Features

End-to-end pipeline

We created reference implementations for all major steps of the pricing optimization pipeline including data preparation, demand forecasting, and various optimization scenarios such as end-of-season sales.

Power of Microsoft Azure. Open source.

Our Pricing Optimization Starter Kit is designed from the ground up for Microsoft Azure to efficiently leverage data processing, model development, and model management services provided by Microsoft. The starter kit is completely open source, so it does not include any proprietary or licensable components.

AutoML under the hood

We use Microsoft Azure’s state-of-the-art Automated ML capabilities to simplify the development and productization of pricing optimization solutions, leveraging built-in capabilities such as easy data exploration, automatic preprocessing, and intelligent feature engineering using deep neural networks to improve model scores.

Advanced modeling features

Our Microsoft Azure Pricing Optimization Starter Kit was created by price management and machine learning experts with extensive domain expertise in retail, manufacturing, and other industries. We incorporate many advanced techniques and best practices that are used by leading B2C and B2B companies.

AI pricing solutions use cases

  • Data-driven price optimization
  • Promotion optimization
  • End-of-season sell-through optimization
  • Dynamic pricing
  • Demand decomposition and analysis
  • Cannibalization analysis

Industries

Apparel retail

Promotions and end-of-season clearance campaigns can be optimized through continuous sales progress tracking and data-driven price adjustments. ML models can be used for both long-term planning (e.g. 52 weeks-ahead) and ongoing adjustments on a weekly basis.

Food & beverage retail

New product pricing and assortment decisions can be optimized through demand forecasting and product similarity models. ML models can be modified to help optimize pricing decisions in early stages of the product life, and to evaluate demand shifts induced by adding or removing items to the assortment.

Hardware retail

Granular short-term demand forecasting at the store level can be used to improve inventory movement from backroom to frontroom. The forecasting solutions are typically integrated with hand-held devices used by store associates to provide instructions on how many units of which items need to be moved.

Manufacturing & B2B

Demand forecasting pipelines can be used to optimize inventory replenishment decisions based on granular store-level and SKU-level forecasts. Typical objectives include maximizing inventory turnover and improving storage and logistics efficiency.

Why develop a pricing optimization solution in Microsoft Azure?

How it works

Learn more

Predictive analytics for promotion and price optimization
Pricing decisions are critically important for any business, as pricing is directly linked to consumer demand and company profits. Even a slightly suboptimal decision-making process inevitably leads to tangible losses, and major mistakes can have grave consequences.
Read more
Retail price modeling for replenishable and seasonal products
Learn more about the common process that retailers follow to set their pricing, the challenges that they are facing in the area of price management, and how they incorporate big data analytics and machine learning into their pricing strategy.
Read more
A guide to dynamic pricing algorithms
Take a deep dive into dynamic pricing algorithms that use dynamic pricing reinforcement learning and Bayesian inference ideas, that were tested at scale by companies like Walmart and Groupon.
Read more

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