Introduction

This report analyzes the impact of advertising spend and display campaigns on revenue. The dataset contains 30 days of display campaign data, including spend, clicks, impressions, transactions, revenue, CTR, and conversion rate.

Hypotheses

  1. Simple Regression Hypothesis
    • H₀: There is no significant relationship between advertising spend and revenue.
    • H₁: Higher advertising spend is associated with increased revenue.

Data Loading and Exploration

library(readr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(stats)

# Load data
df <- read_csv("Display_data.csv")
## Rows: 29 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (8): spend, clicks, impressions, display, transactions, revenue, ctr, co...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# View summary
summary(df)
##      spend           clicks       impressions       display      
##  Min.   : 1.12   Min.   : 48.0   Min.   : 1862   Min.   :0.0000  
##  1st Qu.:28.73   1st Qu.:172.0   1st Qu.: 6048   1st Qu.:0.0000  
##  Median :39.68   Median :241.0   Median : 9934   Median :0.0000  
##  Mean   :44.22   Mean   :257.1   Mean   :11858   Mean   :0.3103  
##  3rd Qu.:55.57   3rd Qu.:303.0   3rd Qu.:14789   3rd Qu.:1.0000  
##  Max.   :91.28   Max.   :593.0   Max.   :29324   Max.   :1.0000  
##   transactions      revenue            ctr           con_rate    
##  Min.   :1.000   Min.   : 16.16   Min.   :1.890   Min.   :0.810  
##  1st Qu.:2.000   1st Qu.:117.32   1st Qu.:1.970   1st Qu.:0.990  
##  Median :3.000   Median :235.16   Median :2.020   Median :1.130  
##  Mean   :2.966   Mean   :223.50   Mean   :2.306   Mean   :1.227  
##  3rd Qu.:4.000   3rd Qu.:298.92   3rd Qu.:2.790   3rd Qu.:1.470  
##  Max.   :6.000   Max.   :522.00   Max.   :3.290   Max.   :2.080

Simple Regression Analysis (Revenue ~ Spend)

library(stats)

# Simple Linear Regression
model_simple <- lm(revenue ~ spend, data = df)
summary(model_simple)
## 
## Call:
## lm(formula = revenue ~ spend, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -145.210  -54.647    1.117   67.780  149.476 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  10.9397    37.9668   0.288    0.775    
## spend         4.8066     0.7775   6.182 1.31e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 86.71 on 27 degrees of freedom
## Multiple R-squared:  0.586,  Adjusted R-squared:  0.5707 
## F-statistic: 38.22 on 1 and 27 DF,  p-value: 1.311e-06

Interpretation

  • The model explains approximately 58.6% of revenue variation.
  • The coefficient for spend is significant (p < 0.001), suggesting higher spending leads to increased revenue.

Multiple Regression Analysis (Revenue ~ Spend + Display)

# Multiple Linear Regression
model_multiple <- lm(revenue ~ spend + display, data = df)
summary(model_multiple)
## 
## Call:
## lm(formula = revenue ~ spend + display, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -176.730  -35.020    8.661   56.440  129.231 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -50.8612    40.3336  -1.261  0.21850    
## spend         5.5473     0.7415   7.482 6.07e-08 ***
## display      93.5856    33.1910   2.820  0.00908 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 77.33 on 26 degrees of freedom
## Multiple R-squared:  0.6829, Adjusted R-squared:  0.6586 
## F-statistic:    28 on 2 and 26 DF,  p-value: 3.271e-07

Interpretation

  • The model explains approximately 68.3% of revenue variation, an improvement over the simple model.
  • Both spend and display are statistically significant predictors.
  • Display campaigns contribute significantly to revenue.

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