Size of wallet estimation: Application of K-nearest neighbour and quantile regression

Size of wallet (SOW) estimation is an important problem to solve from a company's perspective. The total business volume conducted by the customer for a product category across firms is generally unobservable, while the volume of transactions conducted by the customer with the company is mostly...

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Autores principales:	Aashish Jhamtani, Ritu Mehta, Sanjeet Singh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Size of wallet Share of wallet K-nearest neighbour Quantile regression Business HF5001-6182
Acceso en línea:	https://doaj.org/article/ae368d0a6a2e427481f7127a132ed4a3
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spelling	oai:doaj.org-article:ae368d0a6a2e427481f7127a132ed4a32021-11-20T04:55:56ZSize of wallet estimation: Application of K-nearest neighbour and quantile regression0970-389610.1016/j.iimb.2021.09.001https://doaj.org/article/ae368d0a6a2e427481f7127a132ed4a32021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0970389621000914https://doaj.org/toc/0970-3896Size of wallet (SOW) estimation is an important problem to solve from a company's perspective. The total business volume conducted by the customer for a product category across firms is generally unobservable, while the volume of transactions conducted by the customer with the company is mostly accessible. This paper focuses on the estimation of SOW and the estimation of opportunity, which is the difference between the SOW and the actual transactional value of the business that a customer does with a company. K-nearest neighbour (KNN) and quantile regression (QR) are applied here to arrive at the estimations, and their performance is compared. Based on the SOW and opportunity estimates, a company can decide its target segment and design specific marketing strategies accordingly, thereby improving its profitability.Aashish JhamtaniRitu MehtaSanjeet SinghElsevierarticleSize of walletShare of walletK-nearest neighbourQuantile regressionBusinessHF5001-6182ENIIMB Management Review, Vol 33, Iss 3, Pp 184-190 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Size of wallet Share of wallet K-nearest neighbour Quantile regression Business HF5001-6182
spellingShingle	Size of wallet Share of wallet K-nearest neighbour Quantile regression Business HF5001-6182 Aashish Jhamtani Ritu Mehta Sanjeet Singh Size of wallet estimation: Application of K-nearest neighbour and quantile regression
description	Size of wallet (SOW) estimation is an important problem to solve from a company's perspective. The total business volume conducted by the customer for a product category across firms is generally unobservable, while the volume of transactions conducted by the customer with the company is mostly accessible. This paper focuses on the estimation of SOW and the estimation of opportunity, which is the difference between the SOW and the actual transactional value of the business that a customer does with a company. K-nearest neighbour (KNN) and quantile regression (QR) are applied here to arrive at the estimations, and their performance is compared. Based on the SOW and opportunity estimates, a company can decide its target segment and design specific marketing strategies accordingly, thereby improving its profitability.
format	article
author	Aashish Jhamtani Ritu Mehta Sanjeet Singh
author_facet	Aashish Jhamtani Ritu Mehta Sanjeet Singh
author_sort	Aashish Jhamtani
title	Size of wallet estimation: Application of K-nearest neighbour and quantile regression
title_short	Size of wallet estimation: Application of K-nearest neighbour and quantile regression
title_full	Size of wallet estimation: Application of K-nearest neighbour and quantile regression
title_fullStr	Size of wallet estimation: Application of K-nearest neighbour and quantile regression
title_full_unstemmed	Size of wallet estimation: Application of K-nearest neighbour and quantile regression
title_sort	size of wallet estimation: application of k-nearest neighbour and quantile regression
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ae368d0a6a2e427481f7127a132ed4a3
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Size of wallet estimation: Application of K-nearest neighbour and quantile regression

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