Sweet & Coffee in Ecuador: the challenge of market expansion
Bibliographic record
Abstract
Learning outcomes After working through the case and the assignment questions, students will be able to: ▪ Examine Ecuador’s business environment where coffee shops and similar companies operate. ▪ Evaluate the marketing challenges for an enterprise, particularly for a café business operating in Ecuador. ▪ Explain the marketing strategy for a café company to attract a variety of new consumer segments domestically and abroad. ▪ Discuss relevant international market entry strategies given the specificities of the environment in which a company operates. ▪ Describe the advantage of contemporary marketing tools in sustainable market expansion of a café business. Case overview/synopsis Richard Peet and Soledad Hanna turned their coffee shop business, Sweet & Coffee, into a flagship brand in Ecuador. Their coffee shops successfully promoted the culture of consuming coffee and sweets throughout Ecuador and grew exponentially to 129 stores. However, Sweet & Coffee faced significant challenges entering new states in Ecuador, with considerable investment in central kitchens and logistics. Despite the challenges, Peet wanted to continue opening new Sweet & Coffee stores across Ecuador. However, owing to Ecuador’s fast-changing and unpredictable external environment, Peet needed to make new adjustments to its marketing strategy to reposition Sweet & Coffee for a bright future. International market expansion was an option. Complexity academic level This case is helpful for advanced undergraduate or graduate courses in marketing and strategy. Supplementary material Teaching notes are available for educators only. Subject code CSS 8: Marketing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".