BUS 6523 Module 5 Research-Grounded Global Marketing Strategy Example

Earning the Search, Not Just the Ranking: A Research-Grounded Global Marketing Strategy for a Maine Cookware Brand

Student Name

American College of Education

BUS6523: Global Marketing in the Digital Economy

Module 5 Assignment

Instructor Name

June 4, 2029

Introduction

This course has examined consumer behavior mediated by AI, a case of digital disruption, brand equity under algorithms and the ethics of personalization. The closing paper converts what those modules found into a marketing strategy for Harbor and Pine, the composite cast iron and enameled cookware brand from Portland, Maine, used in earlier modules. Most of its revenue still arrives through a single online retailer in three national markets, and its owners want to grow profitably while becoming less dependent on that platform. The strategy sets market priorities, defines how to build brand equity with consumers and algorithms, specifies the role of AI and data and redesigns the customer journey, with measures and a plan for learning.

Starting Position

Harbor and Pine earns about $48 million a year, 71 percent of it through the marketplace. The United States provides 74 percent of sales, Germany 16 percent and Japan 10 percent. The analysis in the third module found strong algorithmic equity in the United States, weak consumer equity in Germany and strong heritage appeal but a thin review base in Japan. The brand's main risks are platform entry into its category with a private label, rising advertising costs on the marketplace and a reputation that rests on ratings it does not control. Its main assets are product quality, low return rates and a credible craftsmanship story.

Setting Market Priorities With the CAGE Framework

Ghemawat (2001) warned that companies inflate the promise of foreign markets when they overlook four kinds of separation between home and host: differences of culture, of law and politics, of physical location and of income and resources. Applying the framework, Germany is administratively close through trade rules but culturally distant in its reliance on specification and independent testing, and it imposes the strictest data rules. Japan is geographically distant, with higher shipping costs, and culturally distinct in its emphasis on detail and craftsmanship, but its consumers value exactly what the brand offers. The strategy therefore defends the United States, invests selectively in Japan, where fit is strongest, and maintains Germany through marketplace sales while testing whether independent product tests can build consumer equity.

Building Consumer Equity

Keller (1993) showed that strong, favorable and unique associations drive brand equity. The brand's distinctive associations are durability, Maine craftsmanship and cooking that lasts for generations. The strategy builds them through content that consumers seek out, such as care guides, recipes developed with cooks from each market and short films from the foundry, distributed on video platforms and the brand's own site. The goal is that more customers search for the brand by name, which protects it against substitution by rankings, private labels and voice assistants, as the third module showed.

Building Algorithmic and Platform Equity

The brand will continue to treat marketplace signals as assets. Packaging and instructions will be revised to cut the few returns that come from misunderstanding enameled care. Post-purchase emails, sent only to customers who opt in, will invite honest reviews, which matters most in Japan, where a few reviews carry heavy weight. Marketplace advertising will shift from broad terms to the brand's own name and specific product terms, protecting branded searches at lower cost. The brand will also enroll in the platform's brand protection program to control its listings and reduce counterfeit risk.

The Role of AI

Huang and Rust (2021) distinguished mechanical, thinking and feeling AI. The strategy uses thinking AI for demand forecasting by market and for on-site recommendations based on the current visit and the customer's own purchases. It uses feeling AI cautiously: a conversational assistant on the website will answer care and cooking questions in English, German and Japanese, with clear disclosure that it is automated and easy handoff to a person. Research on algorithm aversion suggests visible errors erode trust, so the assistant will be limited at first to topics where answers can be checked, such as care instructions, and expanded as accuracy is confirmed.

A Privacy-First Data Policy

The fourth module concluded that personalization should rely on first-party data with transparent consent. The strategy adopts that policy globally, built to the European standard. Martin et al. (2017) found that customer feelings of data vulnerability lead to harmful behaviors and that transparency and control reduce them. The brand will publish a plain-language data notice, offer a preference dashboard, end cross-site retargeting outside short windows with consent and decline purchased life event data. The policy supports the craftsmanship and trust associations the brand is building, turning restraint into part of its positioning.

Redesigning the Customer Journey

Lemon and Verhoef (2016) explained that every purchase passes through stages before, during and after buying, and the contact points along the way may belong to the brand, to its partners, to customers or to independent outsiders. Today, the marketplace owns most of Harbor and Pine's purchase touchpoints. The strategy adds brand-owned touchpoints in the stages where the brand can create the most value: prepurchase content, postpurchase care guidance and a lifetime enamel guarantee registered on the brand's site. Registration creates a direct relationship, and loyal customers will be invited to buy accessories and new products directly, gradually shifting the sales mix.

Budget and Targets

The marketing budget of about $5.8 million will shift over three years from 64 percent marketplace advertising to 45 percent, with the difference funding content, the website, the assistant and guarantee registration. Targets for 2032 include branded search volume up 40 percent in the United States and 80 percent in Japan, direct sales rising from 19 to 32 percent of revenue, return rates below 2 percent, at least 300 reviews per core product in Japan and guarantee registration by 25 percent of buyers. Consumer, algorithmic and platform equity will be reported quarterly by country.

Test and Learn

Several assumptions are uncertain and will be tested before full rollout. Content's effect on branded search will be tested by launching recipe series in some regions first and comparing search trends. The conversational assistant will run as a pilot with a sample of visitors, measuring accuracy, satisfaction and handoffs. In Germany, the brand will submit two products for independent testing and measure the effect of results on sales before deciding on a larger investment. Results will be reviewed every six months, and budgets will move toward what works.

Risks

The largest risk is retaliation or reduced visibility on the marketplace as the brand promotes direct sales; the strategy keeps the marketplace central for discovery and avoids directing customers away from it in ways that breach its rules. A second risk is that content does not raise branded search enough to justify its cost, which the regional tests will reveal early. A third is that the AI assistant makes visible errors, addressed by its limited scope and human handoff.

Conclusion

Harbor and Pine's strategy applies the course's research to one brand: the CAGE framework sets market priorities, brand equity theory guides the building of associations that earn searches by name, platform research shapes how algorithmic signals are managed, AI is used for thinking and carefully scoped feeling tasks, a privacy-first policy supports trust and the customer journey adds brand-owned touchpoints. Budget shifts, targets and a test-and-learn plan let the strategy adapt. The brand aims to be chosen by name, not only ranked.

References

Ghemawat, P. (2001). Distance still matters: The hard reality of global expansion. Harvard Business Review, 79(8), 137-147.

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9

Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22. https://doi.org/10.1177/002224299305700101

Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420

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