| Course | MT362 Artificial Intelligence Applications for the Marketing Professional |
|---|---|
| Unit | Unit 4 |
| Paper type | Privacy and consent review |
| Length | About 1,110 words, 7 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT362 Unit 4
Would a Customer Who Read the Notice Expect This? An MT362 Unit 4 Privacy and Consent Review of Eleven Model Inputs
[Student Name]
Purdue University Global
MT362: Artificial Intelligence Applications for the Marketing Professional
Unit 4 Privacy and Consent Review
[Instructor Name]
[Date]
The chain, its notices and inputs are composite; the laws are described as in effect in October 2026, and this review is a course analysis, not legal advice.
The Laws, With Dates
Fernbrook Pet sells in California and Oregon. In California, the amendments made by the California Privacy Rights Act took effect on January 1, 2023, and are enforced by the California Privacy Protection Agency, whose regulations require that a business's use of personal information be reasonably necessary and proportionate to the purposes it disclosed, or to another purpose compatible with the context of collection (California Privacy Protection Agency, 2023). In Oregon, the Oregon Consumer Privacy Act took effect on July 1, 2024, giving Oregon residents the right to see, fix and erase what a business holds about them, along with opt-outs from targeted advertising, sale and certain profiling (Oregon Legislative Assembly, 2023).
The Test Applied to Each Input
Purpose limitation carries the argument, and it is applied input by input rather than to the model as a whole. Each of the eleven inputs faces the same question: would a customer who read the notice at collection expect this use? Martin and Murphy (2017) review evidence that unexpected uses of data provoke the strongest negative reactions, so the test is also a business one.
Six Inputs That Clear
Purchase history, combining store transactions and Repeat Delivery orders, clears easily. Each policy version from 2016 onward promised that order records would support running and improving the delivery program, and predicting who may cancel in order to keep the program working fits that purpose.
Schedule edits clear for the same reason. A subscriber who changes a delivery interval expects the chain to notice.
Pet profiles clear for timing and offer selection, since customers entered them to personalize the program.
Cancellation-survey answers clear. The survey told customers their answers would help improve the program, and using aggregate patterns to design retention offers is an improvement.
Three Inputs That Wait
Chat transcripts were collected to resolve complaints. Using complaint language to predict cancellation might be compatible with that context, since both concern the customer's service experience, but it might also surprise a customer who wrote in anger. The question goes to counsel: is predicting cancellation from complaint text a compatible purpose under the agency's regulations?
App location was collected to find the nearest store. Using visit patterns to predict cancellation is a different purpose. The question goes to the data owner: can the app's notice be updated before any use, and will customers be asked?
The broker file was bought in 2023 under terms nobody on the team can locate. The question goes to procurement: what do the purchase terms permit? Until the terms are found, the file is not used.
The model proceeds without the three held inputs, because each one waits on a question rather than on a refusal.
Pet Prescriptions, Excluded by Choice
Prescription diet purchases, such as a kidney-support food prescribed by a veterinarian, are the hardest input. California's sensitive categories include information about a consumer's health, but a dog's kidney disease is not the consumer's health, so no statute bars using the purchases. The review drops them anyway and says plainly that this is a policy choice, not a legal requirement: a customer who learned that the chain had used a pet's illness to decide whether to offer a discount would likely find it intrusive.
Returns and refunds clear, since the same policies covered transaction records. Email clicks clear for timing, while opens are excluded as unreliable, a data-quality decision recorded in the Unit 3 inventory rather than a privacy one.
Birth Year, Excluded by Design
Birth year is the eleventh input, and it is excluded from the model by design. The 2019 policy said birth years would be used for birthday offers, and a customer who gave one for a free treat would not expect it to shape a prediction about leaving. There is also a fairness reason: a model that uses age directly could treat older subscribers differently in ways the chain never intended. The field is held for one purpose only, the audit in Unit 7, under a written restriction that keeps it out of every production system. Excluding it from the model does not make age invisible, since other fields can stand in for it, which is exactly what that audit will test.
When a Score Becomes Advertising
Rights come second in the analysis. A churn score used only inside Fernbrook's own emails is neither a sale nor a share of personal information under California's definitions, because no third party receives it. A Global Privacy Control signal, which California treats as an opt-out of sale and sharing, therefore does not block it. The line moves the moment the chain uploads high-risk subscribers to a social platform as an advertising audience. That is cross-context behavioral advertising, the opt-out rights apply, and anyone who has sent the signal must be removed from the upload.
Oregon's Profiling Opt-Out
Oregon lets consumers opt out of profiling used for decisions that produce legal or similarly significant effects, such as decisions about credit, housing or employment. A retention discount on pet food is not such a decision, so the review judges the opt-out inapplicable, while noting that the chain may choose to honor such requests anyway.
Texts Need Their Own Consent
Save offers by text are marketing messages, and under the federal rules implementing the Telephone Consumer Protection Act, automated marketing texts require the subscriber's prior express written consent (Federal Communications Commission, 2012). Only about 41 percent of subscribers hold that consent, so the text audience shrinks accordingly. Email offers carry the unsubscribe link the federal email marketing rules require.
What the Review Means for the Vendor
The rulings above also set terms for any vendor. A vendor receiving cleared inputs must use them only to produce scores for Fernbrook, and the contract must say so, because a vendor free to reuse the data for other clients could turn a service arrangement into a disclosure the notices never described. The vendor comparison in Unit 9 carries that requirement forward as its most heavily weighted criterion.
Open Questions and Owners
Three questions end the review: the compatibility of chat transcripts, assigned to counsel; the app notice update, assigned to the digital product owner; and the broker terms, assigned to procurement. The pilot does not wait on any of them.
References
California Privacy Protection Agency. (2023). California Consumer Privacy Act regulations (Cal. Code Regs. tit. 11, ยง 7000 et seq.). California Privacy Protection Agency.
Federal Communications Commission. (2012). Rules and regulations implementing the Telephone Consumer Protection Act of 1991 (FCC 12-21). Federal Communications Commission.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0495-4
Oregon Legislative Assembly. (2023). Oregon Consumer Privacy Act (S.B. 619). Oregon Legislative Assembly.
Reading the MT 362 Unit 4 instructions
Unit 4 asks students to review a proposed AI use against privacy law and the expectations customers formed when their data was collected. The review should name the laws that apply, with effective dates, and test each data input rather than the system as a whole. Instructors expect purpose limitation to be applied carefully: would the use surprise someone who read the notice? Rights such as opt-outs should be analyzed for when they are triggered. Text messages and email carry their own consent rules. Students may reach rulings that go beyond legal requirements, provided they say so. A course review is not legal advice, and open questions should be assigned to counsel or data owners.
How the MT 362 Unit 4 example is put together
The laws section opens with dates and the California proportionality standard. The test is stated once and applied to eleven inputs, with Martin and Murphy on unexpected uses. Six inputs clear with their notice language. Three wait on questions about purpose and terms, under a highlighted line that each waits on a question rather than a refusal. Prescription purchases are dropped by choice and the reasoning is shown. Birth year is excluded by design and held for audit. Sections then mark where a score becomes advertising, explain why Oregon's profiling opt-out does not apply, set text consent rules and carry vendor terms forward before the open questions close it.
Where the points sit in the MT362 Unit 4 rubric
A privacy review earns its marks through a correct statement of the law, input-by-input application, analysis of rights and honesty about uncertainty. Dates and enforcing agencies stated accurately show care. Applying purpose limitation to each input, not to the model as a whole, earns the central marks. Rubrics reward identifying the moment a use triggers opt-out rights. Separating legal requirements from policy choices shows maturity. Consent rules for texts and email should be accurate. Assigning open questions to owners shows the review can be acted on. Statutes, regulations and research appear in APA 7, and the review notes that it is a course analysis rather than legal advice to the chain.
MT362 Unit 4 help: mistakes that cost points
Reviews in this unit often declare a whole AI system compliant or not, without testing the data that feeds it. Others list privacy principles but never apply them to a specific input. A frequent error is treating every use of customer data as a sale, or no use as one, when the line depends on whether a third party receives it. Policy choices are sometimes presented as legal requirements. Text message consent is often overlooked, as if a phone number on file were permission to market by text. Before you finish, read each ruling and ask whether it rests on the law, on the notice or on a choice the business is making. Label which, and the review becomes far more useful.
Get an MT362 Unit 4 example written to your instructions
Name the jurisdictions that govern the Unit 4 customers and the data uses proposed for them; if a privacy notice or consent language comes with the prompt, include it. Send the rubric too; the first custom review costs nothing, arrives within 24-48h and tests each proposed use separately, with every legal claim dated. 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.
More MT362 papers and BS in Business Administration samples
- MT362 Unit 1: AI Retention Offers Post
- MT362 Unit 2: AI Use Case Assessment
- MT362 Unit 3: Customer Data Inventory
- MT362 Unit 5: Personalization Strategy Memo
- MT362 Unit 6: Seminar Reflection
- MT362 Unit 7: Bias Review
- MT362 Unit 8: Pilot Test Design
- MT362 Unit 9: Vendor Comparison Matrix
- MT362 Unit 10: AI Adoption Proposal
- MT361 Unit 4: Comparative Market Analysis
- MT359 Unit 4: Message Strategy Analysis
- MT358 Unit 4: Paid Social Targeting Memo
- MT357 Unit 4: Owned Digital Asset Audit
MT362 Unit 4 questions, answered
Which privacy laws should the review cite?
The ones that govern the customers in your case, which depends on where they live and the size of the business. For a US retailer, California's CCPA as amended by the CPRA is common, alongside whichever other state laws apply. Give effective dates, name the regulator where one exists, and avoid implying that a single federal privacy law covers consumer marketing data, since none currently does.
Does the review have to reach a legal conclusion?
Usually not a final one. Most sections expect a reasoned reading of how the law applies, with uncertain points flagged for counsel. Stating which facts would change the answer, such as whether data is shared with an ad platform, shows understanding without claiming more certainty than the case allows, and it keeps the review useful to a manager deciding what to do next.
What if the case gives no privacy notice?
Draft the notice you assume the business used and label it as an assumption, or describe the notice content the case implies. Many prompts leave this gap on purpose, to see whether the review notices it. Either way, the test of each use should run against specific notice language rather than against a general sense of what customers expect.
Where can I find a free MT362 Unit 4 sample paper?
This page carries the full MT362 Unit 4 privacy and consent review, testing eleven churn-model inputs against notices and two state laws.
When would a churn score trigger opt-out rights in the MT362 Unit 4 sample?
Used only in the chain's own emails it is neither a sale nor a share, but uploading high-risk subscribers to a social platform as an ad audience would be, and opt-outs would apply.