Investigating the Use of Product Samples as Promotion Tool and Retention of Customers of Digital Television in Ekiti State, Nigeria
Bibliographic record
Abstract
The study assesses the use of samples as promotional tool in enhancing the retention of customers of digital television in Ekiti State, Nigeria. Specifically, the study examined the availability of product samples, understanding interesting programmes in advance, viewing new stations in advance and viewing new quarter programmes in advance. Descriptive research design was adopted for the study. The primary data used for the study was collected through semi-structured questionnaire. The population comprises 8, 334 subscribers of the three major digital television providers in Ekiti State from which 390 respondents were selected. Multi-stage sampling technique was used for the study. Two (2) local government areas (LGAs) were selected from each of the three senatorial districts in Ekiti State using Table of Random Number (TRN); making six (6) LGAs. In all 12, 22 and 31 respondents for Startimes, Dstv and Gotv respectively were selected from each of the six LGAs totaling 390. Data collected were analysed using percentage, frequency table logit. The results establishes that a significant positive relationship exist between availability of product samples, understanding interesting programmes in advance, viewing new stations in advance and viewing new quarter programmes in advance, and retention of customers of digital television. The study concluded that a unit increase in availability of product samples led to a significant increase in retention of customers of digital television in Ekiti State, Nigeria. The study therefore recommends an improvement in the provision of product sample by digital service provide to enhance retention of customers.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".