The Effectiveness of Contextual Competitive Targeting in Conjunction with Promotional Incentives

ABSTRACT

Advancements in Internet technologies have enabled firms to engage in contextual competitive targeting (CCT); that is, the targeted online advertising practice of contextually identifying and poaching prospective customers of competitors. On the basis of two field quasi-experiment studies conducted with Google’s AdSense contextual targeting platform, this article shows that compared to generate targeting, CCT poaching is more effective in raising ad click-through rates (CTRs), but not in inducing conversion rates. Furthermore, promotional incentives can boost CCT’s effects on CTRs, but not conversion rates. The article’s follow-up laboratory survey reveals the reasons for the findings. That is, offering a promotional incentive in CCT ads can arouse customers’ curiosity and thus strengthen the effects of CCT on CTRs. Yet this heightened curiosity does not expand CCT’s effects on conversions because of customer loyalty to the competitor being poached. Collectively the findings suggest different strategies for firms contemplating CCT. When the objective is to attract competitors’ customers to increase their chance of future defect, adding promotional incentives may enhance CCT effectiveness. But when conversion is the main concern, managers can save promotional dollars as CCT poaching induces more sales conversions with less of such incentives.

KEY WORDS AND PHRASES:

• Competitor targeting
• contextual targeting
• Google AdSense
• information incongruity
• online advertising
• promotional incentives
• strategic advertising

Acknowledgments:

The authors acknowledge the financial supports from the National Natural Science Foundation of China (Nos. 71772048, 71522006, 71531006, and 71602026) and China Scholarship Council (No. 201506105044).

Supplemental File

Supplemental data for this article can be accessed on the publisher’s website.

Notes

1. In line with Zhang and Katona [96], we broadly define competitors’ prospective customers as the prospects or leads who are browsing Web pages about the brands and product offerings of competing firms (or rivals of the focal company). These potential customers of rival firms may be real customers of rivals if the focal company cannot poach them with CCT successfully in the short or long run.

2. To verify the difference in the thinking modes between the ad click stage and the conversion stage, we conducted a 2 (Ad Click Stage vs. Conversion Stage) × 2 (CCT Ad vs. Generic Targeting Ad) scenario-base survey study. The results of the survey show that consumers’ thinking mode is indeed significantly more conscious during the conversion stage than during the ad click stage. This significant difference consistently exists in both the CCT group and the Generic group. The details of the survey study are provided in online Appendix F.

3. Quasi-experiments represent a variant of experimental approach in which units are not assigned to conditions randomly; specifically, assignment to conditions is by means of self-selection; that is, where units naturally choose treatment for themselves [8, 81]. This experimental approach has its strengths in that it may afford more empirical realism and ecological validity than typical laboratory experiments [70, 81], and has been employed to provide fruitful insights in various domains, including information systems and e-commerce [23, 84], organizational behavior [37, 63, 68], and marketing [27].

4. The identity of the companies was concealed due to confidentiality requirements.

5. In both studies, the competitor-brand keywords employed are straightforward (i.e., phrases containing competitor brand names); for generic product category keywords, examples are “stock software” and “stock software download” for Experiment 1, and “hotel chain” and “hotel stay” for Experiment 2.

6. Both industries follow “the Rule of Three” [82], which states that markets often become an oligopoly dominated by three large firms.

7. The normal rate of the hotel room is RMB 199 (~ US$32) per night. In the low-promotional incentive condition, customers could order a room at RMB 99.8 for one night, almost half of the original price. In the high-promotional incentive condition, in addition to enjoying the close to 50 percent off normal rate, customers could get an RMB 100 (~ US$16) coupon (for use in their next stay).

8. Before the laboratory survey, we conducted two focus group sessions to ensure that we had captured the alternative explanations that are salient in the context of the study. Eight participants (five women) shared their thoughts in two separate focus group discussions. The focus group guide is provided in online Appendix E (Supplement 2).

9. Where possible, we employed and adapted existing measures from the literature. As some construct measurements were self-developed for this study’s context based on the conceptual definition of the related constructs, we first invited two experienced researchers in the online advertising literature to examine the measurement items for their face validity and appropriateness in the item framing and phrasing. We then conducted unlabeled and labeled sorting sessions with four judges to assess the conceptual validity of the construct measurements. These resulted in minor modifications of the wording used in the measurement items.

10. To rule out an additional alternative explanation that the results could be due to a generally high brand loyalty among the participants (as they indicate the brand to be the first that comes to their mind, i.e., their first-choice brand), we examined the descriptive statistics of this construct. The mean value of brand loyalty is 3.54 with a standard deviation of 1.28. The minimum value is 1.00 while the maximum value is 6.00, indicating a fairly diverse range of values for this construct; that is, consumers are not necessarily highly loyal to the brand they selected as their first choice. But the purchase intention is higher if the consumers perceive the focal company to have a high reputation in the market.

11. http://www.clickz.com/clickz/news/2328886/magna-global-12-facts-on-digital-advertising-in-china (accessed on pril 23, 2014).

Additional information

Notes on contributors

Yiping (Amy) Song

YIPING (AMY) SONG ([email protected]) is an associate professor of marketing at the School of Management, Fudan University, China. Her research interests include digital marketing, machine learning in marketing, and consumer insights based on data analytics. Her research has been published in the Journal of Marketing, Journal of Global Marketing, Journal of Services Marketing, and Journal of Electronic Commerce Research, among others.

Chee Wei (David) Phang

CHEE WEI (DAVID) PHANG ([email protected]) is currently a Professor in the Business School of Nottingham University Ningbo Campus, China. His research interests lie in the areas of social media, mobile commerce, and information technology (IT) in the public sector. His work has been published or is forthcoming in the leading information systems, management, and marketing journals, including Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of the Association for Information Systems, Management Science, and Journal of Marketing, among others.

Shuai Yang

SHUAI YANG ([email protected]) is an associate professor of marketing at Glorious Sun School of Business and Management, Donghua University, China. Her research interests include brand engagement, online advertising, and sharing-economy services. Her work has been published in the Journal of Marketing Management, Journal of Services Marketing, International Journal of Contemporary Hospitality Management, among others.

Xueming Luo

XUEMING LUO ([email protected]; corresponding author) is Charles Gilliland Chair Professor of Marketing, professor of strategic management, and professor of management information systems at Temple University. He is also the founder/director of the Global Center for Big Data in Mobile Analytics at Temple University. His research focuses on mobile consumer behavior, big data marketing strategies, customer analytics with machine learning and networks visualization, gaming and virtual identity, social targeting ads, organizational strategies, and the financial value of marketing metrics. He is an associate editor or editorial board member of several leading marketing journals. His research work has been published in Journal of Marketing, Journal of Marketing Research, Marketing Science, Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Harvard Business Review, and MIT Sloan Management Review, among many others.