Who I am
I am an innovative thinker that knows how to provide real solutions to the problems businesses face today.
I am trained in qualitative and quantitative research aimed at sustainability and technological adoption in the public and private companies.
I deploy an holistic and broad framework to tackle the problem, by combining social science with data analysis, I provide a road map toward success.
I am not afraid of the unknown and I am ready to build the future with you.
Ready to embark on a journey to success? Book your appointment now and let's turn your business challenges into triumphant stories!
My Process
Phase 1 - Getting to Know Each Other
Phase 2 - Project Design
In this phase, we define the project framework and set clear objectives. Together, we align the research methodologies and strategic approach with the desired outcomes to ensure the project is both effective and goal-oriented.
In this initial phase, we take the time to understand each other’s perspectives. We’ll discuss your needs, current challenges, and objectives for our collaboration, while I outline how my expertise and services can best support your goals.
At the conclusion of the project, I will deliver a comprehensive report detailing the process, key findings, and outcomes. Along with summarizing results, I will provide strategic insights on areas for improvement and recommendations for future opportunities based on the project’s conclusions.
In this phase, we put our plan into action. Together, we carry out the agreed steps, including data collection, analysis, and all scheduled activities. Throughout the process, we ensure that both the timeline and objectives are met with precision and consistency.
Phase 4 - Final report and Future Prospects
Phase 3 - Project Execution
Qualitative Methods
Survey Questionnaire
-
This method gathers structured feedback from participants through carefully designed questions. It allows for the collection of quantifiable data on opinions, preferences, and behaviors, providing clear insights that can be analyzed statistically to identify trends and patterns.
Research Interview
-
A research interview is an in-depth, one-to-one conversation used to explore experiences, motivations, and perspectives in detail. Through open-ended questions, it allows participants to express themselves in their own words, generating rich, nuanced insights that go beyond what standardized questionnaires can capture.
Ethnographic Observation
-
This method involves immersive, firsthand observation of participants in their natural environment to capture authentic behaviors and interactions. It provides deep contextual insights into cultural practices, social dynamics, and unspoken patterns that surveys or interviews might miss.
Focus groups
-
This method brings together small groups of participants to discuss specific topics, revealing shared perspectives, attitudes, and dynamics through moderated conversations. It uncovers group consensus, disagreements, and emergent ideas that individual interviews might not surface.
Quantitative Methods and AI
Correlation Analysis and Predictions
-
This method examines statistical relationships between variables to identify patterns and dependencies in data. It enables predictive modeling by forecasting future trends and outcomes based on established correlations, supporting data-driven decision-making.
Cluster Analysis
-
This method groups data into distinct clusters based on similarities or classifies items into predefined categories using statistical patterns. It reveals hidden structures, segments audiences, and supports targeted strategies by identifying natural groupings or predictive labels in datasets.
A/B test
-
This method compares two variants (A and B) of a process, product, or campaign to determine which performs better based on measurable outcomes like conversions or engagement. It provides statistically valid insights to optimize decisions through controlled, data-driven experimentation.
AI development and policy compliance
-
This method integrates AI model development with rigorous compliance checks against regulatory standards like the EU AI Act. It uses quantitative metrics for risk assessment, bias detection, and performance auditing to ensure ethical, transparent, and legally sound AI deployment.