Key Takeaways

• We performed analyses on an FMCG company sales data.
• Main analyses were made on sales figures of categories, cities and segments.
• What we saw was the change of sales figures by loyalty is considerable.
• Another important point declared that Beauty products are the main sale components.
• Also sales by cities can be experienced via our Shiny app on our github page.

Introduction to FMCG Company Sales Analysis World

The following analyses are conducted based on the sales figures of an FMCG company.
Data shows us details such as invoice date, sales amount,purchased item,item categories etc.
It also includes info about customer characteristics like gender, age, address, segmentation, loyalty.
Processed data is 31.2 MB. There are approximately 494K rows with 15 attributes.

Data Fields: INVOICEID, ACCOUNTNUM, GENDER, AGE, LOA (loyalty indicator), CITY, CUSTOMER SEGMENT, BUSINESS, CATEGORY, SEGMENT, SALES, UNIT PRICE, QUANTITY, FIRST ORDER, INVOICE DATE

Analysis-Necessary Packages

Here you may find the necessary libraries. The references are indicated on the Citations.

library(dplyr)                                #[1]
library(tidyverse)                            #[2]
library(ggplot2)                              #[3]
library(scales)                               #[4]
library(readxl)                               #[5]
library(lubridate)                            #[6]
library(treemapify)                           #[7]
library(magrittr)                             #[8]

Excel file is downloaded from github to a local data frame (raw_data) and prepared for analysis.
Here the raw data structure can be seen in the manner of the number of variables, variable types and the number of observations.
There are 15 variables and 494.305 observations to be conducted.

## Observations: 494,305
## Variables: 15
## $ INVOICEID        <dbl> 53927301, 53927301, 53927302, 53927302, 53927...
## $ ACCOUNTNUM       <dbl> 23349830, 23349830, 10915900, 10915900, 10915...
## $ GENDER           <chr> "M", "M", "F", "F", "F", "F", "F", "F", "F", ...
## $ AGE              <dbl> 46, 46, 33, 33, 33, 33, 33, 33, 33, 33, 33, 3...
## $ LOA              <dbl> 25, 25, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2...
## $ CITY             <chr> "Uşak", "Uşak", "Kırklareli", "Kırklareli", "...
## $ Customer_Segment <chr> "E", "E", "B", "B", "B", "B", "B", "B", "B", ...
## $ BUSINESS         <chr> "BEAUTY", "BEAUTY", "BEAUTY", "BEAUTY", "BEAU...
## $ CATEGORY_        <chr> "BODY", "TOILETRIES", "BODY", "TOILETRIES", "...
## $ SEGMENT_         <chr> "MY BODY", "STYLING", "HAND TREATMENT", "WASH...
## $ SALES            <dbl> 223.400, 15.200, 0.758, 0.888, 0.758, 0.942, ...
## $ UNIT_PRICE       <dbl> 22.340, 3.040, 0.758, 0.888, 0.758, 0.942, 0....
## $ QUANTITY         <dbl> 10, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ `FIRST ORDER`    <dttm> 2016-12-23, 2016-12-23, 2018-09-06, 2018-09-...
## $ INVOICE_DATE     <dttm> 2018-09-29, 2018-09-29, 2018-09-29, 2018-09-...

## [1] 494305     15

Exploratory Data Analysis