Increasing television advertising sales for a small market broadcaster
Bibliographic record
Abstract
The purpose of this project is to identify why, fewer businesses in Prince George, use local TV as their primary advertising vehicle when compared to other broadcast media. It will also explore the reasons why these local businesses choose the marketing methods that they do. Through this research I will determine firms' opinions of TV and identify their openness to using TV advertising. I will also determine the level of satisfaction of current TV advertisers. The analysis is going to provide suggestions to improve clients' perception of local TV advertising and propose sales and human resource strategies that will, ideally, increase market share and revenue for the TV sales department of the Jim Pattison Broadcast Group Prince George Division (JPBG). While researching this project, literature was found that focused on the future of TV advertising, selling local TV advertising, sales training, relationship marketing, marketing to small/medium sized businesses and optimal sales force sizes. Research specific to Prince George that addresses TV advertising sales, general advertising or professional outside sales within the market has not, to my knowledge, been published. Because this project is more company, medium and market specific, two additional research methods were used to identify the challenges and provide suggestions for improvement. Thirty anonymous firms were interviewed and surveys were submitted buy [sic] three TV Account Managers who are employed by the JPBG. Reviewing literature, I found that TV is not a dying medium for entertainment and information people are in fact watching more TV than ever before and TV continues to be a viable and effective medium for advertising. Numerous studies discussed the importance of professional sales training, sales systems that are industry specific and the sales force sizing. While interviewing the advertisers, relationship with their account managers was the number one and two reasons why they choose to use the advertising mediums that they do. In a community t
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".