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You have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.

You have been given access to their unicorns database, which contains the following tables:

dates

ColumnDescription
company_idA unique ID for the company.
date_joinedThe date that the company became a unicorn.
year_foundedThe year that the company was founded.

funding

ColumnDescription
company_idA unique ID for the company.
valuationCompany value in US dollars.
fundingThe amount of funding raised in US dollars.
select_investorsA list of key investors in the company.

industries

ColumnDescription
company_idA unique ID for the company.
industryThe industry that the company operates in.

companies

ColumnDescription
company_idA unique ID for the company.
companyThe name of the company.
cityThe city where the company is headquartered.
countryThe country where the company is headquartered.
continentThe continent where the company is headquartered.
Spinner
DataFrameas
df2
variable
SELECT * 
FROM public.companies

The output

Your query should return a table in the following format:

industryyearnum_unicornsaverage_valuation_billions
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------

Where industry1, industry2, and industry3 are the three top-performing industries.

Spinner
DataFrameas
df
variable
WITH top_industries AS (
	SELECT 
    	i.industry, 
        COUNT(i.*)
    FROM industries AS i
    JOIN dates AS d
        ON i.company_id = d.company_id
    WHERE DATE_PART('year', d.date_joined) IN ('2021', '2020', '2019')
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3
),

yearly_ranks AS 
(
    SELECT 
    	COUNT(i.*) AS num_unicorns,
        i.industry,
        DATE_PART('year', d.date_joined) AS year,
        AVG(f.valuation) AS average_valuation
    FROM industries AS i
    JOIN dates AS d
        ON i.company_id = d.company_id
    JOIN funding AS f
        ON d.company_id = f.company_id
    GROUP BY industry, year
)

SELECT 
	industry,
    year,
    num_unicorns,
    ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions
FROM yearly_ranks
WHERE year in ('2021', '2020', '2019')
	AND industry in (SELECT industry FROM top_industries)
GROUP BY industry, year, num_unicorns
ORDER BY year DESC, num_unicorns DESC

Distribution of Unicorns and their Average Valuations Across Over Time

Spinner
DataFrameas
df1
variable
WITH top_industries AS (
	SELECT 
    	i.industry, 
        COUNT(i.*)
    FROM industries AS i
    JOIN dates AS d
        ON i.company_id = d.company_id
    WHERE DATE_PART('year', d.date_joined) IN ('2021', '2020', '2019')
    GROUP BY industry
    ORDER BY count DESC
    LIMIT 3
),

yearly_ranks AS 
(
    SELECT 
    	COUNT(i.*) AS num_unicorns,
        i.industry,
        DATE_PART('year', d.date_joined) AS year,
        AVG(f.valuation) AS average_valuation
    FROM industries AS i
    JOIN dates AS d
        ON i.company_id = d.company_id
    JOIN funding AS f
        ON d.company_id = f.company_id
    GROUP BY industry, year
)

SELECT 
	industry,
    year,
    num_unicorns,
    ROUND(AVG(average_valuation / 1000000000), 2) AS average_valuation_billions
FROM yearly_ranks
WHERE year in ('2021', '2020', '2019')
	AND industry in (SELECT industry FROM top_industries)
GROUP BY industry, year, num_unicorns
ORDER BY year DESC, num_unicorns DESC

Conclusion

Trend in Number of Unicorns

  1. E-commerce & direct-to-consumer: There is a significant increase in the number of unicorns from 2019 (12) to 2021 (47), showing rapid growth in this industry.

  2. Fintech: The number of fintech unicorns also shows impressive growth from 2019 (20) to 2021 (138), indicating a booming interest and investment in financial technology solutions.

  3. Internet software and services: Similar to the other two industries, there is also a marked increase in the number of unicorns, from 13 in 2019 to 119 in 2021.

Trend in Average Valuation (in billions)

  1. E-commerce & direct-to-consumer: The average valuation peaked in 2020 at $4 billion and decreased to $2.47 billion in 2021 despite the increase in the number of unicorns. This might suggest a larger number of smaller-scale startups reaching the unicorn status in 2021 or a general market correction.

  2. Fintech: There is a noticeable decrease in average valuation from $6.80 billion in 2019 to $2.75 billion in 2021. The decrease in valuation, despite the increase in the number of companies, might indicate market saturation or more conservative valuations as more players enter the market.

  3. Internet software and services: The average valuation was highest in 2020 at $4.35 billion, similar to the other industries, but dropped to $2.15 billion in 2021. This drop mirrors the trend observed in the other sectors, suggesting possible market corrections or adjustments in valuation expectations.