Location Based Marketing: A Promising Marketing in Sri Lanka
Bibliographic record
Abstract
In the modern world, many organizations are turning into ground-breaking digital media technologies in order to develop their marketing communication channels, extend effectiveness, and reorganize extra mobile marketing strengths in to the marketing world. In order to achieve powerful competition; some large organizations are investing immensely in the development of mobile marketing. Most of marketer’s dreams were come true by entering many researches and developments into mobile marketing in order to generate an enhanced digital infrastructure. Location based marketing is one of the rapid and critical transformer in the mobile marketing. Location based marketing technology is being developed rapidly every day, opening new incredible opportunities. It supports a company to send their marketing communication messages more successfully to attractive and proper audience, eliminate all redundant “sound” in the communication procedure, cut costs, and more prominently engage its audience in a transferable outcome. In this article, customer recognition, familiarity of LBM, customer trust, customer privacy, customer preference, information accuracy, cooperation have considered as the key factors affecting building location based marketing system in Sri Lanka. These key factors can be helpful for marketers, entrepreneurs to develop their location based marketing model for the better off.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".