4Ps: A Strategy to Secure Customers’ Loyalty via Customer Satisfaction
Bibliographic record
Abstract
This paper explores the role of Marketing mix strategy and its overall positive or negative impact on customer’s satisfaction and loyalty. Product, price, place and promotion variables need to be managed by understating psychological traits of customers buying nature. The literary discussion highlights that customer expectations with regards to product quality, price, and product accessibility are managed by communication techniques using advertising agents. The discussion proceeds in analytical style using previous theory as base point to evaluate the role of marketing mix in relation with customer satisfaction turning into loyalty. Inductive data collection approach has proved a great help to extract gist of past research results. A large proportion of data has been gathered from secondary resources including journals, books and old research papers. The results show that all four aspects of marketing mix are equally important and any imbalance among them can damage overall results. Customers’ buying intentions are greatly affected by his/her expectations in context of a product quality, price, and product accessibility. The relationship between customer satisfaction and loyalty depends on the elimination of perception gap, service gap, operational gap and behavioural gap that needs to be managed by giving focused attention to these matters. This paper reviews prior literature and proposes to think carefully to use marketing mix strategy to met customer expectations by eliminating any communication or perception gaps that further extend customer loyalty.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".