| Course | MT362 Artificial Intelligence Applications for the Marketing Professional |
|---|---|
| Unit | Unit 1 |
| Paper type | Discussion board post |
| Length | About 420 words, 2 pages double-spaced |
| Format | APA 7 discussion post |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT362 Unit 1
Paid to Hesitate: An MT362 Unit 1 Discussion Post on Retention Offers Aimed by Predicted Risk
MT362 · Unit 1 Discussion Board Post
Posted by [Student Name]
The meal-kit episode is described from the writer's own accounts; the pet chain and its figures are composite, and the research cited is real.
Initial Post
The facts first. I set a two-week pause on my meal-kit account on Sunday, October 4. On Wednesday an email offered 30 percent off my next four boxes. My roommate's order history shows 104 consecutive weeks with no pause and no offer of any size. My claim is that a retention offer aimed by predicted risk can teach customers that hesitation pays.
Here is my guess at why, and it is only a guess made from outside. Our unit reading describes churn models as ranking customers by their predicted chance of leaving, using signals such as order gaps and account changes. Neslin et al. (2006) showed that such models vary widely in accuracy, but almost any of them would treat a pause as a strong signal. A system spending its discounts where risk looks highest would reach me and skip my roommate. Ascarza (2018) found that high predicted risk and responsiveness to an offer are different things, so aiming by risk can waste money on people who would have left anyway or stayed anyway.
Customers who never hesitate pay full price, while customers who pause learn that pausing is worth money.
The course question is which marketing decisions a machine should make, so consider Fernbrook Pet, a composite chain of 43 stores in California and Oregon. Anyone who opens the cancel page for its Repeat Delivery program sees $10 off the next order, and roughly one in five accepts. That is already an automated decision, made by a single rule. Would a model choosing who sees the offer save more subscribers, or hand discounts to people who were staying anyway?
What would the chain need to know before letting a model choose who receives a discount?
Reply to Mei
Mei, your fare that dropped after you abandoned a booking fits the same pattern, but the airline's problem is harder than the meal kit's. A paused subscription is a clear signal; an abandoned booking could mean a hesitant traveler or a comparison shopper who has already bought elsewhere. What data would the airline need to tell them apart, and does guessing wrong cost more than the discount it gives away?
References
Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80-98. https://doi.org/10.1509/jmr.16.0163
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204-211. https://doi.org/10.1509/jmkr.43.2.204
The Purdue Global MT362 Unit 1 instructions
The first board of this AI course asks students to describe an automated marketing decision they have seen or suspect and to ask whether a machine should make it. A dated, specific example works better than a general claim about algorithms. Instructors understand that customers rarely see the system behind an offer, so a careful inference is acceptable when it is labeled as one. The unit reading should explain the likely mechanism in managerial terms. Students are encouraged to apply the question to a business they know or a course case. A closing question to classmates is expected instead of a conclusion, and the reply should push a classmate's example toward the data a system would need to act on it.
How the MT 362 Unit 1 example is put together
The opening paragraph gives dates and counts: the Sunday pause, the Wednesday offer and the roommate's 104 weeks. The claim closes that paragraph. The second paragraph explains, as a labeled guess, how a churn model would rank customers, citing Neslin and colleagues and Ascarza, under a highlighted line about loyal customers paying full price. The third paragraph turns to the pet chain's ten-dollar cancel-page rule, notes that it is already an automated decision, and asks whether a model would improve it. A closing question asks what the chain would need to know first. In reply to Mei, the post asks how an airline could separate two kinds of abandoned bookings and what a wrong guess costs.
MT362 Unit 1 rubric: what earns full points
Marks on this opening post generally follow four things: a concrete, dated example; a clearly labeled inference about the system; use of the unit reading; and a question that moves the class toward the course's central issue, which decisions a machine should make. Posts that claim to know how a company's system works, without evidence, lose credibility. Research on churn models and retention targeting adds weight to the worry. Linking the personal example to a business case shows application. A reply earns more when it asks what data a system would need, since that is the question the course returns to. Two cited sources in APA 7 and a reply that tests Mei's airline example finish the work.
MT362 Unit 1 help from the desk
Students often write this post as a general opinion about whether AI is good or bad for customers. That misses the unit's narrower question about a specific decision. Others describe an offer and assert exactly how the company's algorithm produced it, which no outsider can know. Some posts list several examples briefly instead of examining one closely. Replies frequently share a similar anecdote and stop. Before posting, write down the dates and amounts in your example, then one sentence beginning I suspect, which names the signal a system might have used. If you cannot write the second sentence, choose an example where the decision is visible enough to reason about.
Get an MT362 Unit 1 example written to your instructions
Which automated offer reached you, and when? Note the dates and anything that looked tailored, plus your workplace if it runs promotions or subscriptions. Attach the board question as posted and how participation is scored; a post and reply grounded in that example arrive within 24-48h, and nothing is charged for the first custom sample. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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MT362 Unit 1 questions, answered
Does the Unit 1 example have to involve AI I can prove?
No. Most customers cannot see the system behind an offer, and graders know that. Describe what happened, date it, and say what kind of automated decision could explain it, labeling that as your inference. The value lies in reasoning about what the system would need to predict and what data it would use, not in proving how a particular company built it.
Should my post argue for or against using AI?
Neither side is required. Posts tend to earn more when they frame the question a manager would face: what the automation would decide, what it could get wrong and who would notice. A post that ends by naming what the business would need to know before automating reads as more developed than a flat verdict, whichever direction that verdict points.
Can I write about my employer in the post?
Usually, and it often strengthens the post, because the course keeps returning to decisions a real marketing team faces. Describe processes rather than confidential figures, round any numbers you are unsure about sharing, and leave out customer details. If your employer does not use subscriptions or offers, a business you know well as a customer can play the same role.
Where can I find a free MT362 Unit 1 sample paper?
The complete MT362 Unit 1 post and reply are on this page, asking whether retention offers aimed by predicted risk teach customers to hesitate.
What is the pet chain's current rule in the MT362 Unit 1 sample?
Every subscriber who opens the cancel page sees $10 off the next order, an automated decision made by one rule, and about one in five accepts.