In 2013, New York was facing a storm. Uber prices increased 8 folds from the regular prices.
  • This angered citizens and also the celebrities. Salman Rushdie and many others criticized this method online.
  • Because of this incident, post-2015, Uber started setting caps that limited surge pricing in times of crisis.