Introduction

Our Business Question

What is the correlation between purchases of canning supplies and the types of foods being bought concurrently? Additionally, can we identify any patterns or trends in purchasing behavior based on the demographics, such as age and household size, of these customers? How can this information be leveraged to optimize marketing strategies or offer personalized promotions to customers?

How We Got Our Solution

Through targeted transaction filtering, we isolated households that purchase canning supplies. This analysis revealed frequently co-purchased items, seasonal trends in canning popularity, and household sizes most inclined to buy canning supplies.

How Our Proposed Solution Could Help

By strategically targeting customer segments that purchase canning supplies, Regork can aim to optimize profits and develop an effective marketing strategy to attract the right clientele. This comprehensive report will dive into the demographics of these buyers, their purchasing patterns, and the specific items they purchase in tandem with canning products.

Packages Required

CompleteJourney: This package contains all of the data from Regork needed to form our analysis.

Tidyverse: Contains packages used for data visualization and manipulation.

Ggplot2: Useful for creating different types of graphics.

Dplyr: Makes data manipulation easier.

DT: Allows you to create easy to read data tables.

Lubridate: Functions to work with date-times and time-spans

library(completejourney)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(DT)
library(lubridate)
#Load Demographics, Products, and Transactions Data
transactions <- get_transactions()
promotions <- get_promotions()

Data Analysis

Products Frequently Purchased With Canning Supplies

This is the data for the most frequently purchased items in conjunction with canning supplies.

houses_that_can <- transactions %>%
  inner_join(products) %>%
  filter(product_category =="HOME FREEZING & CANNING SUPPLY") %>%
  summarise(household_id) %>%
  distinct(.keep_all = TRUE)

transactions %>%
  inner_join(products) %>%
  filter(household_id == c(1222,771,1372,2310,578,1473,1585,1691,220,1804,1559,338,361,696,1391,2489,2168,165,574,1341,2124,23,1,239,2429,1683,2065,184,456,2058,2173,695,1620,2111)) %>%
  group_by(product_type) %>%
  summarize(total_sales = sum(sales_value), na.rm = TRUE) %>%
  arrange(desc(total_sales)) %>%
datatable( options = list(pageLength = 10))
transactions %>%
  inner_join(products) %>%
  filter(household_id == c(1222,771,1372,2310,578,1473,1585,1691,220,1804,1559,338,361,696,1391,2489,2168,165,574,1341,2124,23,1,239,2429,1683,2065,184,456,2058,2173,695,1620,2111)) %>%
  group_by(product_type) %>%
  summarize(total_sales = sum(sales_value), na.rm = TRUE) %>%
  arrange(desc(total_sales)) %>%
  slice(1:10) %>%
  ggplot(aes(x = product_type, y = total_sales)) +
  geom_col() +
  scale_x_discrete("Product Name") + 
  scale_y_continuous("Amount Sold", labels = scales::dollar) + 
  ggtitle("Products Frequently Purchased With Canning Supplies", subtitle = "This chart illustrates the ten most frequently purchased items in conjunction with canning supplies.")

Based on the insights from this graph, we gain a deeper understanding of the products frequently purchased alongside canning supplies. This knowledge enables us to better comprehend our customer preferences and their want for bundled purchases. Leveraging this information, we can strategically offer special deals or coupons that combine these commonly co-purchased items with canning supplies, enhancing customer satisfaction and optimizing sales.

Which Household Size Most Frequently Purchases Canning Supplies

transactions %>%
    inner_join(products) %>%
    inner_join(demographics) %>%
    filter(household_id == c(1222,771,1372,2310,578,1473,1585,1691,220,1804,1559,338,361,696,1391,2489,2168,165,574,1341,2124,23,1,239,2429,1683,2065,184,456,2058,2173,695,1620,2111)) %>%
    group_by(household_size) %>%
    tally() %>%
    ggplot(aes(x = household_size, y = n)) +
    geom_col() + 
    scale_x_discrete("Household Size") + 
  scale_y_continuous("Sales")

Based on the graph, it is evident that households with two people or less are the primary consumers of canning supplies. This information is valuable for refining our advertising strategy and effectively targeting our audience.

What Age Customer Most Frequently Purchases Canning Supplies

transactions %>%
    inner_join(products) %>%
    inner_join(demographics) %>%
    filter(household_id == c(1222,771,1372,2310,578,1473,1585,1691,220,1804,1559,338,361,696,1391,2489,2168,165,574,1341,2124,23,1,239,2429,1683,2065,184,456,2058,2173,695,1620,2111)) %>%
    group_by(age) %>%
    tally() %>%
    ggplot(aes(x = age, y = n)) +
    geom_col() + 
    scale_x_discrete("Age") + 
  scale_y_continuous("Sales")

Based on the graph, it is evident that ages 45-54 are the primary consumers of canning supplies. This information is valuable for refining our advertising strategy and effectively targeting our audience.

Monthly Canning Supply Sales

  transactions%>%
 inner_join(products)%>%
    filter(product_category =="HOME FREEZING & CANNING SUPPLY")%>%
    mutate( date = month(transaction_timestamp))%>%
    group_by(date)%>%
    summarize(total_sales = sum(sales_value))%>%
    ggplot(aes(x = date,y = total_sales)) +
    geom_col() +
    scale_x_binned("Month") + 
    scale_y_continuous("Total Sales", labels = scales::dollar)

The graph highlights a significant trend: peak sales occur during June and August. Leveraging this insight strategically, we can tailor our promotional offers to align with the summer months. Since sales are already robust during this period, our chances of success are heightened. Customers are more inclined to purchase canning supplies when the weather is warm, making it an opportune time for targeted marketing efforts.

Summary

How We Addressed The Problem

We began by integrating transactional data with product information. Subsequently, we filtered the dataset to focus exclusively on transactions involving canning products. Our investigation then centered on identifying the products most frequently purchased alongside canning items. To gain deeper insights, we linked demographic data to our analysis. Notably, we discovered that households consisting of one or two individuals accounted for a significant share of canning product sales. Lastly, we examined the temporal patterns of canning supplies sales and found that sales peak during the summer months. These insights will inform strategic decisions and guide our business approach.

Our Recommendations

We recommend that Regork offer bundles or coupons for customers to take a percentage off of canning supplies when they purchase items such as gas, white milk, snacks, and soft drinks, because these are very common items purchased year-round. We suggest distributing these coupons to all customers no matter the age in order to drive sales for canning purchases.

Limitations of Our Analysis

Acknowledging limitations is crucial for robust analysis. In this case, our primary constraint lies in the temporal scope of our data, which is restricted to 2017. Given the dynamic nature of purchasing trends, obtaining more recent data would be essential to stay up-to-date with evolving consumer behaviors. By incorporating up-to-date information, we can enhance the accuracy and relevance of our analyses, enabling more informed decision-making.