Optimization

The Optimization module provides a framework that helps you analyze and segment your business, optimize your product portfolio and improve pricing.

Optimization, which supports many data science techniques, performs data profiling and generates segment-specific optimized pricing and price guidance and delivers it to price lists, CPQ, Digital Commerce and ERP systems.

Getting Started

Pricefx delivers several optimization use cases which help you get started much quicker. These sample configurations are easily deployable using Pricefx Accelerators.

Open

PlatformManager Marketplace from where you can deploy Accelerators

Please refer to Accelerator Documentation to learn more about the following use cases:

• Accelerate Clustering Optimization – Provides an easy way to create clusters in order to enrich data by creating data-driven labels on any dimensions of the transactions.

• Accelerate Multifactor Elasticity Optimization – Leverages machine learning model based on past transactions to forecast future sold quantities and impact of price changes.

• Accelerate Negotiation Guidance – Provides price guidance based on segmentation of products, customers or any attributes.

• Accelerate Product Recommendation – Provides products recommendations based on guidance based transaction history.

• Accelerate Price List Impact Simulation – Simulates and assesses the impact of list price or LPG changes.

• Accelerate Product Similarity Optimization – Provides a similarity score between products and regroups them into similar groups which can then be leveraged to apply a pricing strategy.

• Accelerate Price Waterfall Optimization – Positions and adjusts multiple elements of the price waterfall, such as list prices, discounts or rebates.

• Accelerate Markdown Optimization – No longer actively developed.

• Accelerate Shelf Price Optimization – No longer actively developed.

Structure

The Optimization module is highly configurable to meet various business needs. 

The main building blocks are:

• Model Classes – Allow you to build custom specific features, made of sequential steps that the user goes through. In each step, the user can run a calculation and then, in multiple tabs, work with interactive dashboards to explore the calculation results. A Model Class defines a user workflow based on logics and can use an engine, such as Optimization Engine or Python Engine.

  • Engines are a way for the Pricefx platform to run specialized calculations in a virtual environment and give back the results to the platform. Engines are used to run data science and optimization jobs.

• Models – Allow you to create many optimization scenarios based on a specific Model Class.

The flow is as follows: Model Classes bind together the logics and based on this class individual models are created. These models then process (with defined parameters) the specific datasets.

Note on terminology: 

• Pricing Guidance – Used up to version 11.0; now it is replaced by Model Classes. Pricing Guidance was used to create a segmentation model or a product recommendation model.

• Model Types – Used in the initial version of the Optimization module. They were replaced by Model Classes.

• Optimization Engine – It was the previous implementation of Model Classes and Models and was listed in the Optimization menu. Now it is used as a background engine, executed by models.