Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Fuente: arXiv
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Main Authors: Henzel, Joanna, Sikora, Marek
Format: Preprint
Published: 2020
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author Henzel, Joanna
Sikora, Marek
author_facet Henzel, Joanna
Sikora, Marek
contents In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.
format Preprint
id arxiv_https___arxiv_org_abs_2006_04945
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail
Henzel, Joanna
Sikora, Marek
Computers and Society
Machine Learning
In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.
title Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail
topic Computers and Society
Machine Learning
url https://arxiv.org/abs/2006.04945