Understanding Shopper Transaction Data

Tanusondjaja, Arry; Nenycz‐Thiel, Magda; Kennedy, Rachel

doi:10.2501/ijmr-2016-026

Cited by 19 publications

(14 citation statements)

References 21 publications

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“…The second approach is based on the duplication factor (Goodhardt et al, 1984;Ehrenberg et al, 2004;Tanusondjaja et al, 2016), which normalizes the observed joint purchase probability [P(A \ B)] by the expected probability of purchase of both categories under independence, this is P(A)P(B). This metric is also referred to as lift in the association rules literature (Tan et al, 2002) and can be estimated as follows:…”

Section: Definitions and Methodologymentioning

confidence: 99%

“…In particular, most of them show applications for no more than five product categories (Andrews and Currim, 2002;Erdem,1998;Heilman and Bowman, 2002;Ma et al, 2012;Manchanda et al, 1999;Russell and Kamakura, 1997;Russell and Petersen, 2000;Seetharaman et al,1999); while others require the estimation of a considerable number of choice models (Bell and Lattin, 1998;Chib et al, 2002). In contrast, Tanusondjaja et al (2016) apply the duplication of purchase law (Goodhardt et al, 1984 andEhrenberg et al, 2004) to study joint purchase patterns across 28 categories. For a given pair of categories, they compute a duplication factor that measures how the likelihood of buying one of the product categories increases if the other category is also purchased.…”

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confidence: 99%

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Market basket analysis insights to support category management

Musalem

Aburto

Bosch

2018

EJM

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Purpose This paper aims to present an approach to detect interrelations among product categories, which are then used to produce a partition of a retailer’s business into subsets of categories. The methodology also yields a segmentation of shopping trips based on the composition of each shopping basket. Design/methodology/approach This work uses scanner data to uncover product category interdependencies. As the number of possible relationships among them can be very large, the authors introduce an approach that generates an intuitive graphical representation of these interrelationships by using data analysis techniques available in standard statistical packages, such as multidimensional scaling and clustering. Findings The methodology was validated using data from a supermarket store. The analysis for that particular store revealed four groups of products categories that are often jointly purchased. The study of each of these groups allowed us to conceive the retail store under study as a small set of sub-businesses. These conclusions reinforce the strategic need for proactive coordination of marketing activities across interrelated product categories. Research limitations/implications The approach is sufficiently general to be applied beyond the supermarket industry. However, the empirical findings are specific to the store under analysis. In addition, the proposed methodology identifies cross-category interrelations, but not their underlying sources (e.g. marketing or non-marketing interrelations). Practical implications The results suggest that retailers could potentially benefit if they transition from the traditional category management approach where retailers manage product categories in isolation into a customer management approach where retailers identify, acknowledge and leverage interrelations among product categories. Originality/value The authors present a fast and wide-range approach to study the shopping behavior of customers, detect cross-category interrelations and segment the retailer’s business and customers based on information about their shopping baskets. Compared to existing approaches, its simplicity should facilitate its implementation by practitioners.

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Section: Definitions and Methodologymentioning

confidence: 99%

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confidence: 99%

Market basket analysis insights to support category management

Musalem

Aburto

Bosch

2018

EJM

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“…According to Statistics Canada there exist 3 consumer profiles (see [WJ03], [WJ02], and [TNTK16]). The first profile represents consumers who buy only promotional items.…”

Section: Consumer Profilementioning

confidence: 99%

An intelligent shopping list based on the application of partitioning and machine learning algorithms

Tahiri¹,

Mazoure²

2019

Proceedings of the Python in Science Conference

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A grocery list is an integral part of the shopping experience of many consumers. Several mobile retail studies of grocery apps indicate that potential customers place the highest priority on features that help them to create and manage personalized shopping lists. First, we propose a new machine learning model written in Python 3 that predicts which grocery products the consumer will buy again or will try to buy for the first time, and in which store(s) the purchase will be made. Second, we introduce a smart shopping template to provide consumers with a personalized weekly shopping list based on their shopping history and known preferences. As the explanatory variables, we used available grocery shopping history, weekly product promotion information for a given region, as well as the product price statistics.

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“…The Law of Double Jeopardy Ehrenberg, Goodhardt, & Barwise, 1990), household brand repertoire development (e.g. Stocchi, Banelis, & Wright, 2016;Trinh, 2014) and the extent to which brands and SKUs share their customers with competitors (e.g., Tanusondjaja, Nenycz-Thiel, & Kennedy, 2016).…”

Section: Introductionmentioning

confidence: 99%