AI-Powered Dynamic Pricing in Ecommerce for Manufacturers

European manufacturing companies face a dual battle when optimizing pricing strategies. On one hand, they must contend with rising energy prices and supply chain volatility. On the other hand, satisfy customers' demand for low and personalized prices. 

This puts businesses at great risk of turning low profits or even outright losses.  Adopting dynamic pricing strategies driven by AI algorithms is certainly one of the solutions.

In this article, we cover dynamic pricing in ecommerce, the beneficial role of integrating AI algorithms for dynamic price adjustments, and 11 use cases of AI-driven dynamic pricing in manufacturing.

b2b ecommerce efficiency

Dynamic pricing in ecommerce 

You're already familiar with dynamic pricing, even if you don't realise it.

It’s been widely adopted in the B2C sector by companies like Uber and Amazon, which adjust prices up to 2.5 million times daily.

This smart strategy helps them always offer the right price at the right time, based on current demand, how much stock they have, what customers are doing, and what their competitors are charging.

About 25-30% of European retailers have adopted this strategy, benefiting from ecommerce's digital leap. Now, this trend is expanding into B2B, where the stakes are higher due to the sheer volume and volatility of data involved in the process.

Dynamic pricing requires manufacturers to analyse extensive datasets, including market demand trends, competitor pricing, cost data, supply chain information, and more. However, with AI and advanced data analytics tools, this complexity becomes manageable. 

The role of AI in revolutionizing dynamic pricing 

The appeal of integrating artificial intelligence into dynamic pricing lies in its optimization algorithms and real-time data analysis. These elements enable businesses to adjust prices automatically without sacrificing competitiveness or profitability. It is a power many European businesses are tapping into, with 48% of companies in the region using AI to support dynamic pricing in ecommerce in 2023. 

Let's take a closer look at the features and advantages of AI-powered dynamic pricing: 

Real-time market analysis 

AI systems can process large volumes of data on market trends, demand fluctuations, and competitor prices in real-time. This means that all dynamic price adjustments are based on factual information that accurately depicts the state of the market at any given time.  

Predictive analytics 

If there's one thing AI is excellent at, it's identifying patterns and unravelling trends. AI algorithms can predict future market changes, enabling manufacturers to anticipate demand spikes or dips and adjust inventory levels and product prices accordingly. 

Customization at scale 

One advantage of dynamic pricing is that it facilitates the delivery of personalization. AI can segment customers based on purchase history, behaviours, and preferences. Subsequently, AI can offer personalized prices to these segments, enhancing customer satisfaction while maximizing revenue. 

Integration with enterprise software 

Dynamic pricing is most efficient when implemented with a holistic business view. By integrating customer relationship management (CRM), enterprise resource planning (ERP), and production information management (PIM) software, AI can base its pricing decisions on comprehensive data. 

Automation and efficiency 

AI-powered dynamic pricing delivers on all its promises thanks to the automated nature of its promises. It reduces the need for human intervention, reduces the chances of errors, and saves resources for other strategic tasks. 

AI transforms dynamic pricing from a tedious process into one that's driven on autopilot. By incorporating AI algorithms into its pricing strategies, manufacturers can fully unlock the benefits of dynamic pricing. 

Practical applications of AI-driven dynamic pricing for manufacturers 

Now that we have a picture of how AI can supercharge dynamic pricing let us take a look at some of the ways manufacturers can put this pricing strategy into practice: 

Demand-based pricing 

Manufacturers can gather insights on market demand from web searches and sales data and feed it to the AI system for analysis. This will reveal demand patterns for various products and allow corresponding price adjustments. 

The benefit of this approach is that it allows manufacturers to maximize revenue during high-demand periods and drive up sales when demand is low. 

Competitive pricing 

AI can monitor competitors' prices across various channels and uncover insights into their strategies. This can inform price adjustments to ensure the business remains competitive. 

This dynamic pricing strategy ensures that the manufacturer can increase market share without sacrificing profit margins. 

Cost-recovery pricing 

Manufacturers often face fluctuations in the cost of raw materials and supply chain expenses. AI can monitor factors that affect production costs and automatically adjust prices as needed. 

With this method of AI-powered dynamic pricing in place, manufacturers can respond to cost fluctuations and protect profit margins. 

Personalized pricing 

By leveraging datasets like purchase history, browsing behaviour, and purchasing power, AI can segment customers into dynamic segments and offer them different prices. 

This approach helps manufacturers reward their most loyal customers or entice prospective clients to make their first purchase. Personalized dynamic pricing can be the secret to keeping different classes of buyers satisfied and loyal. 

Product life cycle pricing 

A product's lifecycle involves the phases of introduction, growth, maturity, and decline. AI can adjust the prices of products based on their phase in the cycle to ensure the maximum revenue derivable at each point is attained. 

From offering low prices to aid market penetration in the introduction phase, taking advantage of high demand in the growth phase, or clearing the inventory during its decline, AI can make product life cycle pricing successful. 

Inventory management 

AI's optimization algorithms can help manufacturers identify potential overstock situations and products approaching the end of their shelf life. It will go one step further to set the right prices to attract customers to buy these products. 

This strategy reduces storage costs, minimizes losses on overstocking or expiring products, and improves cash flow. 

Innovative dynamic pricing strategies enabled by AI 

The applications of AI-driven dynamic pricing in ecommerce are truly versatile. Beyond the traditional use cases above, manufacturers can use some innovative applications to meet their sales goals. Here are some out-of-the-box use cases: 

Real-Time auctions for manufacturing excess

Excess inventory can tie up capital and increase storage and holding costs, hence the temptation to sell them off at low prices. AI-driven dynamic pricing can power real-time auctions for these products by setting prices that encourage multiple bids. 

Manufacturers can clear precious warehouse space by selling to the highest bidder while making good financial returns. 

Environmental impact pricing

Sustainability and green manufacturing are key in the industry right now. By analyzing lifecycle data and supply chain sustainability metrics, AI can price products with lower environmental impact more attractively. 

This will encourage customers to choose sustainable products, which will help manufacturers meet their sustainability goals and qualify for green tax benefits and subsidies. 

Energy consumption-based pricing

Fluctuating energy costs represent potential risks to profitability for manufacturers. However, AI can help combat this by adjusting product prices to reflect their true cost of production. 

Prices could be lower when renewable energy sources are available and higher during peak energy demand times. 

Personalized pricing for custom orders

Manufacturers that offer customizable products can use AI's dynamic pricing capabilities to tailor prices to individual orders. 

Prices can vary based on the complexity of the customization, current workload, and material costs. Customers can instantly find their personalized quotes that reflect the true cost of their custom orders at checkout. 

Group buying incentives 

Group Buying or bulk purchases can significantly increase sales volume. Manufacturers can use dynamic pricing with AI to offer incentives where the price per unit decreases as more customers commit to purchasing a product. 

Manufacturers will find this an excellent way to push new products, clear excess inventory, and build community buzz around products. 

Dynamic pricing is here to stay  

Dynamic pricing in ecommerce is here to stay. Now is an excellent time for manufacturers to join other industries in implementing this highly beneficial tactic. Artificial intelligence is the perfect ally for businesses planning to try out this pricing strategy because it offers the agility to respond to market changes, customer behaviour, and cost fluctuations in real time. 

Adopting AI-powered dynamic pricing will help manufacturers optimize revenue, satisfy customers, and stay competitive all year round. Applying the traditional and out-of-the-box use cases presented above should immediately set manufacturers on the right path to unlocking the inherent benefits. 

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