PERANCANGAN DAN PENGEMBANGAN PERENCANAAN PEMASARAN
Bibliographic record
Abstract
Suatu perencanaan pemasaran harus direncanakan atau dirancang. Outline rancangan tersebut harus memiliki struktur yang sistematis agar terbentuk suatu perencanaan yang terorganisir dengan baik dan efisien.Analisis lingkungan sangat diperlukan dalam membuat suatu perencanaan pemasaran. Sumber-sumber informasi dalam melakukan analisis lingkungan, antara lain target pasar, pesaing, pembeli individu, pembeli organisasi, sumber daya perusahaan, lingkunga teknologi, lingkungan ekonomi, lingkungan politik, lingkungan hukum, sosial budaya, ancaman serta peluang. Dengan memahaminya kita dapat menetapkan tujuan dan sasaran pemasaran.Efek Pareto merupakan fenomena yang menggambarkan bahwa 80% keuntungan perusahaan berasal dari 20% pelanggan.Segmentasi pasar adalah kelompok pelanggan dengan karakteristik yang sama atau mirip, yang dapat dipuaskan oleh bauran pemasaran tertentu. Suatu perusahaan biasanya tidak dapat melayani dengan sukses dengan kelompok besar segmen.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".