IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v99y2023i3p337-352.html
   My bibliography  Save this article

Effects of online and offline advertising and their synergy on direct telephone sales

Author

Listed:
  • Lu, Steven Qiang
  • Singh, Sonika
  • de Roos, Nicolas

Abstract

Retailers are accelerating direct marketing efforts to reach consumers, and increasingly integrating telephone numbers in online and offline advertisements to generate direct response. The telephone channel is a channel for both lead generation and sales, and yet its effectiveness for direct sales is unexplored. We provide a novel investigation of the effects of online and offline advertising (search advertising, banner advertising, general print advertising and specialty print advertising) on inbound telephone sales using a unique channel-specific telephone sales dataset. We make four findings: banner advertising, typically suited for exposure-based objectives in online and offline channels, is in fact more effective than search advertising to influence inbound telephone sales in both the short term and long term; print advertising is useful for generating inbound telephone sales; there are synergies for advertising in general (non-product focused) and specialty print (product focused) media; and online advertising is complementary to offline advertising and influences inbound telephone sales from print media. These results highlight that the combinations of advertising sources that effectively generate inbound telephone sales are different from those observed to influence sales in other channels, and have important managerial implications.

Suggested Citation

  • Lu, Steven Qiang & Singh, Sonika & de Roos, Nicolas, 2023. "Effects of online and offline advertising and their synergy on direct telephone sales," Journal of Retailing, Elsevier, vol. 99(3), pages 337-352.
    • Handle: RePEc:eee:jouret:v:99:y:2023:i:3:p:337-352
    DOI: 10.1016/j.jretai.2023.06.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435923000283
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretai.2023.06.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stafford, Marla Royne & Lippold, Eric M. & Sherron, C. Todd, 2003. "The Contribution of Direct Mail Advertising to Average Weekly Unit Sales," Journal of Advertising Research, Cambridge University Press, vol. 43(2), pages 173-179, June.
    2. Feng Wang & Li Zuo & Zhi Yang & Yueyan Wu, 2019. "Mobile searching versus online searching: differential effects of paid search keywords on direct and indirect sales," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1151-1165, November.
    3. Thorsten Wiesel & Koen Pauwels & Joep Arts, 2011. "Practice Prize Paper --Marketing's Profit Impact: Quantifying Online and Off-line Funnel Progression," Marketing Science, INFORMS, vol. 30(4), pages 604-611, July.
    4. Lesscher, Lisan & Lobschat, Lara & Verhoef, Peter C., 2021. "Do offline and online go hand in hand? Cross-channel and synergy effects of direct mailing and display advertising," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 678-697.
    5. Allen McDowell, 2004. "From the help desk: Seemingly unrelated regression with unbalanced equations," Stata Journal, StataCorp LP, vol. 4(4), pages 442-448, December.
    6. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, November.
    7. de Haan, Evert & Wiesel, Thorsten & Pauwels, Koen, 2016. "The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 491-507.
    8. Naik, Prasad A. & Peters, Kay, 2009. "A Hierarchical Marketing Communications Model of Online and Offline Media Synergies," Journal of Interactive Marketing, Elsevier, vol. 23(4), pages 288-299.
    9. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, November.
    10. Wolfgang Härdle & Oliver Linton & Wang & Qihua, 2003. "Semiparametric regression analysis with missing response at random," CeMMAP working papers 11/03, Institute for Fiscal Studies.
    11. Daly, Andrew & Dekker, Thijs & Hess, Stephane, 2016. "Dummy coding vs effects coding for categorical variables: Clarifications and extensions," Journal of choice modelling, Elsevier, vol. 21(C), pages 36-41.
    12. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    13. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    Full references (including those not matched with items on IDEAS)

    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. Kalogridis, Ioannis & Van Aelst, Stefan, 2023. "Robust penalized estimators for functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    2. Morteza Amini & Mahdi Roozbeh & Nur Anisah Mohamed, 2024. "Separation of the Linear and Nonlinear Covariates in the Sparse Semi-Parametric Regression Model in the Presence of Outliers," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    3. Mark J. Meyer & Haobo Cheng & Katherine Hobbs Knutson, 2023. "Bayesian Analysis of Multivariate Matched Proportions with Sparse Response," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 490-509, July.
    4. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    5. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    6. Katarzyna Reluga & María‐José Lombardía & Stefan Sperlich, 2023. "Simultaneous inference for linear mixed model parameters with an application to small area estimation," International Statistical Review, International Statistical Institute, vol. 91(2), pages 193-217, August.
    7. Zanin, Luca, 2023. "A flexible estimation of sectoral portfolio exposure to climate transition risks in the European stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    8. Thomas Niemand & Sascha Kraus & Sophia Mather & Antonio C. Cuenca-Ballester, 2020. "Multilevel marketing: optimizing marketing effectiveness for high-involvement goods in the automotive industry," International Entrepreneurship and Management Journal, Springer, vol. 16(4), pages 1367-1392, December.
    9. Gao, Lisa & Shi, Peng, 2022. "Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 161-179.
    10. Yu Liu & Chin-Shang Li, 2023. "A linear spline Cox cure model with its applications," Computational Statistics, Springer, vol. 38(2), pages 935-954, June.
    11. Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).
    12. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    13. Benjamin Owusu & Bettina Bökemeier & Alfred Greiner, 2023. "Assessing nonlinearities and heterogeneity in debt sustainability analysis: a panel spline approach," Empirical Economics, Springer, vol. 64(3), pages 1315-1346, March.
    14. Hamdy F. F. Mahmoud & Inyoung Kim, 2023. "Semiparametric Integrated and Additive Spatio-Temporal Single-Index Models," Mathematics, MDPI, vol. 11(22), pages 1-15, November.
    15. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    16. Timothy K.M. Beatty & Erling Røed Larsen, 2005. "Using Engel curves to estimate bias in the Canadian CPI as a cost of living index," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(2), pages 482-499, May.
    17. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    18. Hyunju Son & Youyi Fong, 2021. "Fast grid search and bootstrap‐based inference for continuous two‐phase polynomial regression models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    19. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
    20. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.

    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:eee:jouret:v:99:y:2023:i:3:p:337-352. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    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.