A classification of live chat service users in the banking industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to classify live chat service users in the banking industry and provide relevant descriptive information on each group to be able to suggest appropriate strategies to managers. Design/methodology/approach A total of 682 panelists from a large Canadian polling firm self-administer a web-based questionnaire. Respondents are users of financial sector live chat services. Two-step cluster analysis was performed. Findings Four groups emerge from the analysis. Young frequent users (Group 1) attach dominant importance to speed of service, whereas computer users (Group 3) and conservative users (Group 4) who avail themselves of live chat services via computer focus on ease of use. Practical implications This study, which details four groups of live chat service users in the banking industry, enables managers to better adapt their strategies to the different market segments with a view to providing customers with better quality service and enhancing their experience. Originality/value The study presents the first live chat service classification to detail user profiles and examine differences at the before, during and after phases of the user experience. Findings enrich the body of academic literature in the service sector, in particular literature focusing on customer service in the banking industry. The paper also provides an interesting managerial framework for the implementation of successful, segment-specific strategies.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".