Evaluation and Priorities the Promotion Mix Elements for Corporate Banking Customers Using MCDM Methods In Eghtesade Novin Bank
Bibliographic record
Abstract
Among the mixed components of marketing, decisions regarding the promotion tools are very important due to their direct and rapid effect on the target market. With regard to the implementation of new models of banking business in recent years in Iran, the purpose of this research is to priorities the promotion mixas antecedent of integrated marketing communication and strongtools in order to influence the target market of corporative banking customers. The objective of promotion mix in the research emphasized the Increase sales volume, attract new customers, grow market share, enhance brand image, strengthen brand loyalty and increase profitability in the long term. For this purpose, with the help of marketing experts and using models of Multiple Criteria Decision Making (MCDM) methods such as AHP, TOPSIS, SAW, ELECTREand using Entropy weighting methods we have prioritized the components of promotion based on business goals. Finally, according to the fact that using the mentioned models had different results, we used the geometric mean of rates methodin finalized the finding ranking. According to the achieved finding, the different elements in order of priority to meet theconsidered business objectives in this studyinclude sales promotion, direct sales by the account managers, public relations and advertising, Objectives or ratings’ criteria.The finding illustrated that although ads are most effective in strengthening the brand, but the other two elements of personal selling and close sales promotion also has the most influence on corporative banking customers. The results of research shows that the most important instruments in advertising is television, in public relations is holding professional seminars for various industries, in advancing the sales is discount in the loan interest rate and fees, and in the in person sales also is verbal marketing by account managers of corporative banking.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".