Experience Delivery & KI-Optimierung
Bayesian-Bandit-Optimierung, 150+ Co-Brand Landing Pages und Echtzeit-Personalisierung auf Web & App. Jedes Erlebnis messbar, jedes Ergebnis nachvollziehbar.
Vorsprung durch intelligentere Optimierung
Anspruchsvolle Experimente über Antragsstrecken, Landing Pages und Checkout-Funnels durchführen — mit statistischer Strenge und automatisierter Analyse.
Multi-Armed-Bandit-Algorithmen leiten Traffic automatisch zu den besten Varianten — schnellere Konvergenz als bei traditionellen A/B-Tests.
Nachgewiesener durchschnittlicher Conversion-Uplift über ACCELERAID-Customer-Journeys — streng gemessen mit Inkrementalitäts-Testing.
Nicht-technische Nutzer können Experimente einrichten, starten und überwachen, ohne Engineering-Ressourcen — inklusive vollständigem Visual Editor.
Von Transaktionsdaten zum personalisierten Angebot — in Millisekunden
Der KI-Layer liest jedes Signal aus der Banking-App, verarbeitet es durch ML-Modelle und liefert das richtige Angebot, bevor der Kunde den Bildschirm schließt.
- Transaktionsdaten-Analyse
- KI-Personalisierungs-Engine
- Customer-Lifecycle-Modelle
- Echtzeit-Decisioning
- Echtzeit-Personalisierung
- Next Best Action
- Cross-Sell & Up-Sell
- Kundenbindung
- Financial Insights
- Umsatzwachstum
Experience-Optimierung: was sie leistet
Antragsstrecken, Onboarding und Landing-Experiences optimieren. Conversion-Drop-off-Punkte identifizieren und mit KI-gestützten Empfehlungen beheben.
Traffic auf stärkere Varianten basierend auf Evidenz leiten. Multi-Armed-Bandit-Algorithmen übertreffen traditionelle A/B-Tests bei Geschwindigkeit und Effizienz.
Uplift bei Conversion und operativen Ergebnissen tracken. Rigoroses Inkrementalitäts-Testing stellt sicher, dass jede Verbesserung echten Business Impact widerspiegelt.
Inhalte, Formularfelder und Calls-to-Action in Echtzeit anpassen, basierend auf Kundenprofil und Kontext aus dem CDP.
Guardrail-Metriken verhindern schädliche Experimente. Automatischer Rollback, wenn die Performance definierte Schwellenwerte unterschreitet.
Nicht-technische Nutzer können Experimente einrichten, starten und überwachen — ohne Engineering-Ressourcen.
Was Optimierung in der Praxis bewirkt
One major European card issuer achieved +120% more credit card applications using ACCELERAID's KI-Allokation and application flow optimisation. The AI found the winning variant and scaled it — without waiting for statistical significance.
- KI-Allokation converges faster than traditional A/B testing
- Personalisierte Flows passen sich an den individuellen Kundenkontext an
- Guardrail-Metriken schützen vor schädlichen Experimenten
- Volle Integration mit CDP-Daten — keine separate Datenschicht nötig
Die Optimierungs-Engine in Aktion erleben
Varianten einrichten, Traffic allokieren und Ergebnisse messen — alles über eine intuitive Oberfläche.
Vorkonfigurierte KI- & Machine-Learning-Modelle
The ACCELERAID platform includes a set of pre-configured predictive scoring and analytics components as part of the licensed modules. All models operate exclusively as decision-support tools — no fully autonomous financial decisions are made by the system. Final campaign activation and business decisions remain under the control of the Client.
2.1 Behavioural Scoring Models
Model family: QuantileTransformer (self-optimising quantile transformation)
Applies a quantile transformation to user-level transaction data to normalise behavioural distributions and derive a standardised behavioural score — ensuring comparability across users with different transaction volumes and spending patterns.
2.1.1 Activity Scoring Model
Purpose: Engagement probability estimation
Estimates customer engagement probability based on historical transaction behaviour. Scores are recalculated automatically with each data refresh cycle.
2.1.2 Predictive Churn Model
Purpose: Early churn & inactivity detection
Estimates the probability of future customer inactivity or churn based on historical behavioural and transaction patterns. Designed to identify early behavioural signals associated with declining engagement.
2.2 Merchant Category Correlation Model
Model family: Statistical correlation analysis / scoring
Identifies behavioural affinities between merchant categories to support targeting and cross-category insights. Enables "customers who spend here also spend there" logic for campaigns.
2.3 Variety Score
Model family: Statistical diversity scoring
Measures diversity of spending behaviour across categories. Provides a single metric reflecting how broadly a customer engages across merchant categories — used for segmentation and targeting.
2.4 Recommendation / Next-Best-Action
Model family: Correlation-based scoring & Bayesian multi-armed bandit
Identifies relevant product or campaign opportunities based on transaction patterns. Outputs are recalculated dynamically — core logic is periodically reviewed and validated.
Contextual Bayesian Bandit Optimisation
Model family: Multi-armed bandit (Reinforcement Learning, Thompson Sampling)
For each audience segment a unique model can be trained. The algorithm optimises the allocation of content variants within a defined target audience segment by dynamically adjusting distribution weights based on observed conversion performance — incrementally shifting traffic towards better-performing variants while maintaining controlled exploration of alternatives.
- Per-segment model training — each audience gets its own optimised allocation
- Thompson Sampling for efficient exploration vs. exploitation trade-off
- Updates continuously during campaign runtime based on incoming feedback
- Outperforms traditional A/B testing in speed and statistical efficiency
- Guardrail metrics prevent degrading experiments from scaling
Baseline version: SaaS v4.23 (Feb 2026)
Verantwortungsvolle KI — Governance & Aufsicht
All ACCELERAID models are built to operate transparently, within client-controlled boundaries, with no autonomous financial decision-making.
All models operate exclusively on data provided and controlled by the Client. No data is shared across clients. No external model training on client data.
No automated credit approval, pricing decision or financial risk decision is performed by any model. All outputs serve as decision-support inputs — final actions remain under Client control.
The Client remains responsible for regulatory compliance regarding the use of model outputs in their jurisdiction. ACCELERAID provides audit documentation on request.
Each model is subject to periodic monitoring of score distribution stability, statistical consistency and predictive accuracy. Drift detection triggers review cycles.
Built on a TensorFlow-based framework enabling development of additional client-specific models where required. Custom models are implemented separately under Professional Services scope.
Questions about model design, governance documentation or regulatory compliance support:
Simon Greiner
AI & Model Governance
sales@acceleraid.ai
Erfahren Sie, wie sichere Kundendaten und verantwortungsvolle KI Wachstum und Compliance verbessern
Ein Gespräch. Ihr Use Case. Echte Zahlen.