6 Types of Dynamic Pricing & How AI Can Improve Them in 2024
Dynamic pricing is the practice of optimizing product and service prices according to supply and demand, competition price, or subsidiary product prices. It gained popularity in the 1980s as the airline industry in the US started developing software to adjust flight prices according to departure time, destination, season, etc. which results in 3-10% in profits according to the used module. The rise in popularity led other industries to leverage dynamic pricing, however, different industries use different dynamic pricing types according to their requirements and customers.
In this article, we explore the different types of dynamic pricing, how to implement them, and industries benefiting from each type.
What are the different types of dynamic pricing?
According to industry requirements, businesses can leverage one or a combination of different dynamic pricing types, which are:
1. Segmented pricing
Segmented pricing, also known as price discrimination, is where businesses set different prices for the same product based on customer data (e.g. age, job, location, purchasing channel, willingness to pay) . For example:
- A plane ticket can be priced lower if the customer is a student because they are less likely to make money at the time.
- A product can be priced higher in wealthy geographical locations where people can afford to buy it.
- A product can be cheaper if you buy it online instead of in-store.
- A product that comes with a coupon can be cheaper because people who use coupons are typically sensitive to higher prices.
2. Time-based pricing
Time-based pricing is where businesses determine the price of a product/service according to a specific time of the month, year, or season. For instance:
- Flight or train tickets are more expensive during the holiday season because people are more likely to travel in holidays.
- Stationery can be priced higher in September at the time where schools and universities start.
- Retailers and ecommerce companies tend to price older products at lower prices to encourage customers to buy them before they expire or go out of style.
3. Peak pricing
Peak pricing relies on market data. It is similar to time-based data such that businesses determine the price according to high demand times, however, businesses also leverage data about competitors, such as inventory or availability. For example:
- Prices can be set higher if a business understands that their competitors are out of stock.
- During heatwaves, electricity suppliers can set higher prices on power consumption.
- Uber sometimes sets higher prices on rides when there are less drivers around.
4. Penetration pricing
Penetration pricing is typically used by businesses that are just entering the market or introducing a new product. In this pricing method, businesses set lower prices compared to competitors in order to attract customers and encourage them to try their product/service. Prices are destined to get higher as the product gets more popular among customers.
5. Competitive pricing
Competitive pricing is the practice of setting prices of business products/services according to market and competitor prices. In a 2019 survey, 70% of respondents stated that competitive pricing was the most important factor that influenced them to shop with a particular online retailer. Businesses can set the prices:
- Above the competitor prices to imply that their product/service is better than their competitors.
- Below competitor prices in hopes that more customers will purchase their product once they compare prices of different vendors.
- At competitor price to avoid loss. Businesses who set their prices at the same rate of the market tend to market their products in versatile ways to differentiate their product from competitor products.
6. Bulk pricing
Bulk pricing is the practice of offering a lower price or a discount to customers who wish to buy a product in bulk numbers. For instance:
- Retailers can offer a buy-2-get-1 package to encourage customers to buy more than one item of the same product.
- Travel agencies offer travel packages which include flight, hotel, and car rent at lower prices in comparison to buying each service alone. This method encourages travelers to buy a package instead of a single service.
How can AI improve dynamic pricing?
Businesses can leverage different AI/ML algorithms and analytics to forecast prices according to:
- Historical data: Historical data provides insights about products and services in demand in certain periods. This data can be leveraged to predict which products will be in demand soon and set prices accordingly. This is especially useful for time-based and peak pricing.
- Market trends: Data about market trends can be collected via web scrapers from news websites, customer reviews, competitor blogs, and annual reports and surveys. Marketing trend data can be input to recommendations system AI models which can generate suggestions about products to be set out to the market, and estimate prices accordingly. Recommendation systems can benefit businesses who rely on penetration and competitive pricing methods.
- Customer behavior: Customer behavior can be understood by collecting data about search queries, purchase habits, preferred items and categories of products/services, customers’ comments and reviews of certain products, etc. Businesses can also leverage natural language processing for sentiment analysis to understand the general market behavior towards certain products, services, or specific features. Analyzing this data enables businesses to target customers with coupons, promotions, and ads in a data-driven manner. Customer behavior can be useful for optimizing segmented pricing strategies.
If you believe that implementing dynamic pricing can increase your profitability, feel free to check our data-driven lists of solutions to help you:
- Web Crawlers
- Buyer Intent Data Tools
- Pricing Software
- Marketing Analytics Tool
- Predictive Analytics Software
And feel free to get in touch so we can guide you through the process:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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