Graciela Galvez
Data-driven strategist focused on insights & growth
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Graciela is ready for their next project!
E-Commerce & Retail Weekly Revenue Performance Dashboard
I engineered a weekly retail metrics dashboard to analyze sales health and geographic performance. Faced with a 1.56% dip in weekly revenue, I isolated a critical operational bottleneck: 'zero sales locations' had climbed from 50% to 60%. I mapped daily transactional trends (peaking on Tuesdays at $1.2M+) and built a predictive impact model demonstrating how adding just a single transaction at dormant locations could recapture lost revenue.
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15-Week Institutional AI Integration Roadmap
I translated a comprehensive AI integration strategy into an actionable, phased 15-week implementation roadmap. The timeline syncs five core operational tracks simultaneously, ranging from governance (Policy Drafting & Annual Refresh) to ground-level deployment (Faculty Sprints, Pilot Cohorts, and License Access).
This asset ensures cross-functional teams remain aligned on milestones from task force formation to school-wide scale.
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Operational Framework: Bridging AI Analytics with Real-World Retail Execution
I developed an operational architecture blueprint to solve workflow and organizational adoption gaps in analytics. The project outlines a multi-layered data model: standardizing highly variable inputs (Grid, Traffic, Pricing), processing them through an AI/ML analytics engine, and passing insights through a 'Human-in-the-Loop' decision framework.
This model helps business leaders maintain accountability while scaling automation.
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Higher Education AI Sentiment & Faculty Behavioral Segmentation Study:
Using longitudinal data from an institutional faculty survey across three consecutive semesters, I performed a psychographic segmentation analysis to understand internal AI adoption dynamics. I identified four distinct faculty personas (The Vanguard, The Wounded, The Purists, and The Converts) mapping each group by key demographic cohorts, years of teaching experience, and specific structural demands (such as clear policy frameworks versus technical training resources).
This synthesized report provided academic leadership with a clear, data-backed roadmap to design targeted AI onboarding and support strategies.
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