Making money from assumption to evidence
Run Continuous Dynamic Experiments While In-Production
The Dynamic Experimentation Module is a production-grade experimentation framework. It enables real-time learning through live experimentation, where each interaction feeds into a dynamic learning engine that tests, compares, and refines strategies in real-time.
Configure multiple alternatives for any decision with or without data. The Module dynamically decisions options as it learns what performs, and what doesn’t.
Close the loop between hypothesis and outcome, to discover optimal pathways faster, reduce risk, and continuously improve engagement without interrupting the customer journey.
Continuous learning for results you don’t have to wait for
Run smarter experiments that adapt in real time at scale
Traditional experimentation assumes access to historical data to define starting points. The Dynamic Experimentation Module flips that assumption—enabling teams to begin testing in brand-new environments with no priors. It leverages behavioral science models and interaction-based signals to infer early preferences and intelligently allocate options from the first engagement onward.
- Start experimentation immediately in new markets, product categories, or digital channels
- Use interaction signals and ecosystem.Ai personalities as cold-start context
- Avoid delay caused by long data-gathering cycles before testing
- Especially valuable for greenfield journeys, onboarding flows, or pilot rollouts
- More traffic is routed to better-performing variants automatically
- Bandit algorithms dynamically balance learning and delivery
- Poor-performing options are deprioritized without manual intervention
- Experiments improve KPIs even before reaching statistical finality
Go beyond A/B. Run complex experiments with multiple strategies, offers, or models simultaneously. Test hypotheses across combinations, segments, and use cases in parallel—without separate deployment workflows.
- Compare multiple options in one configuration (A/B/n, not just A/B)
- Test model vs. model or rule vs. rule in production
- Optimize experiences across different personas or contexts
- Reduce experimentation cycles by testing broadly and intelligently
Embed ecosystem.Ai’s Human Behavioral Algorithms directly into your experiments. Design variations based on cognitive principles like loss aversion or social proof—and let the system measure which framing works best for which customer types.
- Select from prebuilt behavioral models during setup
- Apply psychological principles to shape user responses
- Understand not just what performs, but why it performs
- Tailor experiments to align with Spend or Interaction Personalities
Built for real-time decisioning, not analytics reports
The Dynamic Experimentation Module is actioned by the ecosystem.Ai Prediction Platform.
Configure experiments through a low-code interface to define experiment goals, options, and learning parameters. The Module integrates with the Client Pulse Responder to apply preferences in real-time with Feature Stores to access contextual variables.
Feedback is collected live and updates the decision logic continuously. Experiments are deployed using a unique UUID, and responses can be called via API across touchpoints.
Use Real-Time Recommender or Intelligent Sales Modules to create a more robust decisioning system.
