2026-03-29

What Drives Revenue in Top U.S. Tech Companies?

Purpose

Goal: Understand what drives revenue in top tech companies.

Key questions:

  • Do larger companies generate more revenue

  • Does market value align with revenue

  • Do certain locations dominate performance

Dataset Description

## [1] 50
## [1] 10

The dataset includes 50 leading US tech companies with 10 variables.

Key variables:

  • Revenue (in billions)

  • Market capitalization (in trillions)

  • Employee size

  • Headquarters location

This dataset allows us to analyze how scale, valuation, and geography relate to company performance.

Data Summary

##     Revenue          MarketCap      
##  Min.   :  2.060   Min.   :0.02800  
##  1st Qu.:  7.652   1st Qu.:0.05125  
##  Median : 17.665   Median :0.08250  
##  Mean   : 51.204   Mean   :0.25216  
##  3rd Qu.: 40.815   3rd Qu.:0.16025  
##  Max.   :513.980   Max.   :2.52000

Revenue varies widely across companies, with a few large firms accounting for a significant share of total revenue.

Market capitalization also shows strong variation, suggesting differences in company scale as well as investor expectations about future growth.

Overall, the data is skewed toward a small number of very large companies.

Plot 1: Company Distribution by State

Tech companies are heavily concentrated in a few states, with California clearly dominating. This highlights strong geographic clustering in the industry.

Plot 2: Revenue Distribution by State

Revenue is concentrated in a few states, with Washington and California showing the highest and most variable company performance.

Plot 3: Employees vs Revenue

Revenue generally increases with employee size, but not perfectly. Some companies achieve high revenue with fewer employees, indicating efficiency differences.

Plot 4: 3D Relationship of Key Variables

Companies with higher employees, revenue, and market cap tend to cluster together, showing a strong relationship between scale and performance.

Statistical Analysis

## 
## Call:
## lm(formula = Revenue ~ Employees + MarketCap, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -91.651  -5.844  -2.941   6.451  75.589 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.353e-01  4.188e+00   0.128    0.899    
## Employees   2.527e-04  1.809e-05  13.971   <2e-16 ***
## MarketCap   1.175e+02  8.137e+00  14.440   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.88 on 47 degrees of freedom
## Multiple R-squared:  0.9323, Adjusted R-squared:  0.9294 
## F-statistic: 323.7 on 2 and 47 DF,  p-value: < 2.2e-16

Both employee size and market cap significantly predict revenue, with market cap showing a stronger influence.

Example Code

Below is a simple example of how a bar chart was created:

ggplot(data, aes(x = HQ.State)) +
  geom_bar(fill = "steelblue")

Conclusion

Tech companies are highly concentrated in a few key states, especially California.

Revenue is influenced by both company size and market value.

While larger companies generally generate more revenue, efficiency and valuation also play an important role in explaining differences across firms.