MKT 711 Week 5 Ethics and Data in Customer Relationships Example

Whose Data Is the Herd? Marketing Ethics and Data Privacy in the Customer Relationships of a Dairy Sensor Company

[Student Name]

University of Phoenix

MKT/711: Marketing and Managing the Customer Relationship

Week 5 Assignment

[Instructor Name]

[Date]

HerdSense Technologies, its policies and its customer research are composites written for a model paper.

HerdSense Technologies, the invented Madison company that sells neck-collar sensors and herd health software to dairy farms, now holds years of detailed data on about 410,000 cows: when each eats, ruminates, walks and shows signs of illness. Earlier weeks treated these data as a tool for alerts and for managing relationships. This week considers them as a source of ethical obligation. In a single quarter, three requests reached the leadership team. A milk processor that buys from about 300 of HerdSense's customer farms asked for herd health summaries to support its animal welfare program. A feed company offered $400,000 a year for anonymized rumination data by region. And the sales team proposed using data on falling milk output to identify struggling farms and time financing offers. This paper evaluates these requests using theories of marketing ethics and evidence on data privacy and proposes a data stewardship policy.

A General Theory of Marketing Ethics

Hunt and Vitell (1986) proposed a general theory of marketing ethics in which a marketer facing an ethical problem perceives alternatives and evaluates them in two ways. Deontological evaluation compares each action with norms and duties, such as honesty and fairness. Teleological evaluation weighs the consequences of each action for various stakeholders, considering the probability, desirability and importance of each outcome. Ethical judgment combines both evaluations, and intentions and behavior follow, shaped by personal, organizational and industry factors.

Normative Perspectives

Laczniak and Murphy (2006) laid out principles for responsible marketing practice, including the principles that marketing should put people first, that marketers should go beyond legal requirements, that responsibility extends to all stakeholders and to the common good and that organizations should adopt explicit ethical processes. They emphasized that the law sets a floor, not a standard of excellence. Their framework applies directly to farm data, where the law offers little guidance.

Evidence on Data Privacy

Martin et al. (2017) examined customer data privacy and found that customers' feelings of vulnerability about how firms use their data reduced trust and led to negative behaviors, such as falsifying information and switching, and that data breaches harmed firm performance, including the performance of rivals. Giving customers transparency and control over their data reduced these negative effects. The study links ethics to relationship outcomes: privacy practices are part of the trust that commitment-trust theory places at the center of relationships.

Farmers' Concerns About Data

Wolfert et al. (2017) reviewed research on big data in smart farming and found that data were changing power relationships in agricultural supply chains. They identified concerns about who owns and controls farm data, the risk that technology providers and large agribusinesses would gain at farmers' expense and the need for trust and governance arrangements. In HerdSense's interviews, several farmers said they worried that data on herd health could be used by processors or lenders against them.

Data a farmer creates in the barn can become evidence used against the farmer at the bank or the milk plant.

Dilemma 1: The Processor's Request

Deontologically, sharing herd data without each farm's consent would violate a duty of confidentiality implied by the relationship. Teleologically, the processor's welfare program could benefit animals and help farms meet buyer demands, but it could also expose farms with temporary health problems to penalties. The ethical path is to offer each farm the choice to share specified reports with its processor, with clear explanations of what will be shared and how it may be used. Sharing without consent should be refused.

Dilemma 2: The Feed Company's Offer

Anonymized regional data seem harmless, and the revenue is significant. Yet anonymization of farm data can fail when regions contain few farms, and farmers did not agree to have their data sold. Consequences include possible re-identification and loss of trust if the sale became known. Duties of honesty and respect for the people who generated the data weigh against selling without consent. One acceptable alternative is an opt-in data cooperative in which participating farms share in the revenue and receive the aggregated insights.

Dilemma 3: Targeting Struggling Farms

Using production data to target financing offers at farms in distress raises the strongest concerns. It turns data provided for animal care into a tool for selling to people at their most vulnerable. Teleologically, some farms might benefit from financing, but others could take on debt they cannot carry. Deontologically, it uses customers' data in a way they would not expect or approve. Following Laczniak and Murphy's principle of putting people first, the practice should be rejected. Financing should be offered openly to all customers.

Trade-Offs That Remain

Not every tension disappears. Some farmers may want HerdSense to share data with lenders to secure better loan terms, and the consent model allows that. Processors may push harder for data as welfare standards tighten, which could leave farms that decline to share at a disadvantage with their buyers. HerdSense cannot resolve these market pressures, but it can make sure the choice remains the farmer's.

Stakeholder Map

Stakeholders include farmers, their families and employees, dealers, veterinarians, processors, lenders, feed companies, consumers concerned with welfare and HerdSense's employees and investors. Farmers bear the greatest risk from data misuse and have the least power to control it.

The Business Case

Rejecting the feed company's offer forgoes $400,000 a year, about 1 percent of revenue. Martin et al. (2017) found, however, that perceived data vulnerability reduces trust and loyalty, and Week 2 showed how sensitive customer equity is to churn. A single public controversy about selling farm data could raise churn by several points, costing far more.

A Data Stewardship Policy

HerdSense will adopt six rules. Farmers own the data generated on their farms. No farm data will be shared or sold to any third party without the farm's explicit, revocable consent. Aggregated data will be used only for product improvement and research unless farms opt into sharing. Data on farm finances or performance will never be used to target sales offers. Farmers can download or delete their data at any time. An ethics review group, including two customer farmers and a dairy veterinarian, will review new uses of data each quarter.

Monitoring Compliance

Compliance will be checked through an annual audit of all data access by outside parties, a public summary of data requests received and their outcomes and a question in the annual customer survey about trust in HerdSense's data practices.

Limitations

The analysis rests on one firm's dilemmas and on general research about privacy. Research specific to business customers' data, rather than consumers', remains thin and would strengthen future policy design.

Conclusion

Ethical theory, normative principles and evidence on data privacy point in the same direction for HerdSense: farmers' data should remain under farmers' control. Evaluated through duties and consequences, the processor's request can be met with consent, the feed company's offer requires a cooperative model rather than a sale and targeting struggling farms should be rejected. A stewardship policy built on ownership, consent and transparency protects farmers and strengthens the trust on which HerdSense's relationships depend.

References

Hunt, S. D., & Vitell, S. (1986). A general theory of marketing ethics. Journal of Macromarketing, 6(1), 5-16. https://doi.org/10.1177/027614678600600103

Laczniak, G. R., & Murphy, P. E. (2006). Normative perspectives for ethical and socially responsible marketing. Journal of Macromarketing, 26(2), 154-177. https://doi.org/10.1177/0276146706290924

Martin, K. D., Borah, A., & Palmatier, R. W. (2017). Data privacy: Effects on customer and firm performance. Journal of Marketing, 81(1), 36-58. https://doi.org/10.1509/jm.15.0497

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69-80. https://doi.org/10.1016/j.agsy.2017.01.023