Introduction

This learning path is designed for individuals who need to gain insights into the concepts and capabilities of the Shelf Price Optimization Package and how it allows optimization of shelf prices for end customers. It will highlight the optimizes prices across multiple categories (prices across sales channels, align rules with competitor prices, etc.) and how it outputs a model on shelf prices by product, store and time period. It is separated into multiple tiers that are dependent upon your expected role with the Shelf Price Optimization Package.

Learning Prerequisites

Make sure that you have these aspects covered before moving on to the course.

• Data Management

• Pricing Basics

• Pricing Terminology

• Overview of Accelerators

• Price Optimization Core Learning Path for All Audiences

Learning Synopsis

This learning path is designed to provide a comprehensive overview of the Shelf Price Optimization Accelerator, covering its key features, deployment, architecture, and the step-by-step implementation process. The focus is on helping you understand, install, configure, customization per requirements for the Shelf Price Optimization solution to support your pricing decision-making.

Learning Objectives

Core topics and takeaways:

• Understand the deployed components of the Shelf Price Optimization Accelerator and how it integrates within the Pricefx platform.

• Comprehend the architecture and output of the dashboards provide by the Shelf Price Optimization

• Illustrate how it enables price optimization in this package are based on:

  • Maximization of the revenue and/or profit

  • Setting shelf prices limits

  • Aligning prices across sales channels

  • Adjusting sold quantity depending on price changes (thanks to an advanced elasticity model)

  • Aligning rules with competitor prices

  • Managing stock coverage expectations

Learning Journey

The learning path is structured to guide participants through the critical aspects of the Shelf Price Optimization Accelerator, ensuring they have a solid understanding of its deployment, architecture, and the implementation process. The path is designed to be experimented in a sequential order as it builds knowledge progressively.