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Recommendation for market sizing case

Preplounge!

If you do a market sizing or another case were it does not feel natural to give a recommendation at the end; how do you round up such cases? Any suggestions to a structure for the recommendation of a case like that?

Preplounge!

If you do a market sizing or another case were it does not feel natural to give a recommendation at the end; how do you round up such cases? Any suggestions to a structure for the recommendation of a case like that?

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Hi,

You still have an objective - calculate the number of X. That's how you start:

  1. Our objective was to calculate the number of x
  2. Based on our analysis it will be Y
  3. There is a number of drivers that influence that number: a,b,c
  4. Additionally, if we had more time we can calculate more precisely... / we can make a sanity check / we can calculate from the supply side and triangulate

Best

Hi,

You still have an objective - calculate the number of X. That's how you start:

  1. Our objective was to calculate the number of x
  2. Based on our analysis it will be Y
  3. There is a number of drivers that influence that number: a,b,c
  4. Additionally, if we had more time we can calculate more precisely... / we can make a sanity check / we can calculate from the supply side and triangulate

Best

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Hey there,

In a market sizing case, there often is no "recommendation" per se - rather you would summarise the answer briefly, how you got there, and importantly, make a sense check.

For example:

"Based on our analysis, the market size of rubber bands in the US is approximately $Xbn. This is based on 3 key assumptions: 1..2..3.. In order to sense check this, I can look at what it implies the average american spends on rubber bands per year"...

Then, if based on your sense check, it seems reasonable, you can happily conclude. However, if it seems too high or too low, you should either:

1) if it's simply way too high or too low, you might consider going back to check if you either made any math mistakes, or if you have forgotten to take something major into account

2) Suggest where you may have made simplifying assumptions that could increase/decrease your final number away from it's "true" value.

Hey there,

In a market sizing case, there often is no "recommendation" per se - rather you would summarise the answer briefly, how you got there, and importantly, make a sense check.

For example:

"Based on our analysis, the market size of rubber bands in the US is approximately $Xbn. This is based on 3 key assumptions: 1..2..3.. In order to sense check this, I can look at what it implies the average american spends on rubber bands per year"...

Then, if based on your sense check, it seems reasonable, you can happily conclude. However, if it seems too high or too low, you should either:

1) if it's simply way too high or too low, you might consider going back to check if you either made any math mistakes, or if you have forgotten to take something major into account

2) Suggest where you may have made simplifying assumptions that could increase/decrease your final number away from it's "true" value.

Hi, good question !

As you are a consultant and not a scientist - you want to make sure that the "final" answer (better: answers) is seen:

a) in the light of the assumptions that you took

b) how a change in those assumptions would affect your answer and

c) how you would go about to sense-check or precise your answer going forward (this builds trust with clients and offers opportunity for next business!).

So how could a final solution look like?

1) There are about 45-55 mn bicycles in Europe (as with recommendations, jump straight into the result, no need to talk about anything else here)

2) ...based on a bottom up (45mn) and a top down appraoch (55mn)

3) ...approach 1 seems more exact due to x, y , z, hence the result is more skewed towards 45mn

4) The answer is most sensitive to assumption x. That means that a change of #% in x would alter the answer by y%

5) ...In order to precise the answers going forward, there are 2 possibitlies:

...precise estimate of x by means of xyz

...conduct a 3rd approach (time consuming) and then "triangulate" about the results.

Hope that helps!

Best regards

Hi, good question !

As you are a consultant and not a scientist - you want to make sure that the "final" answer (better: answers) is seen:

a) in the light of the assumptions that you took

b) how a change in those assumptions would affect your answer and

c) how you would go about to sense-check or precise your answer going forward (this builds trust with clients and offers opportunity for next business!).

So how could a final solution look like?

1) There are about 45-55 mn bicycles in Europe (as with recommendations, jump straight into the result, no need to talk about anything else here)

2) ...based on a bottom up (45mn) and a top down appraoch (55mn)

3) ...approach 1 seems more exact due to x, y , z, hence the result is more skewed towards 45mn

4) The answer is most sensitive to assumption x. That means that a change of #% in x would alter the answer by y%

5) ...In order to precise the answers going forward, there are 2 possibitlies:

...precise estimate of x by means of xyz

...conduct a 3rd approach (time consuming) and then "triangulate" about the results.

Hope that helps!

Best regards

(edited)

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Hi,
If you're doing a pure market sizing case, then of course there's no recommandation here.
When concluding if you are prompted to, you can summarise you work :

- starting with the result of the market sizing

- explaining then at high level the methodlogy and hypothese you developped to achieve this results

- highlting the potential weaknesses in your hypotheses.

hope this helps

Best

Benjamin

Hi,
If you're doing a pure market sizing case, then of course there's no recommandation here.
When concluding if you are prompted to, you can summarise you work :

- starting with the result of the market sizing

- explaining then at high level the methodlogy and hypothese you developped to achieve this results

- highlting the potential weaknesses in your hypotheses.

hope this helps

Best

Benjamin

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