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Retail and consumer goods

AI Dynamic Pricing: Optimise your revenue in real time

In today’s fast-moving and hyper-competitive retail environment, traditional pricing strategies are increasingly inadequate. AI Dynamic Pricing is a complex, end-to-end solution powered by the Databricks Data Intelligence Platform that enables businesses to respond to changes in demand and competition in real time. By leveraging advanced machine learning, this solution identifies the "sweet spot" price that maximises total margin, revenue, or capacity fulfillment.

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What is AI Dynamic Pricing?

AI Dynamic Pricing is a process where product or service prices are adjusted in real time based on various market factors and demand. Unlike traditional models that often rely on rigid assumptions or ignore competitor actions, this solution uses Machine Learning (ML) to estimate price elasticity: how quantity demanded changes relative to price shifts—to create highly accurate demand curves for every individual product.

The engine operates through three core pillars:

  • Data Pre-processing, which integrates internal sales and CRM data with external signals like competitor pricing and weather;
  • Demand Estimation, where ML models predict customer behavior;
  • and Price Optimisation, which applies specific business constraints to directly return optimal prices without the need for manual post-processing.

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Large retailers &
e-commerce

For whom: Designed for large-scale operations that need tailored solutions to manage extensive product portfolios across multiple markets.

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Transport & hospitality

 For whom: Optimised for airlines (e.g., Kiwi.com), railways, and events where real-time market shifts and product uniqueness are critical. 

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Accelerator-based delivery

 How: Built on Spark and Python using reusable accelerators, ensuring the solution is delivered faster and more cost-effectively than building from scratch. 

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Modular "White Box" product

 How: A 100% transparent approach that empowers your data teams and integrates into your security-compliant platform (Azure/AWS/GCP) with no vendor lock-in. 

How it works

The deployment follows a structured timeline to ensure maximum ROI and seamless integration. It begins with a Proof of Value (POV) (1 month) to validate the efficiency of the models, followed by the Implementation phase (3 months) where a production-ready pilot is developed. After a rigorous A/B Test (1 month) to measure performance against previous methods, the solution is rolled out across the organisation, building a robust, long-term pricing infrastructure.

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Capture non-linear relationships

Accurately models complex relationships between price and quantity that traditional methods miss.

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Navigate data scarcity

Estimates demand even for new products that lack historical sales data.

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Integrate numerous factors

Factors in seasonality, product cannibalisation, and real-time competitor price shifts.

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Direct, actionable output

Directly returns optimal prices without requiring additional manual post-processing.

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Significantly higher accuracy

Uses sophisticated ML algorithms that consistently outperform classical econometric models.

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Real-time adaptability

Reacts instantly to market volatility, ensuring you never miss a revenue opportunity.

Proven value in the real world 

 Our solution has delivered transformative results for industry leaders. Dr.Max, Europe's second-largest pharmacy chain:

36%

 Increase in revenue in their POC country  

12% 

 proven long-term revenue growth after a full market rollout 

 Increased volume of sales 

Kiwi.com utilised the real-time application to optimise pricing for unique flight products and baggage options

Client testimonial

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"Thanks to the DataSentics team’s technical expertise and good collaboration, we've successfully implemented an AI-powered dynamic pricing solution that optimises prices in real time. The rollout of the dynamic pricing solution across multiple markets has driven significant revenue growth and improved our market responsiveness."

Iva Gergelova

Iva Gergelova

Group Pricing Specialist, Dr.Max

A smarter and strategic path to maximum margin 

Every organisation faces constraints: whether it's low price volatility, intense competition, or complex product interdependencies. AI Dynamic Pricing enables you to innovate within those limits, transforming pricing from a reactive operational task into a proactive strategic advantage without sacrificing speed, security, or control.

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100% European Infrastructure

From hardware to virtualization platform, ensuring sovereignty, compliance, and full auditability through open-source engineering.

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Reliable, Cost-Efficient Performance

Robust and future-proof virtualization solution delivering 60–80% lower TCO compared to traditional options.

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Tailored, Scalable Architecture

Flexible configurations with BullSequana SH or BullSequana SA to meet diverse workload needs.

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High Density and Low Latency

Lightweight hypervisor and optimized design for maximum VM-to-core ratio.

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End-to-End Security

Full supply chain transparency, advanced hardware protection, and trusted execution technologies.

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Energy Efficiency

Reduced power consumption per workload with eco-conscious infrastructure design and smart VM scheduling.

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Easy Expansion and Automation

Modular, composable hardware with API-driven orchestration for seamless scalability.

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Easy Deployment

An already validated reference architecture and a SPOC for hardware-software integration and support.

Technical specifications
Resources
Recommended products and solutions
  • BullSequana SH
  • BullSequana SA
Recommended services
  • Lifecycle services
  • Managed services
  • Consulting
  • Training

FAQ

Traditional methods often rely on cost-plus or simple competitor analysis and disregard actual customer demand. AI Dynamic Pricing uses Machine Learning to capture non-linear relationships between price and quantity, accounts for seasonality and cannibalisation, and provides significantly higher accuracy than classical econometric models.

The engine is designed to optimise specific target metrics based on business needs, including total revenue, margin, customer base expansion, or capacity fulfillment.

The most critical inputs are historical sales and pricing data. Additionally, the system integrates customer loyalty data, product attributes (descriptions and reviews), marketing campaign history, and external signals such as competitor prices, weather, and public holidays.

The AI models are specifically designed to navigate data scarcity. By leveraging attributes from similar products and other integrated features, the system can estimate demand for new items where traditional statistical methods would typically fail.

Yes. Integrating competitor prices and market dynamics is a core pillar of the solution. This ensures the pricing strategy remains competitive and responsive to real-time market shifts.

Optimal pricing is not always the highest possible price; it is the price that maximises total profit or revenue. Because price directly impacts the quantity sold, a lower price point can often drive a volume increase that results in higher overall margins.

The solution is built on the Databricks Data Intelligence Platform using Spark and Python. It is typically hosted in Microsoft Azure, AWS, or GCP to ensure high performance, security, and scalability.

The process follows a structured path: a Proof of Value (POV) to validate ROI (1 month), followed by Implementation of a production-ready pilot (3 months), an A/B Test to measure performance (1 month), and finally a full Roll-out.

It is a "White Box" solution, meaning it is 100% transparent with no vendor lock-in. The business model includes either a one-off license with a monthly support fee or a monthly subscription, combined with custom development for specific integration needs.

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