How do you avoid race-to-the-bottom dynamic pricing?

The model operates within explicit guardrails: a margin floor below which it will not recommend prices, and constraints on how far below competitor pricing the model can go. These guardrails are business rules you define, not something the model decides autonomously. The model maximises revenue or margin within your constraints. If a race-to-the-bottom is happening in your category, the model will flag the margin pressure but will not recommend prices below your defined floor.

Can this handle hundreds of thousands of SKUs?

Yes, at scale. Large-SKU catalogs use a hierarchical approach: detailed elasticity models for high-volume or high-margin SKUs, and category-level models applied to long-tail SKUs where individual SKU data is too sparse for reliable estimation. The recommendation pipeline runs in batch mode on a scheduled cadence and can process a full catalog in hours. We have designed systems for catalogs with over 500,000 SKUs.

How do you incorporate competitor pricing data?

Competitor pricing is incorporated as a feature in the recommendation engine, not as a direct pricing rule. We pull competitor prices via a web scraping pipeline or your existing price monitoring tool (e.g., Prisync, Wiser, DataWeave), and the model uses competitive positioning as one signal alongside your own demand elasticity. The model learns how sensitive your demand is to competitive price gaps, whether customers actively compare and switch, or whether they are relatively insensitive to competitor prices in your category.

What's the difference between pricing optimization and revenue management?

Pricing optimisation focuses on setting the right price for a product given demand elasticity and competitive dynamics. Revenue management is a broader discipline that also includes inventory allocation, capacity planning, and channel mix: common in airlines, hotels, and car rentals. We build pricing optimisation systems. If your use case involves capacity-constrained inventory (seats, rooms, rental units), the scope extends into revenue management territory, which we scope as a more complex engagement.

Use Case

Prices set by what the market will bear, not gut feel.

Static price lists leave revenue on the table in high-demand periods and lose sales in low-demand periods. Most ecommerce and SaaS companies set prices once based on cost-plus logic or competitive benchmarking, and rarely revisit them with data.

Custom pricing optimisation models learn your demand elasticity per SKU, product category, or plan tier, and recommend price adjustments that maximise revenue or margin within your constraints. Every recommendation comes with a projected demand and revenue impact, and runs as a controlled experiment before full deployment.

We build demand elasticity models, price recommendation engines, A/B testing frameworks, and dashboards integrated with your ecommerce platform or pricing system.

How we work View pricing →

Tell us about your pricing challenge.

What the system does.

Six components that replace intuition-based pricing with a data-driven recommendation pipeline.

Demand elasticity model per product

The model estimates how sensitive demand is to price changes for each product, segment, or plan tier. A 10% price increase on one SKU may have minimal demand impact; the same increase on another may be significant. The model learns these elasticities from your historical price and sales data.

Price recommendation engine

The recommendation engine takes elasticity estimates and margin constraints (a floor below which you will not price, a ceiling above which the market rejects) and outputs a recommended price for each product or segment. Recommendations explain the projected revenue and margin impact of the change.

Margin floor and ceiling guardrails

Your pricing team defines the rules: minimum margin per SKU, maximum price premium above a benchmark, and any category or brand constraints. The model cannot recommend a price that violates these guardrails, so the output always satisfies your business constraints regardless of what the elasticity model suggests.

A/B testing framework for price changes

Price changes that have not been tested are run as A/B experiments, exposing a subset of traffic to the new price, measuring the demand and revenue impact versus the control price, and confirming statistical significance before a full rollout. The framework tracks experiment results and controls for confounding factors.

Competitor pricing integration

For ecommerce and marketplace use cases where competitor pricing data is available, the model incorporates competitive positioning as a pricing signal. Competitor prices are pulled from a web scraping pipeline, a price monitoring service, or your existing competitive intelligence data.

Revenue and margin tradeoff dashboard

A dashboard showing current prices versus recommended prices, with projected revenue and margin impacts for each recommendation. Your pricing team reviews recommendations, approves or adjusts them, and the dashboard tracks actuals versus projections after price changes go live.

How we build it.

From historical sales data to live pricing recommendations with A/B testing, the build sequence.

01
Historical pricing and sales data extraction
We pull historical sales data, price history, and any available competitor pricing data from your systems. The data cleaning phase identifies price changes, promotional periods, and seasonal patterns that need to be accounted for in the elasticity model.
02
Demand elasticity estimation
We estimate price elasticity per product, segment, or tier using econometric modelling on your historical data. We use natural price variation experiments in your data (past price changes and their demand impacts) to estimate elasticity without running new experiments.
03
Constraint definition
Your pricing or finance team defines the constraints: margin floors by category, brand positioning constraints, competitive price parity rules, and any hard limits. These are encoded as business rules that govern every recommendation the model outputs.
04
Recommendation engine build
The recommendation engine is built to generate price suggestions at a configurable cadence: weekly, monthly, or event-triggered (e.g., when competitor pricing changes). Each recommendation includes the reasoning, projected demand impact, and revenue/margin forecast.
05
A/B testing framework setup
The experiment framework is set up in your ecommerce platform or pricing system. New price recommendations run as controlled experiments before full deployment. We configure the experiment size, duration, and success criteria for each price change type.
06
Dashboard deployment and integration
The pricing dashboard is deployed. Price recommendations can be approved manually by the pricing team, approved automatically within defined bounds, or pushed directly to Shopify, Stripe, or your custom ecommerce backend depending on your approval workflow.

Who this is for.

Businesses with meaningful pricing discretion and enough historical data to estimate demand responses.

Ecommerce and DTC brands with 500+ SKUs

Managing prices manually across hundreds of SKUs means prices are set infrequently and often wrong. A pricing model that surfaces weekly recommendations per SKU (with projected margin and demand impact) lets your team act on pricing intelligence rather than gut feel.

SaaS companies with usage-based or seat-based pricing

SaaS pricing is often set once and rarely revisited. A model trained on trial conversion data, plan upgrade patterns, and churn by price tier gives pricing teams quantitative input for pricing reviews, rather than intuition and competitor benchmarking alone.

Marketplaces and rental platforms with supply-demand dynamics

Marketplace and rental pricing is inherently dynamic: supply and demand fluctuate by time, location, and category. A custom pricing model learns the demand patterns and recommends prices that capture value in high-demand periods without deterring buyers in low-demand periods.

Who this isn't for.

We’d rather decline than take a project that won’t deliver value.

Businesses with fewer than 12 months of price variation data
Elasticity estimation requires historical price variation, your prices must have changed at different points in time so we can observe demand responses. If you have always charged the same price and have no natural experiments in your data, we cannot estimate elasticity from history alone. We can design a structured price experiment as a starting point, but this is a different engagement.
Categories where pricing is primarily regulatory or contractual
If your prices are set by regulation, long-term contracts, or distributor agreements, market-based pricing optimisation has limited room to operate. The model only operates in the space where you have discretion over price. If that space is small, the ROI of the project is lower and we will say so during discovery.

Pricing.

Pricing optimisation build scope varies with catalog size and integration needs. Lower-end projects cover elasticity modelling for a single product category with a recommendation dashboard. Higher-end projects cover large catalogs, competitor pricing integration, A/B testing infrastructure, and automated price updates pushed to Shopify or Stripe.

The elasticity estimation phase, working with your historical price and sales data — takes three to four weeks. Total build time is seven to ten weeks including A/B testing framework setup and dashboard deployment.

Full pricing breakdown →

Frequently asked questions.

How do you avoid race-to-the-bottom dynamic pricing?: The model operates within explicit guardrails: a margin floor below which it will not recommend prices, and constraints on how far below competitor pricing the model can go. These guardrails are business rules you define, not something the model decides autonomously. The model maximises revenue or margin within your constraints. If a race-to-the-bottom is happening in your category, the model will flag the margin pressure but will not recommend prices below your defined floor.
Can this handle hundreds of thousands of SKUs?: Yes, at scale. Large-SKU catalogs use a hierarchical approach: detailed elasticity models for high-volume or high-margin SKUs, and category-level models applied to long-tail SKUs where individual SKU data is too sparse for reliable estimation. The recommendation pipeline runs in batch mode on a scheduled cadence and can process a full catalog in hours. We have designed systems for catalogs with over 500,000 SKUs.
How do you incorporate competitor pricing data?: Competitor pricing is incorporated as a feature in the recommendation engine, not as a direct pricing rule. We pull competitor prices via a web scraping pipeline or your existing price monitoring tool (e.g., Prisync, Wiser, DataWeave), and the model uses competitive positioning as one signal alongside your own demand elasticity. The model learns how sensitive your demand is to competitive price gaps, whether customers actively compare and switch, or whether they are relatively insensitive to competitor prices in your category.
What's the difference between pricing optimization and revenue management?: Pricing optimisation focuses on setting the right price for a product given demand elasticity and competitive dynamics. Revenue management is a broader discipline that also includes inventory allocation, capacity planning, and channel mix: common in airlines, hotels, and car rentals. We build pricing optimisation systems. If your use case involves capacity-constrained inventory (seats, rooms, rental units), the scope extends into revenue management territory, which we scope as a more complex engagement.

Ready to price based on data, not intuition?

Tell us your approximate SKU count, which platform you sell on, and how pricing decisions are currently made in your business. We'll reply within one business day.