Introduction

path_image <- "C:/Users/Akshay Kher/Desktop/Akshay Kher - Walmart Data Challenge/documents/" # image path
knitr::include_graphics(paste0(path_image,'kroger_logo.png'))

Problem Description

For a particular store and category, what is the right set of products to carry with limited shelf space?

Data

The data analyzed contains household level transactions of over two years from a group of 2,500 households who are frequent shoppers at Kroger. It contains all of each household’s purchases, not just those from a limited number of categories. For certain households, demographic information as well as direct marketing contact history are included. For detailed information, please refer to the following pdf:

https://github.com/akshaykher/Retail-Analysis-Impact-of-Mailer-and-In-Store-Display/blob/master/The_Complete_Journey_guide.pdf

Steps

  1. Data Preparation
  2. Problem Formulation
  3. Data Analysis
    1. Data Exploration
    2. Shelf Optimization
  4. Summary & Future Work

1. Data Preparation

Libraries Required

Loading Libraries

library(data.table) # package to read csv files
library(tidyverse) # package to perform data manipulation
library(kableExtra) # package to output HTML tables
library(DT) # package to output HTML tables
library(ggthemes) # package to change visualization themes
library(treemapify) # package to draw treemaps
library(cowplot) # package for stacking visuals side-by-side
library(plotly) # package to plot interactive visuals
library(arules) # package to implement apriori algorithm
library(arulesViz) # package to visualize results from apriori algorithm
library(stringr) # package to perform string manipulation

Reading Data

Reading Transaction, Product and Demographic data.

path <- "C:/Users/Akshay Kher/Desktop/Akshay Kher - Walmart Data Challenge/data/" # data path

transaction_data <- fread(paste0(path,"transaction_data.csv")) %>% as_data_frame() # transactional data
product <- fread(paste0(path,"product.csv")) %>% as_data_frame() # product data
hh_demographic <- fread(paste0(path,"hh_demographic.csv")) %>% as_data_frame() # demographic data

Aggregating Data

Aggregating Transaction, Product and Demographic data. Also, adding a new variable called product_price.

# Left joining product and demographic data with transaction data. Also adding new variable called product_price.
aggregated_data <-
  transaction_data %>% 
    left_join(hh_demographic, by="household_key") %>% 
    left_join(product, by="product_id") %>% 
  mutate(product_price = (sales_value - (retail_disc + coupon_match_disc))/quantity) %>% 
  select(household_key:curr_size_of_product, product_price, everything())

Data Dictionary

text_tbl <- data.frame (
  Variable = names(aggregated_data),
  Description = c(
    "Uniquely identifies each household",
    "Uniquely identifies a pruchase occasion",
    "Day when the transaction occurred",
    "Uniquely identifies each product",
    "Number of products purchased during the trip",
    "Amount of dollars retailer receives from the sale",
    "Uniquely identifies each store",
    "Discount applied due to retailer's match",
    "Time of the day when the transaction occurred",
    "Week of the transaction. Ranges from 1-102",    
    "Discount applied due to manufacturer coupon",
    "Discount applied due to retailer's loyalty program",
    "Estimated age range of customer",
    "Marital Status of customer (A - Married, B - Single, U - Unknown)",
    "Household Income of customer",
    "Homeowner, renter, etc.",
    "Household composition",
    "Size of household up to 5+",
    "Number of children present up to 3+",
    "Manufacturer code",
    "Groups similar products together (Category)",
    "Indicates Private or National level brand",
    "Groups similar products at a lower level",
    "Groups similar products at the lowest level",
    "Indicates package size (not available for all products)",
    "Indicates price of product"
 )
)

kable(text_tbl) %>%
  kable_styling(full_width = F) %>%
  column_spec(1, bold = T, border_right = T) %>%
  column_spec(2, width = "30em")
Variable Description
household_key Uniquely identifies each household
basket_id Uniquely identifies a pruchase occasion
day Day when the transaction occurred
product_id Uniquely identifies each product
quantity Number of products purchased during the trip
sales_value Amount of dollars retailer receives from the sale
store_id Uniquely identifies each store
retail_disc Discount applied due to retailer’s match
trans_time Time of the day when the transaction occurred
week_no Week of the transaction. Ranges from 1-102
coupon_disc Discount applied due to manufacturer coupon
coupon_match_disc Discount applied due to retailer’s loyalty program
age_desc Estimated age range of customer
marital_status_code Marital Status of customer (A - Married, B - Single, U - Unknown)
income_desc Household Income of customer
homeowner_desc Homeowner, renter, etc.
hh_comp_desc Household composition
household_size_desc Size of household up to 5+
kid_category_desc Number of children present up to 3+
manufacturer Manufacturer code
department Groups similar products together (Category)
brand Indicates Private or National level brand
commodity_desc Groups similar products at a lower level
sub_commodity_desc Groups similar products at the lowest level
curr_size_of_product Indicates package size (not available for all products)
product_price Indicates price of product

First 100 rows

kable(head(arrange(aggregated_data, desc(age_desc)), 100)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "100%", height = "500px")
household_key basket_id day product_id quantity sales_value store_id retail_disc trans_time week_no coupon_disc coupon_match_disc age_desc marital_status_code income_desc homeowner_desc hh_comp_desc household_size_desc kid_category_desc manufacturer department brand commodity_desc sub_commodity_desc curr_size_of_product product_price
1364 26984896261 1 842930 1 2.19 31742 0.00 1520 1 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 69 GROCERY Private CONVENIENT BRKFST/WHLSM SNACKS GRANOLA BARS 10 OZ 2.19
1364 26984896261 1 897044 1 2.99 31742 -0.40 1520 1 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 1075 GROCERY National CRACKERS/MISC BKD FD SNACK CRACKERS 9.5 OZ 3.39
1364 26984896261 1 920955 1 3.09 31742 0.00 1520 1 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 3664 MEAT National BEEF GRND/PATTY - ROUND 3.09
1364 26984896261 1 937406 1 2.50 31742 -0.99 1520 1 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 2209 MEAT-PCKGD National BREAKFAST SAUSAGE/SANDWICHES LINKS - RAW 12OZ 3.49
1364 26984896261 1 981760 1 0.60 31742 -0.79 1520 1 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 69 GROCERY Private EGGS EGGS - X-LARGE 1 DZ 1.39
2397 27023506679 4 1773223 1 1.09 790 0.00 1352 1 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 69 NUTRITION Private WATER SPRING WATER 1 GAL 1.09
2397 27023506679 4 2058885 1 0.99 790 0.00 1352 1 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 5899 NUTRITION National WATER SPRING WATER 1.5 LITER 0.99
2397 27023506679 4 7413002 1 6.63 790 -1.72 1352 1 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 764 GROCERY National LAUNDRY DETERGENTS LIQUID LAUNDRY DETERGENTS 24 LOAD 8.35
1917 27069356878 8 867188 1 1.49 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1046 GROCERY National FROZEN PIE/DESSERTS FRZN WHIPPED TOPPING 8 OZ 1.49
1917 27069356878 8 870106 1 2.29 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DELI Private SALADS/DIPS SAL: DESSERTS-PREPACK 24 OZ 2.29
1917 27069356878 8 906655 1 2.29 32004 -0.50 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private COFFEE GROUND COFFEE 11.5 OZ 2.79
1917 27069356878 8 914126 1 8.99 32004 -1.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DRUG GM Private ADULT INCONTINENCE ADULT INCONTINENCE UNDERGARMEN 9.99
1917 27069356878 8 946964 1 0.89 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private CRACKERS/MISC BKD FD SOUP CRACKERS (SALTINE/OYSTER) 16 OZ 0.89
1917 27069356878 8 971792 1 2.29 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DELI Private SALADS/DIPS SAL: DESSERTS-PREPACK 21 OZ 2.29
1917 27069356878 8 975085 1 7.99 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1420 DRUG GM National DIETARY AID PRODUCTS LIQUID NUTRITION FOR ADULTS 6PK/8OZ 7.99
1917 27069356878 8 1065045 1 0.69 32004 -0.20 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private DRY MIX DESSERTS GELATIN 6 OZ 0.89
1917 27069356878 8 1067614 2 3.99 32004 -3.99 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DRUG GM Private ANALGESICS ADULT ANALGESICS 3.99
1917 27069356878 8 1072680 1 2.50 32004 -1.19 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 2110 GROCERY National FRZN NOVELTIES/WTR ICE CUPS/PUSH UPS/OTHER 11.5 OZ 3.69
1917 27069356878 8 1136199 1 0.69 32004 -0.20 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private DRY MIX DESSERTS GELATIN 6 OZ 0.89
1917 27069356878 8 6633342 1 3.99 32004 0.00 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1646 PRODUCE National SALAD MIX SALAD SPINACH 7 OZ 3.99
1917 27069356878 8 6773118 1 1.99 32004 -0.60 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private MISC. DAIRY REFRIGERATED PUDDING 6PK/3.25OZ 2.59
1917 27069356878 8 6773145 1 1.99 32004 -0.60 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private MISC. DAIRY REFRIGERATED PUDDING 6PK/3.25OZ 2.59
1917 27069356878 8 9296870 1 2.50 32004 -1.19 1737 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 2110 GROCERY National FRZN NOVELTIES/WTR ICE CUPS/PUSH UPS/OTHER 36 OZ 3.69
1364 27081336145 9 915041 1 0.99 31742 -0.40 1554 2 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 69 GROCERY Private BAKED BREAD/BUNS/ROLLS DINNER ROLLS 11 OZ 1.39
1364 27081336145 9 5568828 1 3.19 31742 0.00 1554 2 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 1183 GROCERY National BAKED BREAD/BUNS/ROLLS ENGLISH MUFFINS/WAFFLES 12 OZ 3.19
1364 27081336162 9 9446690 1 1.00 31742 0.00 1558 2 0 0 65+ B 100-124K Homeowner Single Female 1 None/Unknown 6291 DRUG GM National DOLLAR VALUE PRODUCTS DOLLAR DAYS 1.00
1917 27081340474 9 820378 1 3.69 32004 0.00 1029 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 4951 DRUG GM National LAXATIVES LAXATIVES 3.69
1917 27081340474 9 839035 1 4.98 32004 -0.41 1029 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1420 DRUG GM National INFANT FORMULA BABY ISOTONIC DRINKS 5.39
947 27081381160 9 866227 2 1.00 337 0.00 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 2 PASTRY National BREAKFAST SWEETS SW GDS:DONUTS 0.50
947 27081381160 9 895930 1 1.64 337 -0.61 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 3868 DELI National DELI MEATS MEAT: LUNCHMEAT BULK 2.25
947 27081381160 9 901747 3 1.00 337 -0.47 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE BEANS GREEN: FS/WHL/CUT 8 OZ 0.49
947 27081381160 9 913159 1 1.99 337 0.00 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 972 GROCERY National BAG SNACKS MISC BAG SNACKS 4 OZ 1.99
947 27081381160 9 981521 3 2.97 337 -2.10 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 1091 GROCERY National BLEACH LIQUID BLEACH 96 OZ 1.69
947 27081381160 9 1110764 1 1.67 337 -0.32 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 988 GROCERY National MEAT - SHELF STABLE CHILI: CANNED 10.5 OZ 1.99
947 27081381160 9 1110813 1 2.29 337 0.00 1525 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 781 GROCERY National MEAT - SHELF STABLE HASH: CANNED 15 OZ 2.29
2397 27095617427 10 1761524 1 2.69 790 -0.13 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 1251 GROCERY National SOUP RTS SOUP: CHUNKY/HOMESTYLE ET 18.8 OZ 2.82
2397 27095617427 10 1897771 1 3.19 790 0.00 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 159 GROCERY National CRACKERS/MISC BKD FD SPECIALTY CRACKERS 13 OZ 3.19
2397 27095617427 10 1909329 1 2.79 790 -0.20 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 1351 GROCERY National CRACKERS/MISC BKD FD SPECIALTY CRACKERS 7 OZ 2.99
2397 27095617427 10 1963886 1 0.67 790 0.00 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 2 PRODUCE National CARROTS CARROTS - BULK 25 LB 0.67
2397 27095617427 10 1982843 1 1.61 790 0.00 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 2 PRODUCE National VEGETABLES - ALL OTHERS VEGETABLES ALL OTHER 20 LB 1.61
2397 27095617427 10 1991988 1 3.00 790 -0.49 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 1075 GROCERY National CRACKERS/MISC BKD FD SNACK CRACKERS 13 OZ 3.49
2397 27095617427 10 2054623 1 1.99 790 0.00 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 165 GROCERY National FRZN VEGETABLE/VEG DSH FRZN BAGGED VEGETABLES - PLAIN 16 OZ 1.99
2397 27095617427 10 2078883 1 1.99 790 -0.10 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 165 GROCERY National FRZN VEGETABLE/VEG DSH FRZN BOXED VEGETABLES - PLAIN 9 OZ 2.09
2397 27095617427 10 7184502 1 2.69 790 -0.13 1758 2 0 0 65+ B 125-149K Unknown 2 Adults No Kids 2 None/Unknown 1251 GROCERY National SOUP RTS SOUP: CHUNKY/HOMESTYLE ET 18.8 OZ 2.82
1917 27101251324 10 829722 1 4.69 32004 -0.30 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 70 GROCERY National LAUNDRY DETERGENTS LIQUID LAUNDRY DETERGENTS 32 LOAD 4.99
1917 27101251324 10 851779 1 2.29 32004 0.00 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DELI Private SALADS/DIPS SAL: DESSERTS-PREPACK 22 OZ 2.29
1917 27101251324 10 891134 1 4.88 32004 -2.11 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private BATH TISSUES TOILET TISSUE 528 SQ FT 6.99
1917 27101251324 10 891837 1 3.99 32004 -0.90 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 764 GROCERY National LAUNDRY ADDITIVES FABRIC SOFTENER LIQUID 40 LOAD 4.89
1917 27101251324 10 923746 1 1.45 32004 -0.14 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private EGGS EGGS - LARGE 18 CT 1.59
1917 27101251324 10 924262 1 3.99 32004 0.00 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1425 MEAT-PCKGD National BACON BETTER FOR YOU 12 OZ 3.99
1917 27101251324 10 1007895 2 3.98 32004 -1.20 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private MISC. DAIRY REFRIGERATED PUDDING 19.5 OZ 2.59
1917 27101251324 10 1043416 1 1.67 32004 -1.32 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private CHEESE NATURAL CHEESE EXACT WT SLICES 6 OZ 2.99
1917 27101251324 10 5591784 1 4.88 32004 -1.61 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private PAPER TOWELS PAPER TOWELS & HOLDERS 8 ROLL 6.49
1917 27101251324 10 6554027 1 2.49 32004 0.00 1405 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 995 GROCERY National CHEESE MISCELLANEOUS CHEESE 7 OZ 2.49
1917 27101256136 11 866864 1 3.19 32004 0.00 1918 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1266 GROCERY National PNT BTR/JELLY/JAMS PRESERVES JAM MARMALADE 18OZ 3.19
1917 27101256136 11 973476 1 1.39 32004 -0.60 1918 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private PNT BTR/JELLY/JAMS PRESERVES JAM MARMALADE 32 OZ 1.99
1917 27101256136 11 999250 1 3.79 32004 -1.20 1918 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 DELI Private CHICKEN/POULTRY CHIX: ROTISSERIE (HOT) 4.99
1917 27101256136 11 1014599 1 3.19 32004 0.00 1918 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 151 GROCERY National FRZN MEAT/MEAT DINNERS FRZN SS PREMIUM ENTREES/DNRS/N 10 OZ 3.19
1917 27101256136 11 1099802 1 2.50 32004 -0.69 1918 2 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1230 GROCERY National FRZN MEAT/MEAT DINNERS FRZN SS PREMIUM ENTREES/DNRS/N 10.5 OZ 3.19
947 27115235700 12 901747 3 1.00 337 -0.47 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE BEANS GREEN: FS/WHL/CUT 8 OZ 0.49
947 27115235700 12 967373 3 1.00 337 -0.47 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE CORN 8.75 OZ 0.49
947 27115235700 12 981521 2 1.98 337 -1.40 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 1091 GROCERY National BLEACH LIQUID BLEACH 96 OZ 1.69
947 27115235700 12 985034 2 7.00 337 -4.98 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 69 SEAFOOD-PCKGD Private SEAFOOD - FROZEN SEAFOOD-FRZ-BRD SHRIMP 8 OZ 5.99
947 27115235700 12 1039156 1 1.50 337 0.00 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 866 DRUG GM National NEWSPAPER NEWSPAPER 1.50
947 27115235700 12 1070716 1 1.00 337 -0.49 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 608 GROCERY National FRZN MEAT/MEAT DINNERS SS ECONOMY ENTREES/DINNERS ALL 9 OZ 1.49
947 27115235700 12 1113588 2 2.00 337 -0.98 1259 2 0 0 65+ B 35-49K Homeowner Single Male 1 None/Unknown 608 GROCERY National FRZN MEAT/MEAT DINNERS SS ECONOMY ENTREES/DINNERS ALL L 9 OZ 1.49
1917 27130106129 13 857276 1 1.99 32004 -0.50 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE ASPARAGUS 15 OZ 2.49
1917 27130106129 13 991948 1 1.67 32004 -0.32 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private FRZN NOVELTIES/WTR ICE ICE CREAM SANDWICHES 6 CT 1.99
1917 27130106129 13 1011884 3 1.20 32004 -1.17 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE BEANS GREEN: FS/WHL/CUT 15 OZ 0.79
1917 27130106129 13 1030819 2 0.80 32004 -0.78 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE PEAS GREEN 14 OZ 0.79
1917 27130106129 13 1083043 1 3.29 32004 0.00 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1251 GROCERY National CANNED JUICES VEG JUICE (EXCEPT TOMATO) (OVE 5.5 OZ 3.29
1917 27130106129 13 7466806 2 0.80 32004 -0.38 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private YOGURT YOGURT NOT MULTI-PACKS 6 OZ 0.59
1917 27130106129 13 8011282 1 0.40 32004 -0.19 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private YOGURT YOGURT NOT MULTI-PACKS 6 OZ 0.59
1917 27130106129 13 8011285 2 0.80 32004 -0.38 1443 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private YOGURT YOGURT NOT MULTI-PACKS 6 OZ 0.59
362 27130120840 13 819468 1 0.50 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private OLIVES GREEN OLIVES 2.5 OZ 0.50
362 27130120840 13 821007 1 1.39 309 -0.80 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private PNT BTR/JELLY/JAMS JELLY 18 OZ 2.19
362 27130120840 13 821974 1 1.19 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 1675 GROCERY National VEGETABLES - SHELF STABLE MUSHROOMS CND & GLASS 4.5 OZ 1.19
362 27130120840 13 824005 1 1.99 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 673 PRODUCE National MUSHROOMS MUSHROOMS WHITE SLICED PKG 8 OZ 1.99
362 27130120840 13 862826 1 1.10 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 2 PRODUCE National POTATOES POTATOES RED (BULK&BAG) 1.10
362 27130120840 13 915724 3 2.00 309 -0.67 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private BEANS - CANNED GLASS & MW PREPARED BEANS - BAKED W/PORK 16 OZ 0.89
362 27130120840 13 920461 1 0.83 309 -0.36 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private MEAT - SHELF STABLE PASTA: CANNED 15 OZ 1.19
362 27130120840 13 923746 1 1.45 309 -0.14 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private EGGS EGGS - LARGE 18 CT 1.59
362 27130120840 13 933456 2 1.58 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 827 GROCERY National FRUIT - SHELF STABLE PINEAPPLE 8OZ 0.79
362 27130120840 13 1006399 1 1.59 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 764 GROCERY National DISHWASH DETERGENTS LIQUID DISH DETERGENT 12.6 OZ 1.59
362 27130120840 13 1022254 1 0.76 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 2 PRODUCE National POTATOES POTATOES SWEET&YAMS 40 LB 0.76
362 27130120840 13 1038894 1 0.89 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE CORN 15 OZ 0.89
362 27130120840 13 1043006 2 1.18 309 -0.80 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private SOFT DRINKS SFT DRNK 2 LITER BTL CARB INCL 2 LTR 0.99
362 27130120840 13 1052046 1 0.79 309 -0.40 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private FRUIT - SHELF STABLE MANDARIN ORANGES/CITRUS SECT 15 OZ 1.19
362 27130120840 13 1062453 1 0.83 309 -0.36 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 GROCERY Private MEAT - SHELF STABLE PASTA: CANNED 15 OZ 1.19
362 27130120840 13 1086233 1 1.79 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 906 GROCERY National FLUID MILK PRODUCTS REFRIGERATED COFFEE CREAMERS 16 OZ 1.79
362 27130120840 13 1108714 1 3.97 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 DRUG GM Private ANALGESICS ADULT ANALGESICS 3.97
362 27130120840 13 1126045 1 2.99 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 5927 PASTRY National SWEET GOODS & SNACKS SW GDS: PUFF PASTRY-LESS THAN 2.99
362 27130120840 13 1127831 1 2.99 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 5937 PRODUCE National BERRIES STRAWBERRIES 16 OZ 2.99
362 27130120840 13 6703800 1 13.99 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 4951 DRUG GM National LAXATIVES LAXATIVES 13.99
362 27130120840 13 7166817 1 2.07 309 -0.62 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 1251 GROCERY National SOUP RTS SOUP: CHUNKY/HOMESTYLE ET 18.8 OZ 2.69
362 27130120840 13 8276172 1 3.29 309 0.00 1515 3 0 0 65+ U 50-74K Unknown Single Male 1 None/Unknown 69 MEAT-PCKGD Private LUNCHMEAT HAM 10OZ 3.29
1917 27153543688 15 855544 2 1.38 32004 0.00 1212 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE SPINACH & GREENS 14 OZ 0.69
1917 27153543688 15 857276 2 3.98 32004 -1.00 1212 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 69 GROCERY Private VEGETABLES - SHELF STABLE ASPARAGUS 15 OZ 2.49
1917 27153543688 15 933208 1 7.99 32004 0.00 1212 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 1420 DRUG GM National DIETARY AID PRODUCTS LIQUID NUTRITION FOR ADULTS 6PK/8OZ 7.99
1917 27153543688 15 961654 1 0.79 32004 0.00 1212 3 0 0 65+ U 15-24K Unknown Single Female 1 None/Unknown 843 GROCERY National VEGETABLES - SHELF STABLE SPINACH & GREENS 14.5 OZ 0.79

Overall

  • 2.6 million transactions
  • 102 weeks
  • 2,500 households
  • 582 stores
  • 44 categories
  • 2,383 products

Filtering Data

For this analysis, we will consider only a single store (and it’s competitors) and a single category. We will consider that store/category which has the maximum transactions.

  1. Store
  • The plot depicts top 30 stores by number of transactions.
  • Store 367 has the highest number of transactions.
  • Also, stores 406, 356, 381 & 292 have high number of transactions as well. Thus, we will consider these as competitors to store 367.
  • Now, we will limit our analysis to store 367 and it’s four competitors.

Note: Ideally, competitors should be compared on many factors like proximity, total revenue, total visits, total units sold etc. However, due to limited data, we will go ahead with our simplified assumption.

# aggregating transactions by store_id. Also, filtering for top 30 stores.
filter_store <-
  aggregated_data %>% 
  group_by(store_id) %>% 
  summarise(count = n()) %>% 
  arrange(desc(count)) %>% 
  top_n(30) %>% 
  mutate(store_id = as.character(store_id))

# plotting visual
ggplot(filter_store, aes(x=reorder(store_id, count), y=count)) +
  geom_bar(stat='identity') +
  coord_flip() +
  theme_tufte() +
  xlab("Store ID") +
  ylab("")

  1. Category
  • The plot depicts top 10 categories by number of transactions.
  • Category Grocery has the highest number of transactions. Hence, we will limit our analysis to this category only.
# aggregating transactions by category Also, filtering for top 10 categories.
filter_category <-
  aggregated_data %>% 
  group_by(department) %>% 
  summarise(transactions = n()) %>% 
  arrange(desc(transactions)) %>% 
  top_n(11) %>% 
  filter(department != "") %>% 
  select(category = department, transactions)

ggplot(filter_category, aes(area = transactions, label = category)) +
  geom_treemap() +
  geom_treemap_text(fontface = "italic", colour = "orange", place = "centre",
                    grow = TRUE)

Cleaning Data

In the previous step we filtered the data for Stores 367, 406, 356, 381 & 292 and Grocery category. Now we will clean this data.

Converting data types:

  • Age Description to factor
  • Marital Status to factor
  • Income Description to factor
  • Homeowner Description to factor
  • Homeowner Description to factor
  • Household Composition to factor
  • Size of Household to factor
  • Number of children to factor
filtered_data <- 
  aggregated_data %>% 
  filter(store_id %in% c(367, 406, 356, 381, 292), department == "GROCERY") %>% 
    mutate(age_desc = as.factor(age_desc),
           marital_status_code = as.factor(marital_status_code),
           income_desc = as.factor(income_desc),
           homeowner_desc = as.factor(homeowner_desc),
           hh_comp_desc = as.factor(hh_comp_desc),
           household_size_desc = as.factor(household_size_desc),
           kid_category_desc = as.factor(kid_category_desc),
         brand = as.factor(brand))

glimpse(filtered_data)
## Observations: 169,401
## Variables: 26
## $ household_key        <int> 1130, 1130, 347, 347, 347, 347, 347, 347,...
## $ basket_id            <dbl> 27008809354, 27008809354, 27008810686, 27...
## $ day                  <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
## $ product_id           <int> 5568489, 9392700, 916767, 973042, 997101,...
## $ quantity             <int> 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,...
## $ sales_value          <dbl> 4.66, 2.33, 0.50, 0.99, 0.49, 0.99, 0.49,...
## $ store_id             <int> 367, 367, 367, 367, 367, 367, 367, 367, 3...
## $ retail_disc          <dbl> -4.52, -2.26, -0.49, -0.60, -0.10, -0.30,...
## $ trans_time           <int> 1528, 1528, 1710, 1710, 1710, 1710, 1710,...
## $ week_no              <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ coupon_disc          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ coupon_match_disc    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ age_desc             <fct> 55-64, 55-64, NA, NA, NA, NA, NA, NA, NA,...
## $ marital_status_code  <fct> U, U, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ income_desc          <fct> 25-34K, 25-34K, NA, NA, NA, NA, NA, NA, N...
## $ homeowner_desc       <fct> Renter, Renter, NA, NA, NA, NA, NA, NA, N...
## $ hh_comp_desc         <fct> 2 Adults Kids, 2 Adults Kids, NA, NA, NA,...
## $ household_size_desc  <fct> 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ kid_category_desc    <fct> 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
## $ manufacturer         <int> 1208, 1208, 69, 69, 69, 69, 69, 334, 69, ...
## $ department           <chr> "GROCERY", "GROCERY", "GROCERY", "GROCERY...
## $ brand                <fct> National, National, Private, Private, Pri...
## $ commodity_desc       <chr> "SOFT DRINKS", "SOFT DRINKS", "SOFT DRINK...
## $ sub_commodity_desc   <chr> "SOFT DRINKS 12/18&15PK CAN CAR", "SOFT D...
## $ curr_size_of_product <chr> "12 OZ", "12 OZ", "2 LTR", "12 OZ", "1.0 ...
## $ product_price        <dbl> 4.59, 4.59, 0.99, 1.59, 0.59, 1.29, 0.59,...

Missing Values

  • Demographic information like Age Description, Marital Status, Income Description etc. have a lot of missing values. However, the customers might have chosen not to share this information. Thus, we will leave them as missing.
  • Transactions corresponding to missing Product Price have zero units bought and zero spend. These transactions seem erroneous, hence we will remove them.
# calculate missing values
na_table <-
  map_dbl(filtered_data, function(x) sum(is.na(x))) %>%
  sort(decreasing = TRUE) %>%
  data.frame()

# rename column
colnames(na_table) <- c("total_missing")

# display missing value table
kable(na_table) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width = F, position='left')
total_missing
age_desc 72350
marital_status_code 72350
income_desc 72350
homeowner_desc 72350
hh_comp_desc 72350
household_size_desc 72350
kid_category_desc 72350
product_price 267
household_key 0
basket_id 0
day 0
product_id 0
quantity 0
sales_value 0
store_id 0
retail_disc 0
trans_time 0
week_no 0
coupon_disc 0
coupon_match_disc 0
manufacturer 0
department 0
brand 0
commodity_desc 0
sub_commodity_desc 0
curr_size_of_product 0

After removing missing product price transactions, the new missing value table looks like:

# removing missing product price transactions
filtered_data <-
  filtered_data %>% 
    filter(!is.na(product_price))

# calculate missing values
na_table <-
  map_dbl(filtered_data, function(x) sum(is.na(x))) %>%
  sort(decreasing = TRUE) %>%
  data.frame()

# rename column
colnames(na_table) <- c("total_missing")

# display missing value table
kable(na_table) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width = F, position='left')
total_missing
age_desc 72195
marital_status_code 72195
income_desc 72195
homeowner_desc 72195
hh_comp_desc 72195
household_size_desc 72195
kid_category_desc 72195
household_key 0
basket_id 0
day 0
product_id 0
quantity 0
sales_value 0
store_id 0
retail_disc 0
trans_time 0
week_no 0
coupon_disc 0
coupon_match_disc 0
manufacturer 0
department 0
brand 0
commodity_desc 0
sub_commodity_desc 0
curr_size_of_product 0
product_price 0

Outlier Analysis

Observations from summary table:

  • Zero Quantity seems erroneous. We will remove transactions with zero quantity bought.
  • Zero Sales Value is possible when the customer gets 100% discount. However, we will remove those transactions where sum of sales and discount is zero.
  • Product Price is coming as Inf due to division by zero quantity values. Once, we remove zero quantity values, this error should be fixed.
# summary statistics for numerical variables
summary <- data.frame()
for(i in c(5,6,8,11,12,26))
{
  name = colnames(filtered_data)[i]
  min = min(filtered_data[,i], na.rm=TRUE) %>% round(2)
  percentile_1st = quantile(filtered_data[,i,drop=TRUE], probs = 0.01, na.rm = TRUE) %>% round(2) %>% as.numeric()
  mean = mean(filtered_data[,i,drop=TRUE], na.rm=TRUE) %>% round(2)
  median = median(filtered_data[,i,drop=TRUE], na.rm=TRUE) %>% round(2)
  percentile_99th = quantile(filtered_data[,i,drop=TRUE], probs = 0.99, na.rm = TRUE) %>% round(2) %>% as.numeric()
  max = max(filtered_data[,i], na.rm=TRUE) %>% round(2)
  count = sum(!is.na(filtered_data[,i]))
  df = data.frame(name, min, percentile_1st, mean=mean, median=median, percentile_99th, max, count)
  summary <- rbind(summary, df)
}

# printing data
kable(summary) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width = F, position='left')
name min percentile_1st mean median percentile_99th max count
quantity 0.00 1.00 1.33 1.00 5.00 54.00 169134
sales_value 0.00 0.34 2.60 2.00 11.99 191.76 169134
retail_disc -64.00 -3.88 -0.48 -0.19 0.00 0.00 169134
coupon_disc -20.99 -0.50 -0.01 0.00 0.00 0.00 169134
coupon_match_disc -3.50 0.00 0.00 0.00 0.00 0.00 169134
product_price -Inf 0.25 NaN 2.00 10.39 Inf 169134

After making the requisite changes, the summary table looks like:

# quantity >0, spend >0
filtered_data <-
  filtered_data %>% 
  filter(quantity !=0, (sales_value+retail_disc+coupon_disc+coupon_match_disc) !=0)

# summary statistics for numerical variables
summary <- data.frame()
for(i in c(5,6,8,11,12,26))
{
  name = colnames(filtered_data)[i]
  min = min(filtered_data[,i], na.rm=TRUE) %>% round(2)
  percentile_1st = quantile(filtered_data[,i,drop=TRUE], probs = 0.01, na.rm = TRUE) %>% round(2) %>% as.numeric()
  mean = mean(filtered_data[,i,drop=TRUE], na.rm=TRUE) %>% round(2)
  median = median(filtered_data[,i,drop=TRUE], na.rm=TRUE) %>% round(2)
  percentile_99th = quantile(filtered_data[,i,drop=TRUE], probs = 0.99, na.rm = TRUE) %>% round(2) %>% as.numeric()
  max = max(filtered_data[,i], na.rm=TRUE) %>% round(2)
  count = sum(!is.na(filtered_data[,i]))
  df = data.frame(name, min, percentile_1st, mean=mean, median=median, percentile_99th, max, count)
  summary <- rbind(summary, df)
}

# printing data
kable(summary) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width = F, position='left')
name min percentile_1st mean median percentile_99th max count
quantity 1.00 1.00 1.33 1.00 5.00 54.00 168864
sales_value 0.00 0.34 2.61 2.00 11.99 191.76 168864
retail_disc -64.00 -3.87 -0.48 -0.19 0.00 0.00 168864
coupon_disc -17.99 -0.50 -0.01 0.00 0.00 0.00 168864
coupon_match_disc -3.50 0.00 0.00 0.00 0.00 0.00 168864
product_price 0.05 0.25 2.55 2.00 10.39 50.00 168864

2. Problem Formulation

Problem

The retail manager of Store 367 wants to understand the right set of products (out of 610) that should be carried in the shelves pertaining to the Grocery Category. For our analysis we have 102 weeks of data from 245 households who have made a total of 5720 transactions in the Grocery Category.

Optimization Metrics

I believe the solution highly depends upon the definition of “right set of products”. This definition will vary from store to store and location to location. Thus, it is important to define metrics that can help us identify the same:

  1. Popular Products: The retailer might want to include products that are popular among the customers in terms of revenue or visits.
  2. Cross-Selling Products: The retailer might want to include products that are bought in association with the popular products.
  3. Branded Products: The retailer might want to keep only high-end branded products.
  4. Differentiated Products: The retailer might want to keep products that differentiate it from it’s competitors.
  5. Variety of Products: The retailer might want to include variety of products.
  6. Niche Products: The retailer might want to include some niche products for a distinct set of customers.
  7. Economic Products: The retailer would want to include economic products for price sensitive customers.
  8. Non-Perishable Products: The retailer might want to have a more of non-perishable products to reduce risk of spoilage.
  9. Speciality Products: The retailer might want to include some special type of products. Ex: Homemade, Organic, Gluton Free, Egg Free etc.
  10. Local Country Products: The retailer might want to keep products that appeal to the local audience.
  11. Weather Specific Products: The retailer might want to stock items depending upon the weather conditions on that particular day.
  12. Festival Specific Products: The retailer might want to include products depending on the upcoming festival.
  13. Proximity Related Products: The retailer might want to keep specific products depending upon its surroundings. Ex: Keeping university merchandise as it’s closer to a university.
  14. Promotional Products: The retailer might want to stock products which generally have an offer from the manufacturer’s side.
  15. Easy to Carry/Transport Products: The retailers might want to keep products that can be easily transported from the warehouse to the store.

3. Data Analysis

Data Exploration

1. Product Popularity Comparison

The treemap below visualizes top 10 products by revenue for two stores. The size of the rectangle denotes the total revenue and the color denotes the total visits. Some keys observations are as follows:

  • Following products provide high revenue for both stores:
    • Beeralemalt Liquor
    • Milk
    • Soft Drinks
    • Shredded Cheese
    • Juice
    • Toilet Tissue
  • For store 367, Yogurt seems to sell most units whereas for store 292, milk seems to sell most units
  • Store 292 has a sizeable revenue generated from Kids Cereals. Further, Milk has the highest amount of units sold. This might indicate a higher proportion of young couples with children around the store.
  • On the contrary, customers in Store 367 seem to spend highly on Wine. This might indicate that customers have a sizeable disposable income.
  • The demographic profiles for the two stores seem different, which we will explore later.
# Product Popularity Treemap for store 367
p1 <-
filtered_data %>% 
  group_by(store_id, sub_commodity_desc) %>% 
  summarise(revenue = sum(sales_value),
            quantity = sum(quantity),
            visits = n_distinct(basket_id)) %>% 
  arrange(desc(revenue)) %>% 
  filter(store_id == 367) %>% 
  top_n(10, wt = revenue) %>% 
  select(product = sub_commodity_desc, everything()) %>% 
  ggplot(aes(area = revenue, label = product, fill=quantity)) +
    geom_treemap() +
    geom_treemap_text(fontface = "italic", 
                      colour = "white", place = "centre", grow = TRUE) +
  ggtitle("Store 367")

# Product Popularity Treemap for store 292
p2 <-
filtered_data %>% 
  group_by(store_id, sub_commodity_desc) %>% 
  summarise(revenue = sum(sales_value),
            quantity = sum(quantity),
            visits = n_distinct(basket_id)) %>% 
  arrange(desc(revenue)) %>% 
  filter(store_id == 292) %>% 
  top_n(10, wt = revenue) %>% 
  select(product = sub_commodity_desc, everything()) %>% 
  ggplot(aes(area = revenue, label = product, fill=quantity)) +
  geom_treemap() +
  geom_treemap_text(fontface = "italic", 
                    colour = "white", place = "centre", grow = TRUE) +
  ggtitle("Store 292")

plot_grid(p1, p2, nrow = 2)

2. Product Price Comparison

The bargraph compares the prices of products amongst the two stores. It is quite evident, that the prices of products in store 367 are higher than that of store 292 i.e. store 367’s customer are willing to pay higher for the same products. This might indicate that they are less influenced by the price of the product.

# Product Price Comparison Visual
filtered_data %>% 
group_by(store_id, sub_commodity_desc) %>% 
summarise(revenue = sum(sales_value),
          quantity = sum(quantity),
          visits = n_distinct(basket_id),
          price = median(product_price)) %>% 
filter(store_id %in% c(367, 292),
       sub_commodity_desc %in% c('BEERALEMALT LIQUORS',
                                 'POPULAR 750ML WINES',
                                 'KIDS CEREAL',
                                 'SOFT DRINKS 12/18&15PK CAN CAR',
                                 'FRZN SS PREMIUM ENTREES/DNRS/N',
                                 'DAIRY CASE 100% PURE JUICE - O',
                                 'TOILET TISSUE',
                                 'SHREDDED CHEESE',
                                 'YOGURT NOT MULTI-PACKS',
                                 'POTATO CHIPS')) %>% 
  select(store_id, sub_commodity_desc, price) %>% 
  ggplot(aes(reorder(sub_commodity_desc, price), price)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  scale_fill_manual(values=c("#999999", "#E69F00")) +
  ylab("Price") +
  xlab(" ") +
  labs(fill="Store") +
  theme_tufte() +
  coord_flip()

3. Product Variety Comparison

Store 367 seems to have a wider variety of Grocery products as compared to it’s competitors.

# Product Variety Comparison barplot
filtered_data %>% 
  group_by(store_id) %>% 
  summarise(products = n_distinct(sub_commodity_desc)) %>% 
  arrange(desc(products)) %>% 
  ggplot(aes(reorder(store_id, -products), products)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  ylab("Products") +
  xlab("Store") +
  theme_tufte() +
  theme(legend.position="none") +
  scale_fill_brewer(palette="Set1")

4. Transactions by day and hour

  • Most of the Grocery shoppers visit Store 367 on weekends. However, for Store 292 Monday seems to be the busiest day. This might indicate that a higher proportion of Grocery Shoppers for Store 367 are office going and they get time for Grocery Shopping only on weekends.
  • It seems that Store 292 is closed between 1-6 am whereas Store 367 is open 24x7.
  • Thursday and Friday seems to have a low footfall for both stores. Probably customers like Grocery Shopping either at the start of the week or at the end.
  • It would be interesting to analyze all the of the customers for both the stores to make more meaningful conclusions.
####### List of All Day and Time combinations ########
df_mapping <- data.frame(day=3:711, day_of_week = c(rep(1:7, 101), c(1,2)))

df <-
  filtered_data %>%
  mutate(time = as.factor(
    substr(stringr::str_pad(
      trans_time, width=4, side="left", pad="0"),1,2))) %>%
  group_by(day, time) %>%
  summarise(transactions = n()) %>%
  inner_join(df_mapping, by="day") %>%
  group_by(day_of_week, time) %>%
  summarise(transactions = sum(transactions)/n())

df$day <- as.factor(df$day_of_week)
levels(df$day) <- c(4,5,6,7,1,2,3)
levels(df$day) <- c("Friday", "Saturday", "Sunday","Monday", "Tuesday", "Wednesday", "Thursday")
df$day <-
  factor(as.factor(df$day), levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))

df_mapping1 <- distinct(select(df, day, time))

####### Heatmap for Store 367 ########

df <- 
  filtered_data %>% 
  filter(store_id ==367) %>% 
  mutate(time = as.factor(
    substr(stringr::str_pad(
      trans_time, width=4, side="left", pad="0"),1,2))) %>% 
  group_by(day, time) %>% 
  summarise(transactions = n()) %>% 
  inner_join(df_mapping, by="day") %>% 
  group_by(day_of_week, time) %>% 
  summarise(transactions = sum(transactions)/n()) 

df$day <- as.factor(df$day_of_week)
levels(df$day) <- c(4,5,6,7,1,2,3)
levels(df$day) <- c("Friday", "Saturday", "Sunday","Monday", "Tuesday", "Wednesday", "Thursday")
df$day <-
  factor(as.factor(df$day), levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))

df <-
  df %>% 
  right_join(df_mapping1, by=c('day', 'time', 'day_of_week')) %>% 
  mutate(transactions = replace_na(transactions,0))

p3 <-
df %>% 
  ggplot(aes(day, time)) + 
  geom_tile(aes(fill = transactions),colour = "white") + 
  scale_fill_gradient(low = "white",high = "red") +
  theme_tufte() +
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank()) +
  labs(fill="Avg Transactions") +
  ggtitle("Store 367") +
  theme(plot.title = element_text(hjust = 0.5))

####### Heatmap for Store 367 ########

df <- 
  filtered_data %>% 
  filter(store_id ==292) %>% 
  mutate(time = as.factor(
    substr(stringr::str_pad(
      trans_time, width=4, side="left", pad="0"),1,2))) %>% 
  group_by(day, time) %>% 
  summarise(transactions = n()) %>% 
  inner_join(df_mapping, by="day") %>% 
  group_by(day_of_week, time) %>% 
  summarise(transactions = sum(transactions)/n()) 

df$day <- as.factor(df$day_of_week)
levels(df$day) <- c(4,5,6,7,1,2,3)
levels(df$day) <- c("Friday", "Saturday", "Sunday","Monday", "Tuesday", "Wednesday", "Thursday")
df$day <-
  factor(as.factor(df$day), levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))

df <-
  df %>% 
  right_join(df_mapping1, by=c('day', 'time', 'day_of_week')) %>% 
  mutate(transactions = replace_na(transactions,0))

p4 <-
df %>% 
  ggplot(aes(day, time)) + 
  geom_tile(aes(fill = transactions),colour = "white") + 
  scale_fill_gradient(low = "white",high = "red") +
  theme_tufte() +
  theme(axis.title.x=element_blank(),
        axis.title.y=element_blank()) +
  labs(fill="Avg Transactions") +
  ggtitle("Store 292") +
  theme(plot.title = element_text(hjust = 0.5))

####

plot_grid(p3, p4, nrow = 2)

5. Transactions by Week

Clearly, the total number of transactions vary week-on-week for both stores. It would be interesting to identify the trends that affect this variation. Some of the reasons might be:

  1. Festivals
  2. Big Promotional Events
  3. Holiday Seasons
  4. Bad Weather (so people would want to stock up their inventory)
  5. End of month (people get their salaries and thus purchase groceries)
# Transactions by Week Line chart
filtered_data %>% 
  mutate(store_id = as.factor(store_id)) %>%
  filter(store_id %in% c(367, 292)) %>% 
  group_by(store_id, week_no) %>% 
  summarise(transactions = n()) %>% 
  ggplot(aes(x=week_no, y=transactions, group=store_id, color=store_id)) +
  geom_point() +
  geom_line() +
  xlab("Week No") +
  ylab("Transactions") +
  labs(color="Store ID") +
  theme_tufte()

6. Demographic Analysis

(A) Marital Distribution

Store 356 has a large proportion of married Grocery Shoppers. For all the other stores, the proportion seems reasonably similar.

# filtering data
df <-
  filtered_data %>% 
    distinct(household_key,
             store_id,
             age_desc,
             marital_status_code,
             income_desc,
             homeowner_desc,
             hh_comp_desc,
             household_size_desc,
             kid_category_desc) %>%
    na.omit()

# reordering levels
df$store_id <- factor(as.factor(df$store_id), levels = c(356,406,292,367,381))
levels(df$marital_status_code) <- c("Married", "Unmarried", "U")

# Age Distribution Visual
df %>% 
  group_by(store_id, marital_status_code) %>% 
  summarise(count = n()) %>% 
  mutate(freq = count/sum(count)) %>% 
  filter(marital_status_code !="U") %>% 
  ggplot(aes(marital_status_code, freq)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  scale_fill_brewer(palette="Set1") +
  ylab("Proportion") +
  xlab("") +
  labs(fill="Store ID") +
  theme_tufte()

(B) Income Distribution

Store 367 has customers with higher income as compared to customers from Store 292. This observations is consistent with our previous observation that customers in store 367 are less price sensitive.

# reordering levels
df$income_desc <- factor(as.factor(df$income_desc), 
                         levels = c("Under 15K","15-24K","25-34K","35-49K","50-74K",
                                    "75-99K","100-124K","125-149K","150-174K","175-199K"
                                    ,"200-249K","250K+"))

# Income Distribution Visual
df %>% 
  group_by(store_id, income_desc) %>% 
  filter(store_id %in% c(292, 367)) %>% 
  summarise(count = n()) %>% 
  mutate(freq = count/sum(count)) %>% 
  ggplot(aes(income_desc, freq)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  scale_fill_brewer(palette="Set1") +
  ylab("Proportion") +
  xlab("") +
  labs(fill="Store ID") +
  theme_tufte()

(C) No. of Children Distribution

Customer from Store 292 have higher number of children as compared to Store 367. This explains why Kids Cereal is a top selling product for Store 292.

df$kid_category_desc <- factor(as.factor(df$kid_category_desc), 
                         levels = c("None/Unknown","1","2","3+"))

levels(df$kid_category_desc) <- c("0","1","2","3+")


df %>% 
  group_by(store_id, kid_category_desc) %>% 
  filter(store_id %in% c(292, 367)) %>%
  summarise(count = n()) %>% 
  mutate(freq = count/sum(count)) %>% 
  ggplot(aes(kid_category_desc, freq)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  scale_fill_brewer(palette="Set1") +
  ylab("Proportion") +
  xlab("") +
  labs(fill="Store ID") +
  theme_tufte()

(D) Age Distribution

Age distribution for both the stores seems reasonably similar.

df %>% 
  group_by(store_id, age_desc) %>% 
  filter(store_id %in% c(292, 367)) %>%
  summarise(count = n()) %>% 
  mutate(freq = count/sum(count)) %>% 
  ggplot(aes(age_desc, freq)) +
  geom_bar(stat = "identity", aes(fill = as.factor(store_id)), position = "dodge") +
  scale_fill_brewer(palette="Set1") +
  ylab("Proportion") +
  xlab("") +
  labs(fill="Store ID") +
  theme_tufte()

Shelf Optimization

1. Products with High Revenue

A retailer would ideally want to stock those products that bring high revenue. In the visual we can see that approximately 250 products bring 90% of the revenue. Hence, I would advise the retail manager of Store 367 to include these 250 products in the Grocery Shelf.

Hover over the visual for interaction

# initializing empty dataframe
df <- data.frame()

# loop to capture proportion of revenue for top n products
for(n in seq(10,600,10))
{
  top_n_revenue <-
    filtered_data %>% 
    group_by(store_id, sub_commodity_desc) %>% 
    summarise(revenue = sum(sales_value),
              quantity = sum(quantity),
              visits = n_distinct(basket_id)) %>% 
    arrange(desc(revenue)) %>% 
    filter(store_id == 367) %>% 
    top_n(n, wt = revenue) %>% 
    select(product = sub_commodity_desc, everything()) %>% 
    summarise(tot_revenue = sum(revenue)) %>% .$tot_revenue
  
  total_revenue <- sum(filter(filtered_data, store_id == 367) %>% .$sales_value)
  
  # print(top_n_revenue/total_revenue)
  
  df[n/10, 1] = n
  df[n/10, 2] = top_n_revenue/total_revenue
}

# renameing dataframe
names(df) <- c("top_n_products", "percentage_revenue")
df$percentage_revenue <- round(df$percentage_revenue, 2)

# plotting elbow plot
p1 <-
ggplot(df, aes(x=top_n_products, y=percentage_revenue)) +
  geom_point(color = "red", size=1) +
  geom_line() +
  scale_x_continuous(breaks = round(seq(0, 
                                        max(df$top_n_products), by = 50),1)) +
  scale_y_continuous(breaks = round(seq(0, 1, by = 0.1),1), limits = c(0,1),
                     labels = scales::percent) +
  theme_tufte() +
  xlab("Total Products") +
  ylab("% Revenue")

# converting to interactive visual
ggplotly(p1)

List of 250 Products Selected:

df_top_250 <-
  filtered_data %>% 
  group_by(store_id, sub_commodity_desc) %>% 
  summarise(revenue = sum(sales_value),
            quantity = sum(quantity),
            visits = n_distinct(basket_id)) %>% 
  arrange(desc(revenue)) %>% 
  filter(store_id == 367) %>% 
  top_n(250, wt = revenue) %>% 
  ungroup() %>% 
  select(Products = sub_commodity_desc)

kable(df_top_250) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "50%", height = "500px")
Products
BEERALEMALT LIQUORS
POPULAR 750ML WINES
PREMIUM 750ML WINES
FLUID MILK WHITE ONLY
SOFT DRINKS 12/18&15PK CAN CAR
FRZN SS PREMIUM ENTREES/DNRS/N
DAIRY CASE 100% PURE JUICE - O
TOILET TISSUE
SHREDDED CHEESE
YOGURT NOT MULTI-PACKS
ULTRA PREMIUM WINES
ADULT CEREAL
ALL FAMILY CEREAL
AUSTRALIAN/NZ WINES
RTS SOUP: CHUNKY/HOMESTYLE ET
SPRING WATER
PIZZA/PREMIUM
ITALIAN WINES
TORTILLA/NACHO CHIPS
PAPER TOWELS & HOLDERS
LIQUID LAUNDRY DETERGENTS
PREMIUM
SOFT DRINKS 20PK&24PK CAN CARB
POURABLE SALAD DRESSINGS
SFT DRNK MLT-PK BTL CARB (EXCP
SPICES & SEASONINGS
FRZN BAGGED VEGETABLES - PLAIN
KIDS CEREAL
NATURAL CHEESE EXACT WT CHUNKS
IWS SINGLE CHEESE
TUNA
BUTTER
PREMIUM BREAD
NON-CARB FLVRD DRNKNG/MNRL WAT
FACIAL TISSUE & PAPER HANDKE
CEREAL BARS
POTATO CHIPS
SNACKS/APPETIZERS
SNACK CRACKERS
VAC PAC NUTS(CANS)
SUPER PREMIUM PINTS
FRZN MULTI SERVE ENTREES ALL
SUPER PREMIUM WINES
CONDENSED SOUP
FIGHTING VARIETAL WINES
YOGURT MULTI-PACKS
NON-CRBNTD DRNKING/MNERAL WATE
GARBAGE KITCHEN COMPACTOR BA
SFT DRNK 2 LITER BTL CARB INCL
MAINSTREAM WHITE BREAD
FRZN SS PREMIUM ENTREES/DNRS/T
OLIVE OIL
MACARONI & CHEESE DNRS
SCOOP LITTER
SS ECONOMY ENTREES/DINNERS ALL
MEXICAN SAUCESSALSAPICANTEE
GROUND COFFEE
GROUND FLVR REG COFFEE
DAIRY CASE JUICE DRNK UNDER 10
POPULAR 1.5LT WINES
MAINSTREAM
PIZZA/TRADITIONAL
MAINSTREAM WHEAT/MULTIGRAIN BR
CREAM CHEESE
NATURAL CHEESE EXACT WT SLICES
EGGS - LARGE
GRANOLA BARS
CAN CATFD GOURMET/SUP PREM (GR
BAGELS
ENGLISH MUFFINS/WAFFLES
FRENCH WINES
CARBONATED WATER - FLVRD UNSWE
STRING CHEESE
ENERGY DRINK
PEANUT BUTTER
SEMI-SOLID SALAD DRESSING MAY
PRETZELS
INSTANT BREAKFAST
CHEESE CRACKERS (CHEEZ-ITS/GOL
FRUIT BOWL AND CUPS
SOFT DRINKS 6PK/4PK CAN CARB (
DIPS (NON-REFRIGERATED)
SANDWICHES&HANDHELDS
SUGAR
POWDER LAUNDRY DETERGENT
MISCELLANEOUS CHEESE
RICE SIDE DISH MIXES DRY
SNACK CAKE - MULTI PACK
ALL OTHER IMPORTED WINES
COTTAGE CHEESE
PREMIUM COOKIES (EX: PEPPERIDG
SPARKLING WINES
MARGARINE: TUBS AND BOWLS
DRY CAT FOOD SUP PREM (ONE/NUT
MIXERS(CLUB SODA/SELTZERS)FLAV
FRZN BOXED VEGETABLES- VALUE A
AIR CARE - CONTINUOUS ACTION
BUTTER SPRAY CRACKER (RITZ/CLU
TOOLS - FLOOR & FURNITURE
ISOTONIC DRINKS SINGLE SERVE
DAIRY CASE CITRUS PNCH/OJ SUBS
EGGS - X-LARGE
NOODLES DRY
LIQUID DISH DETERGENT
FRZN BAGGED VEGETABLES- VALUE
CANISTER POTATO/TORT CHIPS
CHILI: CANNED
INSTANT OATMEAL
SOUR CREAMS
TRAY PACK/CHOC CHIP COOKIES
BAKING NUTS
DRY CAT FOOD (CAT CHOW/FRISKIE
ISOTONIC DRINKS MULTI-SERVE
INSTANT COFFEE FLAVORED NO SWE
DRY DOG FOOD SUPER PREM (ONE/N
REFRIGERATED PUDDING
PRESERVES JAM MARMALADE
SANDWICH COOKIES
STICKS/ENROBED
AUTHENTIC DRY BEVERAGES W/O SW
SOFT DRINK POWDER POUCHES
PICKLES
WAFFLES/PANCAKES/FRENCH TOAST
AUTO DISH DETERGENT - GEL
BBQ SAUCE
MISCELLANEOUS MILK
VEGETABLE/SALAD OIL
SFT DRNK SNGL SRV BTL CARB (EX
REFRIGERATED COFFEE CREAMERS
FABRIC SOFTENER LIQUID
CORN CHIPS
PIZZA/SINGLE SERVE/MICROWAVE
TEA SWEETENED
BEANS GREEN: FS/WHL/CUT
TOASTER PASTRIES
SPECIALTY CRACKERS
SPECIALTY COOKIES
SWEET GOODS - FULL SIZE
BROTH
MACARONI DRY
SPAGHETTI DRY
BITS & MORSELS
SOUTH AMERICAN WINES
SKILLET DINNERS
RICE - INSTANT & MICROWAVE
PREPARED BEANS - BAKED W/PORK
REFRIGERATED PASTA
RYE BREADS
RICE - DRY BAG AND BOX
FRZN GARLIC BREAD/TOAST/STICKS
FABRIC SOFTENER SHEETS
FROZEN FRUIT
CANNED CAT FOOD (9 LIVES/FRISK
FLAVORED BEAN COFFEE
VANILLA WAFER/KIDS COOKIES
PINEAPPLE
MISC MEAT SAUCES
HOT DOG BUNS
LOAF CHEESE
MOLASSES & SYRUPS
SGL SV/VEND MACH SNACKS CHIP/P
AUTHENTIC THAI FOODS
FRUIT SNACKS
TRADITIONAL
TOMATOES: STEWED/DICED/CRMD
ALUMINUM FOIL
FRZN FRENCH FRIES
CATSUP
GRAPE JUICE (OVER 50% JUICE)
GREEN OLIVES
HAMBURGER BUNS
FREEZER BAGS
MULTI-PACK CRACKERS
FRZN BREADED PREPARED CHICK
REFRIGERATED SPECILATY ROLLS
PLASTIC TRASH BAGS
LIQUID BLEACH
ADULT PREMIUM
REFRIGERATED MEXICAN PRODUCTS
MISC SALES TRANS
MAIN MEAL BREAD
TEA BAGS HERBAL & FLAVORED
PAPER NAPKINS
SOUP CRACKERS (SALTINE/OYSTER)
VALUE GLASS WINE
PASTA: CANNED
PLASTIC/HEAVY PAPER PREM PLATE
RAMEN NOODLES/RAMEN CUPS
PLSTC CTLRYTBLCLTHSTTHPKSST
SANDWICH BAGS
PLASTIC CUPS
SANDWICH BUNS
MEXICAN SOFT TORTILLAS AND WRA
SOFT DRINK CANISTERS
VARIETY BEANS - KIDNEY PINTO
CAT TREATS
VEG JUICE (EXCEPT TOMATO) (OVE
SPECIALTY LAUNDRY
HOT SAUCE
MISC BAG SNACKS
FROZEN PASTA
PIZZA/ECONOMY
CORN
FROZEN ENTREES
REFRIGERATED COOKIES-CHUB
PREMIUM PINTS
ASEPTIC PACK JUICE AND DRINKS
GRATED CHEESE
FRZN MEAT ALTERNATIVES
ITALIAN FOODS
CRANAPPLE CRAN GRAPE JUICE (5
EGGS SUBSTITUTE
NON-IWS SINGLE CHEESE
CLEANING WIPES
MICROWAVABLE SOUPS
COOKING OIL: PEANUT SAFFLOWER
ICE - CRUSHED/CUBED
POTATOES: DRY
PRUNE JUICE (OVER 50% JUICE)
CANNED MILK
SOFT DRINKS CAN NON-CARB (EXCE
NON DAIRY CREAMER: DRY
AIR CARE - AEROSOLS
MIXERS(TONIC WTR/CLUB SODA/GNG
FRZN BOXED VEGETABLES - PLAIN
COOKING SPRAYS
FOOD STORAGE BAGS
SWEETENERS
DOG & CAT TOYS
ALTERNATIVE LITTER
APPLE JUICE & CIDER (OVER 50%
HOT MUSTARD/SPECIALTY MUSTAR
WATER ICE
PURE EXTRACTS
JUICE (UNDER 10% JUICE)
STEAK & WORCHESTER SAUCE
DOG TREATS (SOFT TREATS)
FROZEN FRUIT PIES & COBBLERS
ICE CREAM SANDWICHES
BOUILLON
FRZN PIE SHELLS PASTRY SHELL
HONEY
DAIRY CASE 100% PURE JUICE OTH
MISC REFRIG DOUGH PRODUCTS
CONVENTIONAL LITTER
FLOUR: WHITE & SELF RISING
EGGS - JUMBO
REGULAR BEAN
CENTRAL AMERICAN FOODS
CONES

2. Cross-Selling Products

We have already selected a set of 250 products that bring 90% of the revenue. However, there might be products that don’t bring high revenue, but are frequently bought with these 250 products.

We would use the Apriori Algorithm to identify these cross-selling products. To implement the Apriori Algorithm, we first need to summarise our data at the Basket Level where each row represents a basket and each column represents whether the respective product was bought (1) or not (0).

The top 100 rows are as follows:

# converting to sparse format
df <-
  filtered_data %>% 
  filter(store_id == 367) %>% 
  select(basket_id, sub_commodity_desc) %>% 
  mutate(transaction = 1) %>% 
  unique() %>% 
  spread(sub_commodity_desc, transaction) %>% 
  replace(is.na(.), 0)

kable(head(df, 100)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "100%", height = "500px")
basket_id ABRASIVES ADULT CEREAL ADULT PREMIUM AEROSOL TOPPINGS AIR CARE - AEROSOLS AIR CARE - CANDLES AIR CARE - CONTINUOUS - NON EL AIR CARE - CONTINUOUS ACTION AIR CARE - DISINFECTANT SPRAYS AIR CARE - RUG AIR CARE - SANITIZERS ALL FABRIC DRY BLEACH ALL FABRIC LIQUID BLEACH ALL FAMILY CEREAL ALL OTHER IMPORTED WINES ALTERNATIVE LITTER ALUMINUM FOIL AMMONIA APPLE BUTTER APPLE JUICE & CIDER (50% AND U APPLE JUICE & CIDER (OVER 50% APPLE SAUCE APPLES CRABAPPLES APRICOTS ASEPTIC MILK & MILK DRINKS ASEPTIC PACK JUICE AND DRINKS ASPARAGUS AUSTRALIAN/NZ WINES AUTHENTIC ASIAN FOODS AUTHENTIC DRY BEVERAGES W/O SW AUTHENTIC DRY BEVERAGES W/SWEE AUTHENTIC INDIAN FOODS AUTHENTIC JAPANESE FOODS AUTHENTIC PASTA RICE BEANS AUTHENTIC PEPPERS AUTHENTIC SAUCES SALSA PICAN AUTHENTIC SOUPS BOUILLONS AUTHENTIC THAI FOODS AUTHENTIC VEGETABLES AND FOODS AUTO DISH DETERGENT - GEL AUTO DISH DETERGENT - MONODOSE AUTOMATIC DISHWASHER DETERGENT BAGELS BAGGED CHEESE SNACKS BAGGED POPPED POPCORN BAKING COCOA BAKING CUPS BAKING NUTS BAKING POWDER & SODA BASIC PAPER PLATES BATHROOM CLEANERS - GENERAL BATHROOM CLEANERS - SPECIALTY BBQ SAUCE BEANS GREEN: FS/WHL/CUT BEANS WAX SHELLIES BEEF STEW BEEF/PORK - DRIED SLICED W/GRA BEERALE -NON ALCOHOLIC BEERALEMALT LIQUORS BEETS BERRIES CND (BLUBLKRASP) BEVERAGE WINES BISCUIT FLOUR & MIXES BITS & MORSELS BLENDED JUICE&COMBINATIONS (50 BLENDED JUICE&COMBINATIONS (OV BOUILLON BOX WINES BOXED PREPARED/ENTREE/DRY PREP BREAD:TORTILLAS/PITA BREADINGS COATINGS CRUMBS BREAKFAST CRYSTALS BREAKFAST FOODS BRITISH FOODS BROTH BROWNIE MIX BURRITOS BUTTER BUTTER SPRAY CRACKER (RITZ/CLU BUTTERMILK CAKE DECORS - BIRTHDAY CANDLES CAKE DECORS & ICING CAN CATFD GOURMET/SUP PREM (GR CAN DOG FOOD (SKIPPY/PEDIGREE/ CAN DOG FOOD RATION (TRIX/VETS CAN DOGFD GOURMET/SUPER PREM ( CANDY BAGS-CHOCOCLATE CANISTER POTATO/TORT CHIPS CANNED CAT FOOD (9 LIVES/FRISK CANNED MILK CANOLA OILS CARAMEL COATED SNACKS CARBONATED WATER - FLVRD SWEET CARBONATED WATER - FLVRD UNSWE CARBONATED WATER - UNFLAVORED CARROTS CAT TREATS CATSUP CENTRAL AMERICAN FOODS CEREAL BARS CHEESE CRACKERS (CHEEZ-ITS/GOL CHEESE:CHEESEBALLS/SPREADS CHEESE:PROCESSED PREPORTND CHEESE:SPECIALTY PREPACK CHERRIES (EXCEPT MARASCHINO) CHILI SAUCE COCKTAIL SAUCE CHILI: CANNED CHOCOLATE COVERED COOKIES CHOCOLATE MILK CHUNK MEATS - ALL CLEANING WIPES CLEANSING WIPES CLOSET ACCESSORIES COCKTAIL MIXES-DRY COCKTAIL MIXES-FLUID:ADD LIQ COCKTAIL MIXES-FRZ COCONUT COLLARS AND LEADS CONDENSED SOUP CONES CONVENTIONAL LITTER COOKING BAGS COOKING BAGS WITH SPICES/SEASO COOKING CHOCOLATE (EX SMI-SWT) COOKING OIL: PEANUT SAFFLOWER COOKING SPRAYS COOKING WINES CORN CORN BEEF CORN CHIPS CORN OIL CORN STARCH CORNMEAL COTTAGE CHEESE CRANAPPLE CRAN GRAPE JUICE (5 CRANAPPLE CRAN GRAPE JUICE (O CRANBERRY JUICE (50% AND UNDER CRANBERRY JUICE (OVER 50% JCE) CRANBERRY SAUCE CREAM CHEESE CROUTONS SALAD TOPPERS BREAD CUPS/PUSH UPS/OTHER DAIRY CASE 100% PURE JUICE - O DAIRY CASE 100% PURE JUICE OTH DAIRY CASE CITRUS PNCH/OJ SUBS DAIRY CASE FRUIT DRINKS (NO JU DAIRY CASE JUICE DRNK UNDER 10 DAIRY CASE TEA WITH SUGAR OR S DECAF BEAN: REGULAR DESIGNER PAPER: MEDIUM WEIGHT DESSERTS & VERMOUTH DIET/LIGHT BREAD DINNER ROLLS DIPS (NON-REFRIGERATED) DISTILLED WATER DL SPEC: DRY/REFRIG PASTAS DOG & CAT ACCESSORIES DOG & CAT CHEMICALS DOG & CAT TOYS DOG BISCUITS (HARD BISCUIT) DOG TREATS (SOFT TREATS) DRAIN CARE DRIED FRUIT - OTHER DRY & SPRAY STARCH DRY BEANS/PEAS/BARLEY: BAG & B DRY CAT FOOD (CAT CHOW/FRISKIE DRY CAT FOOD ECONOMY (COST CUT DRY CAT FOOD SUP PREM (ONE/NUT DRY DOG FOOD PREMIUM (ALPO/PUR DRY DOG FOOD SUPER PREM (ONE/N DRY ROAST NUTS DRY SOUP EGG NOG/BOILED CUSTARD EGGS - JUMBO EGGS - LARGE EGGS - MEDIUM EGGS - X-LARGE EGGS SUBSTITUTE ENERGY DRINK ENGLISH MUFFINS/WAFFLES FABRIC REFRESHERS/DRY CLEAN FABRIC SOFTENER LIQUID FABRIC SOFTENER SHEETS FACIAL TISSUE & PAPER HANDKE FIGHTING VARIETAL WINES FITNESS&DIET - BARS FLAVORED BEAN COFFEE FLEA/TICK COLLAR FLOOR - CARPET & UPHOLSTERY FLOOR - DILUTABLES FLOOR - HARD SURFACE FLOUR: MISC SPECIALTY BLEND FLOUR: WHITE & SELF RISING FLUID MILK WHITE ONLY FOAM PLATES FOOD COLORINGS FOOD STORAGE BAGS FORTIFIED/ENERGY WATER FREEZE MIXES PWDRS/LIQUIDS FREEZER BAGS FRENCH WINES FROSTING FROZEN CAKES/ALL TYPES INCLUDI FROZEN CREAM PIES FROZEN ENTREES FROZEN FRUIT FROZEN FRUIT PIES & COBBLERS FROZEN PASTA FRUIT BOWL AND CUPS FRUIT COCKTAIL FRUIT SALAD FRUIT DRINKS: CANNED & GLASS ( FRUIT FILLED COOKIES FRUIT SNACKS FRUIT VARIETAL WINES FRUIT/BREAKFAST BREAD FRZN BAGELS FRZN BAGGED VEGETABLES- VALUE FRZN BAGGED VEGETABLES - PLAIN FRZN BAKED/STUFFED/MASHED&SPEC FRZN BISCUITS FRZN BOXED VEGETABLES- VALUE A FRZN BOXED VEGETABLES - PLAIN FRZN BREAD DOUGH FRZN BREADED PREPARED CHICK FRZN BREADED VEGETABLES FRZN BREAKFAST ENTREES/SANDWIC FRZN BREAKFAST PASTRY FRZN BURGERS/BBQ/MEATBALL FRZN CONC ALLIEDS OVER 50% JUI FRZN CONC UNDER 50% JUICE FRZN CORN ON THE COB FRZN DINNER ROLLS FRZN FRENCH FRIES FRZN GARLIC BREAD/TOAST/STICKS FRZN HASHBROWN POTATOES FRZN MEAT ALTERNATIVES FRZN MULTI SERVE ENTREES ALL FRZN OJ&OJ SUBSTITUTES (OVER 5 FRZN PIE SHELLS PASTRY SHELL FRZN REGIONAL/OTHER FRZN SS PREMIUM ENTREES/DNRS/N FRZN SS PREMIUM ENTREES/DNRS/T FRZN TATER TOTS/OTHER EXTRUDED FRZN WHIPPED TOPPING FURNITURE GARBAGE KITCHEN COMPACTOR BA GARLIC JAR GELATIN GERMAN FOODS GLASS CLEANERS GRAHAM CRACKERS GRANOLA BARS GRAPE JUICE (50% AND UNDER JCE GRAPE JUICE (OVER 50% JUICE) GRAPEFRUIT JUICE (50% AND UNDE GRAPEFRUIT JUICE (OVER 50% JUI GRATED CHEESE GRAVY CAN/GLASS GREEN OLIVES GRITS GROUND COFFEE GROUND DECAFFINATED GROUND FLVR DECAF COFFEE GROUND FLVR REG COFFEE HAMBURGER BUNS HASH: CANNED HISPANIC CARBONATED BEVERAGES HISPANIC COOKIES/CRACKERS HISPANIC JUICE UNDER 50% JUICE HISPANIC SPICES AND SEASONINGS HOMINY HONEY HOT CHOCOLATE COCOA MIX HOT DOG BUNS HOT DOG CHILI SAUCE HOT MUSTARD/SPECIALTY MUSTAR HOT SAUCE ICE - CRUSHED/CUBED ICE CREAM SANDWICHES ICE CREAM TOPPINGS IMITATION EXTRACTS INSTANT BREAKFAST INSTANT COFFEE FLAVORED NO SWE INSTANT COFFEE FLAVORED W/SWEE INSTANT COFFEE REGULAR INSTANT DECAFFINATED INSTANT OATMEAL INSTANT TEA & TEA MIX INSTANT TEA & TEA MIX (W/SUGAR ISOTONIC DRINKS MULTI-SERVE ISOTONIC DRINKS POWDERED ISOTONIC DRINKS SINGLE SERVE ITALIAN FOODS ITALIAN WINES IWS SINGLE CHEESE JELLY JUICE (100% JUICE) JUICE (OVER 50% JUICE) JUICE (UNDER 10% JUICE) JUICE (UNDER 50% JUICE) KIDS CEREAL KITCHEN & BATHROOM DISPENSER C KITCHEN & MULTI-PURPOSE CLEANE KOSHER FOODS KRAUT & CABBAGE LAWN & LEAF BAGS LAYER CAKE MIX LEMON JUICE & LIME JUICE (OVER LIMA BEANS LIQUID BLEACH LIQUID DISH DETERGENT LIQUID LAUNDRY DETERGENTS LITTER ACCESSORIES LOAF CHEESE LUNCHEON MEAT MACARONI & CHEESE DNRS MACARONI DRY MACKEREL MAIN MEAL BREAD MAINSTREAM MAINSTREAM WHEAT/MULTIGRAIN BR MAINSTREAM WHITE BREAD MALTED MLK/SYRUP/PWDRS (EGGNOG MANDARIN ORANGES/CITRUS SECT MARASCHINO CHERRIES MARGARINE STICK MARGARINE: SQUEEZE MARGARINE: TUBS AND BOWLS MARSHMALLOW CREME MARSHMALLOWS MEAT INCLUDED MEAT SNACKS MEDITERRANEAN/GREEK FOODS METAL POLISH&RUST REMOVERS MEXICAN BEANS REFRIED MEXICAN DINNERS AND FOODS MEXICAN PEPPERS CHILIES MEXICAN SAUCESSALSAPICANTEE MEXICAN SEASONING MIXES MEXICAN SOFT TORTILLAS AND WRA MEXICAN TACO TOSTADO SHELLS MICROWAVABLE CUPS MICROWAVABLE SOUPS MICROWAVE MISC MISC BAG SNACKS MISC CHEMICALS MISC CND MEATS MISC DAIRY REFIGERATED MISC GRAIN MIXES MISC MEAT SAUCES MISC REFRIG DOUGH PRODUCTS MISC SALES TRANS MISC SNACKS MISC. CEREAL MISC. CND FRUIT (GRAPESFIGSE MISC. PET SUPPLIES MISC.CND SEAFOODS (CRABETC) MISC: CHEESECAKE MOUSSE MIXES MISC: HERRING PICKLES HORSER MISCELLANEOUS CANDY (INCLUDING MISCELLANEOUS CHEESE MISCELLANEOUS MILK MISCELLANEOUS PACKAGE MIXES MISCELLANEOUS VEGETABLES MIXED VEGETABLES MIXERS(CLUB SODA/SELTZERS)FLAV MIXERS(TONIC WTR/CLUB SODA/GNG MIXERS(TONIC WTR/GNGR ALE)FLAV MOLASSES & SYRUPS MUFFIN & CORN BREAD MIX MULT PK BAG SNACKS MULTI-PACK COOKIES MULTI-PACK CRACKERS MUSHROOMS CND & GLASS NATURAL CHEESE EXACT WT CHUNKS NATURAL CHEESE EXACT WT SLICES NON-ALCOHLIC WINES NON-CARB FLVRD DRNKNG/MNRL WAT NON-CARB FLVRD UNSWEETENED WAT NON-CARB JCE (UNDER 50%JCE) NON-CRBNTD DRNKING/MNERAL WATE NON-IWS SINGLE CHEESE NON DAIRY CREAMER: DRY NON FAT DRY MILK NOODLE SIDE DISH MIXES NOODLES DRY NUTS OTHER OILS/VINEGAR OLIVE OIL ORANGE JUICE (OVER 50% JUICE) ORIENTAL FOODS AND MEALS ORIENTAL NOODLES RICE ORIENTAL OTHER SAUCES MARINAD ORIENTAL SOY SAUCE ORIENTAL VEGETABLES OTHER ASIAN FOODS OTHER CITRUS JUICES (50% AND U OTHER CITRUS JUICES (OVER 50% OTHER ETHNIC FOODS OTHER HOT CEREAL OTHER ITALIAN SAUCE OVEN OVEN CLEANERS PACKAGE DINNERS: OTHER PAILS PANCAKE MIXES PAPER AND FOAM DRINKING CUPS PAPER LUNCH BAGS PAPER NAPKINS PAPER TOWELS & HOLDERS PASTA/RAMEN PASTA: CANNED PEACHES PEANUT BUTTER PEARS PEAS & ONIONS PEAS & CARROT PEAS FRESH PACK CROWDER PEAS GREEN PET TREATS PICKLD VEG PEPPERS ETC PICKLES PIE CRUST MIXES & SHELLS PIE FILLING MINCEMEAT GLAZES PIMENTOS PINEAPPLE PINEAPPLE JUICE (OVER 50% JUIC PITA/TORTILLAS PIZZA MIX DRY PIZZA SAUCE PIZZA/ECONOMY PIZZA/PREMIUM PIZZA/SINGLE SERVE/MICROWAVE PIZZA/TRADITIONAL PLASTIC CUPS PLASTIC TRASH BAGS PLASTIC WRAP (SARANETC.) PLASTIC/HEAVY PAPER PREM PLATE PLSTC CTLRYTBLCLTHSTTHPKSST PLUMS POPULAR 1.5LT WINES POPULAR 750ML WINES PORK-FULLY COOKED POTATO CHIPS POTATOES SWEET POTATOES WHITE POTATOES: DRY POTTED MEATS AND SPREADS POULTRY - STEW W/DUMPLINGS/ A POURABLE SALAD DRESSINGS POWDER LAUNDRY DETERGENT PREMIUM PREMIUM 1.5LT WINES PREMIUM 750ML WINES PREMIUM BREAD PREMIUM COOKIES (EX: PEPPERIDG PREMIUM PINTS PREPARED BEANS - BAKED W/PORK PRESERVES JAM MARMALADE PRETZELS PROCESSED OTHER PRUNE JUICE (OVER 50% JUICE) PUDDING&GELATIN CUPS/CANS PUDDINGS DRY PURE EXTRACTS QUARTS RAMEN NOODLES/RAMEN CUPS RAWHIDE CHEWS REFRIG DIPS REFRIG PIZZA&INGRED N/MEAT DE REFRIGERATED BAGELS REFRIGERATED BISCUITS REGULAR REFRIGERATED COFFEE CREAMERS REFRIGERATED COOKIES-BREAK N B REFRIGERATED COOKIES-CHUB REFRIGERATED COOKIES-SEASONAL REFRIGERATED ENGLISH MUFFINS REFRIGERATED MEXICAN PRODUCTS REFRIGERATED PASTA REFRIGERATED PASTA SAUCE REFRIGERATED PICKLES REFRIGERATED PUDDING REFRIGERATED SPECILATY ROLLS REGULAR BEAN RELISHES RICE - DRY BAG AND BOX RICE - INSTANT & MICROWAVE RICE SIDE DISH MIXES DRY RICOTTA CHEESE RINSE AIDS AND ACCESSORIES RIPE OLIVES ROLLS: DINNER RTS SOUP: CHUNKY/HOMESTYLE ET RYE BREADS SALAD DRESSING MIXES SALAD MUSTARD SALADS CND (BEAN POTATO) SALMON SALT SUBSTITUTES SANDWICH BAGS SANDWICH BUNS SANDWICH COOKIES SANDWICH SAUCE SANDWICH SAUCE HORSERADISH SA SANDWICHES&HANDHELDS SARDINES SAUCE MIXES GRAVY MIXES DRY SCOOP LITTER SEAFOOD-FRE-NON RW-ALL SEAFOOD-MISC-SAUCES/MARIN-DRY SEAFOOD-MISC-SAUCES/MARIN-WET SEAFOOD-MISC-SPICES SEMI-SOLID SALAD DRESSING MAY SEMI MOIST DOG FOOD SFT DRNK 2 LITER BTL CARB INCL SFT DRNK MLT-PK BTL CARB (EXCP SFT DRNK SNGL SRV BTL CARB (EX SGL SV/VEND MACH SNACKS CHIP/P SHREDDED CHEESE SKILLET DINNERS SNACK CAKE - MULTI PACK SNACK CAKE - SINGLE SERVE SNACK CRACKERS SNACK MIX SNACK NUT(BAG) SNACKS/APPETIZERS SNACKS: CRACKERS/COOKIES SOFT DRINK BOTTLE NON-CARB (EX SOFT DRINK CANISTERS SOFT DRINK POWDER POUCHES SOFT DRINKS 12/18&15PK CAN CAR SOFT DRINKS 20PK&24PK CAN CARB SOFT DRINKS 6PK/4PK CAN CARB ( SOFT DRINKS CAN NON-CARB (EXCE SOFT DRINKS SINGLE CANS CARB ( SOIL & STAIN REMOVER SOLID SHORTENING SOUP CRACKERS (SALTINE/OYSTER) SOUR CREAMS SOUTH AMERICAN WINES SPAGHETTI DRY SPARKLING WINES SPECIALTY CAKE SPECIALTY COOKIES SPECIALTY CRACKERS SPECIALTY LAUNDRY SPECIALTY WILD BIRD/SUET SPICES & SEASONINGS SPINACH & GREENS SPRING WATER SS ECONOMY ENTREES/DINNERS ALL STANDARD OATMEAL STEAK & WORCHESTER SAUCE STICKS/ENROBED STRING CHEESE STUFFING MIXES SUGAR SUPER PREMIUM PINTS SUPER PREMIUM WINES SV BEV: BEV/JUIC 10-50% JCE SV BEV: BEV/JUICE 50-100% JUIC SW GDS:DONUTS SWEET GOODS - FULL SIZE SWEETENERS TABLE SALT POPCORN SALTICE C TEA BAGS & BULK TEA TEA BAGS & BULK TEA (SUPPLEMEN TEA BAGS HERBAL & FLAVORED TEA CAN WITH SWEETENER/SUGAR TEA SWEETENED TEA UNSWEETENED TOASTER PASTRIES TOILET BOWL AUTOMATIC TOILET BOWL MANUAL TOILET TISSUE TOMATO JUICE (OVER 50% JCE) TOMATO PASTE TOMATO PUREE & ASPIC TOMATO SAUCE TOMATOES WHOLE TOMATOES: STEWED/DICED/CRMD TOOLS - BATHROOM & TOILET TOOLS - FLOOR & FURNITURE TOPPING MIXES WHIP TOPPING TORTILLA/NACHO CHIPS TRADITIONAL TRAY PACK/CHOC CHIP COOKIES TREATS TUNA ULTRA PREMIUM WINES VAC PAC NUTS(CANS) VALUE VALUE GLASS WINE VANILLA WAFER/KIDS COOKIES VARIETY BEANS - KIDNEY PINTO VEG JUICE (EXCEPT TOMATO) (50% VEG JUICE (EXCEPT TOMATO) (OVE VEGETABLE/SALAD OIL VENDING SIZE/SNGL SERVE CRACKE VENDOR SIZE/SINGLE SERVE COOKI VIENNA SAUSAGE VINEGAR ALL-EXCEPT WINE/RIC VINEGAR WINE/RICE WAFFLES/PANCAKES/FRENCH TOAST WATER ICE WAX PAPER & FREEZER PAPER WEATHERIZATION YEAST: DRY YOGURT MULTI-PACKS YOGURT NOT MULTI-PACKS
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Running Apriori Algorithm to determine cross-selling products

  • Support: The support of an itemset X is defined as the proportion of transactions in the data set which contain the itemset.

  • Confidence: The confidence of a rule is defined as conf(X->Y)=supp(XUY)/supp(X). For example, the rule {milk, bread} -> {butter} has a confidence of 0.5, which means that for 50% of the transactions containing milk and bread the rule is correct. Confidence can be interpreted as an estimate of the conditional probability P(Y |X), the probability of finding the RHS of the rule in transactions under the condition that these transactions also contain the LHS. Association rules are required to satisfy both a minimum support and a minimum confidence constraint at the same time.

  • Lift: Lift is a popular measure of to filter or rank found rules. The lift of a rule is defined as lift(X->Y)=supp(XUY)/(supp(X)*supp(Y)). Lift can be interpreted as the deviation of the support of the whole rule from the support expected under independence given the supports of the LHS and the RHS. Greater lift values indicate stronger associations.

The Apriori Algorithm identifies a set of 14,671 association rules.

# converting to sparse format
df <-
  filtered_data %>% 
  filter(store_id == 367) %>% 
  select(basket_id, sub_commodity_desc) %>% 
  mutate(transaction = 1) %>% 
  unique() %>% 
  spread(sub_commodity_desc, transaction) %>% 
  replace(is.na(.), 0) %>% 
  select(-basket_id)

# converting to arules format
transactions <- as(as.matrix(df), "transactions")

# Run the apriori algorithm
basket_rules <- 
  apriori(transactions,parameter = list(sup = 0.002, conf = 0.5, target="rules"))
## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.5    0.1    1 none FALSE            TRUE       5   0.002      1
##  maxlen target   ext
##      10  rules FALSE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 11 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[610 item(s), 5712 transaction(s)] done [0.00s].
## sorting and recoding items ... [422 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 7 8 done [0.01s].
## writing ... [14671 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

I have kept only those association rules that contain the popular products either on the lhs or the rhs because we want to see the association between the popular and the non-popular products only. Using this filtering condition, we are left with 60 association rules.

# top 250 products by revenue
df <-
  filtered_data %>% 
  group_by(store_id, sub_commodity_desc) %>% 
  summarise(revenue = sum(sales_value),
            quantity = sum(quantity),
            visits = n_distinct(basket_id)) %>% 
  arrange(desc(revenue)) %>% 
  filter(store_id == 367) %>% 
  top_n(250, wt = revenue) %>% 
  ungroup() %>% 
  select(Products = sub_commodity_desc)

# association rules that contain the popular products either on the lhs or the rhs
cross_selling_rules <- subset(basket_rules, subset = (lhs %in% df$Products & (!(rhs %in% df$Products))) |
                          (rhs %in% df$Products & (!(lhs %in% df$Products))) &lift>1)

# converting association rules into dataframe
ruledf = data.frame(
  lhs = labels(lhs(cross_selling_rules)),
  rhs = labels(rhs(cross_selling_rules)), 
  cross_selling_rules@quality)

# printing dataframe
kable(ruledf) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "100%", height = "500px")
lhs rhs support confidence lift count
2 {TEA UNSWEETENED} {YOGURT NOT MULTI-PACKS} 0.0021008 0.6000000 5.848464 12
3 {MEXICAN PEPPERS CHILIES} {SHREDDED CHEESE} 0.0024510 0.7777778 7.529943 14
4 {JUICE (100% JUICE)} {YOGURT NOT MULTI-PACKS} 0.0021008 0.5454545 5.316786 12
5 {BLENDED JUICE&COMBINATIONS (OV} {FLUID MILK WHITE ONLY} 0.0024510 0.6666667 2.518518 14
6 {CARAMEL COATED SNACKS} {MAINSTREAM WHITE BREAD} 0.0028011 0.5161290 7.261402 16
7 {CARAMEL COATED SNACKS} {SOFT DRINKS 12/18&15PK CAN CAR} 0.0036765 0.6774194 7.035308 21
9 {SOLID SHORTENING} {FLUID MILK WHITE ONLY} 0.0021008 0.6000000 2.266667 12
10 {AUTO DISH DETERGENT - MONODOSE} {FLUID MILK WHITE ONLY} 0.0022759 0.6500000 2.455556 13
11 {FRUIT FILLED COOKIES} {FLUID MILK WHITE ONLY} 0.0028011 0.7272727 2.747475 16
12 {ABRASIVES} {DAIRY CASE 100% PURE JUICE - O} 0.0028011 0.6400000 5.305777 16
13 {ABRASIVES} {FLUID MILK WHITE ONLY} 0.0035014 0.8000000 3.022222 20
14 {DAIRY CASE TEA WITH SUGAR OR S} {FLUID MILK WHITE ONLY} 0.0021008 0.5454545 2.060606 12
15 {MISCELLANEOUS PACKAGE MIXES} {FLUID MILK WHITE ONLY} 0.0021008 0.6315789 2.385965 12
17 {REFRIGERATED BAGELS} {CREAM CHEESE} 0.0026261 0.8333333 19.669421 15
18 {FRZN BAKED/STUFFED/MASHED&SPEC} {DAIRY CASE 100% PURE JUICE - O} 0.0029762 0.6800000 5.637387 17
19 {FRZN BAKED/STUFFED/MASHED&SPEC} {FLUID MILK WHITE ONLY} 0.0029762 0.6800000 2.568889 17
20 {STANDARD OATMEAL} {FLUID MILK WHITE ONLY} 0.0028011 0.5714286 2.158730 16
21 {CLEANSING WIPES} {FLUID MILK WHITE ONLY} 0.0021008 0.5454545 2.060606 12
25 {AUTHENTIC PASTA RICE BEANS} {FLUID MILK WHITE ONLY} 0.0024510 0.6086957 2.299517 14
26 {MISCELLANEOUS VEGETABLES} {BEANS GREEN: FS/WHL/CUT} 0.0028011 0.5000000 21.801527 16
30 {BURRITOS} {FLUID MILK WHITE ONLY} 0.0024510 0.5600000 2.115556 14
31 {BATHROOM CLEANERS - GENERAL} {FLUID MILK WHITE ONLY} 0.0029762 0.5862069 2.214559 17
33 {CHOCOLATE MILK} {FLUID MILK WHITE ONLY} 0.0028011 0.5161290 1.949821 16
34 {MALTED MLK/SYRUP/PWDRS (EGGNOG} {FLUID MILK WHITE ONLY} 0.0028011 0.5161290 1.949821 16
43 {GELATIN} {FLUID MILK WHITE ONLY} 0.0026261 0.5172414 1.954023 15
45 {PEAS & ONIONS PEAS & CARROT} {DAIRY CASE 100% PURE JUICE - O} 0.0021008 0.6666667 5.526851 12
46 {PEAS & ONIONS PEAS & CARROT} {FLUID MILK WHITE ONLY} 0.0026261 0.8333333 3.148148 15
53 {PLASTIC WRAP (SARANETC.)} {FLUID MILK WHITE ONLY} 0.0038515 0.6666667 2.518518 22
56 {PANCAKE MIXES} {MOLASSES & SYRUPS} 0.0024510 0.5000000 49.241379 14
57 {PANCAKE MIXES} {FLUID MILK WHITE ONLY} 0.0028011 0.5714286 2.158730 16
58 {DRY ROAST NUTS} {FLUID MILK WHITE ONLY} 0.0033263 0.7037037 2.658436 19
60 {MEXICAN TACO TOSTADO SHELLS} {SHREDDED CHEESE} 0.0040266 0.5750000 5.566780 23
61 {FRUIT/BREAKFAST BREAD} {DAIRY CASE 100% PURE JUICE - O} 0.0031513 0.6000000 4.974165 18
62 {FRUIT/BREAKFAST BREAD} {FLUID MILK WHITE ONLY} 0.0033263 0.6333333 2.392593 19
68 {PIZZA SAUCE} {SHREDDED CHEESE} 0.0054272 0.7209302 6.979582 31
69 {PIZZA SAUCE} {FLUID MILK WHITE ONLY} 0.0038515 0.5116279 1.932816 22
70 {EGGS - MEDIUM} {FLUID MILK WHITE ONLY} 0.0045518 0.5531915 2.089834 26
71 {REFRIG DIPS} {POTATO CHIPS} 0.0036765 0.5250000 12.290164 21
75 {MUSHROOMS CND & GLASS} {SHREDDED CHEESE} 0.0035014 0.5128205 4.964798 20
76 {MUSHROOMS CND & GLASS} {FLUID MILK WHITE ONLY} 0.0038515 0.5641026 2.131054 22
79 {TEA BAGS & BULK TEA} {FLUID MILK WHITE ONLY} 0.0050770 0.6904762 2.608466 29
81 {CANOLA OILS} {FLUID MILK WHITE ONLY} 0.0029762 0.5666667 2.140741 17
82 {PEACHES} {FLUID MILK WHITE ONLY} 0.0036765 0.5250000 1.983333 21
85 {FROSTING} {FLUID MILK WHITE ONLY} 0.0040266 0.5111111 1.930864 23
86 {PUDDINGS DRY} {FLUID MILK WHITE ONLY} 0.0045518 0.6046512 2.284238 26
104 {FOAM PLATES} {FLUID MILK WHITE ONLY} 0.0036765 0.5250000 1.983333 21
105 {NOODLE SIDE DISH MIXES} {FLUID MILK WHITE ONLY} 0.0054272 0.6326531 2.390023 31
106 {LAYER CAKE MIX} {FLUID MILK WHITE ONLY} 0.0052521 0.5084746 1.920904 30
107 {JELLY} {FLUID MILK WHITE ONLY} 0.0054272 0.6200000 2.342222 31
109 {MEXICAN BEANS REFRIED} {SHREDDED CHEESE} 0.0050770 0.5178571 5.013559 29
110 {MEXICAN SEASONING MIXES} {SHREDDED CHEESE} 0.0075280 0.6231884 6.033309 43
120 {MUFFIN & CORN BREAD MIX} {FLUID MILK WHITE ONLY} 0.0056022 0.5423729 2.048964 32
278 {MAIN MEAL BREAD,SHREDDED CHEESE} {PIZZA SAUCE} 0.0029762 0.6538462 86.855098 17
288 {MEXICAN SEASONING MIXES,MEXICAN TACO TOSTADO SHELLS} {SHREDDED CHEESE} 0.0024510 0.6666667 6.454237 14
289 {MEXICAN TACO TOSTADO SHELLS,SHREDDED CHEESE} {MEXICAN SEASONING MIXES} 0.0024510 0.6086957 50.389414 14
312 {FROSTING,LAYER CAKE MIX} {FLUID MILK WHITE ONLY} 0.0033263 0.5428571 2.050794 19
313 {FLUID MILK WHITE ONLY,FROSTING} {LAYER CAKE MIX} 0.0033263 0.8260870 79.976419 19
314 {FLUID MILK WHITE ONLY,LAYER CAKE MIX} {FROSTING} 0.0033263 0.6333333 80.391111 19
431 {CAN CATFD GOURMET/SUP PREM (GR,LIQUID LAUNDRY DETERGENTS} {FOAM PLATES} 0.0021008 0.6000000 85.680000 12
3485 {CAN CATFD GOURMET/SUP PREM (GR,CANNED CAT FOOD (9 LIVES/FRISK,LIQUID LAUNDRY DETERGENTS} {FOAM PLATES} 0.0021008 0.6000000 85.680000 12

The following plot shows the support and confidence for each of the 60 association rules.

# plotting visual
plot(cross_selling_rules)

The following plot shows the association direction between 2 random rules.

# plotting visual
plot(head(sort(cross_selling_rules, by="lift"), 2), method = "graph")

Final list of 44 cross-selling products

After including these 44 products, we have selected a total of 294 products from a set of 610 products till now

# combining lhs and rhs products
prods <- unique(c(as.character(ruledf$lhs), as.character(ruledf$rhs)))
index <- which(str_detect(prods, ","))
prods <- prods[-index]

# removing brackets 
prods <- gsub("\\{", "", prods)
prods <- gsub("\\}", "", prods)

# removing popular products from the list of cross-selling products
index <- which(prods %in% df$Products)
index <- which(prods %in% df$Products)
prods <- as.data.frame(prods[-index])
names(prods) <- "Products"

# printing cross-selling products
kable(prods) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "50%", height = "500px")
Products
TEA UNSWEETENED
MEXICAN PEPPERS CHILIES
JUICE (100% JUICE)
BLENDED JUICE&COMBINATIONS (OV
CARAMEL COATED SNACKS
SOLID SHORTENING
AUTO DISH DETERGENT - MONODOSE
FRUIT FILLED COOKIES
ABRASIVES
DAIRY CASE TEA WITH SUGAR OR S
MISCELLANEOUS PACKAGE MIXES
REFRIGERATED BAGELS
FRZN BAKED/STUFFED/MASHED&SPEC
STANDARD OATMEAL
CLEANSING WIPES
AUTHENTIC PASTA RICE BEANS
MISCELLANEOUS VEGETABLES
BURRITOS
BATHROOM CLEANERS - GENERAL
CHOCOLATE MILK
MALTED MLK/SYRUP/PWDRS (EGGNOG
GELATIN
PEAS & ONIONS PEAS & CARROT
PLASTIC WRAP (SARANETC.)
PANCAKE MIXES
DRY ROAST NUTS
MEXICAN TACO TOSTADO SHELLS
FRUIT/BREAKFAST BREAD
PIZZA SAUCE
EGGS - MEDIUM
REFRIG DIPS
MUSHROOMS CND & GLASS
TEA BAGS & BULK TEA
CANOLA OILS
PEACHES
FROSTING
PUDDINGS DRY
FOAM PLATES
NOODLE SIDE DISH MIXES
LAYER CAKE MIX
JELLY
MEXICAN BEANS REFRIED
MEXICAN SEASONING MIXES
MUFFIN & CORN BREAD MIX

3. Differentiated Products

Differentiated products are those products which are sold in Store 367 but not in competitor stores. The Final list of 24 differentiated products is shown below:

After including these 24 products, we have selected a total of 318 products from a set of 610 products till now

# Unique Products in Store 367
products_367 <-
filtered_data %>% 
  filter(store_id == 367) %>% 
  select(sub_commodity_desc) %>%
  unique() %>% 
  .$sub_commodity_desc

# Unique Products in Stores except 367
products_not_367 <-
  filtered_data %>% 
  filter(store_id != 367) %>% 
  select(sub_commodity_desc) %>%
  unique() %>% 
  .$sub_commodity_desc

# Products in Store 367 that are not in other stores
df <- data.frame(Products = products_367[which(!products_367 %in% products_not_367)])

# Final list of cross-selling products
final_prods <- c(df_top_250$Products, as.character(prods$Products), as.character(df$Products))
final_prods <- unique(final_prods)

# Printing dataframe
kable(df) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "50%", height = "500px")
Products
FRZN BURGERS/BBQ/MEATBALL
CHEESE:CHEESEBALLS/SPREADS
CHERRIES (EXCEPT MARASCHINO)
AUTHENTIC ASIAN FOODS
NON-CARB JCE (UNDER 50%JCE)
FROZEN ENTREES
FREEZE MIXES PWDRS/LIQUIDS
SEAFOOD-MISC-SAUCES/MARIN-WET
CANDY BAGS-CHOCOCLATE
DRIED FRUIT - OTHER
BEEF/PORK - DRIED SLICED W/GRA
SNACKS: CRACKERS/COOKIES
BRITISH FOODS
BOXED PREPARED/ENTREE/DRY PREP
JUICE (OVER 50% JUICE)
NON-ALCOHLIC WINES
BREAKFAST FOODS
GARLIC JAR
PASTA/RAMEN
FITNESS&DIET - BARS
OILS/VINEGAR
COCKTAIL MIXES-DRY
PORK-FULLY COOKED
FORTIFIED/ENERGY WATER
PLUMS

4. High-End Products

High-End Products are those products which are sold at a high price. As the customers in Store 367 are less price sensitive, the likelihood of them buying these products is higher. The final list of 13 high-end products is shown below:

After including these 13 products, we have selected a total of 331 products from a set of 610 products till now

# Top 13 products by price
df <-
filtered_data %>% 
  filter(store_id == 367) %>% 
  group_by(sub_commodity_desc) %>% 
  summarise(price = median(product_price)) %>%
  arrange(desc(price)) %>% 
  top_n(50, price) %>% 
  filter(!sub_commodity_desc %in% final_prods) %>% 
  select(Products = sub_commodity_desc)

# Final list of cross-selling products
final_prods <- c(final_prods, df$Products)

# Printing dataframe
kable(df) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% 
  scroll_box(width = "50%", height = "500px")
Products
BOX WINES
DRY DOG FOOD PREMIUM (ALPO/PUR
PREMIUM 1.5LT WINES
MISC CHEMICALS
DECAF BEAN: REGULAR
TOOLS - BATHROOM & TOILET
FROZEN CREAM PIES
ALL FABRIC LIQUID BLEACH
MULT PK BAG SNACKS
DOG & CAT CHEMICALS
FLEA/TICK COLLAR
MEAT SNACKS
DESSERTS & VERMOUTH

4. Summary & Future Work

Summary

  • We analyzed data containing household level transactions of over two years from a group of 2,500 households who are frequent shoppers at Kroger.
  • We limited our analysis to customers from Store 367 and it’s competitors buying in Grocery Category.
  • Now, the retail manager of Store 367 wants to understand the right set of products that should be carried in the Grocery Shelf. We defined a few metrics that could measure the same:
    • Popular Products
    • Cross-Selling Products
    • Branded Products
    • Differentiated Products
    • Variety of Products
    • Niche Products
    • Economic Products
    • Non-Perishable Products
    • Speciality Products
    • Local Country Products
    • Weather Specific Products
    • Festival Specific Products
    • Proximity Related Products
    • Promotional Products
    • Easy to Carry/Transport Products
  • For our analysis we optimised for the following metrics only:
    • Popular Products
    • Cross-Selling Products
    • Branded Products
    • Differentiated Products
  • Post our analysis, we would recommend the manager of Store 367 to stock 331 products (out of a toal of 610) in the Grocery Shelf. The list of 331 recommended products is shown below:
# converting to dataframe
df <- as.data.frame(final_prods)
names(df) <- "Products"

# Printing dataframe
kable(df) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
  scroll_box(width = "50%", height = "500px")
Products
BEERALEMALT LIQUORS
POPULAR 750ML WINES
PREMIUM 750ML WINES
FLUID MILK WHITE ONLY
SOFT DRINKS 12/18&15PK CAN CAR
FRZN SS PREMIUM ENTREES/DNRS/N
DAIRY CASE 100% PURE JUICE - O
TOILET TISSUE
SHREDDED CHEESE
YOGURT NOT MULTI-PACKS
ULTRA PREMIUM WINES
ADULT CEREAL
ALL FAMILY CEREAL
AUSTRALIAN/NZ WINES
RTS SOUP: CHUNKY/HOMESTYLE ET
SPRING WATER
PIZZA/PREMIUM
ITALIAN WINES
TORTILLA/NACHO CHIPS
PAPER TOWELS & HOLDERS
LIQUID LAUNDRY DETERGENTS
PREMIUM
SOFT DRINKS 20PK&24PK CAN CARB
POURABLE SALAD DRESSINGS
SFT DRNK MLT-PK BTL CARB (EXCP
SPICES & SEASONINGS
FRZN BAGGED VEGETABLES - PLAIN
KIDS CEREAL
NATURAL CHEESE EXACT WT CHUNKS
IWS SINGLE CHEESE
TUNA
BUTTER
PREMIUM BREAD
NON-CARB FLVRD DRNKNG/MNRL WAT
FACIAL TISSUE & PAPER HANDKE
CEREAL BARS
POTATO CHIPS
SNACKS/APPETIZERS
SNACK CRACKERS
VAC PAC NUTS(CANS)
SUPER PREMIUM PINTS
FRZN MULTI SERVE ENTREES ALL
SUPER PREMIUM WINES
CONDENSED SOUP
FIGHTING VARIETAL WINES
YOGURT MULTI-PACKS
NON-CRBNTD DRNKING/MNERAL WATE
GARBAGE KITCHEN COMPACTOR BA
SFT DRNK 2 LITER BTL CARB INCL
MAINSTREAM WHITE BREAD
FRZN SS PREMIUM ENTREES/DNRS/T
OLIVE OIL
MACARONI & CHEESE DNRS
SCOOP LITTER
SS ECONOMY ENTREES/DINNERS ALL
MEXICAN SAUCESSALSAPICANTEE
GROUND COFFEE
GROUND FLVR REG COFFEE
DAIRY CASE JUICE DRNK UNDER 10
POPULAR 1.5LT WINES
MAINSTREAM
PIZZA/TRADITIONAL
MAINSTREAM WHEAT/MULTIGRAIN BR
CREAM CHEESE
NATURAL CHEESE EXACT WT SLICES
EGGS - LARGE
GRANOLA BARS
CAN CATFD GOURMET/SUP PREM (GR
BAGELS
ENGLISH MUFFINS/WAFFLES
FRENCH WINES
CARBONATED WATER - FLVRD UNSWE
STRING CHEESE
ENERGY DRINK
PEANUT BUTTER
SEMI-SOLID SALAD DRESSING MAY
PRETZELS
INSTANT BREAKFAST
CHEESE CRACKERS (CHEEZ-ITS/GOL
FRUIT BOWL AND CUPS
SOFT DRINKS 6PK/4PK CAN CARB (
DIPS (NON-REFRIGERATED)
SANDWICHES&HANDHELDS
SUGAR
POWDER LAUNDRY DETERGENT
MISCELLANEOUS CHEESE
RICE SIDE DISH MIXES DRY
SNACK CAKE - MULTI PACK
ALL OTHER IMPORTED WINES
COTTAGE CHEESE
PREMIUM COOKIES (EX: PEPPERIDG
SPARKLING WINES
MARGARINE: TUBS AND BOWLS
DRY CAT FOOD SUP PREM (ONE/NUT
MIXERS(CLUB SODA/SELTZERS)FLAV
FRZN BOXED VEGETABLES- VALUE A
AIR CARE - CONTINUOUS ACTION
BUTTER SPRAY CRACKER (RITZ/CLU
TOOLS - FLOOR & FURNITURE
ISOTONIC DRINKS SINGLE SERVE
DAIRY CASE CITRUS PNCH/OJ SUBS
EGGS - X-LARGE
NOODLES DRY
LIQUID DISH DETERGENT
FRZN BAGGED VEGETABLES- VALUE
CANISTER POTATO/TORT CHIPS
CHILI: CANNED
INSTANT OATMEAL
SOUR CREAMS
TRAY PACK/CHOC CHIP COOKIES
BAKING NUTS
DRY CAT FOOD (CAT CHOW/FRISKIE
ISOTONIC DRINKS MULTI-SERVE
INSTANT COFFEE FLAVORED NO SWE
DRY DOG FOOD SUPER PREM (ONE/N
REFRIGERATED PUDDING
PRESERVES JAM MARMALADE
SANDWICH COOKIES
STICKS/ENROBED
AUTHENTIC DRY BEVERAGES W/O SW
SOFT DRINK POWDER POUCHES
PICKLES
WAFFLES/PANCAKES/FRENCH TOAST
AUTO DISH DETERGENT - GEL
BBQ SAUCE
MISCELLANEOUS MILK
VEGETABLE/SALAD OIL
SFT DRNK SNGL SRV BTL CARB (EX
REFRIGERATED COFFEE CREAMERS
FABRIC SOFTENER LIQUID
CORN CHIPS
PIZZA/SINGLE SERVE/MICROWAVE
TEA SWEETENED
BEANS GREEN: FS/WHL/CUT
TOASTER PASTRIES
SPECIALTY CRACKERS
SPECIALTY COOKIES
SWEET GOODS - FULL SIZE
BROTH
MACARONI DRY
SPAGHETTI DRY
BITS & MORSELS
SOUTH AMERICAN WINES
SKILLET DINNERS
RICE - INSTANT & MICROWAVE
PREPARED BEANS - BAKED W/PORK
REFRIGERATED PASTA
RYE BREADS
RICE - DRY BAG AND BOX
FRZN GARLIC BREAD/TOAST/STICKS
FABRIC SOFTENER SHEETS
FROZEN FRUIT
CANNED CAT FOOD (9 LIVES/FRISK
FLAVORED BEAN COFFEE
VANILLA WAFER/KIDS COOKIES
PINEAPPLE
MISC MEAT SAUCES
HOT DOG BUNS
LOAF CHEESE
MOLASSES & SYRUPS
SGL SV/VEND MACH SNACKS CHIP/P
AUTHENTIC THAI FOODS
FRUIT SNACKS
TRADITIONAL
TOMATOES: STEWED/DICED/CRMD
ALUMINUM FOIL
FRZN FRENCH FRIES
CATSUP
GRAPE JUICE (OVER 50% JUICE)
GREEN OLIVES
HAMBURGER BUNS
FREEZER BAGS
MULTI-PACK CRACKERS
FRZN BREADED PREPARED CHICK
REFRIGERATED SPECILATY ROLLS
PLASTIC TRASH BAGS
LIQUID BLEACH
ADULT PREMIUM
REFRIGERATED MEXICAN PRODUCTS
MISC SALES TRANS
MAIN MEAL BREAD
TEA BAGS HERBAL & FLAVORED
PAPER NAPKINS
SOUP CRACKERS (SALTINE/OYSTER)
VALUE GLASS WINE
PASTA: CANNED
PLASTIC/HEAVY PAPER PREM PLATE
RAMEN NOODLES/RAMEN CUPS
PLSTC CTLRYTBLCLTHSTTHPKSST
SANDWICH BAGS
PLASTIC CUPS
SANDWICH BUNS
MEXICAN SOFT TORTILLAS AND WRA
SOFT DRINK CANISTERS
VARIETY BEANS - KIDNEY PINTO
CAT TREATS
VEG JUICE (EXCEPT TOMATO) (OVE
SPECIALTY LAUNDRY
HOT SAUCE
MISC BAG SNACKS
FROZEN PASTA
PIZZA/ECONOMY
CORN
FROZEN ENTREES
REFRIGERATED COOKIES-CHUB
PREMIUM PINTS
ASEPTIC PACK JUICE AND DRINKS
GRATED CHEESE
FRZN MEAT ALTERNATIVES
ITALIAN FOODS
CRANAPPLE CRAN GRAPE JUICE (5
EGGS SUBSTITUTE
NON-IWS SINGLE CHEESE
CLEANING WIPES
MICROWAVABLE SOUPS
COOKING OIL: PEANUT SAFFLOWER
ICE - CRUSHED/CUBED
POTATOES: DRY
PRUNE JUICE (OVER 50% JUICE)
CANNED MILK
SOFT DRINKS CAN NON-CARB (EXCE
NON DAIRY CREAMER: DRY
AIR CARE - AEROSOLS
MIXERS(TONIC WTR/CLUB SODA/GNG
FRZN BOXED VEGETABLES - PLAIN
COOKING SPRAYS
FOOD STORAGE BAGS
SWEETENERS
DOG & CAT TOYS
ALTERNATIVE LITTER
APPLE JUICE & CIDER (OVER 50%
HOT MUSTARD/SPECIALTY MUSTAR
WATER ICE
PURE EXTRACTS
JUICE (UNDER 10% JUICE)
STEAK & WORCHESTER SAUCE
DOG TREATS (SOFT TREATS)
FROZEN FRUIT PIES & COBBLERS
ICE CREAM SANDWICHES
BOUILLON
FRZN PIE SHELLS PASTRY SHELL
HONEY
DAIRY CASE 100% PURE JUICE OTH
MISC REFRIG DOUGH PRODUCTS
CONVENTIONAL LITTER
FLOUR: WHITE & SELF RISING
EGGS - JUMBO
REGULAR BEAN
CENTRAL AMERICAN FOODS
CONES
TEA UNSWEETENED
MEXICAN PEPPERS CHILIES
JUICE (100% JUICE)
BLENDED JUICE&COMBINATIONS (OV
CARAMEL COATED SNACKS
SOLID SHORTENING
AUTO DISH DETERGENT - MONODOSE
FRUIT FILLED COOKIES
ABRASIVES
DAIRY CASE TEA WITH SUGAR OR S
MISCELLANEOUS PACKAGE MIXES
REFRIGERATED BAGELS
FRZN BAKED/STUFFED/MASHED&SPEC
STANDARD OATMEAL
CLEANSING WIPES
AUTHENTIC PASTA RICE BEANS
MISCELLANEOUS VEGETABLES
BURRITOS
BATHROOM CLEANERS - GENERAL
CHOCOLATE MILK
MALTED MLK/SYRUP/PWDRS (EGGNOG
GELATIN
PEAS & ONIONS PEAS & CARROT
PLASTIC WRAP (SARANETC.)
PANCAKE MIXES
DRY ROAST NUTS
MEXICAN TACO TOSTADO SHELLS
FRUIT/BREAKFAST BREAD
PIZZA SAUCE
EGGS - MEDIUM
REFRIG DIPS
MUSHROOMS CND & GLASS
TEA BAGS & BULK TEA
CANOLA OILS
PEACHES
FROSTING
PUDDINGS DRY
FOAM PLATES
NOODLE SIDE DISH MIXES
LAYER CAKE MIX
JELLY
MEXICAN BEANS REFRIED
MEXICAN SEASONING MIXES
MUFFIN & CORN BREAD MIX
FRZN BURGERS/BBQ/MEATBALL
CHEESE:CHEESEBALLS/SPREADS
CHERRIES (EXCEPT MARASCHINO)
AUTHENTIC ASIAN FOODS
NON-CARB JCE (UNDER 50%JCE)
FREEZE MIXES PWDRS/LIQUIDS
SEAFOOD-MISC-SAUCES/MARIN-WET
CANDY BAGS-CHOCOCLATE
DRIED FRUIT - OTHER
BEEF/PORK - DRIED SLICED W/GRA
SNACKS: CRACKERS/COOKIES
BRITISH FOODS
BOXED PREPARED/ENTREE/DRY PREP
JUICE (OVER 50% JUICE)
NON-ALCOHLIC WINES
BREAKFAST FOODS
GARLIC JAR
PASTA/RAMEN
FITNESS&DIET - BARS
OILS/VINEGAR
COCKTAIL MIXES-DRY
PORK-FULLY COOKED
FORTIFIED/ENERGY WATER
PLUMS
BOX WINES
DRY DOG FOOD PREMIUM (ALPO/PUR
PREMIUM 1.5LT WINES
MISC CHEMICALS
DECAF BEAN: REGULAR
TOOLS - BATHROOM & TOILET
FROZEN CREAM PIES
ALL FABRIC LIQUID BLEACH
MULT PK BAG SNACKS
DOG & CAT CHEMICALS
FLEA/TICK COLLAR
MEAT SNACKS
DESSERTS & VERMOUTH

Future Work

  • Come up with more metrics that could define what the right set of products could mean for a particular store in a particular location.
  • Develop algorithms specific to each of these metrics or a range of metrics.
  • Write production level codes that can perform shelf optimisation for multiple stores and categories in real-time.
  • Invest resources to collect more demographic level data for current customers and data related to competitors.