Abstract
While bricks-and-mortar-only retailers do not offer online purchasing, they often take advantage of multi-channel management strategies to reach consumers in a pre-purchase phase. We investigate whether paid search can increase the sales of brick-and-mortar retailers who promote their offers via an informational website. Although a sizeable one third of all retailers still trade without an online-shop, previous work has been silent about the effects of paid search for them. We make use of a randomized field experiment and an end-to-end tracking mechanism to investigate the cross-channel behavior of individual consumers. Our empirical results suggest that, whilst paid search increases the number of potential customers through enhancing the reach of marketing initiatives, store sales are not increased. We conclude that customers who search online to buy offline primarily use paid search as a navigational shortcut to the retailer’s website. Consequently, bricks-and-mortar-only retailers seeking to increase store purchases should approach paid search with caution.
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References
Abraham, M. (2008). The off-line impact of online ads. Harvard Business Review, 86(4), 28.
Ailawadi, K. L., Neslin, S. A., & Gedenk, K. (2001). Pursuing the value-conscious consumer: Store brands versus National Brand Promotions. Journal of Marketing, 65(1), 71–89. https://doi.org/10.1509/jmkg.65.1.71.18132
Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An Empiricist’s companion. Princeton: Princeton University Press.
Animesh, A., Viswanathan, S., & Agarwal, R. (2011). Competing “creatively” in sponsored search markets: The effect of rank, differentiation strategy, and competition on performance. Information Systems Research, 22(1), 153–169. https://doi.org/10.1287/isre.1090.0254
Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60–76. https://doi.org/10.1509/jmkr.45.1.60
Bandiera, O., Barankay, I., & Rasul, I. (2011). Field experiments with firms. The Journal of Economic Perspectives, 25(3), 63–82. https://doi.org/10.1257/jep.25.3.63
Bawa, K., & Shoemaker, R. W. (1987). The coupon-prone consumer: Some findings based on purchase behavior across product classes. Journal of Marketing, 51(4), 99–110. https://doi.org/10.2307/1251251
Baye, M. R., Santos, l., de, B., & Wildenbeest, M. R. (2016). Search engine optimization: What drives organic traffic to retail sites? Journal of Economics & Management Strategy, 25(1), 6–31. https://doi.org/10.1111/jems.12141
Berman, R., & Katona, Z. (2013). The role of search Eengine optimization in search marketing. Marketing Science, 32(4), 644–651. https://doi.org/10.1287/mksc.2013.0783
Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119(1), 249–275. https://doi.org/10.1162/003355304772839588
Bitkom. (2017). Digitalisierung im Stationären Einzelhandel. Retrieved from http://www.esales4u.de/2017/digitalisierung-im-stationaeren-einzelhandel.php.
Blake, T., Nosko, C., & Tadelis, S. (2015). Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica, 83(1), 155–174. https://doi.org/10.3982/ECTA12423
Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249–267. https://doi.org/10.1509/jmkr.40.3.249.19241
Chan, D. X., Yuan, Y., Koehler, J., & Kumar, D. (2011). Incremental clicks: The impact of search advertising. Journal of Advertising Research, 51(4), 643–647. https://doi.org/10.2501/JAR-51-4-643-647
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale: Erlbaum.
Dholakia, R. R., Zhao, M., & Dholakia, N. (2005). Multichannel retailing: A case study of early experiences. Journal of Interactive Marketing, 19(2), 63–74. https://doi.org/10.1002/dir.20035
Dholakia, U. M., Kahn, B. E., Reeves, R., Rindfleisch, A., Stewart, D., & Taylor, E. (2010). Consumer behavior in a multichannel, multimedia retailing environment. Journal of Interactive Marketing, 24(2), 86–95. https://doi.org/10.1016/j.intmar.2010.02.005
Dinner, I. M., Van Heerde, H. J., & Neslin, S. A. (2014). Driving online and offline sales: The Cross-Channel effects of traditional, online display, and paid search advertising. Journal of Marketing Research, 51(5), 527–545. https://doi.org/10.1509/jmr.11.0466
Frambach, R. T., Roest, H. C., & Krishnan, T. V. (2007). The impact of consumer internet experience on channel preference and usage intentions across the different stages of the buying process. Journal of Interactive Marketing, 21(2), 26–41. https://doi.org/10.1002/dir.20079
Georgallides, G. (2017). 4 Ways Brick-and-Mortar Stores can Outsell Online Retailers. Retrieved from https://www.entrepreneur.com/article/290766.
Google. (2014). Understanding Consumers’ Local Search Behavior. Retrieved from https://think.storage.googleapis.com/docs/how-advertisers-can-extend-their-relevance-with-search_research-studies.pdf.
He, Z., Cheng, T., Dong, J., & Wang, S. (2016). Evolutionary location and pricing strategies for service merchants in competitive O2O markets. European Journal of Operational Research, 254(2), 595–609. https://doi.org/10.1016/j.ejor.2016.03.030
Inman, J. J., & McAlister, L. (1994). Do coupon expiration dates affect consumer behavior? Journal of Marketing Research, 31(3), 423. https://doi.org/10.2307/3152229
Katona, Z., & Sárváry, M. (2010). The race for sponsored links: Bidding patterns for search advertising. Marketing Science, 29(2), 199–215. https://doi.org/10.1287/mksc.1090.0517
Kireyev, P., Pauwels, K., & Gupta, S. (2016). Do display ads influence search? Attribution and dynamics in online advertising. International Journal of Research in Marketing, 33(3), 475–490. https://doi.org/10.1016/j.ijresmar.2015.09.007
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.1080/01621459.1952.10483441
Lal, R., & Sarvary, M. (1999). When and how is the internet likely to decrease price competition? Marketing Science, 18(4), 485–503. https://doi.org/10.1287/mksc.18.4.485
Lewis, R., & Reiley, D. (2014). Online ads and offline sales: Measuring the effect of retail advertising via a controlled experiment on yahoo! Quantitative Marketing and Economics, 12(3), 235–266. https://doi.org/10.1007/s11129-014-9146-6
Liang, T.-P., & Huang, J.-S. (1998). An empirical study on consumer acceptance of products in electronic markets: A transaction cost model. Decision Support Systems, 24(1), 29–43. https://doi.org/10.1016/S0167-9236(98)00061-X
Lilien, G. L., Kotler, P., & Moorthy, K. S. (1992). Marketing Models. New Delhi: Prentice Hall of India Private Ltd..
Liu, J., Abhishek, V., & Li, B. (2016). The Impact of Mobile Adoption on Customer Omni-Channel Banking Behavior. ICIS 2016 Proceedings. Retrieved from http://aisel.aisnet.org/icis2016/EBusiness/Presentations/10.
Lu, X., & Zhao, X. (2014). Differential effects of keyword selection in search engine advertising on direct and indirect sales. Journal of Management Information Systems, 30(4), 299–326. https://doi.org/10.2753/MIS0742-1222300411
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491.
Mittal, B. (1994). An integrated framework for relating diverse consumer characteristics to supermarket coupon redemption. Journal of Marketing Research, 31(4), 533–544. https://doi.org/10.2307/3151881
Monetate. (2017). Conversion Rate of Online Shoppers Worldwide as of 1st Quarter 2017. Retrieved from https://www.statista.com/statistics/439576/online-shopper-conversion-rate-worldwide/.
Nielsen. (2016). Global Connected Commerce. Retrieved from http://www.nielsen.com/us/en/insights/reports/2016/global-connected-commerce.html.
Nottorf, F., & Funk, B. (2013). A cross-industry analysis of the spillover effect in paid search advertising. Electronic Markets, 23(3), 205–216. https://doi.org/10.1007/s12525-012-0117-z
Rampell, A. (2010). Why Online2Offline Commerce is a Trillion Dollar Opportunity. Retrieved from https://techcrunch.com/2010/08/07/why-online2offline-commerce-is-a-trillion-dollar-opportunity/.
Rubin, D. B. (1986). Comment: Which ifs have causal answers. Journal of the American Statistical Association, 81(396), 961–962. https://doi.org/10.1080/01621459.1986.10478355
Rutz, O., Trusov, M., & Bucklin, R. E. (2011). Modeling indirect effects of paid search advertising: Which keywords lead to more future visits? Marketing Science, 30(4), 646–665. https://doi.org/10.1287/mksc.1110.0635
Sayedi, A., Jerath, K., & Srinivasan, K. (2014). Competitive poaching in sponsored search advertising and its strategic impact on traditional advertising. Marketing Science, 33(4), 586–608. https://doi.org/10.1287/mksc.2013.0838
sdworx. (2017). Two Thirds of Retailers have Readily Embraced e-Commerce. Retrieved from https://www.sdworx.com/en-us/press/2017-04-03-e-commerce.
Skrovan, S. (2017). Why researching online, Shopping offline is the new norm. Retrieved from http://www.retaildive.com/news/why-researching-online-shopping-offline-is-the-new-norm/442754/.
Strebel, J., Erdem, T., & Swait, J. (2004). Consumer search in high technology markets: Exploring the use of traditional information channels. Journal of Consumer Psychology, 14(1–2), 96–104. https://doi.org/10.1207/s15327663jcp1401&2_11
Sudhir, K. (2016). Editorial - the exploration-exploitation tradeoff and efficiency in knowledge production. Marketing Science, 35(1), 1–9. https://doi.org/10.1287/mksc.2015.0974
Thomas, J. S., & Sullivan, U. Y. (2005). Managing marketing communications with multichannel customers. Journal of Marketing, 69(4), 239–251. https://doi.org/10.1509/jmkg.2005.69.4.239
TNS Infratest. (2016). Consumer Barometer Survey. Retrieved from https://www.statista.com/statistics/372970/search-engine-pre-purchase-germany/.
Tsai, T.-M., Wang, W.-N., Lin, Y.-T., & Choub, S.-C. (2015). An O2O commerce service framework and its effectiveness analysis with application to proximity commerce. Procedia Manufacturing, 3, 3498–3505. https://doi.org/10.1016/j.promfg.2015.07.668
Valentini, S., Montaguti, E., & Neslin, S. A. (2011). Decision process evolution in customer channel choice. Journal of Marketing, 75(6), 72–86. https://doi.org/10.1509/jm.09.0362
Vaver, J., & Koehler, J. (2011). Measuring Ad Effectiveness Using Geo Experiments. Retrieved from http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/38355.pdf.
Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007). Multichannel customer management: Understanding the research-shopper phenomenon. International Journal of Research in Marketing, 24(2), 129–148. https://doi.org/10.1016/j.ijresmar.2006.11.002
Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307–333. https://doi.org/10.2307/1912557
Wiesel, T., Pauwels, K., & Arts, J. (2011). Marketing’s profit impact: Quantifying online and off-line funnel progression. Marketing Science, 30(4), 604–611. https://doi.org/10.1287/mksc.1100.0612
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. https://doi.org/10.2307/3001968
Zenith. (2017). Prognose zu den Investitionen in Internetwerbung weltweit in den Jahren 2016 bis 2019 nach Segmenten (in Milliarden US-Dollar). Retrieved from https://de.statista.com/statistik/daten/studie/209291/umfrage/investitionen-in-internetwerbung-weltweit-nach-segmenten/.
Acknowledgements
We are grateful for comments on a previous version of this paper received by the editors, reviewers and conference participants at the 13th International Conference “Wirtschaftsinformatik “in St. Gallen. We thank our partnering firm finke Das Erlebnis-Einrichten Gmbh & Co KG for giving us the opportunity to conduct the field experiment as well as the special issue editors and the review team of Electronic Markets who provided us with insightful feedback and suggestions throughout the review rounds. Beyond that, we also benefited from helpful discussions with René Fahr, Behnud Mir Djawadi and Dominik Gutt.
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Appendices
Appendix 1: Top 100 Search Terms
In accordance with Rutz et al. (2011) we made use of generic search terms as well as brand-specific requests and placed ads for both categories in our experiment. Furthermore, we excluded search requests containing the retailer’s name to enhance the probability of new customers being exposed to the crafted ad. The list below provides the 100 search terms which received the most clicks in the course of the experiment. Please note that search terms were translated from German to English. Location names were replaced by “<location name>“. Naturally, furniture brand names (marked with the prefix “b:”) were not translated. A “+” indicates the applied modified broad match option of Google AdWords.
armchair | bench +online | corner sofa | furniture +online | shelf |
---|---|---|---|---|
b: bretz | bookshelf | corner wardrobe | glass tables | side table |
b: hülsta | bowls | couch | guest bed | sideboard |
b: jensen | box spring bed | cushion | henders hazel | single bed |
b: kare | box spring sofa | cutlery | innerspring mattress | slatted frame |
b: kare + < location name> | bunk bed | decoration | kitchen table | sliding door cabinet |
b: loddenkemper | bureau | dining room table | leather sofa | sliding door cabinet +cheap |
b: musterring | cabinet | dining set | library | sofa bed |
b: rolf benz | canopy bed | dining table | lounge suite | solid wood cabinet |
b: stressless | cantilever | dishes | loungers | table |
bar table | CD shelf | double bed | mattress | television armchair |
beanbag | coffee table | dressing table | mattress + < location name> | television furniture |
bed | cold foam mattress | DVD shelf | metal bed | upholstered beds |
bed + < location name> | commode | extending table | pans | upholstered furniture |
bed +buy | console table | frame + < location name> | rattanbed | upholstered furniture + < location name> |
bed +cheap | corner bench | frame +buy | recamiere | wardrobe |
bed +online | corner bench group | functional bed | relax arm chair | wardrobe + < location name> |
bedstead | corner cabinet | furniture | relax loungers | wardrobe +buy |
bench | corner display case | furniture + < location name> | room divider | wing chair |
bench + < location name> | corner shelf | furniture +cheap | round tables | wooden bench |
Appendix 2: Experimental Design: Grouping Combinations
To allow for valid claims in regard to the offline impact of paid search, a matched-subject design is applied. Grouping was performed based on two key performance indicators, namely (1) advertising reach and (2) revenue per customer. In order to determine the most appropriate candidate group all possible grouping combinations were evaluated based on the performance indicators. The Table below depicts all possible grouping combinations. We chose the grouping (BDF-ACE) as this grouping yielded the lowest group differences in terms of advertising reach as well as revenue per customer. To prevent selection biases, we chose the group BDF-ACE instead of group ACE-BDF as this group yields a lower but statistically insignificant (p = 0.854) advertising impact (−1.2%) as well as a reduced but statistically insignificant (p = 0.455) revenue per customer (−0.3%).
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Schlangenotto, D., Kundisch, D. & Wünderlich, N.V. Is paid search overrated? When bricks-and-mortar-only retailers should not use paid search. Electron Markets 28, 407–421 (2018). https://doi.org/10.1007/s12525-018-0287-4
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DOI: https://doi.org/10.1007/s12525-018-0287-4
Keywords
- Paid search
- Consumer behavior across channels
- Substitution effects
- Field experiment
- Bricks-and-mortar-only