About
About
Steven Rencher, Founder of Monadux
I spent a decade doing market research — leading insights functions at Heatseeker, BILL, and Optum, building research capabilities that drove revenue decisions and GTM strategy at scale.
I spent a decade doing market research — leading insights functions at Heatseeker, BILL, and Optum, building research capabilities that drove revenue decisions and GTM strategy at scale.
What I kept seeing, across companies of every size: teams with the right data making the wrong calls, because nothing in their stack connected the signals across sources. Product said one thing. Marketing said another. Support said a third. No one was synthesizing across all three.
What I kept seeing, across companies of every size: teams with the right data making the wrong calls, because nothing in their stack connected the signals across sources. Product said one thing. Marketing said another. Support said a third. No one was synthesizing across all three.
That’s why I founded Monadux. Monadux takes its name from Leibniz’s philosophical concept of the monad: an individual, indivisible unit that reflects the whole it belongs to. That’s the principle Monadux is built on — bringing every piece of your data together until the complete picture emerges.
That’s why I founded Monadux. Monadux takes its name from Leibniz’s philosophical concept of the monad: an individual, indivisible unit that reflects the whole it belongs to. That’s the principle Monadux is built on — bringing every piece of your data together until the complete picture emerges.
What I built
Scry, Monadux’s first product.
Scry, Monadux’s first product.
Scry, Monadux’s first product.
Scry is a cross-source intelligence layer for growth-stage teams — named for the old word for looking into a reflective surface to find what’s hidden. It surfaces the patterns, connections, and opportunities buried in data your company has already collected but never fully seen.
Scry is a cross-source intelligence layer for growth-stage teams — named for the old word for looking into a reflective surface to find what’s hidden. It surfaces the patterns, connections, and opportunities buried in data your company has already collected but never fully seen.
You set a standing objective — what your team is focused on right now. You bring exports from your existing tools — product analytics, CRM, customer support, and others. Scry analyzes everything simultaneously and delivers a structured synthesis report: a single top-line conclusion that changes how you approach the next 90 days, ranked root causes with directional revenue impact, a phased action plan with named owners, and documented gaps.
You set a standing objective — what your team is focused on right now. You bring exports from your existing tools — product analytics, CRM, customer support, and others. Scry analyzes everything simultaneously and delivers a structured synthesis report: a single top-line conclusion that changes how you approach the next 90 days, ranked root causes with directional revenue impact, a phased action plan with named owners, and documented gaps.
No integrations. No engineering. No lengthy onboarding. Just the answers your stack has been sitting on.
Before Monadux
A decade in market research, at every scale.
A decade in market research, at every scale.
A decade in market research, at every scale.
Heatseeker
I pushed the envelope with behavioral AI experimentation — supporting ongoing development, leading client implementations, and presenting results so enterprise teams understood what they meant. I managed a portfolio of enterprise accounts, including ServiceNow, Cisco, L’Oréal, and KPMG.
I pushed the envelope with behavioral AI experimentation — supporting ongoing development, leading client implementations, and presenting results so enterprise teams understood what they meant. I managed a portfolio of enterprise accounts, including ServiceNow, Cisco, L’Oréal, and KPMG.
BILL
Built the Market Intelligence function from scratch
Built the Market Intelligence function from scratch
I started and owned BILL’s Market Intelligence function. I identified and sized a $300M/yr market opportunity, then worked cross-functionally to bring the resulting products to market. I led BILL’s yearly thought leadership program, resulting in ~$3M/yr in net new recurring revenue. I cut sample costs ~88% using new sampling methods, 3x’ed research capacity, and managed a $500,000 budget.
I started and owned BILL’s Market Intelligence function. I identified and sized a $300M/yr market opportunity, then worked cross-functionally to bring the resulting products to market. I led BILL’s yearly thought leadership program, resulting in ~$3M/yr in net new recurring revenue. I cut sample costs ~88% using new sampling methods, 3x’ed research capacity, and managed a $500,000 budget.
I started and owned BILL’s Market Intelligence function. I identified and sized a $300M/yr market opportunity, then worked cross-functionally to bring the resulting products to market. I led BILL’s yearly thought leadership program, resulting in ~$3M/yr in net new recurring revenue. I cut sample costs ~88% using new sampling methods, 3x’ed research capacity, and managed a $500,000 budget.
Optum
Longitudinal research on login and registration flows
During UHC’s yearly Open Enrollment Period, I led longitudinal research on login and registration flows, along with longitudinal competitive monitoring of how competitors handled the same journeys. That work fed a user flow change that increased enrollment 20% — worth ~$56M in revenue. I led a team of 4 researchers to build training completed by roughly 100 researchers org-wide.
During UHC’s yearly Open Enrollment Period, I led longitudinal research on login and registration flows, along with longitudinal competitive monitoring of how competitors handled the same journeys. That work fed a user flow change that increased enrollment 20% — worth ~$56M in revenue. I led a team of 4 researchers to build training completed by roughly 100 researchers org-wide.
During UHC’s yearly Open Enrollment Period, I led longitudinal research on login and registration flows, along with longitudinal competitive monitoring of how competitors handled the same journeys. That work fed a user flow change that increased enrollment 20% — worth ~$56M in revenue. I led a team of 4 researchers to build training completed by roughly 100 researchers org-wide.
TDS Telecommunications
TDS Telecommunications
I directed TDS’s market research, CSAT, NPS, and loyalty programs and managed a $300,000 research budget.
I directed TDS’s market research, CSAT, NPS, and loyalty programs and managed a $300,000 research budget.
Market research, CSAT, NPS, and loyalty
Independent
Fortune 500 to five-person startup
Fortune 500 to five-person startup
I provided market research, market intelligence, and competitive intelligence to companies from Amazon, Etsy, Expedia, and NMDP down to small, fast-growing teams most people have never heard of. Many of those smaller companies have since been acquired, and some aren’t around anymore. That range, Fortune 500 to five-person startup, is where I learned what actually breaks as a company scales.
I provided market research, market intelligence, and competitive intelligence to companies from Amazon, Etsy, Expedia, and NMDP down to small, fast-growing teams most people have never heard of. Many of those smaller companies have since been acquired, and some aren’t around anymore. That range, Fortune 500 to five-person startup, is where I learned what actually breaks as a company scales.
I hold an MA in Psychology from Minnesota State University, Mankato.
I hold an MA in Psychology from Minnesota State University, Mankato.
Behavioral AI experimentation
Behavioral AI experimentation
Why I’m doing this differently now
Why I’m doing this differently now
Enterprise-grade synthesis, without the enterprise budget.
Enterprise-grade synthesis, without the enterprise budget.
Enterprise-grade synthesis, without the enterprise budget.
Working with small and growing teams for years, I kept seeing the same ceiling: they carried the same fragmented-data problem as the giants, with none of the budget or headcount giants use to paper over it. That contrast is the niche I learned to operate in.
Working with small and growing teams for years, I kept seeing the same ceiling: they carried the same fragmented-data problem as the giants, with none of the budget or headcount giants use to paper over it. That contrast is the niche I learned to operate in.
Optum showed me the other side of that gap — an enterprise with dedicated teams and infrastructure built to keep signals connected across a massive surface area, resources no ten-person startup will ever have. BILL and Heatseeker each showed me a different way to close that gap without the enterprise budget. At BILL, new methods — synthetic sample, AI-assisted tooling — cut research costs without cutting quality, for some questions, not all, and knowing where that line sits is most of the job. At Heatseeker, I watched a team scale customer success through a stretch of 25% month-over-month growth without adding headcount, by building automation and self-service into the system instead of throwing people at it.
Optum showed me the other side of that gap — an enterprise with dedicated teams and infrastructure built to keep signals connected across a massive surface area, resources no ten-person startup will ever have. BILL and Heatseeker each showed me a different way to close that gap without the enterprise budget. At BILL, new methods — synthetic sample, AI-assisted tooling — cut research costs without cutting quality, for some questions, not all, and knowing where that line sits is most of the job. At Heatseeker, I watched a team scale customer success through a stretch of 25% month-over-month growth without adding headcount, by building automation and self-service into the system instead of throwing people at it.
Optum showed me the other side of that gap — an enterprise with dedicated teams and infrastructure built to keep signals connected across a massive surface area, resources no ten-person startup will ever have. BILL and Heatseeker each showed me a different way to close that gap without the enterprise budget. At BILL, new methods — synthetic sample, AI-assisted tooling — cut research costs without cutting quality, for some questions, not all, and knowing where that line sits is most of the job. At Heatseeker, I watched a team scale customer success through a stretch of 25% month-over-month growth without adding headcount, by building automation and self-service into the system instead of throwing people at it.
Scry is where all of that comes together: enterprise-grade synthesis, built to run without an enterprise budget.
Scry is ready.
If your data is, let's talk.
Reach out directly to hello@monadux.com or tell us a little about your business.
Scry is ready.
If your data is,
let's talk.
Reach out directly to hello@monadux.com or
tell us a little about your business.