Business Question

The primary business question is: Which sporting event (Atlanta-based NASCAR race or NCAA College Football post-season bowl) provides the best sponsorship opportunity based on consumer sentiment and return on marketing investment (ROMI)?

Data Import and Cleaning

library(ggplot2)
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(readr)
library(tidyr)
library(readxl)

# Load dataset
file_path <- "segmentation_analysis (1).xlsx"
df <- read_excel(file_path, sheet = "segmentation_analysis")

# View the first few rows of the data
head(df)
## # A tibble: 6 × 3
##   Consumer NASCAR NCAA_College_Football
##      <dbl>  <dbl>                 <dbl>
## 1        1      6                     1
## 2        2      5                     4
## 3        3      3                     4
## 4        4      2                     6
## 5        5      3                     9
## 6        6      5                     1

Data Visualization

We will plot the consumer sentiment scores for each event on a Cartesian coordinate plane.

ggplot(df, aes(x = NASCAR, y = NCAA_College_Football)) +
  geom_point(alpha = 0.6) +
  labs(
    title = "Consumer Attitudes Towards NASCAR and NCAA Football",
    x = "NASCAR Sentiment (1-7)",
    y = "NCAA Football Sentiment (1-7)"
  ) +
  theme_minimal()

Clustering Analysis

Using visual inspection and clustering methods to identify consumer segments:

library(cluster)

# Perform k-means clustering (assuming 3 segments)
set.seed(123)
kmeans_result <- kmeans(df[, c("NASCAR", "NCAA_College_Football")], centers = 3)
df$Cluster <- as.factor(kmeans_result$cluster)

# Add cluster centers to the dataframe for visualization
centroids <- as.data.frame(kmeans_result$centers)

# Plot clusters with centroids
ggplot(df, aes(x = NASCAR, y = NCAA_College_Football, color = Cluster)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_point(data = centroids, aes(x = NASCAR, y = NCAA_College_Football),
             color = "black", size = 5, shape = 8) +
  labs(
    title = "Consumer Segments with Centroids",
    x = "NASCAR Sentiment (1-7)",
    y = "NCAA Football Sentiment (1-7)"
  ) +
  theme_minimal()

Consumer Segments and Recommendations

Identified Segments

  1. NASCAR Loyalists - High NASCAR scores, low NCAA scores.
  2. NCAA Enthusiasts - High NCAA scores, low NASCAR scores.
  3. Dual Fans - High scores for both events.

Recommendation

  • If budget allows, targeting the Dual Fans group provides maximum reach.
  • If choosing one event, NCAA College Football has broader appeal among the target demographic.
  • Consider a tailored marketing strategy for each segment to optimize engagement.

Conclusion

Based on the analysis, the company should prioritize sponsorship of the NCAA College Football event while leveraging NASCAR for secondary engagement. This ensures brand alignment with the most favorable consumer segment, maximizing return on marketing investment (ROMI).