Marketing Challenges of Satisfying Consumers Changing Expectations and Preferences in a Competitive Market
Bibliographic record
Abstract
In a prevalent business environment which is highly competitive, firms pay more attention to the needs of customers and offer them quality products to satisfy their ever-rising expectations. Marketers often face challenges from a rapidly changing market condition. Satisfying customers’ ever-rising and changing expectations, discovering customers’ current needs is a complex process. It involves translating the voice of the customer (VOC) into product features; translating the voice of the business (VOB) into generating profits, through new and improved products; translating the voice of the engineers (VOE) which deals with technical requirement and constraints into physical products. In fact, customer satisfaction and total quality management requires a company ability to accurately determine customer requirements and successfully transform these requirements into finished quality products. Customer satisfaction is considered to affect customer retention and therefore, profitability and competitiveness. This study examines the variables that pose as marketing challenges of satisfying consumers changing expectation and preferences in a competitive market, such as market turbulence, technology turbulence, general economy, competition, management training and intelligence response. The descriptive statistics and multiple regression were used to ascertain how these variables influence customers changing expectations and preferences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".