Neuere Ansätze der Planung und Kontrolle von Marketing-Maßnahmen im Online-Banking
Bibliographic record
Abstract
In den letzten Jahren konnten die größtenteils erst Ende der Neunziger Jahre gegründeten Direktbanken einen beachtlichen Kundenstamm aufbauen. Insbesondere im Online-Brokerage-Bereich erzielten einige dieser jungen Institute hohe Wachstumsraten. Eine deutliche Beschleunigung erfuhr dieses Wachstum Anfang des Jahres 2000, als die Kurse am Neuen Markt zu Höhenflügen ansetzten. Die hohe Affinität potentieller Online-Brokerage-Kunden zur Internet-Nutzung verhalf den Online-Banking-Anbietern mit ihren zeitgemäßen Trading-Lösungen zu einem bemerkenswerten Wachstumsschub. Mit dem Verfall der Kurse am Neuen Markt im Herbst 2000 nahm allerdings die Zahl der Neukunden bei diesen Instituten trotz intensiver Marketing-Aktivitäten rapide ab (siehe Abb. 1). Dieser Trend setzte sich im Jahr 2001 weiter fort.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".