Beyond mean estimates of price and promotional effects in scanner-panel sales–response regression

Haupt, Harry; Kagerer, Kathrin

doi:10.1016/j.jretconser.2012.06.002

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Macé (2012) performed an empirical analysis using consumer goods scanner data to model nonlinearity in sales response by grouping them into three price level segments. Haupt and Kagerer (2012) in a similar study, ranked them as Premium, Mid-priced and Low-Price Segments.…”

Section: Modelmentioning

confidence: 96%

“…Like Haupt and Kagerer (2012), an empirical analysis is carried out to model the response of sales using data from consumer goods scanners. The information is collected and integrated from the different FMCG retailers in the Spanish market included in the study aligning them at product level (EAN code).…”

Section: Datamentioning

confidence: 99%

“…Following Haupt and Kagerer (2012), who proposed a regression framework that allowed the direct estimation of price impacts addressing the heterogeneity of prices and promotional effects, models are contrasted by type of MSRP endings. In contrast, a time series regression model (TSML) is compared since they will be applied on laser data from scanner in order to study for each product the statistical significance of the price variables ending in 9 in real demand as opposed to types of random prices (Blattberg and Wisniewski, 1987;Chu et al, 2008), for three types of retailers depending on their sales channels.…”

Section: Modelmentioning

confidence: 99%

“…Following Haupt and Kagerer (2012) who proposed a regression framework addressing price heterogeneity and promotional effects, time series regression models (TSML) are contrasted by types of Manufacturer Suggested Retail Price (MSRP) endings that are applied to scanner data series data with the objective of studying for each product the significance of 9-ending and rounded prices variables in real demand compared to types of random prices (Blatberg and Wisniewski, 1987;Chu et al, 2008). Other independent variables taken into consideration are "Retailer types," "Group by MSRP levels," and "Promotion Communication.…”

Section: Introductionmentioning

confidence: 99%

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Demand Impact for Prices Ending With “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels

Lopez-Pastor

García‐Madariaga²,

Sánchez³

et al. 2020

IRMM

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Studies on demand impact for 9-ending and rounded prices have so far offered controversial results, with hardly any research focusing on their effect on online commerce or in a multichannel sales context. Our study aims to fill this gap by analyzing the conditions that influence the strategy behind setting such type of pricing in the multichannel retail business of fast-moving consumer goods (FMCG). To test the formulated hypotheses, scanner data from FMCG retailers are used. In addition to "demand" and "price", "promotion communication", "retailer type" and "price level" are included as moderators between 9-ending, rounded prices and demand. The results aim to provide, both in the academic and business fields, systematic findings of pricing relationships between online and offline channels which contribute to a better management strategy for "9-ending" and "rounded" prices.

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Section: Modelmentioning

confidence: 96%

Section: Datamentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Demand Impact for Prices Ending With “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels

Lopez-Pastor

García‐Madariaga²,

Sánchez³

et al. 2020

IRMM

View full text Add to dashboard Cite

show abstract

“…Rao, ; Hanssens et al ., ). Focusing on estimation Haupt and Kagerer () find nonlinearities in pricing effects using spline‐based quantile estimation. The empirical results of all studies of this stream indicate that price response in the FPCG category may indeed show complex nonlinearities (caused, for example, by the existence of threshold and/or saturation effects) which are difficult or not at all to capture by parametric models.…”

Section: Introductionmentioning

confidence: 99%

Smooth Quantile-Based Modeling of Brand Sales, Price and Promotional Effects From Retail Scanner Panels

2013

Self Cite

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SUMMARYSemiparametric quantile regression is employed to flexibly estimate sales response for frequently purchased consumer goods. Using retail store-level data, we compare the performance of models with and without monotonic smoothing for fit and prediction accuracy. We find that (a) flexible models with monotonicity constraints imposed on price effects dominate both in-sample and out-of-sample comparisons while being robust even at the boundaries of the price distribution when data is sparse; (b) quantile-based confidence intervals are much more accurate compared to least-squares-based intervals; (c) specifications reflecting that managers may not have exact knowledge about future competitive pricing perform extremely well.

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Non- and Semiparametric Regression Models

Heerde

2017

International Series in Quantitative Marketing

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Beyond mean estimates of price and promotional effects in scanner-panel sales–response regression

Cited by 9 publications

References 9 publications

Demand Impact for Prices Ending With “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels

Demand Impact for Prices Ending With “9” and “0” in Online and Offline Consumer Goods Retail Trade Channels

Smooth Quantile-Based Modeling of Brand Sales, Price and Promotional Effects From Retail Scanner Panels

Non- and Semiparametric Regression Models

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