Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data

Suhara, Yoshihiko; Bahrami, Mohsen; Bozkaya, Burçin; Pentland, Alex

doi:10.1089/big.2020.0161

Cited by 11 publications

(10 citation statements)

References 45 publications

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“…Thus, executive board members are more than cautious to locate new physical stores and decorate them with different budget levels when planning to enter a new market. For example, in 2019, Adidas opened 147 new stores in total, among which, 142 were decorated as factory outlets and 5 as concession corners stores; meanwhile, it also closed 9 concept stores (Statista, 2021). A similar emphasis on physical stores' decisions was also identified in Nike's business practice (Emma, 2019).…”

Section: Introductionmentioning

confidence: 90%

Locating facilities under competition and market expansion: Formulation, optimization, and implications

Lin¹,

Tian²,

Zhao

2022

Production and Operations Management

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As the ongoing battle between brick‐and‐mortar stores and e‐commerce shops escalates, managers of the former are becoming more cautious regarding their strategic store site selection and decoration decisions, particularly if foreseeable competition from rival companies exists. This paper investigates a bilevel competitive facility location (BCFL) problem, where two companies, a leader and a follower, plan to enter a market sequentially. Each company has a budget to open and design facilities. The goal is to maximize expected revenue that is forecasted through a discrete choice model. To reflect a practical environment, we further consider a situation with elastic demand, explaining the market expansion effect when customers are offered better service due to the opening of new facilities. We formulate the problem as a nonlinear 0‐1 bilevel program. Because of the bilevel structure and the market expansion effect, this problem is so challenging that we are unaware of any exact algorithms in the literature. Motivated by this gap, we develop an exact framework that leverages the state‐of‐the‐art value‐function–based approach. However, this framework requires solving a mixed‐integer nonconvex optimization problem (MINOP) at each iteration, which is computationally prohibitive even for medium‐scale instances. To mitigate the intractability, we propose a new framework that avoids MINOP and tackles instances with hundreds of location variables. Finally, we conduct extensive computational studies to show the efficiency and effectiveness of our method as well as provide insightful guidance for managers to have win–win/dominate outcomes and choose an appropriate market size function when dealing with expansion decisions in chained business operations.

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Section: Introductionmentioning

confidence: 90%

Locating facilities under competition and market expansion: Formulation, optimization, and implications

Lin¹,

Tian²,

Zhao

2022

Production and Operations Management

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“…As discussed above, store location selection is never an outdated topic for retailers. Apart from empirical studies, theories and models have been gradually developed to quantify the commercial environment and consumer preferences ( Suhara, Bahrami, Bozkaya, & Pentland, 2021 ) that cover techniques involving market share ( Drezner, 2006 ). This theoretical framework can be traced back to 1931 when Reilly (1931 ) proposed the law of retail gravitation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When consumers can choose more efficient means of transportation or there is no significant difference in the distances between consumers and two stores, it is unrealistic to judge the attractiveness of different stores by considering the distance alone. Deterministic utility models are aggregated models assuming the consumer patronizes the store with the highest weighted utility ( Behrens et al, 2012 , Drezner, 1994a , Suhara et al, 2021 ). According to the random utility model, consumers’ preferences towards various brands will impact store selections, as they tend to patronize specific chains to which they are loyal.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, parameter calibration plays an essential role in improving the accuracy of Huff gravity model applications. The ordinary least squares technique (OLS), geographically weighted regression (GWR), and particle swarm optimization (PSO) are the main approaches used in the literature so far ( Eberhart and Kennedy, 1995 , Huff and McCallum, 2008 , Liang et al, 2020 , Nakanishi and Cooper, 1974 , Suárez-Vega et al, 2015 , Suhara et al, 2021 ). OLS is sensitive to outliers and GWR disregards consumer preferences by focusing on location.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using gravity model to make store closing decisions: A data driven approach

Bahrami¹,

Xu²,

Tweed³

et al. 2022

Expert Systems with Applications

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“…The Huff gravity model generates a discrete (area-level) surface of the visit probability for each store as calculated from store size and travel distance from surrounding areas (9) . The model has been widely used to convert quantities of store-level sales into surrounding areas for the past four decades among retail management scientists (10,11) .…”

mentioning

confidence: 99%

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada

Mamiya

Schmidt

Moodie

et al. 2021

Public Health Nutr.

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Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet-soda, flavoured (sugar-added) yogurt, and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data. Setting: Greater Montreal, Canada in 2012 Participants: Neighbourhood districts (n=193). Results: The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1.08, 95% Credible Interval [CI]: 1.02-1.14), while that of plain yogurt had a negative association (0.93, 95% CI: 0.89-0.96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1,765 with the indicators, 1,772 without). Conclusion: Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation.

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Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data

Cited by 11 publications

References 45 publications

Locating facilities under competition and market expansion: Formulation, optimization, and implications

Locating facilities under competition and market expansion: Formulation, optimization, and implications

Using gravity model to make store closing decisions: A data driven approach

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada

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