Case Studies
Proven results, built and still running
A selection of engagements where VICI built the capability, shipped it, and moved the number. Client names are withheld by design; the work and the outcomes are real.
Building an analytics function from zero
A roughly $1.9B-asset credit union had no central data infrastructure, no enterprise reporting capability, and no analytics leadership. Finance, operations, marketing, and branch management each worked from disconnected spreadsheets, with no consistent source of truth for performance.
- Designed and built the first enterprise data warehouse from scratch, including architecture, data sourcing, and integration
- Stood up enterprise reporting that served finance, operations, marketing, and branch management
- Built a proprietary member-benchmarking framework to measure performance against peer institutions
- Trained cross-functional teams to use analytics for performance management and decisions
- Established a scalable data governance foundation the organization could grow into
- The benchmarking framework was adopted as an official, marketable product, not just an internal tool
- Teams moved from manual spreadsheets to data-driven decision cycles
- The analytics function went from non-existent to mature in under three years
- Created lasting institutional IP that outlived the engagement
Weather-adjusted demand forecasting
A PE-backed home-services roll-up was guessing on staffing and service demand. Seasonal spikes were unpredictable, over- and under-staffing was routine, and the business had no model that accounted for the biggest variable in the trade: the weather.
- Designed and built a weather-adjusted seasonality demand model, integrating historical weather patterns with service-call volume
- Created an operational forecasting system that predicted demand windows with meaningful accuracy
- Built it to be maintainable and extensible, a production tool rather than a one-time analysis
- Translated model outputs into executive-ready dashboards and decision frameworks for ops leadership
- Went live in 2019 and is still in production today, seven-plus years of active use
- Enabled proactive staffing during demand peaks and shoulder seasons
- Reduced reactive overstaffing costs during slow periods
- Became an embedded operational asset, not a deliverable that sat on a shelf
AI governance for regulated financial services
Fortune 500 financial-services clients were under pressure to adopt AI from regulators, boards, and competitors, but lacked the frameworks to deploy it responsibly in regulated environments. Compliance, audit, and legal were blocking AI initiatives with no clear path to approval.
- Ran a weekly AI briefing cadence, translating fast-moving foundation-model and enterprise-vendor developments into governance-ready frameworks
- Designed retrieval-augmented generation (RAG) deployment patterns compatible with compliance and audit requirements
- Developed permission-aware retrieval and human-in-the-loop workflows that allowed AI adoption without regulatory exposure
- Advised C-suite stakeholders on AI readiness, vendor evaluation, and responsible deployment sequencing
- Clients moved from AI-blocked to AI-ready, with frameworks that satisfied compliance, legal, and audit
- Multiple clients advanced from strategy to active implementation within the engagement
- Consistently exceeded commercial targets, proving governance-forward AI advisory produces outcomes, not compliance theater
Predictive analytics and data governance at scale
A large fintech and payments organization needed its e-commerce and commercial mid-market divisions to move beyond historical reporting to forward-looking decisions: predictive models for sales trends and attrition risk, stronger data governance, and an advisor credible across both the technical and executive layers.
- Served as strategic analytics advisor to the e-commerce and commercial mid-market divisions, translating complex data into executive-ready insight
- Built predictive models for sales-trend forecasting and customer-attrition risk
- Enhanced data governance standards and controls that improved data reliability across reporting
- Delivered actionable analysis on market dynamics, pipeline health, and product performance
- The attrition model gave sales and retention teams advance visibility into at-risk accounts, enabling outreach before churn
- Governance improvements reduced reporting errors and raised leadership confidence in the numbers
- Established a repeatable analytics-advisory model leaders could engage on demand