Distributing portuguese table wine in the USA : José Maria da Fonseca, starting in Azeitão and managing foreign distribution channels
Bibliographic record
Abstract
José Maria da Fonseca is the Portuguese company in which this teaching case in based on. The Case Study is followed by a teaching note, and is intended to support Marketing classes, both in undergraduate or graduate levels. The company, the oldest table wine producer in the country and owned by the same family for seven generations, exports more than 70% of its production. It has clear international roots, exporting since the nineteenth century. Among its main markets there are countries as different as Brazil, Sweden, Canada or the United States of America. The focus of this case study is on the U.S. market. In 2011 the U.S. was the major wine consumer in the world, particularly diverse concerning the population and with State-specific regulations in what comes to distribution. The three-tier distribution system and all the collected taxes increase the complexity of the exports to this market and turn price into a very sensitive element to distribution changes. In order to find the best approach to increase the success in such an overseas market, JMF Marketing Director goes, during the case, through an analysis of the marketing environment, consumer trends and competition. Particularly considering the importance of a Representative in the overseas market, and the key relevance of relationships with the chosen Distributor, students are invited to equate different distribution possibilities and advantages, the resulting prices and the adequateness of the Product Portfolio to the segments, their needs and the competitive environment. This case provides students, mainly those in the Marketing field, with an opportunity to increase their experience on marketing strategy issues in exporting, understanding issues that firms face when formulating an implementing marketing plans, particularly in what comes to distribution and product. Recommendations cover topics such as product line stretching, different disintermediation levels and pricing calculations supported by quantitative and qualitative data. Getting closer to real-life business decisions in export markets, the case readers are given the tools to solve the presented dilemma and recommend the case protagonist the most strategically defensible action possibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".