The First Chicago Method A variation on the Venture Capital method involves the “First Chicago” approach. Start ups and early stage companies often use a WAG (wild ass guess) approach. What the First Chicago Method (FCM) does is then evaluate the Best, Expected and Worst case scenarios. It then tries to attach probabilities to each case and uses these probabilities to attain a separate valuation and pricing strategies for each case. These can then, in turn, be averaged to achieve a general valuation and pricing.
The First Chicago Method is therefore a combination of the DCF (Discounted Cash Flow) and relative valuation. To achieve probabilities for each case, we will use the general accepted Law of Diffusion of Innovation by Everett Rogers. This diffusion is represented below
For GroceryNet, we can use this theory and apply it to the current available market data: To do this, we use the following market share data: Marktaandelen 2010-2012 IRI
Formule
2010 2011 2012
Albert Heijn
33,4
33,6
33,7
C1000
11,5
12,1
12,0
1%
Marketshare 2012
1% 1% 0% 1%
1%
0%
0%
Albert Heijn
Superunie
29,6
29,5
29,2
Discounters
14,3
14,2
14,7
Plus
5,9
6,0
5,8
Coop
2,4
2,6
2,7
Deen
2,0
2,0
2,0
Discounters
Hoogvliet
2,0
2,0
2,1
Plus
Vomar
1,6
1,6
1,5
Spar
1,5
1,5
1,4
Jan Linders
1,0
1,0
1,0
Poiesz
1,0
1,0
1,0
Attent
0,3
0,3
-
Agrimarkt
0,2
0,2
0,2
3%
1%
Others 19%
C1000 Superunie
8%
16%
Coop Deen 7%
41%
Hoogvliet Vomar
As stated in the given data about GroceryNet, we cannot include the AH customers in our customer analysis since AH already has a distribution service similar to GroceryNet. This means that the potential customer distribution ratio (in comparison to customers at AH) would look like the following:
Potential Customer Distribution
31% Albert Heijn Others 69%
Now to make a funded analysis of the potential customers, we also need data regarding the amount of customers. We approximate this by using the absolute number of receipts that each supermarket prints over a given period, together with some (older) information about the number of customers per year: Number of receipts per week
20 0 9
2 01 0
2011
30,000
29,000
28,000
27,000 ) 0 0 0 . 1 x ( n e n n o b a s s a k l a t n a A
26,000
25,000
24,000
23,000
22,000
21,000
20,000 1 2 3 4 5 6 7 8 9 10 11 11 12 13 13 14 15 15 16 17 17 18 19 19 20 21 21 22 23 23 24 25 25 26 27 27 28 29 29 30 31 31 32 33 33 34 35 35 36 37 37 38 39 39 40 41 41 42 43 43 44 45 45 46 47 47 48 49 49 50 51 51 52 53 53
weken in het jaar
Number of households that buy in a certain supermarket (approximation)
Using this data, we can see that there are approximately 26000 per week and 7.39 million customers per year. Applying our market share analysis to this data gives us the following result: Data (best case scenario) Number of potential receipts Number of potential customers Number of potential users
Calculation 26000*52*0.69 (7.39-6.25)*10^6 7390000*0.69
Result 932880 receipts per year 1140000 potential customers 5099100 users per year
If we now take a WAG estimate of the potential amount of customers that can use GroceryNet per year, we get about 1.5 million potential customers. Now, applying The Law of Diffusion of Innovation , we achieve the following key numbers: Group Innovators Early Adaptors Early Majority Late Majority Laggards
Calculation 0.025*1 500 000 0.135*1 500 000 0.340*1 500 000 0.340*1 500 000 0.160*1 500 000
Share 37 500 customers 202 500 customers 510 000 customers 510 000 customers 240 000 customers
This means that setting up the business should target at least 37 500 customers (per year). Then, after initial start-up, the next group: the Early Adaptors need to be convinced. This will needs either capital injection or large marketing campaigns. The Early Majority should then follow automatically. Combining these two observations, we see that the ‘nudging’ of 240 000 customers (per year) is needed before the concept will reach the majorities. This ‘nudging’ involves exhaustion of capital.
Since the The Law of Diffusion of Innovation follows a normal distribution, we can now calculate the Best, Expected and Worst case Scenario based of the following hypothesis: The total number of customers is normally distributed. Of these customers, the chance that any given customers is an innovator of the population is 2.5%. The chance that any given customer is an early adopter is 13.5%. Now we define the best, expected and worst case scenario’s as follows (complemented with calculations and the eventual number of customers GroceryNet needs to target):
Stage 1 scenarios (per year) Scenario Best case Expected Worst case
Consists of Innovators + Early Adopters Innovators 0.5% of total population
Probability 0.16 0.025 0.005
# customers 240 000 37 500 7 500
Probability 0.5
# customers 750 000
0.16 0.01
240 000 15 000
Probability 0.84
# customers 1 260 000
0.5
750 000
0.025
37 500
Stage 2 scenarios (per year) Scenario Best case
Expected Worst case
Consists of Innovators + Early Adopters + Early Majority Innovators + Early Adopters 1% of total population
Stage 3 scenarios (per year) Scenario Best case
Expected Worst case
Consists of Innovators + Early Adopters + Early Majority + Late Majority Innovators + Early Adopters + Early Majority 2.5% of total population
Now we can adjust pricing and DCF according to these projections.
References
Ge, D., Mahoney, Mahoney, J.M., Mahoney, J.T. (2005). New Venture Valuation by Venture Capitalists: An Integrative Approach. Link: http://business.illinois.edu/Working_Papers/papers/050124.pdf NACVA (1999). VALUING A BUSINESS:THE VENTURE CAPITAL METHOD . Link: http://www.mcleanllc.com/pdf/vcarticle99.pdf Sinkek, S. (2010). How great leaders inspire action. Link: http://www.youtube.com/watch?v=qp0HIF3SfI4 Khor, A. (2011). The Law of Diffusion of Innovation. Link: http://thebadcompanyblog.wordpress.com/2011/03/09/the-law-of-diffusion-of-innovation2/ Distrifood/Nielsen (2011). Marktaandelen Supermarkten. Link: http://www.distrifood.nl/Service/Marktaandelen/ Centraal Bureau Levensmiddelenhandel (2011). Consumententrends 2011. Link: http://www.supermarkt.nl/cblopleidingen/bronnen/Consumententrends2011.pdf Gfk (2013). GfK Supermarktkengetallen 2011. Link: http://publications.gfk.nl/News/GfK%20Publicaties/supermarktkengetallen/2011/Fast%20M ov.%20Cons.%20Goods/GfK/03-062011%20GfK%20Supermarktkengetallen%20MEI%202011.ppt.. 2011%20GfK%20Supermarktkengetallen%20MEI%202011.ppt
