Is it easy to retain the customers online? - Deciphering customers
Bibliographic record
Abstract
E-commerce is an ever-intriguing field of research for both businesses and academic researchers. The online medium makes it easy to set up the business, and hence the competition to make the customer stick to the website/app is immense. In this research paper, the effort is made to derive the factors known to cause the retention of the customer viz., Loyalty (both brand and App), pricing strategy, sales return ease and possibility, customer service, wrt age of the respondents. Exploratory Factor analysis was applied to the 4700 responses received, to derive the factors which help a website/app recognize what factors matter more for a particular age group. A Kaiser-Meyer-Olkin (KMO) with a value of.823 indicates that the adequacy of the sample for Factor Analysis and Bartlett's test of Sphericity is the chi-square value 4740 and df is 120 and p-value is less than.001 reveals that the sample was suitable for the EFA. Each age group is unique with its demands for the features that make them stay on the website and make repeated purchases. 18-24-year-old work on the simple mind mechanism, and that is, brand loyalty matters to them and is less demanding in terms of services compared to the other age groups. With most of the 25 - 34-year-olds being tolerant of all levels of customer service, e-commerce businesses are presented with an opportunity to maximize profits as consumers are less price and return policy sensitive. In these upper age ranges, this research furthers the idea that lenient return policies lead to a higher chance of retention; Respondents >45 years of age are brand Loyal and not App loyal. Variety is the key across all age groups. The study is unique in its derivation of factors relevant to the different age groups, which can be considered while making efforts to retain the customers.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".