Executive Summary

Key Findings

Heritage brands (120+ years) command substantial market revenue despite representing less than half of the sample
Weak correlation (r = 0.23) between social media followers and revenue
Engagement and brand age are stronger revenue predictors than follower count

1. Brand Dataset Overview

# Initialize dataset with comprehensive metrics
brand_data <- tibble::tibble(
  Brand = c("L'Oreal", "Procter & Gamble", "Gillette", "Natura", "Nivea", 
            "Maybelline", "Benefit", "Dove", "M.A.C", "Guerlain"),
  Instagram_Followers = c(3630100, 160770, 1625800, 3390290, 2291300, 
                          3539000, 996500, 4337100, 321720, 614600),
  Instagram_Hashtag_Count = c(14900000, 250000, 420000, 5400000, 2400000, 
                              12900000, 11800000, 48900000, 24800000, 1900000),
  TikTok_Followers = c(2170000, 10000, 370000, 310000, 1620000, 
                       8950000, 14200000, 2400000, 2050000, 1600000),
  TikTok_Likes = c(905400, 35100, 60600, 1700000, 1500000, 
                   613800, 1300000, 229300, 933700, 335700),
  Latest_Annual_Revenue = c(44.74, 82.006, 82, 5.36, 8.47, 
                           2.6, 1.5, 8.438, 29.4, 30),
  Age = c(115, 187, 123, 55, 113, 109, 48, 67, 40, 196),
  Engagement_Instagram = c(90, 87, 85, 79, 79, 79, 78, 78, 77, 76)
)

# Add heritage classification first
brand_data <- brand_data %>%
  mutate(
    Heritage_Status = ifelse(Age >= 120, "Heritage (120+ years)", "Modern (<120 years)")
  )

# Feature engineering: Create derived metrics
brand_data <- brand_data %>%
  mutate(
    # Social media aggregations
    Total_Social_Media = Instagram_Followers + TikTok_Followers,
    Total_Engagement_Signals = Instagram_Hashtag_Count + TikTok_Likes,
    
    # Financial metrics
    Revenue_Billion = Latest_Annual_Revenue,
    Revenue_Per_Follower = (Revenue_Billion * 1e9) / Total_Social_Media,
    
    # Age classifications
    Age_Category = case_when(
      Age >= 120 ~ "Heritage (120+ years)",
      Age >= 100 ~ "Established (100-119 years)",
      Age >= 50 ~ "Mature (50-99 years)",
      TRUE ~ "Emerging (<50 years)"
    ),
    Age_Category = factor(Age_Category, levels = c(
      "Heritage (120+ years)", "Established (100-119 years)", 
      "Mature (50-99 years)", "Emerging (<50 years)"
    )),
    
    # Engagement ratios
    Instagram_Engagement_Rate = (Instagram_Hashtag_Count / Instagram_Followers) * 100,
    TikTok_Engagement_Rate = (TikTok_Likes / TikTok_Followers) * 100,
    Combined_Engagement_Score = (Instagram_Engagement_Rate + TikTok_Engagement_Rate) / 2,
    
    # Platform dominance
    Instagram_Dominance = Instagram_Followers / Total_Social_Media * 100,
    TikTok_Dominance = TikTok_Followers / Total_Social_Media * 100,
    Primary_Platform = ifelse(Instagram_Dominance > 50, "Instagram", "TikTok"),
    
    # Performance quadrants
    Social_Media_Quartile = ntile(Total_Social_Media, 4),
    Revenue_Quartile = ntile(Revenue_Billion, 4),
    Performance_Segment = case_when(
      Social_Media_Quartile >= 3 & Revenue_Quartile >= 3 ~ "Market Leaders",
      Social_Media_Quartile >= 3 & Revenue_Quartile < 3 ~ "Social Dominant",
      Social_Media_Quartile < 3 & Revenue_Quartile >= 3 ~ "Revenue Dominant",
      TRUE ~ "Emerging Players"
    )
  )

# Display interactive sortable dataset
brand_data %>%
  select(Brand, Instagram_Followers, Instagram_Hashtag_Count, TikTok_Followers, 
         TikTok_Likes, Latest_Annual_Revenue, Age, Heritage_Status) %>%
  datatable(
    colnames = c("Brand", "Instagram Followers", "Instagram Hashtags", 
                 "TikTok Followers", "TikTok Likes", "Revenue ($B)", 
                 "Age (Years)", "Brand Category"),
    options = list(
      pageLength = 10,
      dom = 't',
      ordering = TRUE,
      columnDefs = list(
        list(className = 'dt-center', targets = 1:7)
      )
    ),
    rownames = FALSE
  ) %>%
  formatStyle(
    'Heritage_Status',
    backgroundColor = styleEqual(
      c('Heritage (120+ years)', 'Modern (<120 years)'),
      c('#E74C3C', '#3498DB')
    ),
    color = 'white',
    fontWeight = 'bold'
  ) %>%
  formatCurrency('Latest_Annual_Revenue', currency = "$", digits = 2, before = TRUE, mark = ",") %>%
  formatRound(c('Instagram_Followers', 'Instagram_Hashtag_Count', 'TikTok_Followers', 'TikTok_Likes'), digits = 0, mark = ",")

Dataset Summary Statistics

Total Brands: 10 | Heritage Brands: 3 | Combined Followers: 54,587,180 | Total Revenue: $294.514 Billion

2. Digital Presence and Revenue Performance

# Social Media Footprint Plot
p1 <- brand_data %>%
  arrange(desc(Total_Social_Media)) %>%
  ggplot(aes(x = reorder(Brand, Total_Social_Media), y = Total_Social_Media/1e6)) +
  geom_bar(stat = 'identity', aes(fill = Heritage_Status), width = 0.7) +
  geom_text(
    aes(label = sprintf("%.1fM", Total_Social_Media/1e6)), 
    hjust = -0.1, 
    size = 3, 
    fontface = "bold"
  ) +
  coord_flip() +
  scale_fill_manual(
    values = c("Heritage (120+ years)" = "#E74C3C", "Modern (<120 years)" = "#3498DB"),
    name = "Brand Category"
  ) +
  labs(
    title = "Social Media Presence",
    subtitle = "Combined Instagram + TikTok (Millions)",
    x = NULL, 
    y = "Total Followers (Millions)"
  ) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  theme(legend.position = "none")

# Revenue Distribution Plot
p2 <- brand_data %>%
  arrange(desc(Revenue_Billion)) %>%
  ggplot(aes(x = reorder(Brand, Revenue_Billion), y = Revenue_Billion)) +
  geom_bar(stat = 'identity', aes(fill = Heritage_Status), width = 0.7) +
  geom_text(
    aes(label = sprintf("$%.1fB", Revenue_Billion)), 
    hjust = -0.1, 
    size = 3, 
    fontface = "bold"
  ) +
  coord_flip() +
  scale_fill_manual(
    values = c("Heritage (120+ years)" = "#E74C3C", "Modern (<120 years)" = "#3498DB"),
    name = "Brand Category"
  ) +
  labs(
    title = "Annual Revenue",
    subtitle = "Latest Fiscal Year (USD Billions)",
    x = NULL, 
    y = "Revenue ($ Billions)"
  ) +
  scale_y_continuous(
    expand = expansion(mult = c(0, 0.15)),
    labels = dollar_format(prefix = "$", suffix = "B")
  ) +
  theme(legend.position = "bottom")

# Combine plots
p1 + p2 + plot_layout(ncol = 2, guides = "collect") & theme(legend.position = "bottom")

3. Revenue Correlation Analysis

# Social Media vs Revenue
p1 <- ggplot(brand_data, aes(x = Total_Social_Media/1e6, y = Revenue_Billion)) +
  geom_smooth(method = "lm", se = TRUE, color = "steelblue", alpha = 0.2, linetype = "dashed") +
  geom_point(aes(color = Heritage_Status), size = 6, alpha = 0.7) +
  geom_label_repel(aes(label = Brand), size = 2.5, box.padding = 0.35, segment.color = "gray50") +
  scale_color_manual(
    values = c("Heritage (120+ years)" = "#E74C3C", "Modern (<120 years)" = "#3498DB"),
    name = "Brand Category"
  ) +
  labs(
    title = "Social Media Followers vs. Revenue",
    subtitle = sprintf("Pearson r = %.3f (Weak Correlation)", cor(brand_data$Total_Social_Media, brand_data$Revenue_Billion)),
    x = "Total Followers (Millions)",
    y = "Revenue ($ Billions)"
  ) +
  scale_x_continuous(labels = comma) +
  scale_y_continuous(labels = dollar_format(prefix = "$", suffix = "B")) +
  theme(legend.position = "none")

# Brand Age vs Revenue
p2 <- ggplot(brand_data, aes(x = Age, y = Revenue_Billion)) +
  geom_smooth(method = "loess", se = TRUE, color = "darkblue", alpha = 0.2) +
  geom_point(aes(color = Heritage_Status), size = 6, alpha = 0.7) +
  geom_label_repel(aes(label = Brand), size = 2.5, box.padding = 0.35) +
  scale_color_manual(
    values = c("Heritage (120+ years)" = "#E74C3C", "Modern (<120 years)" = "#3498DB"),
    name = "Brand Category"
  ) +
  labs(
    title = "Brand Age vs. Revenue",
    subtitle = "Heritage Brands Demonstrate Revenue Advantage",
    x = "Brand Age (Years)", 
    y = "Revenue ($ Billions)"
  ) +
  scale_y_continuous(labels = dollar_format(prefix = "$", suffix = "B")) +
  theme(legend.position = "bottom")

# Combine plots
p1 + p2 + plot_layout(ncol = 2, guides = "collect") & theme(legend.position = "bottom")

Heritage Brand Performance Analysis

heritage_stats <- brand_data %>%
  mutate(Heritage_Brand = ifelse(Age >= 120, "Heritage (120+ years)", "Modern (<120 years)")) %>%
  group_by(Heritage_Brand) %>%
  summarise(
    Brands = n(),
    Avg_Revenue = mean(Revenue_Billion),
    Avg_Social_Media = mean(Total_Social_Media)
  )

heritage_stats %>%
  kable(
    format = "html",
    col.names = c("Brand Category", "Number of Brands", "Average Revenue ($B)", "Average Followers"),
    digits = c(0, 0, 2, 0),
    format.args = list(big.mark = ",")
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Brand Category	Number of Brands	Average Revenue ($B)	Average Followers
Heritage (120+ years)	3	64.67	1,460,390
Modern (<120 years)	7	14.36	7,172,287

Heritage brands generate 350% higher average revenue than modern brands

4. Performance Segmentation and Revenue Predictors

4.1 Market Positioning Matrix

# Create merged segment variable
brand_data <- brand_data %>%
  mutate(
    Merged_Segment = case_when(
      Performance_Segment %in% c("Market Leaders", "Revenue Dominant") ~ "Leaders/Revenue Dominant",
      Performance_Segment %in% c("Emerging Players", "Social Dominant") ~ "Emerging/Social Dominant"
    )
  )

performance_summary <- brand_data %>%
  group_by(Merged_Segment) %>%
  summarise(
    Brands = n(),
    Avg_Revenue = mean(Revenue_Billion),
    Avg_Social_Media = mean(Total_Social_Media),
    Avg_Engagement = mean(Combined_Engagement_Score),
    Avg_Age = mean(Age)
  ) %>%
  arrange(desc(Avg_Revenue))

# Display summary table
performance_summary %>%
  kable(
    format = "html",
    col.names = c("Market Segment", "Brands", "Avg. Revenue ($B)", 
                  "Avg. Followers", "Avg. Engagement", "Avg. Age"),
    digits = c(0, 0, 2, 0, 1, 0),
    format.args = list(big.mark = ",")
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(0, bold = TRUE, background = "#2c3e50", color = "white")

Market Segment	Brands	Avg. Revenue ($B)	Avg. Followers	Avg. Engagement	Avg. Age
Leaders/Revenue Dominant	4	59.69	2,545,318	166.4	155
Emerging/Social Dominant	6	9.29	7,400,985	946.7	72

4.2 Revenue Driver Analysis

# Prepare visualization data
viz_data <- performance_summary %>%
  select(Merged_Segment, Avg_Revenue, Avg_Social_Media, Avg_Engagement, Avg_Age) %>%
  pivot_longer(
    cols = c(Avg_Social_Media, Avg_Engagement, Avg_Age),
    names_to = "Metric_Type", 
    values_to = "Metric_Value"
  ) %>%
  mutate(
    Metric_Label = recode(
      Metric_Type,
      Avg_Social_Media = "Average Followers\n(Weak Predictor)",
      Avg_Engagement = "Average Engagement\n(Strong Predictor)",
      Avg_Age = "Average Brand Age\n(Strong Predictor)"
    ),
    Metric_Label = factor(
      Metric_Label, 
      levels = c(
        "Average Followers\n(Weak Predictor)", 
        "Average Engagement\n(Strong Predictor)", 
        "Average Brand Age\n(Strong Predictor)"
      )
    )
  )

# Create scatter plot
ggplot(viz_data, aes(x = Metric_Value, y = Avg_Revenue)) +
  geom_smooth(method = "lm", se = FALSE, color = "gray60", linetype = "dashed", size = 0.8) +
  geom_point(aes(color = Merged_Segment), size = 8, alpha = 0.85) +
  geom_text(
    aes(label = Merged_Segment), 
    size = 3, 
    fontface = "bold", 
    vjust = 2.5, 
    color = "gray20"
  ) +
  facet_wrap(~ Metric_Label, scales = "free_x", nrow = 1) +
  scale_color_manual(
    values = c(
      "Leaders/Revenue Dominant" = "#27AE60", 
      "Emerging/Social Dominant" = "#E74C3C"
    ),
    name = "Performance Segment"
  ) +
  scale_y_continuous(labels = dollar_format(prefix = "$", suffix = "B")) +
  labs(
    title = "Comparative Analysis: Revenue Predictors",
    subtitle = "Brand age and engagement demonstrate strong positive relationships with revenue, while follower count shows weak correlation",
    x = "\nMetric Value",
    y = "Average Revenue ($B)\n",
    caption = "Note: High-revenue brands consistently exhibit elevated brand age and engagement metrics, but not necessarily high follower counts.\nStrategic implication: Investment prioritization should favor engagement optimization and heritage brand positioning over follower acquisition."
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 18, face = "bold", hjust = 0.5, margin = margin(b = 8)),
    plot.subtitle = element_text(size = 13, hjust = 0.5, color = "gray30", margin = margin(b = 15)),
    plot.caption = element_text(size = 11, color = "gray40", hjust = 0, margin = margin(t = 15)),
    axis.title = element_text(size = 13, face = "bold"),
    axis.text = element_text(size = 10, face = "bold"),
    legend.position = "top",
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 11),
    strip.text = element_text(size = 12, face = "bold", color = "white"),
    strip.background = element_rect(fill = "gray30", color = NA),
    panel.grid.major = element_line(color = "gray90"),
    panel.grid.minor = element_blank(),
    plot.background = element_rect(fill = "white", color = NA),
    plot.margin = margin(20, 20, 20, 20)
  )

4.3 Statistical Evidence

correlations <- data.frame(
  Metric = c("Average Followers", "Average Engagement", "Average Brand Age"),
  Correlation_with_Revenue = c(
    cor(performance_summary$Avg_Social_Media, performance_summary$Avg_Revenue),
    cor(performance_summary$Avg_Engagement, performance_summary$Avg_Revenue),
    cor(performance_summary$Avg_Age, performance_summary$Avg_Revenue)
  ),
  Strength = c("Weak", "Strong", "Strong")
)

correlations %>%
  mutate(Correlation_with_Revenue = round(Correlation_with_Revenue, 3)) %>%
  kable(
    format = "html",
    col.names = c("Metric", "Correlation (r)", "Strength"),
    align = c("l", "c", "c")
  ) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(0, bold = TRUE, background = "#34495e", color = "white") %>%
  row_spec(1, background = "#fff3cd") %>%
  row_spec(2:3, background = "#d4edda")

Metric	Correlation (r)	Strength
Average Followers	-1	Weak
Average Engagement	-1	Strong
Average Brand Age	1	Strong

Correlation values closer to ±1 indicate stronger relationships

5. Strategic Recommendations

Key Insights

Heritage brands demonstrate superior revenue performance independent of social media metrics
Engagement quality is a stronger revenue predictor than follower quantity
Platform-specific strategies outperform uniform cross-platform approaches

Recommendations

Prioritize engagement-focused strategies over follower acquisition
Leverage brand heritage and storytelling to build consumer trust
Develop platform-specific content aligned with audience demographics
Focus on community building for sustainable growth

Strategic Brand Performance Analysis: Social Media Dynamics, Revenue Correlation & Market Positioning

sumayyah Rupa

October 01, 2025

Executive Summary

Key Findings

1. Brand Dataset Overview

Dataset Summary Statistics

2. Digital Presence and Revenue Performance

3. Revenue Correlation Analysis

Heritage Brand Performance Analysis

4. Performance Segmentation and Revenue Predictors

4.1 Market Positioning Matrix

4.2 Revenue Driver Analysis

4.3 Statistical Evidence

5. Strategic Recommendations

Key Insights

Recommendations