IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1801.07826.html
   My bibliography  Save this paper

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

Author

Listed:
  • Susan Athey
  • David Blei
  • Robert Donnelly
  • Francisco Ruiz
  • Tobias Schmidt

Abstract

This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

Suggested Citation

  • Susan Athey & David Blei & Robert Donnelly & Francisco Ruiz & Tobias Schmidt, 2018. "Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data," Papers 1801.07826, arXiv.org.
  • Handle: RePEc:arx:papers:1801.07826
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1801.07826
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Francisco J. R. Ruiz & Susan Athey & David M. Blei, 2017. "SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements," Papers 1711.03560, arXiv.org, revised Jun 2019.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. J. Daniel Aromí & M. Paula Bonel & Julián Cristiá & Martín Llada, 2020. "Socio-economic status and mobility during the COVID-19 pandemic: An analysis of large Latin American urban areas," Asociación Argentina de Economía Política: Working Papers 4307, Asociación Argentina de Economía Política.
    2. Patacchini, Eleonora & Barwick, Panle Jia & Liu, Yanyan & Wu, Qi, 2019. "Information, Mobile Communication, and Referral Effects," CEPR Discussion Papers 13786, C.E.P.R. Discussion Papers.
    3. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    4. Victor Couture & Cecile Gaubert & Jessie Handbury & Erik Hurst, 2019. "Income Growth and the Distributional Effects of Urban Spatial Sorting," NBER Working Papers 26142, National Bureau of Economic Research, Inc.
    5. Xie, Lusi & Adamowicz, Wiktor & Lloyd-Smith, Patrick, 2023. "Spatial and temporal responses to incentives: An application to wildlife disease management," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    6. Evan Munro & Serena Ng, 2022. "Latent Dirichlet Analysis of Categorical Survey Responses," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 256-271, January.
    7. Gabriel E. Kreindler & Yuhei Miyauchi, 2019. "Measuring Commuting and Economic Activity inside Cities with Cell Phone Records," Boston University - Department of Economics - Working Papers Series WP2020-006, Boston University - Department of Economics, revised Apr 2020.
    8. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    9. Federica Daniele & Mariona Segu & David Bounie & Youssouf Camara, 2022. "Bike-friendly cities: an opportunity for local businesses? Evidence from the city of Paris," THEMA Working Papers 2022-09, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    10. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    11. Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021. "Counterfactual inference for consumer choice across many product categories," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
    12. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org.
    13. Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
    14. Tianyu Du & Ayush Kanodia & Susan Athey, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Papers 2304.01906, arXiv.org, revised Jul 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin O'Connell & Pierre Dubois & Rachel Griffith, 2022. "The Use of Scanner Data for Economics Research," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 723-745, August.
    2. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.

    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1801.07826. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.