The Effect of Marketing Communications on the Sales Performance of Ghana Telecom (Vodafone, Ghana)
Bibliographic record
Abstract
One of the problem areas in marketing of great practical and theoretical significance about which much remains to be learned is the nature of market response to a firm's marketing mix. Marketers are therefore concerned about the coordination between communications and their sales and the expenses thereof. This study sought to examine the relationships existing between marketing communications activities and the sales performance of Vodafone. The study also made use of simple statistical tools such as tables, graphs, together with multiple regression analysis to determine the degree of variation between the dependent (sales volume) and independent variable (communication tools). The results indicated strong relationships between sales promotion, advertising budgets and total sales. There was however an inverse relationship between TV advertisements and sales. In addition, a negative relationship was also found to exist between sponsorship budget and total sales. The outcome indicates that Vodafone was not paying much attention to its total communication costs and the return on investment (ROI) on such expenditures. It is recommended therefore that management and other marketers in the industry regularly evaluate the marketing communications activities they engage in as this will inform them on how effective their communications activities are and what returns to expect on these marketing communications activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".