Win Rate Optimization 1.0

This document summarizes major improvements and fixes introduced in the Accelerate Win Rate Optimization package release version.

Version

1.0

Release Date

New Features and Improvements

New Feature Description

ID

A heatmap chart is now available to show the Pairwise impacts of features on the Win Rate model.

PFPCS-9416

A portlet in the Definition tab now summarizes data rows filtered out due to invalid Win/Lost values.

PFPCS-9449

The dropdown menus for Win/Lost values are now limited to the first 100 distinct values to prevent the UI from freezing.

PFPCS-9476

If discount % is selected without Revenue at List Price, the model informs the user and removes discount % from the model.

PFPCS-9489

The accelerator now checks for and forbids the use of reserved names or selecting the same feature as both categorical and numerical.

PFPCS-9490

Expired feature tables from previous calculations are now automatically dropped before a new training run.

PFPCS-9512

The Pairwise Impact inputs now dynamically allow only existing pairs while keeping full choices for Feature 1 for better UX.

PFPCS-9516

The model evaluation now displays the optimized price.

PFPCS-9528

The Evaluation Dashboard now includes a feature importance view to highlight win rate drivers.

PFPCS-9529

The feature importance visualization now uses a red-to-green color scale.

PFPCS-9536

Constant features are handled by removing them from the model and informing the user.

PFPCS-9545

Using a cost of zero is forbidden to avoid invalid profit calculations.

PFPCS-9601

The evaluation allows entering and assessing a new categorical value.

PFPCS-9695

Price optimization supports targeting either revenue or profit.

PFPCS-9824

Numerical inputs below the training minimum are handled using the minimum’s adjustment in Evaluation.

PFPCS-9837

The price adjustment logic correctly handles high price values.

PFPCS-9898

Revenue and profit are split into separate charts in Evaluation.

PFPCS-9899

All chart portlets now include a Data tab.

PFPCS-9960

Fixed Issues

Bug Description

ID

The density bar for null or empty string values in categorical features displays incorrectly in the Feature Impact chart.

PFPCS-9683

An "Invalid input value" error for an unknown category occurs in the Evaluation tab.

PFPCS-9889

The Python script fails when the test set contains only wins or only losses.

PFPCS-9896

The win rate curve in the "Win Rate and Optimal Price" portlet incorrectly displays values at its extremities.

PFPCS-9929

Creating temporary tables with long feature names causes an error.

PFPCS-9992