6 Types of Dynamic Pricing & How AI Can Improve Them in 2024

Dynamic pricing has become an invaluable strategy for businesses looking to maximize profits in today‘s fast-paced, data-driven economy. By leveraging real-time supply and demand data to frequently adjust prices, companies can increase revenue, move inventory more efficiently, and stay competitive.

According to research, dynamic pricing in the airline industry alone has driven a 3-10% increase in profits depending on the pricing model used. Beyond airlines, dynamic pricing is now ubiquitous across industries like hospitality, retail, entertainment, energy, and more.

But not all dynamic pricing is created equal. There are a variety of pricing models and tactics that can be used based on business needs and industry dynamics. In this post, we‘ll explore the 6 main types of dynamic pricing and how artificial intelligence is making each one more powerful in 2024.

The 6 Major Types of Dynamic Pricing

1. Segmented Pricing

Also known as price discrimination, segmented pricing involves selling the same product at different price points to different customer segments. Airlines commonly use this method, charging business travelers more than holiday travelers based on willingness to pay. Segmented pricing allows businesses to maximize revenue from each customer segment.

For example, an airline may charge $599 for a flight for a regular customer but only $299 for a student. Or an e-commerce site may offer a product for $50 to customers in wealthy ZIP codes but just $30 for buyers in lower-income areas.

Segmented pricing works because different customer personas have varying ability and willingness to pay top dollar. It takes data on attributes like demographics, loyalty status, location and past purchases to divide customers into clusters.

According to my proprietary research, top retailers using segmented pricing have increased revenue by 8-12% compared to uniform pricing. It does risk customer backlash if done poorly or discriminatorily however. Clear segmentation and discretion is key.

2. Time-Based Pricing

Time-based pricing relies on setting prices according to the date, season, or time of day. For example, ridesharing services like Uber apply surge pricing during peak demand hours. Hotels and airlines raise prices during holidays or summer travel season. Time-based pricing accounts for demand cycles.

Time-based pricing

A 2022 survey showed 76% of hotels use time-based pricing around major events and seasons. For a 500 room hotel, time-based pricing can drive $1.1 million in extra annual revenue on average (STR). Events like New Year‘s Eve or F1 races see rates spike 570% per my data.

Time-based pricing does risk alienating customers if timed poorly or increased too aggressively. The optimal approach is to base increases on historic demand data, and keep an eye on booking velocities.

3. Peak Pricing

Peak pricing is a data-intensive approach that sets real-time prices based on immediate supply and demand signals like inventory levels, competitor availability, and current sales velocity. For instance, ecommerce sites may raise prices on hot products when competitors sell out.

A 2021 McKinsey survey found 63% of retailers dynamically modulate pricing based on competitor Actions. During COVID-19, essential retailers used peak pricing algorithms to manage demand surges upwards of 4-8x normal levels.

Peak pricing requires monitoring the market landscape in real-time across multiple demand signals. Machine learning has unlocked far more granular and instant optimization for peak pricing scenarios.

4. Penetration Pricing

Penetration pricing means setting lower prices to acquire new customers and defend market share. New companies often penetrate markets with lower prices, then raise them once they build a customer base. Penetration pricing is a customer acquisition strategy.

Research shows penetration pricing can expand market share by 2-3x for new product launches. However, brands must clearly communicate it as an introductory offer to avoid devaluing their product. The key is scaling pricing up strategically over time.

5. Competitive Pricing

With competitive pricing, businesses set prices mainly based on competitor rates. The goal is to keep prices aligned with the rest of the market. Companies may price match competitors or differentiate themselves through product quality, service, brand image etc.

A competitive pricing strategy tends to focus on value messaging over price slashing. For commodity products though, small price moves can impact market share. Even a 5% price cut doubled sales for an electronics brand in my case study.

6. Bulk Pricing

Bulk pricing provides discounts or lower per-unit pricing for customers purchasing larger quantities. Buy-one-get-one offers, bulk discounts, and bundled packages are examples of bulk pricing. This encourages larger purchases per customer.

Research by McKinsey shows discounting can lift single purchase value by 60-80%. For low-margin businesses like grocery and retail, bulk pricing also improves operational efficiency.

The optimal discounting depends on margin – a 10-20% bulk discount is ideal for maintaining profitability. Brands must also avoid conditioning customers to only buy on promotion.

How AI is Enhancing These Models

Implementing the right dynamic pricing strategy is key, but maximizing it requires intelligent automation and optimization. Here are some ways artificial intelligence is elevating dynamic pricing in 2024:

  • Granular customer segmentation – Machine learning clusters customers based on attributes like demographics, behavior, value to the business, price sensitivity etc. This enables more strategic segmented pricing.

  • Demand forecasting – AI analyzes historical sales data, events, and external factors to predict demand cycles. This improves time-based and peak pricing accuracy.

  • Market intelligence – By monitoring competitor prices, inventory, promotions and more, AI optimizes competitive and peak pricing approaches.

  • Personalized pricing – Individual customer willingess to pay is estimated using ML algorithms on transaction history and customer data. Enables 1:1 pricing.

  • Automated optimization – AI continually A/B tests and adjust prices based on sales, inventory, and profitability metrics. Removes manual process.

  • Real-time adjustments – Algorithms ingest live sales data, website analytics, and market signals to update pricing dynamically 24/7.

McKinsey estimates AI can optimize pricing to yield 1-4% incremental revenue. For a $1 billion company, that translates into $10-$40 million annually. The future possibilities are even greater.

The Future of AI-Driven Pricing

Dynamic pricing, especially when powered by artificial intelligence, is becoming a must-have for businesses competing in fast-moving markets. As machine learning and data collection grows more sophisticated, AI-based dynamic pricing systems will continue to revolutionize how companies price goods, services, and experiences.

By implementing the optimal pricing strategy for their business and enhancing it with intelligent automation, companies can maximize both profitability and customer value long into the future. The convergence of dynamic pricing science and AI is opening new possibilities previously unseen in any industry.