Manajemen Strategi Pemasaran Produk Manulife Saving Protector pada PT Asuransi Jiwa Manulife Surabaya Pusat
Bibliographic record
Abstract
One of the best insurance companies in Indonesia is PT Asuransi Jiwa Manulife Indonesia, a financial services provider from Canada that operates in Asia, Canada and the United States. Headquartered in Toronto, Canada, Manulife has been serving customers for more than 155 years. Manulife is traded under the symbol ‘MFC’ on the Toronto, New York and Philippine stock exchanges, and under the symbol ‘945’ in Hong Kong. Manulife Indonesia has offered a variety of financial services including life insurance, accident and health insurance, investment services and pension funds to individual customers and business actors in Indonesia. Manulife Indonesia has served more than 2 million customers in Indonesia through a network of more than 11,000 employees and professional agents spread across 30 marketing offices. Given the increasing market potential in Indonesia, many insurance companies, both local and foreign, are trying to reach the market in Indonesia. In order for the company to be able to compete with other insurance companies, a sales strategy and product are needed that can attract the interest of its customers. One of the most popular products owned by PT AJ Manulife Indonesia is Manulife Saving Protector, which is the only individual dual-purpose life insurance product (endowment) that can provide Insurance Money of up to IDR 1.5 billion without having to undergo a medical examination first. The insurance industry in 2023 is projected to increase along with improving economic conditions in Indonesia. However, the level of public distrust can hinder the development of this industry. This causes many people to tend to delay opening an insurance policy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".