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How to come up with more solid Assumptions?

Anonymous A asked on Sep 13, 2018 - 2 answers

Hey there,

When it comes to market sizing questions, I don't struggle to come up with a logical structure that makes sense (issue tree). However, I tend to pick numbers in the estimation of my parameters that are too big/too small (even if the end result does not matter that much, sometimes I'm off by 10X or more because of wrong guestimation i.e. wrong assumptions). How can I improve the accuracy of my assumptions? I have learnt the obvious ones like households etc. however there are aIso more tricky ones.
My strategy now is to solve as many market sizing questions as possible and always not down a tipical assumption which was not correct and is not that obvious.


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updated his answer on Sep 14, 2018
VC @ Data Collective and First Round's Dorm Room Fund | ex-Cleantech Entrepreneur in Residence | ex-McK JEM, 50+ cases | Haas MBA
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Hey Anonymous,

You're right -- the numbers don't matter that much. It's always good to see candidates have solid general knowledge when computing their assumptions, but it's a bit odd if they happen to know how many golf balls are sold in France every year off the top of their head.

My suggestion is to start from the basics that everyone does (or should) know -- population size, people per household, age distribution trends, etc. From those, you should be able to make 'laddering' assumptions that essentially convert the units from things you know (number of people) to things you want to know (how many golfballs sold in France).

So, population of France is about 70m. Assuming that 10% of the population plays golf at least once a year, that means 7m people play 18 holes. Lets say that, on average, there are 1.5 games per person per year. So, now we're at 27 holes per person per year, times 7m. If a ball gets lost every 9th hole, that means each person goes through 3 balls per year. 3 times 7m is 21m, which suggests 21m golf balls are sold every year in France.

That sounds high to me, so at that point I'd ask the interviewer if any of my assumptions are terribly off.

Again, the point isn't to know a bunch of truly random trivia, but rather to show a good broad knowledge base and facility with both computing and communicating simple math. After all, in a real world setting the thing we're measuring for is "will this person be a good calculator and collaborator on market size estimations? how can I tell without that person having a computer in front of them today?"

The ability to go through an example like this confidently, quickly, and with clear communication far outweighs whatever brownie points you might score for getting close to the right answer. So focus on that.

Hope this helps!


replied on Sep 14, 2018
Experienced strategy consultant, now running own consulting business
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Hi Ananymous,

very good answer by Adam.

I would add that there are a couple of rules of thumb that show up in nature and in business that are good to keep in mind. There are a million exceptions to any of these (well, except the rule of 70, because that's just math). But they're good first sanity checks...

  • First and foremost there's the famous Pareto 80/20 rule
  • Rule of 70: To calculate the time it takes for something to double, divide 70 by the rate of growth (i.e. at 5% interest your investment would double roughly withing 14 years). Works only for growth rates up to roundabout 10%
  • The 1% rule: If you're asked to calculate something of high exclusivity (luxury products, golf, ...) just make it a thing of the top 1%.
  • Economic profits: As a very rough rule, no market or business, (with the exception of very strong network effect businesses like Facebook or Google) will sustainably deliver profits (EBIT) above let's say 10-15% and EBITDA of 25-40%. I mean, these figures are very rough ballparks, but if it were not so, others would enter the market and drive down profits. I know that there are probably a million examples of businesses that beat my theory, but of you assume a profit margin ov 10% you are rarely laughed out of the room.
  • Inverse relationships beween investment and EBITDA: A business that has very high upfront investments (think chip manufacturing, telecom networks, sofware development, ...) will have relatively high EBITDA ratios (35, 40, even 60%). Businesses with low investments but high operational expenditures (eny service business, most general manufacturing) will have MUCH lower EBITDA. And by and large, EBIT rates wil be more or less similar.
  • Span of control: Most managers can't manage more than 7-8 direct reports. So in most organisations, the layers of management are more or less 6-8 times larger with every step. So if you have 5 CXOs, you might have 30-40 VPs, about 200 department heads, maybe 1500 team leads and 10000 worker bees. Very rough estimates, but helpful as a rule of thumb. So if you see an organisation with 100k employees, you can roughly calculate the number of management layers.

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