Dynamic pricing is the practice of adjusting prices according to market and customer data. e-Commerce business leaders, such as eBay and Amazon, leverage different types of dynamic pricing to attract more customers and increase profitability.
In this article, we explore why dynamic pricing is important for e-commerce platforms, how it works, and case studies such as Amazon and E-Bikeshop.
Why is dynamic pricing important in e-commerce?
It’s been estimated that ~2.1B people worldwide are expected to buy products on e-commerce platforms, and 81% of consumers conduct online research before making a purchase to understand price differences and extra costs. Dynamic pricing helps e-commerce businesses:
- Attract more customers: Providing competitive prices and offers enables businesses to attract competitors’ clients as well as retain their own.
- Increase sales growth: More potential customers bring more sales opportunities. McKinsey claims that leveraging dynamic pricing models has helped their customers increase their sales up to 5%.
- Reflect product demand: Dynamic pricing helps customers understand the level of interest in a certain product. Customers that monitor product price changes on e-commerce platforms can understand if a product is in demand if they see its price rising, which may increase their interest in purchasing this product like their peers.
How does dynamic pricing work in e-commerce?
To implement a dynamic pricing strategy in an e-commerce platform, business owners need to:
- Collect market data: The first step in implementing a dynamic pricing strategy is to collect market data including competitors’ prices, customer reviews of similar products, and purchase trends during different time periods.
- Choose the right dynamic pricing module: There are different dynamic pricing modules for different product categories. Some modules are based on the type of data available, and customer perceptions and expectations.
- Choose the right type of dynamic pricing: There are also different types of dynamic pricing which differ based on season, competitors, stock availability, etc. Businesses can choose to apply one or more dynamic pricing types according to their needs.
- Apply dynamic pricing algorithms on collected data: A dynamic pricing algorithm analyzes the collected data and provides prices or pricing ranges for different products accordingly. Old dynamic pricing algorithms rely on classical statistic methods such as Bayesian strategies, whereas more recent algorithms leverage ML methods such as deep learning or reinforcement learning for more accurate results.
- Update data and optimize: Prices change often, therefore, price data needs to be collected frequently. Businesses can leverage web crawlers to automate the extraction of pricing data in order to obtain real-time data.
What are some examples of e-commerce platforms applying dynamic pricing?
Here are the top examples of e-commerce platform owners applying dynamic pricing:
Amazon is one of the largest global e-commerce platforms with ~300,000,000 active users. It’s been estimated that Amazon changes its prices more than 2.5 million times a day to set their prices lower than their competitors. In 2016, it was claimed that dynamic pricing helped Amazon increase profits by 25%.
E-Bikeshop, a UK bike supplier platform, leverages dynamic pricing to provide attractive prices in comparison to UK competitors such as ElectricRider and Electric Bike World.
For more on dynamic pricing
To explore dynamic pricing and pricing optimization in detail, feel free to read our in-depth articles:
- Dynamic pricing is key to enterprise profitability
- Dynamic pricing: What it is, Why it matters & Top Pricing Tools
To explore e-commerce technologies, feel free to check our:
And we can help you choose the right tool and vendor for your business:
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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