Challenge
The pricing team of clients used to work based on fixed rules and spreadsheets that were updated manually by category managers. It was not easy to adjust to changes in the competitors or respond to market moves in real time. There was a slow rate of updating price and it varied in different channels and was a guesswork many times.
The catalog became more and more complicated with its growth. It was also physically impossible to have a manual approach to dynamic pricing on >200,000 SKUs, resulting in lost margin, over-discounting, and underpriced high-traffic products. They required a system, which would be enabled to respond quicker, learn on a real-time basis, and plan intelligent pricing strategies.
This case exposed the growing limitations of traditional models — and highlighted the need for dynamic pricing AI and smarter AI pricing solutions that could adapt to market pressure without constant human intervention.
Solution
The solution centered on implementing a flexible AI dynamic pricing system capable of reacting to high-frequency market changes. The platform was a continuous compilation of up-to-date information regarding the pricing by competitors, stocks, and the performance of products along with seasonality. This data was then fed to machine learning models, which dynamically optimize prices of over 200,000 SKUs, assuring goals such as optimization of margins, competitive position, and conversion.
In the verticals that were highly dynamic such as electronics, competitiveness was in play; in the slower categories, the algorithm maximized margin retention. Human override was integrated in a centralized dashboard to allow pricing managers to review pricing logic recommendations, deploy overrides, review history of prices, and reject prices.
The system integrated cleanly with the client’s ecommerce backend and PIM, ensuring minimal friction and no downtime. The entire solution was designed for long-term scalability and transparency.
More about their approach to retail AI can be found in this article, which outlines practical applications of AI pricing solutions for modern commerce.
This project became a model for AI powered dynamic pricing at scale — demonstrating how to use AI for dynamic pricing not just as automation, but as a strategic business lever.
Results
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The adoption of AI and dynamic pricing delivered both measurable and strategic improvements across pricing operations:
- Performance Gains
- +7% average margin increase across monitored SKUs
- +9% revenue uplift in high-velocity categories
- 60% less manual input required for daily price changes
- Faster response to competitor movements through automated logic
- Operational Wins
- Improved team agility and decision-making confidence
- Replaced manual updates with dynamic pricing using AI
- Enabled continuous testing of price elasticity in real-world environments
- Strategic Benefits
- Pricing managers reported higher trust in the system’s logic
- The platform evolved into a strategic control center — not just automation
- A strong example of ai-driven dynamic pricing supporting scale without complexity
Why Bintime
Bintime has a proven track record of delivering AI-based dynamic pricing systems for ecommerce businesses dealing with high SKU complexity. Our solutions are designed for real-world operations — integrating seamlessly with PIM, ERP, and ecommerce platforms to enable smarter, faster decisions.
With experience in AI in dynamic pricing, we don’t just build algorithms — we solve pricing problems. Our tools support pricing managers with dashboards, override logic, and strategic controls, making automation feel intuitive rather than disruptive.
Ready to unlock the power of AI in pricing? Let’s discuss your goals.
