Visagio's methodology of growth and innovation
Visagio's approach to innovation and growth emphasises a structured cycle of experimentation applied to both incremental and disruptive ideas: experiments with existing data and innovative ones with new data sources. The core of this approach is three pillars: governance, people development, and technological advancement.

Key Components
Experimentation Cycle
- Guidelines and Levers: Establish guidelines and identify key drivers to ensure experiments are aligned with strategic goals.
- Ideation, Pipeline and Prioritisation: Generating new ideas through dynamic and engaging activities, building a structured funnel for ideas to be evaluated and prioritised, and using the ICE Score (Impact, Confidence, Effort) to prioritise initiatives.
- Experimentation: Implementing and testing ideas in controlled environments.
- Analysis: Evaluating results and iterating based on data.
Pillars
- Governance
- Ensure strong governance with a clear framework to support dynamic operations and alignment towards results.
- Utilise agile methodologies, including regular sprints and meetings to maintain progress and address any impediments.
- Incorporate stakeholder feedback and ensure continuous improvement.
- Ensure strong governance with a clear framework to support dynamic operations and alignment towards results.
- People Development
- Build a multidisciplinary team with strong technical capabilities and continuous training.
- Promote a culture of collaboration and knowledge sharing within the team.
- Develop leadership through hands-on experience and structured training programs.
- Build a multidisciplinary team with strong technical capabilities and continuous training.
- Technology
- Develop tools to automate the tracking of results and ensure consistency across experiments.
- Implement dashboards for real-time monitoring and analysis of experiments.
- Ensure robust data management practices to support informed decision-making.
- Develop tools to automate the tracking of results and ensure consistency across experiments.


