An Effect Analysis Model for Corporate Marketing Mix Based on Artificial Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Under the boom of market economy in China, marketing mix is being updated constantly to facilitate the all-round development of various industries. To accurate evaluate the effect of corporate marketing mix, this paper designs an effect analysis model for corporate marketing mix based on artificial neural network (ANN). Firstly, a complete evaluation index system (EIS) was created for corporate marketing mix, and the weight of each index was assigned through analytic hierarchy process (AHP). Then, a marketing mix of big marketing + service marketing was proposed, and subject to fuzzy comprehensive evaluation (FCE). Finally, an FCE model was constructed for the effect of corporate marketing mix, based on three-layer backpropagation neural network (BPNN). The effectiveness of the model was verified through experiments. The research findings provide a reference for the application of ANN in other fields of effect analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it