An emotion-based segmentation of bank service customers
Bibliographic record
Abstract
Purpose Emotional and affective responses are experienced during service use that determine customer behavior; and for this reason, bank services require an better understanding of the emotions customers feel in service experiences. This research aims to examine whether different customer segments exist in the bank services industry, based on the emotions they experience when using the service. Design/methodology/approach The factors were examined through confirmatory factor analysis (CFA). Then, two-step clustering analysis was developed for customer segmentation on data from 451 bank service customers. Finally, an Anova test was conducted to confirm the differences among the obtained customer segments. Findings Our findings show that the emotion-based segmentation is meaningful in terms of behavioral outcomes in bank services. Further, research findings indicate that bank service customers cannot be perceived as a homogenous group, since four customer clusters emerge from our research namely “angry complainers”, “pragmatic uninvolved”, “emotionally attached customers” and “happy satisfied customers”. Research limitations/implications Our findings show that the emotion-based segmentation is meaningful in terms of behavioral outcomes in bank services. Further, research findings indicate that bank service customers cannot be perceived as a homogenous group, since four customer clusters emerge from our research namely “angry complainers”, “pragmatic uninvolved”, “emotionally attached customers” and “happy satisfied customers”, being the “angry complainers” the most challenging customer group. Originality/value The study is the first one to specifically segment bank customers based on the emotions they experience when using the service.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".