BUSI 442 Week 1 Research Paper 1 Example

Knowing Whom to Contact: The Direct Response and Database Foundations of Internet Marketing

Introduction

Long before email, direct mail marketers learned to ask a specific question of every campaign: who responded, and was it worth it? Roberts and Zahay (2018) describe direct response marketing as communication that seeks a measurable action and database marketing as the practice of recording each customer's interactions so that future communication can be targeted and evaluated. Internet marketing inherited both. Email, online ads and websites made contacts cheaper and faster, but they did not change the underlying logic: know your customers, contact the right ones and measure the result. This paper asks what the direct response and database tradition teaches a business marketing online, and it answers through three studies and a composite online garden retailer, Northfield Garden Supply, deciding whom to email in its spring campaign.

Purchase History Is the Most Valuable Data

The first lesson is that what customers have done predicts what they will do far better than who they are. Rossi et al. (1996) examined how much different kinds of information improved target marketing for coupon delivery. They found that demographic information alone added relatively little to targeting value, while even a short purchase history substantially increased the value of targeted offers, because past behavior revealed customers' preferences and price sensitivity.

For Northfield, the implication is direct. The company could segment its 60,000 email subscribers by age or region, but the more useful data are already in its order records: which customers bought vegetable seeds last spring, which bought only houseplants, and which bought once and never returned. A customer's last order says more about the next one than any census category can. An email offering tomato seedlings to last year's vegetable gardeners will outperform the same email sent to everyone in a region.

Selecting Customers by Expected Profit

The second lesson is that the goal is profit, not response. Bult and Wansbeek (1995) developed a method for selecting which households to include in a direct mail campaign by estimating each household's probability of responding and the profit a response would bring, and choosing households whose expected profit exceeded the cost of mailing. They showed that this approach produced higher profits than conventional selection methods.

Email is far cheaper than mail, which tempts businesses to send everything to everyone. But email has costs that do not appear on an invoice: each irrelevant message teaches customers to ignore the next one, and some unsubscribe. Northfield should therefore decide whom to include in each campaign by expected value. A promotion for expensive raised garden beds belongs in the inboxes of customers who have bought large items before, not in every inbox on the list.

Recency, Frequency and Monetary Value

The third lesson concerns how to estimate what each customer is worth. Direct marketers have long ranked customers by recency, frequency and monetary value, known as RFM. Fader et al. (2005) connected these simple measures to customer lifetime value, showing that recency and frequency together, with monetary value, can be used to estimate each customer's expected future purchases and that customers with the same lifetime value can have quite different RFM patterns, which they drew as curves of equal value. Their work gives the familiar RFM table a firmer foundation: it becomes a way to forecast value, not only to sort a list.

Northfield can apply this by scoring every customer each month. A customer who bought three times last season and most recently six weeks ago is worth far more attention than one who bought once two years ago, even if both spent the same amount. The company can reserve its richest offers, such as early access to rare seed varieties, for high-value customers, and send simpler reactivation messages to those whose value is fading.

Testing and Measurement

Direct response marketing always measures. Each of Northfield's spring emails should be sent first to a sample of the target group in two versions, with the better version sent to the rest, and a small control group should receive no email at all so the company can see how many orders the campaign truly added. Roberts and Zahay (2018) emphasize that the measurability of internet marketing is its great advantage, but only if businesses design campaigns to be measured.

Permission and Privacy

The database tradition also carries obligations. Commercial email sent in the United States falls under the CAN-SPAM Act, so Northfield's messages must identify the sender truthfully, avoid misleading subject lines, include a working opt-out that is honored promptly and carry a postal address. Beyond the law, customers expect their data to be used for their benefit. Northfield should collect only the information it uses, explain how purchase history shapes the emails customers receive, and let subscribers choose topics, such as vegetables, flowers or houseplants, so that targeting feels like service rather than surveillance. Permission obtained honestly is also good business, since subscribers who chose to hear from a company read its messages more often than those added without asking.

A Biblical Perspective: The One and the Ninety-Nine

Database marketing can tempt marketers to see customers only as rows in a table or scores in a model. Jesus told of a shepherd who left the ninety-nine sheep to search for the one that was lost, and rejoiced when it was found (English Standard Version Bible, 2001/2016, Luke 15:3-7). The parable speaks of God's love for each person, not of marketing, but it challenges a habit of mind that treats people as aggregates. The best database marketing honors that challenge by using data to serve each customer more personally: remembering what a gardener planted last year, noticing when a loyal customer has gone quiet, and reaching out with something useful rather than another discount. Data used this way respects the individual instead of reducing the individual to a number.

Conclusion

The direct response and database tradition gives internet marketers three durable practices. First, build targeting on purchase history, which predicts behavior far better than demographics. Second, select whom to contact by expected profit, remembering that even cheap channels carry hidden costs when messages are irrelevant. Third, estimate customer value from recency, frequency and monetary value, and allocate attention accordingly. Combined with disciplined testing and a commitment to treating each customer as a person, these practices turn an email list into a relationship with thousands of individuals. For Northfield, the first step is modest: score every customer by recency, frequency and spending before the spring campaign, send each segment an offer matched to what it bought last year, and hold back a small control group to learn what the campaign truly earned.

References

Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378-394. https://doi.org/10.1287/mksc.14.4.378

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415

Roberts, M. L., & Zahay, D. (2018). Internet marketing: Integrating online and offline strategies (4th ed.). Cengage Learning.

Rossi, P. E., McCulloch, R. E., & Allenby, G. M. (1996). The value of purchase history data in target marketing. Marketing Science, 15(4), 321-340. https://doi.org/10.1287/mksc.15.4.321