We use statistical analysis to examine business performance, evaluate initiatives, and estimate potential outcomes. Our work includes AI adoption and impact evaluation, predictive modeling, segmentation, and business case analysis.
We select methods that fit the question and the available data, then explain what the results support and where uncertainty remains.
We examine usage, adoption patterns, and business outcomes, applying causal methods where the data and evaluation design support them.
We use regression, machine learning, and scenario analysis to estimate outcomes and explore how they change under different assumptions.
We develop customer cohorts, clusters, composite indices, and scoring models to support comparison and prioritization.
We analyze feedback, reviews, and location data to investigate customer perceptions, market patterns, and operational differences.
We assess costs, benefits, and potential returns, testing the assumptions that drive the financial case.
Examine how adoption and usage relate to business outcomes, using causal methods where the data and evaluation design support estimates of AI’s contribution.
Evaluate whether patterns in customer, operational, or market data can support useful forecasts, with validation that shows how well the models perform.
Explore how changes in costs, adoption, and expected benefits affect potential returns, highlighting the assumptions that have the greatest influence on the result.
Examples of how we evaluate impact, identify meaningful segments, and build repeatable analytical models.
We evaluated an internal AI assistant across sales and customer service, combining causal study design with large-scale operational data. Placebo checks, sensitivity tests, and explicit measurement boundaries helped distinguish supported findings from claims the evidence could not establish.
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Within a policy advisory firm’s Gates Foundation program, we compared clustering approaches to develop seven employer personas. Statistical checks and expert validation connected the segmentation to job-quality gaps and practical program priorities.
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We combined market-penetration analysis across 3,142 U.S. counties with a repeatable geographic model matching more than 10,000 prospects to nearby branches. The outputs connected local opportunity, proximity, and branch coverage for comparison.
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Assess market opportunities, understand competitors, and define where to compete through market sizing, segmentation, pricing research, and commercial due diligence.
Understand customer needs, buying decisions, and product preferences through survey design, quantitative analysis, pricing research, and customer segmentation.
Estimate revenue potential, compare customer groups, and assess upsell and cross-sell opportunities through account-level modeling and customer analysis.
