The math behind
the move.
Market research and quantitative intelligence for private equity, financial institutions, banks and nonprofits. Built by economists, mathematicians and data scientists.
Research that moves capital.
From the first question to a defensible recommendation, our work brings scientific depth to the decisions that matter.
Different mandates demand different lenses. Every engagement begins with the same commitment: understand the question, interrogate the data, and make the conclusion useful.
Four sectors. One standard of evidence.
Every assumption deserves scrutiny; every recommendation should survive it.
Built by scientists.
Our work draws on economists, mathematicians, statisticians, and data scientists.
That composition shapes the questions we ask, the methods we choose, and how candidly we communicate uncertainty. We do not mistake a compelling chart for a defensible conclusion. Every assumption deserves scrutiny; every recommendation should survive it.
From question to conviction.
A disciplined sequence designed to reduce uncertainty without hiding complexity.
Step 1 of 5
Frame
Turn an uncertain decision into a testable question.
We begin with the decision, not the dataset. In a scoping workshop, we surface competing hypotheses and identify the evidence that could change the answer. We agree on success measures, constraints, and the boundaries of the inquiry before fieldwork begins. The result is a research plan that makes trade-offs in time and budget explicit.
What we do
- Run a decision-focused scoping workshop
- Build a hypothesis tree and define decision criteria
- Identify stakeholders, audiences, and evidence gaps
- Set success metrics and research priorities
- Agree on governance, plan, and budget
- Methods & tools
- Stakeholder interviews · Hypothesis trees · Evidence gap mapping · Research design
- What you receive
- Decision brief · Hypothesis tree · Research plan and budget
- Typical duration
- Typically 1–2 weeks
Step 2 of 5
Collect
Gather the right evidence, not simply the most available data.
Collection combines what people say with what they do. We design primary research around the populations that matter, while assessing secondary and alternative sources for relevance and coverage. Permissions, nondisclosure agreements, and data handling are established before sensitive information moves. Recruitment and instruments are checked so later analysis rests on a credible foundation.
What we do
- Design surveys, interview guides, and sampling plans
- Recruit experts, customers, donors, or field panels
- Gather transaction, web, geospatial, and filing data where appropriate
- Assess provenance, bias, and missingness
- Put consent, NDAs, and data governance in place
- Methods & tools
- Survey design · Panel sourcing · Expert interviews · Customer and donor interviews · Secondary and alternative data review
- What you receive
- Fieldwork summary · Source inventory · Documented dataset and data dictionary
- Typical duration
- Typically 2–5 weeks
Step 3 of 5
Analyze
Separate durable patterns from noise and convenient assumptions.
We clean and weight the evidence before asking a model to explain it. Analytical choices follow the question: segmentation for distinct needs, choice models for trade-offs, forecasting for change over time. Where data is sparse, we show how estimates borrow strength and where they remain uncertain. Scenarios translate model outputs into decision-relevant alternatives.
What we do
- Clean, link, and weight the data
- Build behavioral segments and test stability
- Model demand, price sensitivity, and adoption
- Forecast under explicit economic assumptions
- Compare scenarios against the decision criteria
- Methods & tools
- Clustering and segmentation · Conjoint / MaxDiff · Econometric models · Bayesian hierarchical models · Scenario analysis
- What you receive
- Analysis workbook · Segment profiles · Scenario model and assumptions
- Typical duration
- Typically 2–4 weeks
Step 4 of 5
Validate
Challenge the result under different scenarios and competing explanations.
A plausible model is not yet a defensible finding. We back-test where histories permit, reserve holdout evidence where feasible, and probe sensitivity to key assumptions. Independent review challenges the design, code, and interpretation. We report confidence ranges and limitations next to the conclusion, not in a footnote.
What we do
- Back-test forecasts and use holdout samples where feasible
- Run sensitivity and Monte Carlo scenarios
- Triangulate primary, secondary, and behavioral evidence
- Arrange independent senior or PhD-level peer review
- State confidence ranges and limitations plainly
- Methods & tools
- Holdout / back-testing · Sensitivity analysis · Monte Carlo simulation · Source triangulation · Independent peer review
- What you receive
- Validation memo · Sensitivity ranges · Reviewed assumptions and limitations
- Typical duration
- Typically 1–2 weeks
Step 5 of 5
Advise
Translate the analysis into decisions and a clear path forward.
We communicate findings in the language of the decision and the people responsible for it. The executive readout separates what is known, what remains uncertain, and what should happen next. Teams receive the models and definitions needed to revisit the reasoning rather than a static presentation alone. Follow-up can test implementation signals at 30, 60, and 90 days when appropriate.
What we do
- Write a board-ready decision memo and report
- Lead an executive readout and discussion
- Prioritize options and implementation dependencies
- Hand off models, definitions, and assumptions
- Agree on a 30/60/90-day follow-up plan
- Methods & tools
- Decision synthesis · Executive workshop · Model hand-off · Implementation planning
- What you receive
- Board-ready report · Executive readout · Decision memo · Model hand-off
- Typical duration
- Typically 1–2 weeks
Capabilities built for real decisions.
Research design, fieldwork, analysis, and advice are integrated rather than handed off across silos.
What matters is not how much we measure, but what the evidence allows us to say.
The work behind the answer.
Illustrative composite engagements show the questions behind the work and how we approach them.
Understanding recurring donor growth
A donor-centered view of declining recurring support and where durable growth might come from.
Testing a healthcare-services acquisition thesis
A grounded assessment of market demand, pricing power, and downside risk for a specialty healthcare-services platform.
Rethinking deposit strategy and product demand
A differentiated view of deposit price sensitivity, customer attrition, and Shariah-compliant product demand.
Brands we've helped grow.
Alongside our research practice, we support brands with marketing strategy, creative and paid media.
Data reimagines the world.
Decisions once made on instinct can now be measured against evidence.
Patterns hidden in transactions, surveys, and behavior become visible when we ask better questions of the data.
Rigor does not erase uncertainty. It turns it into ranges that people can understand and act on.
Our role is to translate those signals into decisions for funds, banks, and nonprofits.
Beyond AI.
Artificial intelligence is moving from tools that assist analysts toward systems that may one day reason across entire markets at once — what many now call superintelligence (SI).
We believe that shift will change how capital is allocated, how institutions manage risk, and how causes understand the people they serve. Our work is built for that future: rigorous data foundations, models that can be audited, and human experts — economists, mathematicians and scientists — who know when to trust a machine and when to question it. As machines grow more capable, judgment becomes more valuable, not less.
As machines grow more capable, judgment becomes more valuable, not less.
Ideas worth testing.
Why TAM estimates fail in due diligence
The error is often not arithmetic. It is the market definition that came before it.
Deposit pricing after the rate cycle
Why the next deposit decision requires more than a competitor-rate comparison.
What mathematicians bring to market research
A model is most useful when its assumptions can be challenged.
12 years of research.
Since 2014, our questions have broadened. Our commitment to careful evidence has not.
We hire mathematicians.
And economists, statisticians, data scientists, and curious minds who care about the answer.
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