BUSI 424 Week 5 Discussion Board Forum 5 Example

Discussion Board Forum 5: E-Commerce Marketing and Advertising Concepts

BUSI 424 E-Commerce, Discussion Board Forum 5 Thread and Replies

Thread by [Student Name]

The forum prompt, the retailer and the three classmates are composites; the textbook and the research cited are real, and the structure follows Liberty's public course guide for BUSI 424.

Thread

Laudon and Traver (2017) describe a long list of online marketing metrics, from impressions and click-through rates to conversion rates and return on ad spend. These numbers are easy to collect, and that is part of the problem. They show what happened after people saw an ad, not what the ad caused. I want to argue that the honest measure of online advertising is incrementality: the sales that would not have happened without the ad.

The difference can be enormous. Blake et al. (2015) ran a large field experiment at eBay, turning off paid search ads on searches for the eBay brand name in some regions. Sales barely changed, because people searching for "eBay" were going to eBay anyway, and the ads simply put a paid link above the free one. Ads on non-brand searches did more, but mostly for new and infrequent customers. The ads looked profitable on the dashboard because the dashboard credited them with sales that the customers' own intentions had already produced.

Retargeting shows a similar pattern. Lambrecht and Tucker (2013) studied ads shown to people who had visited a travel website and compared generic brand ads with dynamic ads showing the exact hotel a person had viewed. On average the generic ads worked better. The detailed ads helped only for shoppers who had already narrowed their preferences, such as those who had visited a review site. Showing someone the exact product they looked at is not automatically persuasive.

A composite outdoor gear retailer offers a practical case. Its dashboard says brand search ads return eight dollars for every dollar spent, its best campaign. A simple test would turn those ads off for a random half of its regions for four weeks and compare total sales. If sales hold steady, the budget can move to channels that reach customers who do not already know the brand. The textbook's metrics are still useful, but only after a test shows which ones reflect real influence.

The same logic applies to any channel the textbook describes, including display ads, affiliate programs and email. A metric becomes trustworthy only after a test shows that it moves when the spending moves. Without that check, a business may keep paying for its most loyal customers to click on an ad on their way to buying.

Reply to Camila

Camila, your thread on influencer marketing for a skincare brand was well researched, and I agree that influencers can reach audiences traditional ads miss. I would apply this week's idea of incrementality to your example, though. Many brands judge influencer campaigns by discount code redemptions, but some of the people who use an influencer's code were already planning to buy and simply found a coupon. A better test would compare sales in a period or market where the influencer posted with a similar one where they did not, or track how many code users were first-time customers. If most redemptions come from existing customers, the campaign may be giving away margin rather than creating sales. Influencers who bring genuinely new customers are worth more than their redemption counts suggest, and those who reach only the brand's fans are worth less. It also helps to look at the influencer's audience before paying, since follower counts can be inflated and some audiences are mostly in countries where the brand does not ship. A smaller creator whose followers closely match the brand's customers often produces more new buyers than a famous one. Asking the influencer for audience data before signing is a reasonable request. How did the brand in your example decide which influencers to keep?

Reply to Joshua

Joshua, I agree with your argument that email marketing remains one of the most valuable channels for small online stores, and I think the chapter supports you for a reason you did not mention. Email reaches people who have chosen to hear from the business, so the store owns the relationship rather than renting attention from a search engine or social platform. That ownership means the cost of each message is close to zero, and the store is not exposed to a platform suddenly raising prices or changing its algorithm. The weakness, as the textbook notes, is that email lists must be built over time and can be damaged by sending too often, which raises unsubscribe rates. I would also apply incrementality here: a store can hold back a random slice of its list from a promotion and compare purchases, which shows how much the email itself added. Segmenting the list adds value too. A customer who bought hiking boots last month should not get the same email as one who only browsed, and the chapter's discussion of personalization suggests that relevant messages earn more opens and fewer unsubscribes. Sending fewer, better messages usually beats sending more. Has your example business ever tested its emails that way?

Reply to Abby

Abby, you raised a fair objection to my thread: a small business may not have enough customers or budget to run a clean experiment, and you are right that a holdout test with tiny numbers can produce misleading results. I would not give up on the idea, though. A small shop can still learn from simpler comparisons, such as pausing a campaign for two weeks and watching whether total sales, not just ad-attributed sales, change, or asking new customers how they heard about the store. Those methods are less precise than the large experiments in the research, but they protect against the biggest error, paying for sales that would have happened anyway. Small firms can also borrow conclusions from published experiments, such as the finding that brand search ads add little, when they cannot test everything themselves. Another option is to test only the largest line item. If one campaign takes most of the budget, even a rough before-and-after comparison on that campaign can save more money than careful measurement of many small ones. Even imperfect tests beat trusting the dashboard alone. A small shop can also compare two similar weeks with and without a promotion. Which of those approaches do you think would fit the bakery in your example?

References

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

Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, 50(5), 561-576. https://doi.org/10.1509/jmr.11.0503

Laudon, K. C., & Traver, C. G. (2017). E-commerce 2016: Business, technology, society (12th ed.). Pearson.