AI Pricing Models for e-commerce
Balance between new prices and the one working well
Scale your current revenue and margin levels with automated pricing processes
Competitive edge
Faster exploration
and longer optimal play
Leverage data from orders, inventory, customer behavior and competition to train the AI models
Price Testing
Out-of-the-box AI pricing models that pull in demand analytics to optimize prices immediately!
- Bayesian A/B testing
- Run price experiments — from structured tests to AI-driven optimization
- Demand response and elasticity insights
- Value-based pricing automation
Stock Optimizer
The model is constantly training on inventory and demand data to improve pricing policies. You can employ ‘Stock Optimizer’ as a category manager assistant to:
- Improve inventory turnover
- Reduce storage and holding costs
- Account for marketing and acquisition spend
- Adjust prices based on current stock availability
Adaptive Pricer
Adapt to dynamic markets and apply your pricing strategy within different contexts, like:
- Page visits and conversion rates
- Holidays and days of the week
- Competitor pricing changes
- Customer segments and buying behavior
Markdown Runner
Optimize discount timing to maximize revenue while clearing inventory efficiently.
- Pre-configured optimal discount ladders
- Encourage earlier purchases by structuring future price reductions strategically
- Gradual price reductions based on demand signals
- Sequential markdown pricing to clear inventory profitably
Demand-Based Multipricer
Optimize prices across your catalog while balancing revenue growth and profitability.
- Analyze the revenue–profit tradeoff
- Set revenue and profit targets at the category level
- Automatically update prices as demand changes
- Apply business rules like margin guards, competitor benchmarks, and price rounding
Manage and optimize your business retail cycle
Adapt to changing markets with pricing automation policies and models.
Get in Touch with Us
Please fill out the form and we will contact you back with more details.
AI Pricing Models FAQs
Several of our models utilize the Bandit framework, a form of fast reinforcement learning. This allows the AI to "learn" the optimal price by balancing exploration (testing new prices) and exploitation (sticking to the best-performing price) based on real-time market signals.
The models are optimized for established merchants who handle a significant volume of orders and transactions. The algorithms rely on data flow to make accurate decisions, making them less suitable for low-volume stores.
No. All our models are sequential, meaning they do not discriminate between buyers. At any given moment, every customer sees the exact same price. The price changes over time based on demand and strategy, but not based on the specific user visiting the site.
It depends on the specific model you choose. Some models require historical data to analyze past price-volume relationships, while others are designed to work from a "cold start" and learn from current experience.
If you are launching new products or lack past data, you can use the Price Explorer, Adaptive Pricer, or Stock Optimizer. These models use reinforcement learning to learn from experience in real-time.
The Demand-Based Multipricer and the Markdown Runner require historical price-volume data. They analyze past performance to calculate the next-best prices for revenue or profit optimization.
All models include strict guardrails to protect your business. You can define Min-Max prices and set Margin Guards to ensure that prices never drop below a profitable threshold or exceed a competitive ceiling.
Yes. There is a comprehensive Activity Log that tracks every single price change. It provides full transparency into how the model is behaving.
All price changes are saved within the system. You have the ability to revert changes if necessary, giving you full control over the final output.
Yes. The Activity Log records who from the merchant side created the instance of the model, providing accountability for internal teams.
The Price Explorer is designed to discover optimal prices through experimentation (learning from experience), making it ideal for new items. The Demand-Based Multipricer relies on analyzing existing historical data to find the efficient frontier between revenue and profit.
The Stock Optimizer uses reinforcement learning to adjust prices based on inventory levels and sales velocity. It is ideal when your primary goal is managing inventory turnover or clearing stock while maximizing return.
The Markdown Runner uses historical data to determine the most effective path for discounting products. It is generally used for end-of-life products or seasonal clearance to maximize revenue before inventory is depleted.
Yes. When setting up a model instance, you can define when it starts and how long it will operate. Once the duration expires, the model stops adjusting prices automatically.