The challenge
Pricing is one of the fastest levers for profit. But most teams can't move it fast enough.
Pricing by gut feel and last year's spreadsheet
Your pricing team is experienced, but they're making thousands of decisions with a handful of data points. When product catalogs run into tens of thousands of SKUs, manual rules can't keep up. And every slow decision is margin left behind.
Promotions that cost more than they earn
Blanket discounts and one-size-fits-all campaigns spend marketing budget on customers who would have bought anyway. While missing the ones who just need the right offer. The result: lower margins and no clear signal of what actually worked.
No way to test before you commit
Price changes go live based on assumptions. You find out what worked. And what didn't. Only after the quarter closes. Without a feedback loop, every pricing decision is a one-shot bet.
How we solve it
From transaction data to running pricing models in production. Typically in 8–12 weeks.
Find where the margin is hiding
We start with your transaction history, customer segments, and product catalog. The goal isn't a generic analysis. It's finding the specific pricing decisions where a model will outperform manual rules. In one engagement, this initial analysis identified a 15–20% profit uplift opportunity that the client's team hadn't been able to quantify.
Build models matched to your pricing problem
There's no single pricing model. We've built auction-based dynamic pricing for wholesale, demand-driven personalized offers for retail loyalty, and predictive cost models for industrial service contracts. The model fits your business logic. Not the other way around.
Validate with controlled experiments
Models are tested against real data and, where possible, in controlled A/B tests before they touch production pricing. One retail engagement ran 9 months of continuous A/B testing across 800K customers to validate lift before scaling. You don't go live on faith.
Run in production and improve continuously
The model runs as part of your operations. Integrated into your ERP, pricing system, or campaign workflow. Performance is tracked through dashboards, and models retrain as new data flows in. We've maintained production pricing systems for over a year with ongoing optimization runs.
Proven results
Production systems with measured business impact. Not proof-of-concepts gathering dust.
Personalized coupon offers matched to individual customer behavior across 800K active loyalty customers. 256% increase in coupon usage, 5.3% more store visits, 3.5% sales increase. With improved margins from lower average discount amounts. Validated over 9 months of continuous A/B testing. The system runs in production on Azure and now extends to promotional leaflet optimization and assortment decisions.
Wholesale Distributor
AI-powered auction pricing in sealed-bid wholesale markets. The model optimizes dynamically based on demand patterns and inventory levels, delivering 15–20% profit increase and 10%+ sales volume growth. Built from scratch. Data analysis, feature engineering, model development, and production deployment. In under six months.
Predictive pricing model for maintenance contracts in industrial HVAC and pumping systems. The model uses equipment data, usage patterns, and historical maintenance costs to price service agreements more accurately. Replacing manual estimation with data-driven contract pricing. Built through a design sprint and pilot in 12 weeks.
Our loyalty customers now get offers they care about. This means more visits, more sales, and 256% more coupon use. We turned customer insights into action. And real business results.
Where it applies
Pricing optimization works wherever you have transaction history and pricing decisions at scale.
Retail & E-commerce
Personalized promotions, dynamic catalog pricing, assortment optimization. Best fit when you have a loyalty program or customer-level purchase data.
Wholesale & Distribution
Auction pricing, bid optimization, demand-driven replenishment pricing. High impact when you're pricing thousands of SKUs across customer segments.
Industrial & Field Services
Service contract pricing, maintenance cost prediction, spare parts pricing. Works especially well when pricing depends on equipment profiles and usage data that's been collected but never modeled.
Consumer Goods
Trade promotion optimization, channel pricing, pack-price architecture. Relevant when you're managing pricing across multiple retail partners.
What you get
A working pricing model in production. Not a slide deck about what could be possible.
