Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model

Agrawal, Deepak; Schorling, Christopher

doi:10.1016/s0022-4359(96)90020-2

Cited by 124 publications

(44 citation statements)

References 13 publications

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“…Also, gravity models, in which consumer patronage is stated to be in direct proportion with the utility factors (square footage, representing assortment factors) and in inverse proportion to disutility factors (distance) (Converse 1949;Stanley and Sewall 1976) explore retail patronage. More complex discrete choice models and conjoint analysis (Agrawal and Schorling 1996;Arnold, Roth and Tigert 1980) apply stepwise linear regression to explain the consumers' shopping center choice.…”

Section: Research Problemmentioning

confidence: 99%

See 1 more Smart Citation

An Assessment of Consumers’ Willingness to Patronize Foreign-Based Business Format Franchises: An Investigation in the Fast-Food Sector

Ertekin

Pelton

2014

Proceedings of the 2010 Academy of Marketing Science (AMS) Annual Conference

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Section: Research Problemmentioning

confidence: 99%

“…(Converse 1949;Reilly 1931), to more complex discrete choice models and conjoint analysis (Agrawal and Schorling 1996;Arnold, Roth and Tigert 1981;Finn and Louviere 1990). These studies apply stepwise linear regressions to explain the consumers' shopping center choice.…”

Section: Sharma and Stafford (2000)mentioning

confidence: 99%

An Assessment of Consumers’ Willingness to Patronize Foreign-Based Business Format Franchises: An Investigation in the Fast-Food Sector

Ertekin

Pelton

2014

Proceedings of the 2010 Academy of Marketing Science (AMS) Annual Conference

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“…Barksdale and Hilliard found that successful inventory management depends to a large extent on the accurate forecasting of retail sales [2]. Works in [3] and [4] also pointed out that accurate demand forecasting plays a critical role in profitable retail operations and poor forecasting results in under-stock or over-stock that directly affect profitability and competitive position of the retailer.…”

Section: Existing Workmentioning

confidence: 99%

Purchase-driven Classification for Improved Forecasting in Spare Parts Inventory Replenishment

Bala¹

2010

IJCA

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Performance of inventory management depends on the accuracy of demand forecasting. There are many techniques used for forecasting demand in retail sale. Advances in data mining application systems have given rise to the use of business intelligence in various domains of retailing. The current research captures the knowledge of classification of the customers using the purchase-based data of customers for improved forecasting. The model developed in this work suggests a technique for forecasting of demands which results in improved performance of inventory. The suggested forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. Moreover, the model makes use of purchase driven information instead of customers" demographic profile or other personal data for developing the decision tree for forecasting.

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“…More accurate forecasts of aggregate retail sales may improve portfolio investors" ability to predict movements in the stock prices of retailing chains (Barksdale and Hilliard, 1975;Thall;Alon et al, 2001). However, poor forecasting would result in redundant or insufficient stock that will directly affect the revenue and competitive position (Agrawal and Schorling, 1996).…”

Section: Introductionmentioning

confidence: 99%

Forecasting aggregate retail sales: The case of South Africa

Aye

Balcılar

Gupta

et al. 2015

International Journal of Production Economics

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Forecasting aggregate retail sales may improve portfolio investors" ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa"s aggregate seasonal retail sales. We use data from 1970: 01 -2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the previous literature on retail sales forecasting, we not only look at a wider array of linear and nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight forecast error in booms and recessions. Focusing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, the combination forecasts models, especially the discounted mean-square forecast error method which weighs current information more than past, produced not only better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with the fact that individual nonlinear models performed better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models which not only incorporates learning behaviour, but also allows the behavioural parameters of the model to be state-dependent, to account for regime-switching behaviour across alternative states of the economy.

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Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model

Cited by 124 publications

References 13 publications

An Assessment of Consumers’ Willingness to Patronize Foreign-Based Business Format Franchises: An Investigation in the Fast-Food Sector

An Assessment of Consumers’ Willingness to Patronize Foreign-Based Business Format Franchises: An Investigation in the Fast-Food Sector

Purchase-driven Classification for Improved Forecasting in Spare Parts Inventory Replenishment

Forecasting aggregate retail sales: The case of South Africa

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