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What is happening to my nearby stores? The own- and cross-effect of a radical store transformation on existing customers

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A Correction to this article was published on 28 June 2023

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Abstract

Brick-and-mortar grocery retailers that undertake major format changes often do so in a staggered rollout and radically transform just one store at a time. This approach begs two questions: What effects does a radical store transformation have on existing customers’ sales at the transformed store (own-effect) and at the chain’s nearby untransformed stores (cross-effect)? Do the effects vary with customer characteristics? These questions are investigated using a quasi-field experiment of a staggered radical store transformation of a German retailer. Conventional wisdom would predict cannibalization of nearby untransformed stores’ sales. However, applying our proposed theoretical framework shows, for this empirical case, a negative own- but a positive cross-effect on existing customers. Further, existing customers who had a greater preference for and shopped more at the old format are most likely to migrate. Thus, nearby untransformed stores can help retain existing customers who may get turned off by a radical store transformation.

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Notes

  1. The closest next (untransformed) store is located about 20 km away from the transformed store, so it is not “nearby”, as was confirmed by the retail chain’s management.

  2. Aggregate sales = number of visits * spending per visit by existing customers. The number of visits is the consequence of their ‘Likelihood to shop’ which captures whether an existing customer will visit a store in that week, and the (conditional) ‘spending’ which captures the weekly expenditures given a visit.

  3. We summed the sales of the two nearby untransformed stores, to keep the analyses and findings tractable and because we are not interested in differential effects across these two nearby stores.

  4. The main results remain robust to a different operationalization that accounts for the overlap of the first four weeks, such that we respecify the Afteropendummy variable to equal 1 only after the first four weeks following opening and 0 before, and the Afteropenweeks variable to take values of 1–80 after the first four weeks following opening and 0 before. The model is also robust when we use ln(Afteropenweeks) and when we add two dummies that equal 1 in the first or last two weeks of the construction period, respectively. Also accounting for possible sales changes due to press exposure (dummy variable, equals 1 in the weeks before the reopening when the format change was mentioned in the press, 7 total) did not change the main results. The press exposure dummy variable was not significant.

  5. To depict how the transformation effect varies over time, we ran a model with 84 time dummies instead of the Afteropendummy and the Afteropenweeks variables. We plot the coefficients in Web Appendix B, Fig. B.2. They substantiate our findings.

  6. The requirement of a minimum of four purchases per year is supported by company practice; some data are available only for customers who reach this minimum. We additionally selected households with a minimum of two purchases in the initialization period, or about half of the before period.

  7. As in Study 1, we summed spending in the two nearby untransformed stores. The percentage of observations with visits to both nearby stores in the same week is very small, just 0.41% of all observations.

  8. In the transformed store model, sales equal 0 during the closure period, so we cannot estimate the store closure effect for treatment households. Accordingly, the transformed store model contains 73 coefficients, whereas the nearby untransformed stores model has 74 coefficients.

  9. The control variables (Treati * Aftert * RelDistancei) point to a broadening of the catchment area for the transformed store: Existing customers who live relatively farther away from the transformed store are more likely to visit it, and if they do, they also spend more. For untransformed stores, existing customers who live relatively closer are more likely to visit it, but if they do, they spend less.

  10. In Table 5, we report whether coefficients are unaffected or become (in)significant. A detailed table of the results of the robustness checks is available on request.

  11. Remodeling research indicates negative effects during the remodeling, such as due to noise or inconvenience (Brueggen et al., 2011; Dagger & Danaher, 2014; Ferraro et al., 2017).

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Acknowledgements

The authors would like to thank our corresponding retailer for data access. They also thank AiMark for data support used in the matching procedure of Study 1. They further wish to thank support by KU Leuven grant C14/20/018 and like to thank Ngoc Ha Vu and Jan Boßlet, two student assistants for their help.

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Authors and Affiliations

  1. Faculty of Economics and Business (FEB), KU Leuven, Leuven, Belgium

    Els Breugelmans

  2. Institute for Management Research, Radboud University Nijmegen, Nijmegen, The Netherlands

    Marleen Hermans

  3. Chair of Marketing Management, University of Muenster, Münster, Germany

    Manfred Krafft, Mirja Kroschke & Felix Lehmkuhle

  4. University of Auckland, Auckland, New Zealand

    Manfred Krafft

  5. Otto GmbH & Co KG, Hamburg, Germany

    Mirja Kroschke

  6. KU School of Business, University of Kansas, Lawrence, KS, USA

    Murali Mantrala

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  1. Els Breugelmans
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Correspondence to Els Breugelmans.

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The original online version of this article was revised: The Eq. 2 was not correct.

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Breugelmans, E., Hermans, M., Krafft, M. et al. What is happening to my nearby stores? The own- and cross-effect of a radical store transformation on existing customers. J. of the Acad. Mark. Sci. 52, 217–238 (2024). https://doi.org/10.1007/s11747-023-00946-2

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