Research on Factors Affecting Performance Indicators of Telemarketers Based on Talk Time in the Life Insurance Market: The Case of Korea
Bibliographic record
Abstract
The telemarketing industry is gradually expanding its area as the telecommunication industry has rapidly developed. Especially the telemarketing sector of the insurance companies has been showing the most outstanding growth. They not only increase investment to achieve good telemarketing performance, but also benchmark practice of other competitors and aim for further improvement via their own knowhow. According to the survey by American Report, expenses related to the telemarketers comprise 62% of the telemarketing cost. This indicates that effective management of telemarketers is more important than deployment of system equipment and various solutions. There are correlations between the effective management of telemarketers and the amount of their average income generated as well as their turnover due to resignation and/or moving to another company. Savings in payment to telemarketers in advance may be interpreted also as a performance indicator for insurance companies. Then, the performance indicators of insurance companies can be expressed in detail into commissions of telemarketers, cases of new sales, and amount of first premiums. In this study, we analyzed actual data related to telemarketing performance indicators to assess such performance indicators. Multiple regression analysis was applied, based on one year records, after confirming correlations among talk time, experiences, contact time, sex, age, and education all of which are telemarketing performance indicators. It is shown that there is a meaningful correlation between commissions, first premiums and new sales cases which are the business achievement of telemarketers, and total talk time and work experiences which are determinants of performance. Talk time, experiences, contact were turned out to be significant, while personal characteristics were not. In order to improve the total talk time based on this analysis, we propose to manage performance indicators by working month and training, and to introduce improved so called
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".