Specifics of studying the target audience for effective allocation of advertising budgets in companies providing services
Bibliographic record
Abstract
This work makes an attempt at a systematic comprehension of modern methodological approaches to target audience research that underlie optimal advertising budget allocation. The aim of the research is to develop and substantiate a unified methodological toolkit for precise identification and segmentation of service consumers, ensuring a significant increase in the efficiency of marketing investments. As the theoretical and methodological platform, principles of systems analysis, methods of statistical processing of big data and cohort analysis, as well as an integrative review of specialized publications, were used. As a result of the analysis of existing practices, strengths and weaknesses of common approaches to segmentation and targeting were identified. Based on the obtained conclusions, a multi-level model of target audience research is proposed. Implementation of the proposed model allows for enhancing the accuracy of identifying relevant segments and rationalizing budget distribution among communication channels, which ultimately leads to an increase in the overall effectiveness of advertising campaigns. The scientific novelty of the research lies in the synthesis of behavioral, psychographic and predictive methods for dynamic audience segmentation in the service sector, which expands the existing theoretical toolkit and opens new opportunities for practical application in marketing. The results obtained will be of interest both to researchers specializing in marketing communications and consumer behavior and to executives and practitioners in the sphere of service provision.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".