Modeling The Effects Of Socio-Demographic, Psychographic And Relationship Characteristics On Share Of Wallet For Financial Services
Bibliographic record
Abstract
<h5 style="page-break-after: auto; text-align: justify; line-height: normal; margin: 0in 0.5in 0pt; mso-pagination: none; tab-stops: .5in;"><span style="font-size: 10pt; font-weight: normal;"><span style="font-family: Times New Roman;">Customer Loyalty is frequently conceptualized as customer retention even though polygamous loyalty to multiple products/brands is commonplace and customers are unlikely to shift all of their purchases to another supplier following dissatisfaction.<span style="mso-spacerun: yes;">&nbsp; </span>In this study, loyalty is operationalized as share of wallet (SOW) and modeled in relation to socio-demographic, psychographic and relationship predictors.<span style="mso-spacerun: yes;">&nbsp; </span>Results indicate that chequing account SOW was negatively skewed and leptokurtic with a mean of 83.3%.<span style="mso-spacerun: yes;">&nbsp; </span>In contrast, mean credit card SOW was 59.5% and the distribution was negatively skewed and platykurtic.<span style="mso-spacerun: yes;">&nbsp; </span>Relationship length, depth and satisfaction, and financial attitudes, values and lifestyles, but not consumer socio-demographics, emerged as key predictors of share of wallet.<span style="mso-spacerun: yes;">&nbsp; </span></span></span></h5>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".