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The Effect of Sales Assistance on Purchase Decisions: An analysis using retail video data

Author

Listed:
  • Aditya Jain

    (Baruch College)

  • Sanjog Misra

    (University of Chicago Booth School of Business)

  • Nils Rudi

    (Yale University)

Abstract

We investigate the role of sales assistance in driving customer’s purchase decision using unique observational video data from a cosmetics retail chain. The data contain visual descriptors of customers and their in-store activities including the time they spent in interacting with salespersons (sales assistance), and are linked to their purchase decisions. Our empirical specification is based on the process of customer deciding to engage in sales assistance to acquire information meaningful toward purchase decision. Thus, we treat sales assistance as endogenous construct, and employ a control function approach to correct for this endogeneity using instruments pertaining to the supply of sales assistance. Our analysis quantifies the role of sales assistance in driving a customer’s purchase decision, and it shows that the effectiveness of sales assistance diminishes with its amount. In addition to highlighting the importance of sales assistance towards purchase decisions, our results also quantify how the retailer can influence sales assistance by increasing the availability of salespersons. We also examine the effect of sales assistance on in-store search carried out by the customer. Finally, we offer number of context specific insights into the heterogeneity of customers’ shopping and purchasing behavior.

Suggested Citation

  • Aditya Jain & Sanjog Misra & Nils Rudi, 2020. "The Effect of Sales Assistance on Purchase Decisions: An analysis using retail video data," Quantitative Marketing and Economics (QME), Springer, vol. 18(3), pages 273-303, September.
    • Handle: RePEc:kap:qmktec:v:18:y:2020:i:3:d:10.1007_s11129-020-09223-w
    DOI: 10.1007/s11129-020-09223-w
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    Cited by:

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    3. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    4. Gauri, Dinesh K. & Jindal, Rupinder P. & Ratchford, Brian & Fox, Edward & Bhatnagar, Amit & Pandey, Aashish & Navallo, Jonathan R. & Fogarty, John & Carr, Stephen & Howerton, Eric, 2021. "Evolution of retail formats: Past, present, and future," Journal of Retailing, Elsevier, vol. 97(1), pages 42-61.
    5. Haofeng Jin & Zhentong Lu, 2021. "Measuring the Effectiveness of Salespeople: Evidence from a Cold-Drink Market," Staff Working Papers 21-40, Bank of Canada.
    6. Wayne Taylor & Brett Hollenbeck, 2021. "Leveraging loyalty programs using competitor based targeting," Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 417-455, December.
    7. Guo, Xiongfei & Chen, Jing & Wu, Jie & Zhang, Tinglong & Zhang, Hui, 2024. "Returns policy, in-store service, and contract strategies in the presence of customer returns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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    More about this item

    Keywords

    Sales assistance; Customer search; Video analytics;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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