Cargando…

Forecasting Key Retail Performance Indicators Using Interpretable Regression

Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents a regression method that is able...

Descripción completa

Detalles Bibliográficos
Autores principales: Panay, Belisario, Baloian, Nelson, Pino, José A., Peñafiel, Sergio, Frez, Jonathan, Fuenzalida, Cristóbal, Sanson, Horacio, Zurita, Gustavo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962459/
https://www.ncbi.nlm.nih.gov/pubmed/33800166
http://dx.doi.org/10.3390/s21051874
_version_ 1783665474399109120
author Panay, Belisario
Baloian, Nelson
Pino, José A.
Peñafiel, Sergio
Frez, Jonathan
Fuenzalida, Cristóbal
Sanson, Horacio
Zurita, Gustavo
author_facet Panay, Belisario
Baloian, Nelson
Pino, José A.
Peñafiel, Sergio
Frez, Jonathan
Fuenzalida, Cristóbal
Sanson, Horacio
Zurita, Gustavo
author_sort Panay, Belisario
collection PubMed
description Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents a regression method that is able to predict these three indicators based on previous data. The previous data includes values for the indicators in the recent past; therefore, it is a requirement to have gathered them in a suitable manner. The previous data also considers other values that are easily obtained, such as the day of the week and hour of the day of the indicators. The novelty of the approach that is presented here is that it provides a confidence interval for the predicted information and the importance of each parameter for the predicted output values, without additional processing or analysis. Real data gathered by Follow Up, a customer experience company, was used to test the proposed method. The method was tried for making predictions for up to one month in the future. The results of the experiments show that the proposed method has a comparable performance to the best methods proposed in the past that do not provide confidence intervals or parameter rankings. The method obtains RMSE of [Formula: see text] for foot traffic prediction, [Formula: see text] for conversion rate forecasting, and [Formula: see text] for sales prediction.
format Online
Article
Text
id pubmed-7962459
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79624592021-03-17 Forecasting Key Retail Performance Indicators Using Interpretable Regression Panay, Belisario Baloian, Nelson Pino, José A. Peñafiel, Sergio Frez, Jonathan Fuenzalida, Cristóbal Sanson, Horacio Zurita, Gustavo Sensors (Basel) Article Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents a regression method that is able to predict these three indicators based on previous data. The previous data includes values for the indicators in the recent past; therefore, it is a requirement to have gathered them in a suitable manner. The previous data also considers other values that are easily obtained, such as the day of the week and hour of the day of the indicators. The novelty of the approach that is presented here is that it provides a confidence interval for the predicted information and the importance of each parameter for the predicted output values, without additional processing or analysis. Real data gathered by Follow Up, a customer experience company, was used to test the proposed method. The method was tried for making predictions for up to one month in the future. The results of the experiments show that the proposed method has a comparable performance to the best methods proposed in the past that do not provide confidence intervals or parameter rankings. The method obtains RMSE of [Formula: see text] for foot traffic prediction, [Formula: see text] for conversion rate forecasting, and [Formula: see text] for sales prediction. MDPI 2021-03-08 /pmc/articles/PMC7962459/ /pubmed/33800166 http://dx.doi.org/10.3390/s21051874 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Panay, Belisario
Baloian, Nelson
Pino, José A.
Peñafiel, Sergio
Frez, Jonathan
Fuenzalida, Cristóbal
Sanson, Horacio
Zurita, Gustavo
Forecasting Key Retail Performance Indicators Using Interpretable Regression
title Forecasting Key Retail Performance Indicators Using Interpretable Regression
title_full Forecasting Key Retail Performance Indicators Using Interpretable Regression
title_fullStr Forecasting Key Retail Performance Indicators Using Interpretable Regression
title_full_unstemmed Forecasting Key Retail Performance Indicators Using Interpretable Regression
title_short Forecasting Key Retail Performance Indicators Using Interpretable Regression
title_sort forecasting key retail performance indicators using interpretable regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962459/
https://www.ncbi.nlm.nih.gov/pubmed/33800166
http://dx.doi.org/10.3390/s21051874
work_keys_str_mv AT panaybelisario forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT baloiannelson forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT pinojosea forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT penafielsergio forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT frezjonathan forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT fuenzalidacristobal forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT sansonhoracio forecastingkeyretailperformanceindicatorsusinginterpretableregression
AT zuritagustavo forecastingkeyretailperformanceindicatorsusinginterpretableregression