Why I Built Clarivant: The Mid-Market Analytics Gap
- Retail & eCommerce
- Consumer Goods (CPG)
- Manufacturing & Supply Chain
TL;DR
Clarivant is an analytics consultancy built for mid-market companies — roughly $10M to $500M in revenue — that are too large for Excel and off-the-shelf BI tools but too small for enterprise consulting budgets and multi-year enterprise data programs. It was founded on the observation that mid-market executives don't lack data, they lack trusted, timely answers to simple questions like customer lifetime value or marketing spend versus forecast. Clarivant delivers senior-practitioner-led engagements that produce documented, version-controlled systems the client's own team fully owns and can run after the engagement ends, rather than a dependency that requires the consultant to stay involved indefinitely.
Key takeaway — Every mid-market company I’ve talked to has the same story: business is good, someone asks a simple question, and nobody can answer it with data. The problem isn’t the data — it’s the gap between having it and using it. That’s why I built Clarivant.
Someone in the C-suite asks a simple question:
“What’s our customer lifetime value by region?”
Silence.
“How much did we spend on marketing last quarter compared to forecast?”
Someone mumbles something about “pulling that together” and “getting back to you.” Three weeks later, an Excel file appears. It’s wrong. Nobody trusts it. Decisions get delayed.
I’ve watched this happen at every mid-market company I’ve talked to. And it’s not because they lack data — they’re drowning in it. The gap is between having data and using it to make decisions.
That gap is why Clarivant exists.
Why the gap persists
Big companies solve this by writing large checks. They hire a name-brand consulting firm, buy the enterprise stack, build a 10-person data team, and wait 18 months for results.
Mid-market companies can’t do that — and shouldn’t have to.
They’re too big for Excel and Power BI. Too small for enterprise budgets. Too lean to wait a year and a half. So they limp along with spreadsheets, manual reports, and decisions made on gut feel.
The conventional wisdom — that enterprise-grade analytics requires enterprise-level spend — is wrong. The frameworks that power billion-dollar operations aren’t inherently expensive to run. They’re expensive because of how they’re delivered: layers of project management, junior consultants learning on your dime, and discovery phases that produce nothing but slide decks.
Strip all that away, and what’s left is a pattern. A very repeatable one.
My own path to this
I discovered data analytics the way most people do: out of desperation.
Early in my career, someone handed me an Excel file with 60,000+ rows. The file wouldn’t open — Excel at the time couldn’t handle it. That moment forced me into Access, then SQL, then automation. What started as a workaround became a revelation: if you knew pivot tables and conditional formatting, you were in the top 1% of any company’s Excel users. If you could automate it with macros, you became indispensable.
That curiosity became a pattern. At P&G, when Excel and Access weren’t enough, I learned R, KNIME, and Hadoop. At eBay, when I needed to process millions of transactions across 15 countries, I learned machine learning at scale. The progression wasn’t planned — it was driven by increasingly complex problems.
And here’s what I realized along the way: the tools matter far less than knowing which problem you’re actually solving.
Every mid-market executive I spoke to said some version of the same thing: “We need what you built at those companies, but we can’t afford the enterprise price tag.” And they were right — they couldn’t afford the delivery model. But they could absolutely afford the solution.
What Clarivant stands for
I’m not building a consultancy that scales by adding headcount. There’s a version of this work where the consultant becomes a dependency — where the client needs you forever because you built something only you understand. That’s not what I’m after.
Every system I build, you own. Every pipeline is documented, tested, version-controlled. When I leave, your team runs it.
What that actually looks like in practice: I show up as a senior practitioner, not a partner who sells and a junior who delivers. I measure success against business metrics — margin, cost, churn, revenue — not dashboard counts. And I work fast enough that something useful exists within months, not fiscal years.
That’s the model I’d want to buy if I were on the other side of the table.
The point of view
The question I start every engagement with isn’t “what tool should we buy?” — it’s “what decision are you trying to make, and what’s stopping you from making it with confidence?”
Start there, and the architecture follows. Skip it, and you end up with a warehouse full of tables nobody trusts and dashboards nobody opens.
Most analytics projects don’t fail because the technology is wrong. They fail because nobody asked the right question first. That’s the lens I bring — not a methodology deck, but “what’s actually broken, and what’s the fastest path to you trusting your own numbers?”
Frequently asked questions
What is Clarivant and who is it for?
Clarivant is an analytics consultancy built specifically for mid-market companies, roughly $10M to $500M in revenue, that have outgrown spreadsheets and basic BI tools but can't justify enterprise-scale consulting spend or multi-year data platform buildouts. It exists to close the gap between having data and actually using it to make decisions — the gap that shows up when a simple question like customer lifetime value by region gets met with silence or a three-week-late, untrusted Excel file.
Why was Clarivant founded?
Its founder spent years across companies including P&G and eBay building analytics capability, from an early career moment forced by a 60,000-row Excel file that wouldn't open, through learning SQL, R, KNIME, Hadoop, and machine learning at scale. Repeatedly, mid-market executives said some version of needing what was built at those larger companies but being unable to afford the enterprise price tag — and the realization was that they couldn't afford the enterprise delivery model, but could afford the underlying solution. Clarivant was built to deliver that solution without the enterprise-scale overhead.
How is Clarivant's delivery model different from a traditional consulting firm?
Traditional enterprise analytics engagements involve name-brand consulting firms, large teams, junior consultants learning on the client's dime, and 18-month timelines before results appear. Clarivant is built around a senior practitioner delivering work directly — not a partner who sells the work while a junior team delivers it — measured against business metrics like margin, cost, churn, and revenue rather than dashboard counts, and working fast enough that something useful exists within months rather than fiscal years.
Does working with Clarivant create a long-term dependency on the consultant?
No — that's an explicit design principle. Every system built is documented, tested, and version-controlled so the client's own team can run it after the engagement ends. The stated alternative it avoids is a consultancy that scales by adding headcount and where the client needs the consultant indefinitely because only the consultant understands what was built. The goal is for the client to own and operate what was delivered, not to remain reliant on outside help.
What kinds of companies does Clarivant work best with?
Mid-market companies roughly in the $10M to $500M revenue range that are past the point where a single analyst with Excel can answer most business questions, but haven't reached the scale or budget where enterprise consulting firms and platforms make sense. These are companies that are, in the founder's words, too big for Excel and Power BI, too small for enterprise budgets, and too lean to wait a year and a half for results.
What question does Clarivant start every engagement with?
Not what tool should we buy, but what decision are you trying to make, and what's stopping you from making it with confidence. The premise is that most analytics projects fail not because the technology is wrong but because nobody asked the right question first, and skipping that question leads to a warehouse full of tables nobody trusts and dashboards nobody opens. Architecture and tooling choices follow from answering that question, not the other way around.