Consumer Loyalty as a Factor of Establishing the Competitive Advantages in a Company under the Market Conditions
Bibliographic record
Abstract
The objective of the work is the theoretical rationale for the ratio of the customer loyalty to a number of the major factors of establishing the competitive advantages of a company and developing the practical guidelines for managing the consumer loyalty. As a result of the study it has been found, that the consumer loyalty determines their commitment to the company and its products, which is developed while building the relationships between the company and its consumers through the relationship marketing. There are four major factors of establishing the competitive advantages of a company under the market conditions: product quality, service quality, product price and consumer loyalty. In this study the following guidelines are suggested to companies in order to ensure consumer loyalty: impress the consumers, ensure consistently high quality of goods and services, earn confidence of the consumers, hear the consumers' criticism, build your own distribution network and surprise them with understanding. While managing the consumer loyalty companies not only draw new consumers, entice consumers away from the rival companies, but also retain its customers. While retaining the consumers companies get a double advantage, as: the process of retaining consumers is less complicated and costly, than the process of drawing and particularly enticing the consumers away from the competitors; the consumers, who are loyal to the company for a long period of time, regularly buy its products, ensuring the stability of its sales, profit and profitability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".