An IIT engineer calculated market size to the exact dollar during her Bain interview. $2,847,293,412. Twelve minutes of flawless computation. He got rejected.
Meanwhile, a history major estimated the same market as “roughly $3 billion” in 90 seconds. He got the offer.
The feedback that the IIT engineer got after his rejection email “Lacking business judgment.”
Here’s what nobody tells you: Consulting math isn’t about being right. It’s about being useful.
A private equity partner needs to know if a market is worth entering. The difference between $2.8 billion and $3.2 billion? Irrelevant. The difference between $2 billion and $20 billion? Everything.
Yet candidates waste precious minutes calculating precise answers to imprecise questions.
Watch this disaster: A candidate spent eight minutes deriving that a coffee chain needs exactly 1,847 locations for breakeven. The interviewer asked: “So roughly 2,000?” The candidate insisted: “No, 1,847.”
That precision added zero value. But it revealed poor judgment about what matters in executive decisions.
CEOs don’t care about the third decimal place. They care about order of magnitude and direction.
A candidate faced this question: “Our client’s costs grew from $43 million to $67 million while revenue grew from $213 million to $248 million. Should they be concerned?”
The amateur approach: Calculate exact growth rates. Cost grew 55.8%, revenue grew 16.4%. Compare ratios. Derive precise margin degradation.
The consultant approach: “Costs grew about 50%, revenue maybe 15-20%. Costs are growing 3x faster than revenue. Yes, they should be concerned. Let me identify which cost category is driving this.”
Thirty seconds versus three minutes. Same decision. Better use of time.
In real consulting, we round everything. But strategically.
$4.87 billion becomes $5 billion (easy math, minimal error) 33.2% becomes “roughly a third” (clearer communication) $97 million becomes $100 million (3% error, 50% easier)
But notice: We round 33.2% to “a third,” not 30%. Why? Because “a third” is actually more accurate (33.3%) than 30%.
A candidate got hired partly because she said: “Market share is basically a quarter well, 23%, but let’s call it a quarter for easy math.” She showed both precision awareness and practical judgment.
Most calculations have invisible breakpoints where precision suddenly matters. A retailer needs 15% margins to satisfy investors. Current margins are 14.7%.
Here, the difference between 14.7% and 15% determines everything. Rounding would be catastrophic. But if margins were 8%? Whether they’re precisely 8.2% or 7.8% doesn’t matter. They’re far below target either way.
Strong candidates identify breakpoints before calculating: “What margin level triggers action? 15%? Then let me check if we’re clearly above, clearly below, or borderline.”
Big numbers paralyze candidates. Scientific notation liberates them.
Question: “2 billion products, 20 grams each, material costs €1,100 per ton. Total cost?”
Without scientific notation: drowning in zeros
With scientific notation:
Clean. Fast. Verifiable.
A physics PhD told me: “I got my McKinsey offer the moment I wrote 10⁹ on the whiteboard. The interviewer said ‘Finally, someone who won’t drown in zeros.'”
Every calculation needs a reasonableness test. Yet most candidates skip it.
A candidate calculated that a food truck generates $45 million annually. Instead of catching the error, he built recommendations around this impossible number.
Simple sanity check: $45 million ÷ 365 days = $125,000 daily. Would a food truck selling $15 meals can potentially serve 8,000 customers daily? Obviously not.
The right answer was $450,000. He’d moved a decimal. The candidate couldn’t recover.
Build sanity checks into your process:
Numbers without narrative are worthless.
“Revenue is $743 million” So what? “Revenue of $743 million is 30% above our target, driven by unexpected success in Brazil” Now we’re talking.
A candidate analyzed pricing options. Instead of just calculating “$50 price points generates $2.5 million in revenue,” she said: “$50 positions us below premium competitors but maintains our quality perception. At this price, we need only 50,000 customers to break even that’s 1% of our addressable market.”
The interviewer stopped her: “You just passed. That’s exactly how consultants think.”
Clients constantly ask about compound growth. Most candidates panic.
The exact formula: Final = Initial × (1 + rate)^years
The consultant shortcut: For rates under 20% and periods under 5 years, just add the percentages.
5% for 3 years? About 15% total (actually 15.76%)
7% for 4 years? About 28% total (actually 31.08%)
Yes, it’s wrong. But for strategic decisions, it’s close enough and 10x faster.
One candidate faced: “10% growth for 6 years, what’s the total?”
Instead of computing 1.1^6, she said: “Roughly 60% using linear approximation, probably closer to 70% with compounding. Either way, we’d double in about 7 years.”
Interviewer: “Exactly right. Let’s move on.”
Made a calculation error? How you handle it matters more than the mistake itself.
Bad recovery: “Oh no, let me recalculate everything from scratch…”
Good recovery: “I see the error I used 50 instead of 500. That means our result should be 10x larger, so $200 million not $20 million. This actually strengthens our recommendation because…”
A candidate multiplication error changed market size from $3 billion to $300 million. Instead of panicking, she said: “Interesting even if I’m off by 10x and it’s only $300 million, that still exceeds our hurdle rate. The decision remains robust.”
She got the offer.
Candidates love showing off discounting skills. Usually unnecessarily.
If cash flows are similar across years, skip NPV calculations. If the decision timeline is under 2 years, ignore time value. If returns are obviously above hurdle rate, don’t precisely calculate IRR.
A candidate was asked about a 3-year project with steady returns. Instead of complex NPV math, he said: “Returns significantly exceed our cost of capital, and payback is under 18 months. Time value won’t change our decision. Should I calculate NPV anyway or focus on implementation risks?”
The interviewer smiled: “Let’s discuss risks.”
Stop practicing complex multiplication. Start practicing:
Tomorrow, take any business article with numbers. Round everything. Convert to scientific notation. Check reasonableness. Explain what the numbers mean, not just what they are.
Partners don’t remember candidates who calculated perfectly. They remember candidates who knew when perfect calculation was worthless. Your Excel model can be precise. Your interview math should be useful.
Real impact comes from knowing when to be precise, staying calm in ambiguity, and acting decisively without all the answers.
That’s not just good interview technique. That’s the job. Stop trying to be a human calculator. Start thinking like someone who advises CEOs.
The math will follow. The offer will too.
If you want to learn when precision matters, when it doesn’t, and how to communicate numbers the way CEOs actually think, I can help. As a former Bain recruiter, I’ve coached hundreds of candidates to transform their case math from over-engineered to executive-ready. 🎯 Book a free intro call today, and let’s build the math intuition that interviewers reward.
As a coach with consulting experience, I can provide you with more tips and one-on-one practice to sharpen your estimation techniques. Book a intro session with my team to know more.
