Please subscribe to get updates about this report and other ecosystem report
Av. das Forças Armadas
1649-026 Lisboa, Portugal
geral@bgi.pt

Copyright © 2017 BGI.

  • Facebook
  • Twitter
  • YouTube
  • Instagram

1.    The ventures were ranked based on 5 factors: 


a.    Total funding (TF), 
b.    Total revenue generated (TR), 
c.    Number of Employees (TE) 
d.    Capital turnover ratio (CR) from 2013 to 2018
e.    Time to Market. 


2.    A standardised score (z-score) was first calculated for the chosen factors (i.e. TF, TR, TE & CR) for each start-up. This z-score evaluates the distance from the mean using standard deviations and allows each of the start-ups to be compared with each other.


3.    Weights were then assigned to each factor for each venture. The objective of the weighting process was to indicate the importance of each factor in the ranking. The importance of each factor was determined as a matter of practical relevance. Weights were determined using Rank Sum Method (See Annex for details). The weights when summed are equal to one. However, before weights were assigned to each factor (i.e. TF, TR, TE & CR), we took into consideration technology intensity or time to develop technology. We have proxied the latter by the time it takes to generate revenue alongside capital intensity (cash burn).


4.    The weighting process was based on industry research which led to some of our assumptions; we assumed that deep technology companies usually take longer to generate revenue because they generally have larger CAPEX values, in order to develop their technology and reach the market. 


5.    Given our period of analysis (i.e. 5 years) we assumed the average time to develop technology to be 2 years after year of foundation. We also assumed that the time to develop technology was dependent on the vertical, i.e. some ventures in a vertical were more likely to generate revenue more quickly than others. Taking these assumptions into consideration we divided our sample into 2 categories. The 1st category included ventures that have been operational for 3 years or more. While the 2nd category included ventures that have been operational for 2 years or less.


6.    In the first category, weights were only assigned to the TR z-score of start-ups who started generating significant revenue (US$30k) in their 3rd year of operation. Ventures in this category who started generating significant revenue in years exceeding the third year received lesser weights. We essentially ignore the revenues generated in the first 2 years of operation for start-ups in this category.


7.    In the 2nd category, weights were assigned only to the TR z-score of start-ups who generated significant revenue (US$10k) in either the 1st or 2nd years of operation. For instance, Ventures in this category who generated significant revenues in the 1st year received greater weights than those which started generating revenue in the subsequent year.


8.    By following this process, we produced a score that takes into consideration year of operation and time to market which we term the “Time Weighted Revenue Score”


9.    We go further to assign final weights (step 3) to the “Time Weighted Revenue Score”, TF z-score, TE z-score, TR z-score and CR z-score, which we sum up to produce the rank score. In general, total capital received the largest weight followed by total revenue received and capital turnover ratio and finally number of employees.


10.    We then ranked the start-ups based on the final rank score to determine the hierarchy of the Top 25 Portuguese scaleups. Following the above steps, we have the resulting algorithm:


a.    {EQ 1} WT*(z-score of Total revenue) = Time Weighted Revenue Score
b.    {EQ 2} W1*(Time Weighted Revenue Score) + W2*(z-score of total funding) + W3*(z-score of capital turnover ratio) + W4*(z-score of Total number of Employees) = Rank Score
c.    Given W1 + W2 + W3 = 1


This report uses data from Germany, Spain, Italy, France and the United Kingdom for basis of comparison. All of the financial figures are reported in Euros except otherwise stated. Some data (e.g. Total revenue) was deliberately not presented individually for confidentiality reasons, however such data was used aggregately. The data on total revenue, capital and number of employees was the sum of all total revenue and capital received from the foundation of each start-up (i.e. from 2013 - 2018), as well as the cumulative sum of the number of employees during this period.