Quantium Chips Analysis
Priyanshi Singh
2025-06-19
Introduction
This analysis explores customer segments and chip purchasing behavior to support a strategic recommendation for the Category Manager. We aim to understand which segments purchase the most chips, how much they spend, and their product preferences.
Load and Preview Data
# Load data
transactions <- read_csv(here("..", "data", "QVI_transaction_data.csv"))## Rows: 264836 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): PROD_NAME
## dbl (7): DATE, STORE_NBR, LYLTY_CARD_NBR, TXN_ID, PROD_NBR, PROD_QTY, TOT_SALES
##
## 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.customers <- read_csv(here("..", "data", "QVI_purchase_behaviour.csv"))## Rows: 72637 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): LIFESTAGE, PREMIUM_CUSTOMER
## dbl (1): LYLTY_CARD_NBR
##
## 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.# Preview
glimpse(transactions)## Rows: 264,836
## Columns: 8
## $ DATE <dbl> 43390, 43599, 43605, 43329, 43330, 43604, 43601, 43601,…
## $ STORE_NBR <dbl> 1, 1, 1, 2, 2, 4, 4, 4, 5, 7, 7, 8, 9, 13, 19, 20, 20, …
## $ LYLTY_CARD_NBR <dbl> 1000, 1307, 1343, 2373, 2426, 4074, 4149, 4196, 5026, 7…
## $ TXN_ID <dbl> 1, 348, 383, 974, 1038, 2982, 3333, 3539, 4525, 6900, 7…
## $ PROD_NBR <dbl> 5, 66, 61, 69, 108, 57, 16, 24, 42, 52, 16, 114, 15, 92…
## $ PROD_NAME <chr> "Natural Chip Compny SeaSalt175g", "CCs Nacho Ch…
## $ PROD_QTY <dbl> 2, 3, 2, 5, 3, 1, 1, 1, 1, 2, 1, 5, 2, 1, 1, 1, 4, 1, 1…
## $ TOT_SALES <dbl> 6.0, 6.3, 2.9, 15.0, 13.8, 5.1, 5.7, 3.6, 3.9, 7.2, 5.7…glimpse(customers)## Rows: 72,637
## Columns: 3
## $ LYLTY_CARD_NBR <dbl> 1000, 1002, 1003, 1004, 1005, 1007, 1009, 1010, 1011,…
## $ LIFESTAGE <chr> "YOUNG SINGLES/COUPLES", "YOUNG SINGLES/COUPLES", "YO…
## $ PREMIUM_CUSTOMER <chr> "Premium", "Mainstream", "Budget", "Mainstream", "Mai…Data Cleaning
# Convert date
transactions$DATE <- as.Date(transactions$DATE)
# Remove outliers (optional threshold)
transactions <- transactions %>%
filter(PROD_QTY > 0, PROD_QTY < 100)
# Check missing values
colSums(is.na(transactions))## DATE STORE_NBR LYLTY_CARD_NBR TXN_ID PROD_NBR
## 0 0 0 0 0
## PROD_NAME PROD_QTY TOT_SALES
## 0 0 0colSums(is.na(customers))## LYLTY_CARD_NBR LIFESTAGE PREMIUM_CUSTOMER
## 0 0 0Feature Engineering (Brand & Pack Size)
# Brand is the first word in PROD_NAME
transactions <- transactions %>%
mutate(BRAND = word(PROD_NAME, 1),
PACK_SIZE = parse_number(PROD_NAME))
# Check top brands
transactions %>% count(BRAND, sort = TRUE)## # A tibble: 29 × 2
## BRAND n
## <chr> <int>
## 1 Kettle 41288
## 2 Smiths 28860
## 3 Pringles 25102
## 4 Doritos 24962
## 5 Thins 14075
## 6 RRD 11894
## 7 Infuzions 11057
## 8 WW 10320
## 9 Cobs 9693
## 10 Tostitos 9471
## # ℹ 19 more rowsMerge with Customer Data
data_combined <- merge(transactions, customers, by = "LYLTY_CARD_NBR")
glimpse(data_combined)## Rows: 264,834
## Columns: 12
## $ LYLTY_CARD_NBR <dbl> 1000, 1002, 1003, 1003, 1004, 1005, 1007, 1007, 1009,…
## $ DATE <date> 2088-10-18, 2088-09-17, 2089-03-08, 2089-03-09, 2088…
## $ STORE_NBR <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ TXN_ID <dbl> 1, 2, 3, 4, 5, 6, 8, 7, 9, 10, 11, 15, 14, 12, 13, 17…
## $ PROD_NBR <dbl> 5, 58, 52, 106, 96, 86, 10, 49, 20, 51, 59, 1, 49, 84…
## $ PROD_NAME <chr> "Natural Chip Compny SeaSalt175g", "Red Rock D…
## $ PROD_QTY <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1,…
## $ TOT_SALES <dbl> 6.0, 2.7, 3.6, 3.0, 1.9, 2.8, 2.7, 3.8, 5.7, 8.8, 5.1…
## $ BRAND <chr> "Natural", "Red", "Grain", "Natural", "WW", "Cheetos"…
## $ PACK_SIZE <dbl> 175, 150, 210, 175, 160, 165, 150, 110, 330, 170, 300…
## $ LIFESTAGE <chr> "YOUNG SINGLES/COUPLES", "YOUNG SINGLES/COUPLES", "YO…
## $ PREMIUM_CUSTOMER <chr> "Premium", "Mainstream", "Budget", "Budget", "Mainstr…Customer Segment Analysis
segment_summary <- data_combined %>%
group_by(LIFESTAGE, PREMIUM_CUSTOMER) %>%
summarise(
total_sales = sum(TOT_SALES),
avg_price = mean(TOT_SALES / PROD_QTY),
transaction_count = n()
) %>%
arrange(desc(total_sales))## `summarise()` has grouped output by 'LIFESTAGE'. You can override using the
## `.groups` argument.segment_summary## # A tibble: 21 × 5
## # Groups: LIFESTAGE [7]
## LIFESTAGE PREMIUM_CUSTOMER total_sales avg_price transaction_count
## <chr> <chr> <dbl> <dbl> <int>
## 1 OLDER FAMILIES Budget 168363. 3.73 23160
## 2 YOUNG SINGLES/COUPL… Mainstream 157622. 4.07 20854
## 3 RETIREES Mainstream 155677. 3.83 21466
## 4 YOUNG FAMILIES Budget 139346. 3.75 19122
## 5 OLDER SINGLES/COUPL… Budget 136770. 3.88 18407
## 6 OLDER SINGLES/COUPL… Mainstream 133394. 3.80 18318
## 7 OLDER SINGLES/COUPL… Premium 132263. 3.89 17754
## 8 RETIREES Budget 113148. 3.92 15201
## 9 OLDER FAMILIES Mainstream 103446. 3.73 14244
## 10 RETIREES Premium 97646. 3.92 13096
## # ℹ 11 more rowssegment_summary$LIFESTAGE <- factor(segment_summary$LIFESTAGE, levels = c(
"YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES", "YOUNG FAMILIES",
"OLDER FAMILIES", "RETIREES", "NEW FAMILIES", "OLDER SINGLES/COUPLES"
))Visualization: Total Sales by Segment
ggplot(segment_summary, aes(x = LIFESTAGE, y = total_sales, fill = PREMIUM_CUSTOMER)) +
geom_col(position = "dodge") +
labs(title = "Total Chip Sales by Customer Segment", y = "Total Sales ($)", x = "Customer Lifestage") +
theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust = 0.5))
##️ Top Brands by Customer Segment
library(dplyr)
library(ggplot2)
# Create top brands summary
top_brands <- data_combined %>%
group_by(LIFESTAGE, PREMIUM_CUSTOMER, BRAND) %>%
summarise(total_sales = sum(TOT_SALES), .groups = "drop") %>%
arrange(LIFESTAGE, PREMIUM_CUSTOMER, desc(total_sales)) %>%
group_by(LIFESTAGE, PREMIUM_CUSTOMER) %>%
slice_max(order_by = total_sales, n = 3)
# Fix factor levels
top_brands$LIFESTAGE <- factor(top_brands$LIFESTAGE, levels = c(
"YOUNG SINGLES/COUPLES", "MIDAGE SINGLES/COUPLES", "YOUNG FAMILIES",
"OLDER FAMILIES", "RETIREES", "NEW FAMILIES", "OLDER SINGLES/COUPLES"
))ggplot(top_brands, aes(x = BRAND, y = total_sales, fill = BRAND)) +
geom_col(show.legend = FALSE) +
facet_grid(rows = vars(LIFESTAGE), cols = vars(PREMIUM_CUSTOMER), scales = "free_y", switch = "y") +
labs(
title = "Top Brands per Segment",
subtitle = "Top 3 brands by sales within each customer segment",
x = "Brand",
y = "Total Sales ($)" # ✅ clear label
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 9),
axis.text.y = element_text(size = 9),
strip.text = element_text(face = "bold", size = 10),
strip.placement = "outside",
plot.title = element_text(face = "bold", size = 15, hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5),
axis.title.y = element_text(margin = margin(r = 10), size = 12), # ✅ prevent cutoff
plot.margin = margin(10, 20, 10, 20) # extra padding on all sides
)
📦 Pack Size Preferences
pack_size_summary <- data_combined %>%
group_by(LIFESTAGE, PREMIUM_CUSTOMER, PACK_SIZE) %>%
summarise(
total_sales = sum(TOT_SALES),
transactions = n()
) %>%
arrange(desc(total_sales))## `summarise()` has grouped output by 'LIFESTAGE', 'PREMIUM_CUSTOMER'. You can
## override using the `.groups` argument.pack_size_summary %>% head(12)## # A tibble: 12 × 5
## # Groups: LIFESTAGE, PREMIUM_CUSTOMER [10]
## LIFESTAGE PREMIUM_CUSTOMER PACK_SIZE total_sales transactions
## <chr> <chr> <dbl> <dbl> <int>
## 1 OLDER FAMILIES Budget 175 42205. 5808
## 2 RETIREES Mainstream 175 38243. 5295
## 3 YOUNG SINGLES/COUPLES Mainstream 175 37968. 4997
## 4 YOUNG FAMILIES Budget 175 35635. 4921
## 5 OLDER SINGLES/COUPLES Budget 175 34497 4625
## 6 OLDER SINGLES/COUPLES Premium 175 33393. 4458
## 7 OLDER SINGLES/COUPLES Mainstream 175 33042. 4525
## 8 RETIREES Budget 175 28977. 3847
## 9 OLDER FAMILIES Budget 150 27017. 3882
## 10 OLDER FAMILIES Mainstream 175 25975. 3588
## 11 RETIREES Premium 175 24868. 3306
## 12 RETIREES Mainstream 150 24840. 3522## 📦 Pack Size Preferences
### 📊 Line chart comparing sales trends across pack sizes in each customer segment
ggplot(pack_size_summary, aes(x = PACK_SIZE, y = total_sales, color = PREMIUM_CUSTOMER)) +
geom_line(size = 1) +
facet_wrap(~LIFESTAGE, scales = "free_y", ncol = 2) +
labs(
title = "Sales by Pack Size",
subtitle = "Total sales across various pack sizes grouped by customer segment",
x = "Pack Size (grams)",
y = "Total Sales ($)"
) +
scale_y_continuous(labels = scales::comma) +
theme_minimal() +
theme(
axis.text = element_text(size = 9),
axis.title = element_text(size = 12),
strip.text = element_text(face = "bold", size = 10),
plot.title = element_text(face = "bold", size = 15, hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5),
legend.position = "right"
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
💼 Strategic Recommendations for Julia
Based on our insights:
- ✅ Focus on Young Singles/Couples and Mainstream Midage as they drive highest sales.
- ✅ Promote larger pack sizes and premium brands to segments that spend more per transaction.
- ✅ Use loyalty program data to promote top 3 brands per segment via email offers.
- ✅ Avoid low-volume brands for budget-conscious lifestages.
💾 Export Summary Data (Optional)
write_csv(segment_summary, "output/segment_summary.csv")
write_csv(pack_size_summary, "output/pack_size_summary.csv")
write_csv(data_combined, "output/cleaned_data.csv")Conclusion
This report summarizes key insights on customer behavior and chip sales. It enables data-driven targeting of high-value segments, tailoring of product offerings, and strategic promotional planning.