An Optimization Model For The Market-Mix Problem In The Banking Industry
Bibliographic record
Abstract
For many years banks designed their promotional efforts to aim at the broadest possible markets in hopes of recruiting new clients. Recently, competitive measures have forced them to focus instead on a strategy of market segmentation designed to sell specific products to markets that present the best present and future opportunity for profit. Consequently, banks have begun developing marketing strategies similar to those of the retail industry. In one large Canadian bank, a new marketing approach called the ’street-corner strategy’ was proposed for selling the right product to the right client. However questions remain unanswered. What kinds of clients do banks want? Which products should they sell them? This article proposes a model enabling bankers to find the market-mix that will maximize profits, under the constraints of feasible marketing strategy, competition, and the effects of changing interest rates. We have designed a deterministic optimization model that quickly finds a solution that maximizes bank profits while respecting operational constraints. The model was tested on the data for the province of Quebec and it yielded potential profit increase of 25% for a three-year period. This model can be used to help marketing departments assess their constraints, to evaluate changes in product costs, and to adjust their strategies to increase profitability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".