The ACCELERAID Platform
Experience Optimisation & Personalisation
Bayesian bandit optimisation, 150+ co-brand landing pages and real-time personalisation, delivered on web, app, email, push and in branch. Every variant is measured against a control group.
How It Works
From variant to measured uplift.
Five steps, from setup to measurement.
01
Set up variants
Set up variants for a journey, content or offer in the visual editor.
02
Allocate traffic
AI allocation (multi-armed bandit with Thompson sampling) distributes traffic across the variants.
03
Keep learning
The allocation adapts to measured conversion during the run, per target segment.
04
Safeguard
Guardrail metrics limit experiments. If performance falls below defined thresholds, an automatic rollback follows.
05
Measure
Every variant is measured against a control group, with impact measured across channels rather than by last click.
Key Capabilities
Experience Optimisation: what it delivers
Journey optimisation
Optimise application journeys, onboarding and landing experiences. Identify conversion drop-off points and fix them with AI-supported recommendations.
AI allocation
Route traffic to stronger variants based on evidence. Multi-armed bandit algorithms outperform traditional A/B tests in speed and efficiency.
Measurement & uplift
Track uplift in conversion and operational results. Rigorous incrementality testing ensures every improvement reflects real business impact.
Personalised flows
Adapt content, form fields and calls to action in real time, based on customer profile and context from the CDP.
Safe experimentation
Guardrail metrics prevent harmful experiments. Automatic rollback when performance falls below defined thresholds.
No-code editor
Business teams set up, launch and monitor experiments in the visual editor.
The algorithm
Contextual Bayesian Bandit — Optimisation
Model family: Multi-armed bandit (Reinforcement Learning, Thompson Sampling)
A separate model can be trained for each audience segment. The algorithm optimises the allocation of content variants within a defined target segment by dynamically adjusting distribution weights — based on observed conversion performance. Traffic is shifted incrementally to better-performing variants, with controlled exploration of alternatives.
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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 harmful experiments from scaling
Thompson
Sampling — proven RL approach
Per segment
Individual model per audience
Real-time
Updates during campaign runtime
Data: Campaign response data (clicks, conversions) · performance metrics per variant · aggregated behavioural interaction signals
Channels
One decision layer for every channel
Web, app, email and branch use the same data, scores and rules. The decision API responds in under 100 ms, and the impact of each channel is measured across channels instead of by last click.
Case example
What optimisation achieves in practice
A European credit card issuer achieved +120% more card applications with AI allocation and an optimised application flow. The allocation shifted traffic to the stronger variant while the test was still running.
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AI allocation converges faster than traditional A/B testing
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Personalised flows adapt to individual customer context
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Guardrail metrics protect against harmful experiments
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Full integration with CDP data — no separate data layer needed
+120%
Card applications at one issuer
< 100 ms
Decision API response time
AI
Allocation (not manual A/B)
Rollback
Automatic when guardrails are breached
Visual Editor
See the optimisation engine in action
Set up variants, allocate traffic and measure results — all through an intuitive interface.
Read on
