Introduction

This learning path is designed for individuals who need to gain insights into the concepts and capabilities of the Multifactor Elasticity Accelerator that will illustrate how it uses machine learning to forecast demand and assess price elasticity and assist in forecasting quantity that would be sold at a given price.

It is separated into multiple learning paths that are dependent upon your expected role with this Pricefx Accelerator.

✅ Learning Prerequisites

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

• Pricing Basics

• Pricing Terminology

• Accelerator Overview

• Price Optimization Core Learning Path for All Audiences

🧭 Learning Synopsis

These learning paths on Multifactor Elasticity Accelerator will explore how this Accelerator deploys a powerful machine learning approach to assess elasticity from a forecasting model and provides the features and functions that will allow it to forecast the quantity that would be sold at a given price

It will highlight how to install and configure this Accelerator, map the incoming data, build a forecasting model, train the model, predict quantity for the next period and then fit an elasticity curve to match expected quantity to price variations.

🎯 Learning Objectives

Core topics and takeaways from these learning paths:

• Illustrate how to deploy and configure this Accelerator into a partition.

• Depict the mandatory data requirements in our transactional data that are needed for this Accelerator.

• Understand the step-by-step process for instantiating the machine learning model.

• Define the steps for running our calculation and initiate the training of the elasticity model.

• Highlight the underlying technical components and libraries that are used by developers for building the logics involved in these calculations.

🚘 Learning Journey

This learning path is separated into sections that provide a basic roadmap for building your knowledge and understanding of the technical aspects of Multifactor Elasticity Accelerator.

The components in this path are designed to be experienced in a sequential order, as each subsequent level builds upon the knowledge acquired in the preceding learning path(s). This structured approach ensures that learners have the necessary foundation before progressing to more advanced concepts, optimizing your learning experience.