Grid Dynamics joins forces with Mutual Mobile.



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 Google Cloud. Open source.

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


AutoML under the hood

We use Google Cloud’s state-of-the-art capabilities to simplify the development and productization of pricing optimization solutions. In particular, we leverage AutoML capabilities provided by Google Cloud Vertex AI to automate model selection and parameter tuning.

Advanced modeling features

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


AI pricing solution use cases

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


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 Google Cloud?

How it works

The Price Optimization Starter Kit that Grid Dynamics is unveiling today is an example of the innovation excellence upon which the company has built its reputation. This starter kit helps companies leverage the Vertex AI platform more efficiently and achieve compelling business results in a much quicker timeframe.

June Yang - VP, Cloud AI and Industry Solutions, Google Cloud at Grid Dynamics
June Yang VP, Cloud AI and Industry Solutions, Google Cloud

Learn more

Price optimization notebook for apparel retail using Google Vertex AI
Google Cloud and Grid Dynamics teamed up to develop a reference price optimization solution in Vertex AI platform.
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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