Most analytics work produces dashboards. Dashboards describe what happened. They do not tell you what to do. The work we do is different. We build the models that predict outcomes, identify the levers that move them, and quantify the cost of getting it wrong.

Workforce analytics and retention modeling. Predict individual employee flight risk, segment by driver, and identify the interventions that move the numbers. Built on XGBoost, SHAP, and hierarchical Bayes conjoint methodology. Deployed across federal, provincial, and Fortune 100 environments.

Conjoint analysis and preference research. Force real tradeoffs in employee benefits, customer purchase decisions, voter choice, and stakeholder policy acceptance. Output is prescriptive: change this attribute, expect this shift in choice.

Quasi-experimental program evaluation. Difference-in-differences, natural experiments, and small-cluster inference. The right design choice depends on the data structure and the question. We pick the one that holds up to scrutiny, not the one that produces the cleanest chart.

Synthetic research. Where time or budget does not allow for full fieldwork, AI-generated profiles produce research-grade insights in days. Useful for early-stage decisions and time-sensitive calls.

Most consulting analytics is descriptive dressed up as insight. Our work is predictive and prescriptive. We isolate what causes what, not just what correlates with what. We use small-cluster corrections when the cluster count is small. We apply false discovery rate corrections when we run multiple tests. The methodology survives review by people who know what to look for, because the people on the other side of the table often do.

Major hospital system: Cut turnover 50% with a benefits package 75% of employees preferred, saving $1,300 per employee.

Federal transportation agency: Forecast individual employee flight risk at 85% accuracy across a 6,500-person workforce.

Long-shot political candidate: Took to party leader using prescriptive conjoint research instead of conventional polling.

Large retailer: Linked employee engagement to store profit and revenue per square foot.

Custom analytics engagements scoped to the question. Typical projects run six to sixteen weeks. Pricing scales with scope, ranging from $25,000 for targeted diagnostic work to $150,000 and up for full analytics deployments. Ongoing advisory relationships available.

We take on a small number of analytics engagements per year. If the problem is hard and the stakes are real, start the conversation: website@CleverTrout.com