Triente. Management advisory, data science, and primary market research.
Triente brings together strategy, analytics, and research to help leaders find the move worth making.
The U.S. Sentiment Signal · June 2026
About Triente
Better decisions when stakes are high.
Triente is a strategy firm that brings its own analytics and primary research to a company's hardest decisions. founded it in 2012 to do that work for the decisions that are too consequential to leave to standard methods.
Three decades across strategy, fintech, and AI · SVP at Fiserv · Principal and GTM lead at AWS · Associate Partner at McKinsey · four patents.
Read full bio →Principal-led
A few people who take the decision as seriously as you do, sit with the messy version of it, and don't hand you back the obvious answer. One lean team, led by David Rose, close enough to the work to be accountable for the result.
Data-forward
Every engagement pairs our own analytics with primary research among the executive teams and customers who live the market, resolved into one recommendation.
Partner-scaled
SaaS analytics platforms put models into production; marketing-research firms field primary studies. Capacity without the overhead.
Founded
2012
Clients
Mid-market to Fortune 500, private equity sponsors, and government
Decision-makers
Boards, and the CEO, CMO, COO, or GM who owns the number
Industries
Banking & Insurance · Consumer Financial Products · AI & Machine Learning Platforms · Government & Nonprofits · Private Equity
The Triente Ethos
The bigger a decision and the harder it is to reverse, the sharper the analysis behind it has to be.
The accepted story is usually wrong
Most strategic advice runs on pattern-matching and borrowed frameworks, defended with confidence the evidence doesn't support. We set it aside and rebuild the picture from raw data and primary research. It's usually wrong in at least one place that matters.
Disagreement is the finding
A model that contradicts the executive teams and customers living the market is wrong. A story the data won't back is also wrong. Where the analysis and the market agree, you can act. Where they don't, the gap is the finding.
What We Do
Services & Capabilities
Clients hire us for one hard, expensive decision and the plan to act on it. We lead with strategy, grounded in the system behind the decision. What makes it trustworthy is that we test it against our own analytics and primary research in the market, and the move worth making is the one all three point to. Each can be engaged on its own when a decision needs just one. On a decision that is large and hard to reverse, we run all three. The stakes set the depth. The deliverables under each are examples, not a full list.
Strategy
We frame the decision, size the market, and map where the profit is. The deliverable is a recommendation on the move that matters most, with the plan to act on it.
- Where to play, across markets, segments, and the portfolio
- Market sizing and profit-pool mapping
- Growth and market-entry strategy
- Value-creation case, with the economics behind it
- Implementation plan, with motions at 30, 60, and 90 days
Analytics
We build the models that measure what moves the objective and predict what comes next. Where cause matters, the deliverable is a simulator the client can run.
- Demand and volume forecasts
- Predictive models for attrition, response, and marketing mix
- System-dynamics simulators the client can operate
- Experiment design and causal testing
- Optimization and decision models for pricing, allocation, and capacity
Research
We go to the executive teams and customers who live the market and quantify what they want and will pay for. The evidence is firsthand.
- Customer segmentation and needs discovery
- Conjoint and willingness-to-pay studies
- Voice-of-customer and executive interviews
- Concept and product testing, and pilots
- Customer-journey and demand mapping
An Example
How they come together
The work is one team in the same room on one problem, the strategy, the analytics, and the market argued out until they point the same way. Take a decision to enter a new market. Strategy sizes it and maps the profit pools. Analytics models demand and how it responds to price and competition. Research tests what customers actually want and will pay for. The three converge on one recommendation, with the motions to act on it.
The same method runs underneath, on every engagement.
How we thinkWhen strategy, analytics, and the market point to the same lever, that's the one to pull. When they disagree, the disagreement is the finding.
How we thinkIndustry Expertise
We concentrate on five sectors, from both the portfolio and operating-company perspective, where the wrong answer is most expensive.
Banking & Insurance
Growth strategy, peer benchmarking, and risk diagnostics for banks and insurers.
Consumer Financial Products
Product economics, pricing, and customer behavior across lending, payments, and deposits.
AI & Machine Learning Platforms
Go-to-market, adoption, and product strategy for companies that sell AI.
Government & Nonprofits
Evidence for policy and program decisions, built on public data.
Private Equity
Commercial due diligence and value creation for portfolio companies across the hold.
Selected Work
Case Studies
Anonymized accounts of client engagements. We don't name clients or employers. Each covers the problem, the results, and the techniques behind the work.
AI & Machine Learning Platforms
Turning billions of payment records into a new data business
How a payments processor built a defensible new data business from transaction data it already held.
Read case study →Banking & Insurance
Defining the analytics platform community banks were missing
A positioning strategy that turned a community-bank analytics leader's data assets into a new platform, plus a recommendation to pass on an acquisition priced above its worth.
Read case study →Consumer Financial Products
Predicting customer attrition early enough to act
A logistic-regression model that flags at-risk clients early, with a plain-language reason for every score.
Read case study →Consumer Financial Products
Forecasting payment-type volume and simulating what drives it
A statistical forecast paired with a system-dynamics simulator the client could run themselves.
Read case study →Consumer Marketing
Allocating a $100M TV budget, week by week
How a national consumer brand turned a $100 million television budget into weekly, market-by-market allocation decisions that lifted its return by $10 to $15 million a year.
Read case study →Banking & Insurance
Diagnosing a claims-cost spike in a warranty book
A fast diagnostic that showed a worrying claims-cost spike was concentrated and temporary, not structural, and pointed to the specific models and repair providers behind it.
Read case study →Banking & Insurance
Finding the data to launch a new bank-marketing business
A data-asset assessment for a company trying to deepen narrow banking relationships into a new analytics business, and the focused set of bank data that made one viable.
Read case study →Banking & Insurance
Deciding whether to build or buy a way into analytics
A build-versus-buy assessment that ruled out an organic build and screened the analytics market down to a shortlist, one of which the company later acquired.
Read case study →Banking & Insurance
Running the diligence behind an analytics acquisition
An acquisition diligence that moved from weighted criteria and candidate ranking to on-site visits and board-ready materials for a decision the company acted on.
Read case study →Research & Perspective
Insights
Triente combines data science with what's happening in markets and how leaders actually decide.
A few tools we've built and maintain. Each one shows how public data, modern APIs, and a small amount of AI can compress a long research cycle. Each illustrates a stage of getting a decision right. One assembles the facts of an industry. One reads what widely followed measures mean when brought together. One is a working model of an industry where the players respond. Short articles on the work live in the articles section, and anonymized examples of client engagements and operating work live in the case studies.
Interactive Demonstration · Public Data
The U.S. Sentiment Signal
A monthly gauge of the mood of the United States, built from federal economic data and national polling in a single index. It tracks economic health, government trust, and social confidence, and flags shifts in the national outlook.
Interactive Demonstration · Public Data
The U.S. Banking Industry Data Explorer
Explore FDIC call report data across every FDIC-insured institution. Search, filter, benchmark, and compare banks by asset size, charter type, state, and financial performance. Public APIs and generative AI turn filings from every U.S. bank into answers in seconds.
Interactive Demonstration · Public Data
The U.S. Banking Industry Simulator
Explore the financial dynamics of banks under varying conditions, such as continuing consolidation, NIM pressure, and other macroeconomic scenarios. A system-dynamics model shows how those conditions play out over time.
Contact Us
Get in touch
The good problems are the ones without a clean answer yet. If you're sitting with one of those, tell me what you're working toward. If I can help, I'll say how. If I can't, I'll say so.
Engagements are scoped to the decision, whether that's a two-week diagnostic or a multi-month strategy effort.
Prefer a conversation? Schedule a meeting with David Rose →