Evolving Pricing Strategies in the Internet Era

Let’s talk about how businesses set the price of a product or service in the pre-Internet era

Before the Internet era, prices were calculated with the same formula: Price = Cost + Markup. In other words, you must evaluate your costs (materials, labor, overhead) and determine your desired profit. Sound simple, but you must consider many other factors, like the demand for your product, who your customers are, and how much your competition is selling their products. Before the Internet, this information wasn’t available in real time or available just with a click here or there.

The Internet didn’t change the pricing formula. The formula is more-less the same; I meant how to calculate the price of a product. But what was changed is how to obtain and access the information you need to set up the price of a product. The massive data available in real-time about markets, demand, demographics, consumer behavior, competitors, etc., have allowed businesses to set prices in real-time or what the experts called the dynamic pricing model. “Digital platforms have enabled businesses to offer dynamic pricing, where prices fluctuate based on market demand, competition, and other factors.” according to Frank Rojas from Forbes Magazine.

The digital era has also allowed customers to compare prices on the spot. A client can shop for shoes and check prices online by taking pictures of the shoes or a bottle of wine. The price of products is adjusted in real-time based on supply and demand. For consumers, the Internet can help them to make more informed decisions.

Three common pricing strategies employed by businesses in the Internet era

Dynamic Pricing Strategy: 

Dynamic pricing involves real-time adjusting prices based on factors such as demand, supply, competition, and customer behavior. This strategy allows businesses to optimize revenue and remain competitive in dynamic market conditions.

Case: Uber’s Surge Pricing

Uber, the ride-hailing service, employs dynamic pricing known as “surge pricing” during periods of high demand. When demand exceeds the available supply of drivers, Uber automatically increases prices to incentivize more drivers to come online. This strategy ensures a balance between supply and demand while maximizing revenue.

Pros:

• Maximizes revenue during peak demand periods.
• Balances supply and demand effectively.
• Encourages drivers to join the platform during high-demand periods.

Cons:

• Customers may perceive surge pricing as unfair or exploitative.
• Potential backlash and negative customer sentiment during surge pricing instances.
• Requires sophisticated algorithms and data analysis capabilities to accurately determine pricing adjustments.

Freemium Pricing Strategy

The freemium pricing strategy involves offering a basic version of a product or service for free while charging for premium features or advanced functionalities. This strategy helps attract a large user base and encourages conversion to paid subscriptions.

Spotify’s Freemium Model

Spotify, the music streaming platform, offers free and premium subscription options. The free tier allows users to access a limited platform version with occasional ads. At the same time, the premium subscription offers an ad-free experience, offline listening, and additional features. Spotify’s freemium model has been instrumental in acquiring a massive user base and converting a significant portion of them to paying subscribers.

Pros:

• Attracts a large user base with a free offering, increasing brand exposure.
• Encourages user engagement and loyalty through the free tier.
• Provides a pathway to convert free users into paying subscribers.

Cons:

• Potential revenue limitations from a large user base primarily using the free tier.
• Balancing the features offered in the free and premium tiers can be challenging.
• Frequent updates and value additions to the premium tier are essential to retain paying subscribers.

Personalized Pricing Strategy:

Personalized pricing involves tailoring prices to individual customers based on demographics, purchase history, browsing behavior, or location. This strategy aims to optimize pricing decisions by offering customized offers and discounts.

Case: Amazon’s Personalized Pricing

The e-commerce giant Amazon leverages customer data and browsing behavior to offer personalized pricing. As a result, the prices displayed to customers may vary based on their purchase history, location, and browsing patterns. Amazon’s customized pricing approach allows them to provide targeted offers and discounts, enhancing the customer experience and encouraging repeat purchases.

Pros:

• Increases customer satisfaction through customized offers and discounts.
• Enhances customer loyalty and repeat purchases.
• Optimizes revenue by tailoring prices based on customer segments and behavior.

Cons:

• Privacy concerns and potential backlash regarding the collection and use of customer data.
• The risk of appearing discriminatory if pricing variations are not transparently communicated.
• Requires robust data analytics capabilities and ethical data handling practices.

The Internet offers a vast place to find any information and disinformation; marketers and businesses should use this Big Data available not just to set up a price of a product or service but also to gain a better understanding of their customers, and leading to more targeted and personalized marketing strategies that can help in the decision-making process.