Proposed Digital Marketing Strategy And Food Product Assortment To Increase Sales Performance Of Coffee Shop
Bibliographic record
Abstract
This study focuses on the growing food and beverage industry in Indonesia, which contributed considerably to the overall gross domestic product (GDP) of the country and experienced growth of 0.22% in the second quarter of 2020. As people drink more coffee, the coffee shop business has expanded, and Poseidon Coffee in Cikarang is one of the businesses that has benefited from this trend. Despite this, the COVID-19 pandemic had a significant effect on the industry, which resulted in decreasing sales as a direct result of increased government regulations and limitations. Poseidon Coffee's sales have been slowly climbing back up, although they still have not reached the levels they were at before the outbreak. This study seeks to provide recommendations to improve Poseidon Coffee's sales performance by applying digital marketing tactics and expanding their line of food products. These recommendations will be provided as part of this study. The investigation makes use of a thorough methodology, which incorporates such concepts as the Resource Based View, the Segmenting, Positioning, and Targeting model, the Marketing Mix, Porter's Five Forces, Competitor Analysis, and Customer Analysis, amongst others. When developing plans in response to both internal and external analyses, the TOWS Matrix is an invaluable tool. The findings of the research provide significant insights that may be used to address difficulties faced by businesses and increase sales success. Poseidon Coffee intends to capitalize on the growing demand for coffee in Indonesia by using innovative marketing methods in the near future. The company places an emphasis on high-quality products, skilled baristas, and a location that is advantageous for business in order to overcome commercial hurdles. It is vital to engage in promotional activities and social media engagement, in addition to collaborating with local businesses, in order to expand the culinary offers and compete with other businesses that are particularly strong in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".