BUSI 330 Week 7 Discussion Board Forum 4 Thread and Replies Example

Discussion Board Forum 4: Measuring the Effectiveness of Digital Promotion

BUSI 330 Principles of Marketing, Discussion Board Forum 4 Thread and Replies

Thread by [Student Name]

The prompt and the classmates are composites; the textbook and research cited are real.

Thread

The question

Digital advertising dashboards report clicks, conversions and return on ad spend in real time, which makes promotion look more measurable than ever. Kerin and Hartley (2020) describe integrated marketing communications as coordinating promotional elements to deliver a clear, consistent message, and they stress setting objectives and evaluating results. The harder question is whether the results on a dashboard were caused by the ads or would have happened anyway.

What a field experiment found

Blake et al. (2015) addressed that question at eBay, which had long bought search ads on its own brand name. The company stopped those ads in some markets and kept them in others. Traffic that had arrived through paid brand ads largely shifted to the free search results directly below them, and sales barely changed. Ads on other keywords had small effects, concentrated among infrequent users who did not already know the site. Many of the customers the ads were credited with would have arrived anyway. The dashboard had been counting customers the ads did not create.

Why measurement is hard

Lewis and Rao (2015) explain why even sophisticated advertisers struggle with this. Individual purchases vary so much that the effect of an ad campaign is small relative to that noise, so even experiments with very large samples can have difficulty measuring returns precisely. As a result, many campaigns can be neither proven profitable nor proven wasteful.

Testing honestly

These findings suggest a practical approach. Set objectives in terms of incremental sales, not clicks. Hold out a randomly chosen group of customers or markets that does not see a campaign, and compare their purchases with those who do. Focus spending where ads reach people who would not otherwise find the product. That discipline fits the textbook's emphasis on evaluation, and it often moves budget from familiar, easy-to-measure channels to ones that genuinely reach new customers.

Small businesses can apply the same idea without a data science team. A local gym could pause its paid social ads for two weeks in one neighborhood while continuing them in another, then compare new memberships. The test is imperfect, but it replaces a dashboard's assumption with an actual comparison. Most owners who try it learn that some channels matter far more than they thought and others far less, and the budget they free up can fund experiments with channels they had never tested. The point is not to stop advertising but to learn which advertising earns its cost, so the next campaign starts from evidence instead of habit.

Reply to Jasmine

Jasmine, your report recommended that the cosmetics brand use highly targeted, eye-catching display ads with video and pop-ups, and your audience targeting plan was well thought out. I would add evidence that targeting and intrusiveness may work against each other.

Goldfarb and Tucker (2011) analyzed a large set of online display ad campaigns and found that ads matched to the content of the website and ads that were visually obtrusive each increased purchase intent on their own, but that combining the two reduced effectiveness. Their interpretation was that consumers who saw an ad that was both closely targeted and hard to ignore felt manipulated or concerned about privacy. The effect was strongest for people who were more privacy-sensitive.

That suggests a refinement to your plan: use precise targeting with quieter, less intrusive formats, or use bold formats on broader placements, but avoid doing both at once. Kerin and Hartley stress that promotion should build a consistent, positive relationship with the brand, and an ad that makes customers feel watched could undermine that goal even if it attracts attention.

How do you think the brand's younger customers would react to a pop-up video ad that clearly reflected their recent browsing? It would be worth testing both versions with a small budget before committing, since the research suggests the answer may differ by audience and by how privacy-conscious the customers are.

Reply to Owen

Owen, your report proposed that the outdoor gear company create social media content that customers would share, rather than paying for more ads, and the examples of trail videos were creative. Research on sharing can help decide which content to produce.

Berger and Milkman (2012) studied which New York Times articles made the most emailed list over several months. Content that evoked high-arousal emotions, such as awe, anger or anxiety, was more likely to be shared, while content evoking low-arousal emotions such as sadness was less likely to spread. Positive content was generally shared more than negative content. Awe-inspiring material, which fits an outdoor brand well, performed especially strongly.

For the gear company, that points toward dramatic landscapes, ambitious trips and stories that create a sense of awe, rather than product demonstrations alone. Connecting this to the textbook's integrated approach, the shared content should still carry the brand's consistent message so that the attention it draws reinforces the brand rather than simply entertaining.

Which kind of content do you think would make the company's followers feel awe, and how would you measure whether sharing led to sales? One approach would be to give shared posts a unique discount code, so the company can see how many purchases trace back to content passed between friends rather than to its own paid posts.

References

Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205. https://doi.org/10.1509/jmr.10.0353

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

Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and obtrusiveness. Marketing Science, 30(3), 389-404. https://doi.org/10.1287/mksc.1100.0583

Kerin, R. A., & Hartley, S. W. (2020). Marketing: The core (8th ed.). McGraw-Hill Education.

Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941-1973. https://doi.org/10.1093/qje/qjv023