The value of competitive information in forecasting FMCG retail product sales and the variable selection problem

Huang, Tao; Fildes, Robert; Soopramanien, Didier

doi:10.1016/j.ejor.2014.02.022

Cited by 87 publications

(70 citation statements)

References 59 publications

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“…Using the weekly data from a large U.S. retail chain, they included within category competitive (substitutive) promotional information into their Autoregressive Distributed Lag (ADL) model and empirically checked the forecasting improvements compared to the model without competitive information. The key similarities of this study and that by Huang et al (2014) are that both studies aim to improve the forecasting accuracy for retailers at SKU level by integrating extra promotional information from other products. At the same time, there are some important differences.…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

“…In practice, many retailers use a base-times-lift approach to forecast product sales at the SKU level (Cooper et al, 1999;Huang et al, 2014). The approach is a two-step procedure which initially generates a baseline forecast from a simple time series models and then makes adjustments for any incoming promotional events.…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

“…This is small compared to planning for the 30,000 items that are in stock at any given time for a retailer." An exception is Huang, Fildes and Soopramanien (2014). Using the weekly data from a large U.S. retail chain, they included within category competitive (substitutive) promotional information into their Autoregressive Distributed Lag (ADL) model and empirically checked the forecasting improvements compared to the model without competitive information.…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

“…They also employed a regression model capturing the effects of such variables as past sales, trend, own and competitor prices and promotional variables, and seasonality. In recent research, Huang, Fildes and Soopramanien (2014) proposed effective methods to forecast retail SKU sales by incorporating competitive information including prices and promotions. They found that the proposed methods generate substantially more accurate forecasts across a range of product categories.…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

“…Therefore, univariate forecasting methods are usually adopted as a benchmark model in many studies (Gür Ali et al, 2009;Huang et al, 2014).…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

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Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Fildes

Huang

2016

European Journal of Operational Research

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131

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In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts, but no research has put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra-and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 14.3 percent over the model using only the SKU's own predictors. But of the improvements achieved, 88.1 percent of it comes from the intra-category information, and only 11.9 percent from the inter-category information. The substantive marketing results also have implications for promotional category management.

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Section: Sku Sales Forecastingmentioning

confidence: 99%

Section: Sku Sales Forecastingmentioning

confidence: 99%

Section: Sku Sales Forecastingmentioning

confidence: 99%

Section: Sku Sales Forecastingmentioning

confidence: 99%

“…Therefore, univariate forecasting methods are usually adopted as a benchmark model in many studies (Gür Ali et al, 2009;Huang et al, 2014).…”

Section: Sku Sales Forecastingmentioning

confidence: 99%

See 3 more Smart Citations

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Fildes

Huang

2016

European Journal of Operational Research

Self Cite

131

View full text Add to dashboard Cite

show abstract

Robust forecasting in spatial autoregressive model with total variation regularization

Jiang

2022

Journal of Forecasting

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In recent decades, feature selection has attracted great attention in data science due to curse of dimensionality, which arises in larger, complex, and heterogeneous data. However, existing researches of feature grouping on spatial autoregressive model are rare. To address this challenge, this paper investigates robust spatial autoregressive model with feature grouping and robust forecasting achieved automatically. The proposed novel methodology borrows strength from check loss function in quantile regression and total variation regularization. A simple‐to‐implement algorithm following double‐level alternative method of multipliers design is derived computationally. The empirical studies demonstrate the effectiveness of the proposed methods via comparing with other competing forecasting techniques.

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Retail Promotion Forecasting: A Comparison of Modern Approaches

Bojer

Dukovska‐Popovska

Christensen

et al. 2019

IFIP Advances in Information and Communication Technology

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Promotions at retailers are an effective marketing instrument, driving customers to stores, but their demand is particularly challenging to forecast due to limited historical data. Previous studies have proposed and evaluated different promotion forecasting methods at product level, such as linear regression methods and random trees. However, there is a lack of unified overview of the performance of the different methods due to differences in modeling choices and evaluation conditions across the literature. This paper adds to the methods the class of emerging techniques, based on ensembles of decision trees, and provides a comprehensive comparison of different methods on data from a Danish discount grocery chain for forecasting chain-level daily product demand during promotions with a four-week horizon. The evaluation shows that ensembles of decision trees are more accurate than methods such as penalized linear regression and regression trees, and that the ensembles of decision trees benefit from pooling and feature engineering.

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The value of competitive information in forecasting FMCG retail product sales and the variable selection problem

Cited by 87 publications

References 59 publications

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

Robust forecasting in spatial autoregressive model with total variation regularization

Retail Promotion Forecasting: A Comparison of Modern Approaches

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