Something in the plan isn't working.
You can feel it. You just can't isolate exactly what.
Most founders at this point have data, opinions, and a strategy — and still feel like they're guessing. I work where the strategic question meets the analytical answer: go-to-market, commercial activation, unit economics. A specific answer. Not a framework.
First conversation is informal, off the record, no obligation.
Most analysis answers the wrong question precisely. The first step is establishing what decision the data needs to serve.
I work with a limited number of people at a time so the quality doesn't dilute. What you share stays between us.
The immediate and the structural problem need different thinking. Conflating them is why go-to-market stalls at activation.
No translation loss between analyst and strategist. I build the unit economics, and tell you what it means for the decision.
Pick the closest problem. Each engagement starts the same way — the right framework, filled with your data.
Tap a card to see the approach.
Your internal data defines the Situation. Market data reveals the Complication — the gap that makes it a problem now. Once both are grounded in evidence, the Question becomes unambiguous.
A precise problem statement the whole team can align on, before any solution is attempted.
Fifteen years at Deloitte, Citigroup, and Gartner solving business problems that arrived unclear: which market, which buyer, whether the model holds, why growth stalled. Strong analytics behind the answer — enough to settle the question rather than argue it.
Sanitised versions of frameworks used in real engagements. No email required.
Which one do I need, and how to use each →
Turn an unclear problem into a precise, actionable question. Built on SCQA: Situation, Complication, Question, Answer.
Price, costs and volume in; contribution margin, break-even volume and your gap to break-even out. Know whether the business works before scaling it.
The first seven days of a launch: daily activities by role, expected outcomes, and how each day feeds the next.
"The measure of good analytical work is whether the answer holds when the person who built it isn't in the room."
The activation plan existed before the first candidate conversation. Built from analysis, not from instinct. Open any stage to see how it was built.
Founder was trying to solve one problem. It was actually two: fill Batch 1 across 10+ centres in sixty days, and build the job-placement architecture that makes the model sustainable. Separating them was the first analytical act.
Segmentation across four candidate profiles identified three groups structurally motivated to join without a placement guarantee — each with different channels, timelines, and messaging.
Which segments to target, through which channels, in what sequence — all derived from the model, not instinct. Channel choices came from where the demand signal was strongest.
Cost per lead, break-even by centre, minimum viable batch size — all calculated before the playbook was written. Nothing went in that didn't hold in the model first.
Every daily target existed before the first candidate conversation — derived, not guessed. The playbook was the model, translated into daily action.
10+ sites, four roles each, one tracking structure built from the same model that generated the targets. Deviations were visible before they compounded.
When Batch 1 data arrives, the model already has the structure to absorb it. The sixty-day problem is addressed; the eighteen-month problem is now the right question.
"We had double digit centres to activate simultaneously — no historical data, and a tight timeline. Divya separated what looked like one problem into two: the immediate batch activation and the long-term sustainability model beneath it. She validated the unit economics before writing a single line of the playbook. Every target had an analytical basis before we committed to it. That changed how we thought about the expansion, not just how we ran it."
— Founder, MGrow
Three years of earnings transcripts. Forty competitors. No structured database had touched any of it.
NLP analysis surfaced a pricing-pressure signal twelve to eighteen months before it appeared in any structured market report. Analyst processing effort down roughly forty per cent.
A research report given away free every year — did it have standalone commercial value?
Regression modelling quantified patterns competitors had only described. Dataset grew from two hundred to five hundred companies. Converted to a paid research product, fully subscribed in cycle one.
The full scope of services.
Finding what is actually wrong before recommending anything.
The choices that determine how revenue gets built — structured around options, resolved with data.
Bottom-up, built from real demand signals and sector benchmarks.
Whether the model works before scaling it — under different assumptions and volumes.
The capability that makes insight repeatable — a system, not a one-time analysis.
From strategy to day one. Every target derived from the model.
CM1 to CM3, RTO, marketplace take rates, CAC and cash — one illustrative D2C brand, worked through in rupees.
Not motivation. Not a plan. Looking at what your own business is telling you that you have stopped noticing — and building the decision on that.
Five to seven paying customers is the point at which most B2B founders make their most consequential — and least examined — decision.
With a thirty-minute conversation, off the record, no obligation. If there's a clear fit, I scope the specific question and what answering it requires.
Scoped per engagement, based on the question and the depth of analysis it needs. Discussed openly before any commitment.
The sixty-day problem and the eighteen-month problem need different timelines. Most diagnostic work resolves in two to four weeks; activation work runs alongside execution.
Yes. First conversations are off the record, and client names are only shared with permission. What you share stays between us.
Yes — past work spans the US, UK, and EU alongside India, though most founder-led engagements are India-based.
Informal, off the record, no pitch. Bring the real problem — the one you're not sure how to frame yet.
Or reach me directly:
divyakaushik1@outlook.comTypically responds within one business day.