1  STARTUPS 2021

Published

November 13, 2022

In this report, we will explore the data set of start-ups getting investments from a variety of domestic and international investors. Data is gathered from KPMG and 212’s Turkish Startup Investments Review 2021 report.

KEY TAKEAWAYS OF THE REPORT

  1. In the seed stage, Gaming sector has the lead, in the acquistion sector, SaaS is the leader sector
  2. Gaming is winner of number of investment category, but if we look at total deals Ecommerce Enabler sector is winner. This is possibly due to the trendyol,hepsiburada investments which are outliers of this category. Delivery & Logistic sector is also ahead of Gaming, this is also due to the getir, which is also a outlier in terms of deal amount.
  3. Getir also stands out as a start-up that gets investments more than one stage (Later VC Stage, Early VC Stage) and more than once in 2021. Servislet is another start-up that getting investments both Seed Stage and Early VC Stage stage in 2021. These two start-ups draw attention with their rapid growth in 1 year.
  4. Gaming sector has the highest number of investors
  5. Agritech sector is the most multi-cultural sector in terms of different number of investor origins in one deal
  6. Most of the investments made in December and August, and the most quite months are May and October, as quarters, number of total investments are increasing in Q3,Q4 if we compare Q1 and Q2.
  7. Fintech startups’ 73% of investments coming from Financial Investors. Interestingly, 86% of Delivery & Logistics investments and 78% of Healthtech investments coming from Financial Investors which are greater than Fintech.
  8. Seed-Stage is mostly invested by Financial investors. This may be due to financial investors’ willing to invest in the beginning of the start-ups to get more profit.

DATA PREPROCESSING

Call necessary libraries

Show the code 
#install.packages("readxl")
library(readxl)
library(lubridate)
library(dplyr)
library(tidyverse)
library(ggplot2)

Load the data

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data = read_excel("data//startup_deals_2021.xlsx")

Rename the column names

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data <- data %>% 
  rename(Stage = 'Investment Stage',
         Company = 'Target Company',
         DealValue = 'Deal Value (USD)',
         Financial = 'Financial Investor',
         Date = 'Announcement Date',
         Origin ="Investor's Origin",
         Stake = "Stake (%)"
  )
colnames(data)
[1] "Company"   "Sector"    "Investor"  "Date"      "Financial" "Origin"   
[7] "Stake"     "DealValue" "Stage"
  • Convert “Deal Value (USD)” type to numeric data
  • Convert Date column to date type, and drop year 2021, since all date values have same year, 2021
  • Drop % sign for Stake, and convert to numeric data type
Show the code 
options(scipen=999)
data$DealValue <- suppressWarnings(as.numeric(data$DealValue))

data$Date <- my(data$Date)


data <- data %>% 
  mutate(Stake = str_replace_all(Stake, "%", ""))
data$Stake <- suppressWarnings(as.numeric(data$Stake))
sapply(data, class)
    Company      Sector    Investor        Date   Financial      Origin 
"character" "character" "character"      "Date" "character" "character" 
      Stake   DealValue       Stage 
  "numeric"   "numeric" "character"

Sector feature has inconsistent values like Diğital Comparison, Artificial intelligence, Cybersec urity, Telecpm, Artificial Intelligence, B lockchain.

Show the code 
data_incons <- data %>%
  filter(Sector %in% c('Diğital Comparison',
'Artificial intelligence',
'Cybersec urity',
'Telecpm',
'Artificial Intelligence',
'B lockchain'
))
unique(data_incons$Sector)
[1] "Artificial Intelligence" "Artificial intelligence"
[3] "B lockchain"             "Diğital Comparison"     
[5] "Cybersec urity"          "Telecpm"

Inconsistent Sector values are updated with the right values.

Show the code 
data$Sector[data$Sector == 'Artificial intelligence'] <- 'Artificial Intelligence'
data$Sector[data$Sector == 'Telecpm'] <- 'Telecom'
data$Sector[data$Sector == 'B lockchain'] <- 'Blockchain'
data$Sector[data$Sector == 'Diğital '] <- 'Dijital'
data$Sector[data$Sector == 'Cybersec urity '] <- 'Cybersecurity'
data$Sector[data$Sector == 'Ecommerce enabler'] <- 'Ecommerce Enabler'
data$Sector[data$Sector == 'I mage process'] <- 'Image process'
data$Sector[data$Sector == 'Ecommerce enabler'] <- 'Ecommerce Enabler'
data$Sector[data$Sector == 'Data analytics'] <- 'Data Analytics'

Now the data is ready for the EDA,

Show the code 
knitr::kable(head(data))
Company Sector Investor Date Financial Origin Stake DealValue Stage
Abonesepeti SaaS Keiretsu Forum, Berkan Burla 2021-06-01 Yes Turkey 5 100000 Seed Stage
Abrakadabra Gaming WePlay Ventures 2021-12-01 Yes Turkey 5 250000 Seed Stage
Ace Games Gaming Actera Group, NFX, Kristian Segerstrale, Alexis Bonte, Kaan Günay (Private Investors) 2021-04-01 Yes Turkey, USA NA NA Seed Stage
Adlema Internet of things TR Angels 2021-06-01 Yes Turkey NA 120000 Seed Stage
Agave Games Gaming 500 Istanbul (Fund II), Akin Babayiğit (Private Investor) 2021-09-01 Yes Turkey NA 100000 Seed Stage
Agrio Fintech lnnovate21st.com 2021-06-01 Yes Turkey NA 1000000 Seed Stage

EXPLORATORY DATA ANALYSIS

Gaming is a good start!

If we look at the number of deals Gaming sector is at the top in Seed stage and also in all stages!

Show the code 
df <- data %>% 
  group_by(Sector) %>% 
  summarise(TotalNumberofDeals=n()) %>%
  top_n(n=10) 
ggplot(data=df, aes(x=Sector, y=TotalNumberofDeals)) +
  geom_bar(position="dodge",stat="identity") + 
  ggtitle("Number of Deals vs Sectors (Top 10)")+
  theme(axis.text.x = element_text(angle = 45))

Show the code 
sector_stage <- data %>%
  group_by(Stage,Sector) %>%
  summarise(Total = n()) %>% 
  arrange(desc(Total)) %>%
  slice_max(Total,n=3)
knitr::kable(sector_stage)
Stage Sector Total
Acquisition SaaS 10
Acquisition Gaming 5
Acquisition Fintech 4
Early VC Stage Fintech 3
Early VC Stage Ecommerce Enabler 2
Early VC Stage Foodtech 2
Early VC Stage Gaming 2
Later VC Stage Delivery & Logistics 3
Seed Stage Gaming 44
Seed Stage SaaS 17
Seed Stage Fintech 16
Show the code 
ggplot(data=sector_stage, aes(x=Stage, y=Total, fill=factor(Sector))) +
  geom_bar(position="dodge",stat="identity") + 
  ggtitle("Investment Stage and Investment Sectors")

Deal Amounts: A General Look

Ecommerce Enabler is the winning sector if we look at the Total Deal Value(USD).

Show the code 
df1 <-  data %>%
  mutate(TotalDeal = sum(DealValue,na.rm = TRUE))

 
df2 <- df1 %>%
  group_by(Sector) %>%
  summarise(RateOfDeal=(sum(DealValue,na.rm = TRUE)/TotalDeal)*100) %>%
  arrange(desc(RateOfDeal)) 
  

knitr::kable(head(unique(df2)))
Sector RateOfDeal
Ecommerce Enabler 58.7822478
Delivery & Logistics 27.1997590
Gaming 5.8547176
SaaS 2.2243645
Fintech 0.7646592
Marketplace 0.7006893
Show the code 
df3 <- df2 %>% filter(RateOfDeal>1)
df3 <- unique(df3)
ggplot(df3, aes(x="Sector", y=RateOfDeal, fill=Sector))+
geom_bar(width = 1, stat = "identity") +
  ggtitle("Deal(USD) Rates of Sectors (greater than 1%)")

Deal Amounts: Who makes the difference?

Gaming has the lead at number of deals, but e-commerce enablers sector is at the top of deal amounts. So, Deal Value category is investigated with a boxplot.

Show the code 
df <-  data %>%
  group_by(Company,Sector) %>%
  summarise(Total=n(),TotalDealValue=sum(DealValue))  %>%
  arrange(desc(TotalDealValue))
ggplot(df) +
  aes(x = "", y = log(TotalDealValue)) +
  geom_boxplot(fill = "#0c4c8a") +
  theme_minimal()

There are upper outliers and a 1 lower outlier. Let’s look deeper at the upper outlier, to see which companies make the difference.

Which companies make the difference?

Show the code 
lower_bound <- quantile(df$TotalDealValue, 0.01,na.rm = TRUE)
upper_bound <- quantile(df$TotalDealValue, 0.99,na.rm = TRUE)

upper_outlier_ind <- which(df$TotalDealValue > upper_bound)


knitr::kable(df[upper_outlier_ind, ],caption = "Companies Making Difference")
Companies Making Difference
Company Sector Total TotalDealValue
trendyol Ecommerce Enabler 1 1435000000
Getir Delivery & Logistics 4 1018000000
hepsiburada Ecommerce Enabler 1 761481000

Companies which were invested in different stages

Show the code 
Stage_v1 <- data %>% group_by(Company,Stage) %>%
  summarise(Investment_Count = n(),
  TotalDealValue=sum(DealValue)          
  )

Stage_v2 <- Stage_v1 %>% group_by(Company) %>%
  summarise(StageCount = n()) %>%
  filter(StageCount>1)

Morethanonestage <-data %>%
                    select(Company,Sector,DealValue,Date,Stage) %>%
                    filter(Company %in% Stage_v2$Company) 

knitr::kable(Morethanonestage,caption = "Companies which invested in different stages")
Companies which invested in different stages
Company Sector DealValue Date Stage
Getir Delivery & Logistics 555000000 2021-06-01 Later VC Stage
Getir Delivery & Logistics 300000000 2021-03-01 Later VC Stage
Getir Delivery & Logistics 128000000 2021-01-01 Early VC Stage
Getir Delivery & Logistics 35000000 2021-08-01 Later VC Stage
Servislet Marketplace 12000000 2021-12-01 Early VC Stage
Servislet Marketplace 500000 2021-07-01 Seed Stage

Investors

If we look at the number of investors, Gaming is also winner for this category. Investor Numbers by Sectors

Show the code 
data_investor <- mutate(data,
       investor_number = sapply(strsplit(as.character(data$Investor), ","), length))

investor_gg <- data_investor %>%
  group_by(Sector) %>%
  summarise(TotalNumberInvestor=sum(investor_number))  %>%
  arrange(desc(TotalNumberInvestor)) %>%
  slice_max(TotalNumberInvestor,n=10)

ggplot(data=investor_gg, aes(x=Sector, y=TotalNumberInvestor)) +
  geom_bar(position="dodge",stat="identity") + 
  ggtitle("Number of Investors by Sectors") +
  theme(axis.text.x = element_text(angle = 90))

Investor Numbers by Origins : Most Multi-cultural Sector : Agritech

Agritech sector is the most multi-cultural sector in terms of different number of investor origins in one deal

Show the code 
data_inv_origin <- mutate(data,
       origin_number = sapply(strsplit(as.character(data$Origin), ","), length)) 

inv_or_sec <- data_inv_origin %>%
  group_by(Sector) %>%
  summarise(MaxOriginNumber = max(origin_number),MeanOriginNumber = mean(origin_number)) %>%
  filter(MaxOriginNumber>1) %>%
  arrange(desc(MaxOriginNumber)) %>%
  top_n(5)

 
knitr::kable(inv_or_sec)
Sector MaxOriginNumber MeanOriginNumber
Agritech 5 1.625000
Ecommerce Enabler 4 1.666667
Delivery & Logistics 3 1.307692
Advertising 2 1.500000
Foodtech 2 1.222222

Investors: Financial or Non-Financial?

Fintech startups’ 73% of investments coming from Financial Investors. Interestingly, 86% of Delivery & Logistics investments and 78% of Healthtech investments coming from Financial Investors which are greater than Fintech.

Show the code 
df1 <- data %>%
  group_by(Sector) %>%
  summarise(
    Total = n(),
    Financial = sum(Financial == "Yes")
  ) %>%
  mutate(FinancialInvestorPercentage =(Financial/Total)*100 ) %>%
  filter(Total>1) %>%
  arrange(desc(Financial),desc(FinancialInvestorPercentage))

knitr::kable(head(df1,10))
Sector Total Financial FinancialInvestorPercentage
Gaming 51 27 52.94118
SaaS 28 19 67.85714
Fintech 23 17 73.91304
Delivery & Logistics 13 11 84.61538
Healthtech 14 11 78.57143
Marketplace 17 11 64.70588
Artificial Intelligence 14 9 64.28571
Ecommerce Enabler 9 7 77.77778
Foodtech 9 7 77.77778
Biotech 6 6 100.00000

When the investments take place most?

Total number of deals are increasing in Q3,Q4 if we compare Q1 and Q2.

Show the code 
df <-  data %>%
  mutate(Month = month(data$Date, label=TRUE)) %>%
  mutate(Quarter = paste("2021 Q", quarter(data$Date)))  %>%
  group_by(Quarter) %>%
  summarise(Total=n())%>%
  arrange(Quarter) 

ggplot(data=df, aes(x=Quarter, y=Total, group=1)) +
  geom_line()+
  geom_point() +
  ylim(0,100) +
  ggtitle("Number of Investments Quarters - 2021")

Most of the investments made in December and August, and the most quite months are May and October.

Show the code 
df1 <-  data %>%
  mutate(Month = month(data$Date, label=TRUE)) %>%
  group_by(Month) %>%
  summarise(Total=n())%>%
  arrange(Month) 

ggplot(data=df1, aes(x=Month, y=Total, group=1)) +
  geom_line()+
  geom_point() +
  ylim(0,100) +
  ggtitle("Number of Investments Monthly - 2021")

Which state to invest? Non-financial and financial investors

We can say that Seed Stage has been mostly invested by Financial investors and Acquisition stage mostly invested by Non-Financial investors.

Show the code 
  ggplot(data,aes(x = Stage, y=Sector, fill = Financial)) +
    geom_dotplot(binaxis = "y", stackdir = "center",
                 dotsize=1,binwidth = 1)

Which investor making the highest number of investment according to sector in every investment stage

Show the code 
data_investor %>% separate_rows(Investor,sep = ",") %>%  head()
# A tibble: 6 × 10
  Company   Sector Inves…¹ Date       Finan…² Origin Stake DealV…³ Stage inves…⁴
  <chr>     <chr>  <chr>   <date>     <chr>   <chr>  <dbl>   <dbl> <chr>   <int>
1 Abonesep… SaaS   "Keire… 2021-06-01 Yes     Turkey     5  100000 Seed…       2
2 Abonesep… SaaS   " Berk… 2021-06-01 Yes     Turkey     5  100000 Seed…       2
3 Abrakada… Gaming "WePla… 2021-12-01 Yes     Turkey     5  250000 Seed…       1
4 Ace Games Gaming "Acter… 2021-04-01 Yes     Turke…    NA      NA Seed…       5
5 Ace Games Gaming " NFX"  2021-04-01 Yes     Turke…    NA      NA Seed…       5
6 Ace Games Gaming " Kris… 2021-04-01 Yes     Turke…    NA      NA Seed…       5
# … with abbreviated variable names ¹​Investor, ²​Financial, ³​DealValue,
#   ⁴​investor_number
Show the code 
df_investor<-data_investor %>% separate_rows(Investor,sep = ",")
df_investor <- df_investor %>% select(Investor,Stage,Sector)
df_investor %>% group_by(Stage,Sector,Investor) %>% summarise(Number_of_invesmment=n()) %>% arrange(desc(Number_of_invesmment))
# A tibble: 628 × 4
# Groups:   Stage, Sector [73]
   Stage          Sector               Investor                   Number_of_in…¹
   <chr>          <chr>                <chr>                               <int>
 1 Seed Stage     Gaming               "Games United"                          8
 2 Seed Stage     Gaming               "WePlay Ventures"                       7
 3 Acquisition    Gaming               "Rollic"                                3
 4 Acquisition    SaaS                 "Verusaturk GSYO"                       2
 5 Early VC Stage Gaming               " Balderton Capital"                    2
 6 Early VC Stage Gaming               " Makers Fund"                          2
 7 Later VC Stage Delivery & Logistics " Goodwater Capital"                    2
 8 Later VC Stage Delivery & Logistics " Tiger Global Management"              2
 9 Seed Stage     Advertising          " twozero Ventures"                     2
10 Seed Stage     Agritech             "Startup Wise Guys"                     2
# … with 618 more rows, and abbreviated variable name ¹​Number_of_invesmment

** So if we have a company in the seed stage and in the gaming sector, it is very meaningfull to aim ınvestors like “Games United” and “WePlay Ventures” in order to get investment **