A&B Co. Case Study
Jeremy Hattendorf
2023-07-08
Introduction Prompt
You’re a Data Scientist / Business Analyst working for a new eCommerce company called A&B Co. (similar to Amazon) and you’ve been asked to prepare a presentation for the Vice President of Sales and the Vice President of Operations that summarizes sales and operations thus far. The summary should include (at a minimum) a summary of current state the business, current customer satisfaction, and a proposal of 2-3 areas where the company can improve. Here are some facts:
Company is US-based, but launched in Brazil (which is why some information is in Portuguese)
It’s currently September 2018 (e.g., you can ignore all data after September 2018)
The company’s inception was January 2017 (so you can ignore all data before January 2017)
You can assume all orders are delivered (so ignore the order state field)
Your presentation should not have more than 10 slides of content, and the presentation itself should only take ~15 minutes.
Rundown
I’m downloading data from here to determine the status of the business, and customer satisfaction. Then afterwards I will make a powerpoint presentation of the findings here.
Prepare/Process
Steps
load relevant packages
create joined tables
clean and manipulate data
graph and model the data
Loading relevant package*
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsReading in all .csv files and getting a glimpse
files <- list.files("~/RStudio_Data/A&B/archive", ".csv", full.names = TRUE)
for (i in seq_along(files))
{
csvs <- read.csv(files[i])
names <- basename(files[i])
assign(names, csvs)
}
for (file in ls(pattern = ".csv")) {
cat(paste0("**File: ", file, "**\n"))
glimpse(get(file))
}## **File: olist_customers_dataset.csv**
## Rows: 99,441
## Columns: 5
## $ customer_id <chr> "06b8999e2fba1a1fbc88172c00ba8bc7", "18955e83…
## $ customer_unique_id <chr> "861eff4711a542e4b93843c6dd7febb0", "290c77bc…
## $ customer_zip_code_prefix <int> 14409, 9790, 1151, 8775, 13056, 89254, 4534, …
## $ customer_city <chr> "franca", "sao bernardo do campo", "sao paulo…
## $ customer_state <chr> "SP", "SP", "SP", "SP", "SP", "SC", "SP", "MG…
## **File: olist_geolocation_dataset.csv**
## Rows: 1,000,163
## Columns: 5
## $ geolocation_zip_code_prefix <int> 1037, 1046, 1046, 1041, 1035, 1012, 1047, …
## $ geolocation_lat <dbl> -23.54562, -23.54608, -23.54613, -23.54439…
## $ geolocation_lng <dbl> -46.63929, -46.64482, -46.64295, -46.63950…
## $ geolocation_city <chr> "sao paulo", "sao paulo", "sao paulo", "sa…
## $ geolocation_state <chr> "SP", "SP", "SP", "SP", "SP", "SP", "SP", …
## **File: olist_order_items_dataset.csv**
## Rows: 112,650
## Columns: 7
## $ order_id <chr> "00010242fe8c5a6d1ba2dd792cb16214", "00018f77f2f03…
## $ order_item_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,…
## $ product_id <chr> "4244733e06e7ecb4970a6e2683c13e61", "e5f2d52b80218…
## $ seller_id <chr> "48436dade18ac8b2bce089ec2a041202", "dd7ddc04e1b6c…
## $ shipping_limit_date <chr> "2017-09-19 09:45:35", "2017-05-03 11:05:13", "201…
## $ price <dbl> 58.90, 239.90, 199.00, 12.99, 199.90, 21.90, 19.90…
## $ freight_value <dbl> 13.29, 19.93, 17.87, 12.79, 18.14, 12.69, 11.85, 7…
## **File: olist_order_payments_dataset.csv**
## Rows: 103,886
## Columns: 5
## $ order_id <chr> "b81ef226f3fe1789b1e8b2acac839d17", "a9810da82917…
## $ payment_sequential <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ payment_type <chr> "credit_card", "credit_card", "credit_card", "cre…
## $ payment_installments <int> 8, 1, 1, 8, 2, 2, 1, 3, 6, 1, 8, 1, 1, 5, 4, 10, …
## $ payment_value <dbl> 99.33, 24.39, 65.71, 107.78, 128.45, 96.12, 81.16…
## **File: olist_order_reviews_dataset.csv**
## Rows: 99,224
## Columns: 7
## $ review_id <chr> "7bc2406110b926393aa56f80a40eba40", "80e641a11…
## $ order_id <chr> "73fc7af87114b39712e6da79b0a377eb", "a548910a1…
## $ review_score <int> 4, 5, 5, 5, 5, 1, 5, 5, 5, 4, 5, 5, 4, 4, 3, 5…
## $ review_comment_title <chr> "", "", "", "", "", "", "", "", "", "recomendo…
## $ review_comment_message <chr> "", "", "", "Recebi bem antes do prazo estipul…
## $ review_creation_date <chr> "2018-01-18 00:00:00", "2018-03-10 00:00:00", …
## $ review_answer_timestamp <chr> "2018-01-18 21:46:59", "2018-03-11 03:05:13", …
## **File: olist_orders_dataset.csv**
## Rows: 99,441
## Columns: 8
## $ order_id <chr> "e481f51cbdc54678b7cc49136f2d6af7", "53c…
## $ customer_id <chr> "9ef432eb6251297304e76186b10a928d", "b08…
## $ order_status <chr> "delivered", "delivered", "delivered", "…
## $ order_purchase_timestamp <chr> "2017-10-02 10:56:33", "2018-07-24 20:41…
## $ order_approved_at <chr> "2017-10-02 11:07:15", "2018-07-26 03:24…
## $ order_delivered_carrier_date <chr> "2017-10-04 19:55:00", "2018-07-26 14:31…
## $ order_delivered_customer_date <chr> "2017-10-10 21:25:13", "2018-08-07 15:27…
## $ order_estimated_delivery_date <chr> "2017-10-18 00:00:00", "2018-08-13 00:00…
## **File: olist_products_dataset.csv**
## Rows: 32,951
## Columns: 9
## $ product_id <chr> "1e9e8ef04dbcff4541ed26657ea517e5", "3aa071…
## $ product_category_name <chr> "perfumaria", "artes", "esporte_lazer", "be…
## $ product_name_lenght <int> 40, 44, 46, 27, 37, 60, 56, 56, 57, 36, 54,…
## $ product_description_lenght <int> 287, 276, 250, 261, 402, 745, 1272, 184, 16…
## $ product_photos_qty <int> 1, 1, 1, 1, 4, 1, 4, 2, 1, 1, 1, 4, 3, 2, 4…
## $ product_weight_g <int> 225, 1000, 154, 371, 625, 200, 18350, 900, …
## $ product_length_cm <int> 16, 30, 18, 26, 20, 38, 70, 40, 27, 17, 16,…
## $ product_height_cm <int> 10, 18, 9, 4, 17, 5, 24, 8, 13, 10, 10, 19,…
## $ product_width_cm <int> 14, 20, 15, 26, 13, 11, 44, 40, 17, 12, 16,…
## **File: olist_sellers_dataset.csv**
## Rows: 3,095
## Columns: 4
## $ seller_id <chr> "3442f8959a84dea7ee197c632cb2df15", "d1b65fc7de…
## $ seller_zip_code_prefix <int> 13023, 13844, 20031, 4195, 12914, 20920, 55325,…
## $ seller_city <chr> "campinas", "mogi guacu", "rio de janeiro", "sa…
## $ seller_state <chr> "SP", "SP", "RJ", "SP", "SP", "RJ", "PE", "SP",…
## **File: product_category_name_translation.csv**
## Rows: 71
## Columns: 2
## $ product_category_name <chr> "beleza_saude", "informatica_acessorios"…
## $ product_category_name_english <chr> "health_beauty", "computers_accessories"…Joining tables together
# translating
product_translated <- left_join(product_category_name_translation.csv, olist_products_dataset.csv,
by = "product_category_name", "product_category_name_english")
product_translated2 <- left_join(product_translated, olist_order_items_dataset.csv,
by = "product_id", "order_id")
# joining other tables together
CustomerSellerProduct <- inner_join(olist_order_items_dataset.csv, product_translated) %>%
inner_join(product_translated2, olist_order_reviews_dataset.csv,
by = "order_id", relationship = "many-to-many") %>%
inner_join(olist_order_reviews_dataset.csv, olist_order_reviews_dataset.csv,
by = "order_id", relationship = "many-to-many")## Joining with `by = join_by(product_id)`Cleaning and Manipulating
Filtering the data as directed…
CustomerSellerProduct <- na.omit(distinct(CustomerSellerProduct))
min(CustomerSellerProduct$shipping_limit_date.x)## [1] "2016-09-19 00:15:34"max(CustomerSellerProduct$shipping_limit_date.x)## [1] "2020-04-09 22:35:08"CustomerSellerProduct <- filter(CustomerSellerProduct,
shipping_limit_date.x > as.Date("2017-01-01") &
shipping_limit_date.x < as.Date("2018-09-01") &
shipping_limit_date.y > as.Date("2017-01-01") &
shipping_limit_date.y < as.Date("2018-09-01") &
review_creation_date > as.Date("2017-01-01") &
review_creation_date < as.Date("2018-09-01") &
review_answer_timestamp > as.Date("2017-01-01") &
review_answer_timestamp < as.Date("2018-09-01")
)
min(CustomerSellerProduct$shipping_limit_date.x)## [1] "2017-01-09 11:56:06"max(CustomerSellerProduct$shipping_limit_date.x)## [1] "2018-08-31 19:50:11"Formatting the data
# Year, Month, Day, Time, -to- , Year, Month.
head(CustomerSellerProduct)## order_id order_item_id.x
## 1 00010242fe8c5a6d1ba2dd792cb16214 1
## 2 00018f77f2f0320c557190d7a144bdd3 1
## 3 000229ec398224ef6ca0657da4fc703e 1
## 4 00024acbcdf0a6daa1e931b038114c75 1
## 5 00042b26cf59d7ce69dfabb4e55b4fd9 1
## 6 00048cc3ae777c65dbb7d2a0634bc1ea 1
## product_id.x seller_id.x
## 1 4244733e06e7ecb4970a6e2683c13e61 48436dade18ac8b2bce089ec2a041202
## 2 e5f2d52b802189ee658865ca93d83a8f dd7ddc04e1b6c2c614352b383efe2d36
## 3 c777355d18b72b67abbeef9df44fd0fd 5b51032eddd242adc84c38acab88f23d
## 4 7634da152a4610f1595efa32f14722fc 9d7a1d34a5052409006425275ba1c2b4
## 5 ac6c3623068f30de03045865e4e10089 df560393f3a51e74553ab94004ba5c87
## 6 ef92defde845ab8450f9d70c526ef70f 6426d21aca402a131fc0a5d0960a3c90
## shipping_limit_date.x price.x freight_value.x product_category_name.x
## 1 2017-09-19 09:45:35 58.90 13.29 cool_stuff
## 2 2017-05-03 11:05:13 239.90 19.93 pet_shop
## 3 2018-01-18 14:48:30 199.00 17.87 moveis_decoracao
## 4 2018-08-15 10:10:18 12.99 12.79 perfumaria
## 5 2017-02-13 13:57:51 199.90 18.14 ferramentas_jardim
## 6 2017-05-23 03:55:27 21.90 12.69 utilidades_domesticas
## product_category_name_english.x product_name_lenght.x
## 1 cool_stuff 58
## 2 pet_shop 56
## 3 furniture_decor 59
## 4 perfumery 42
## 5 garden_tools 59
## 6 housewares 36
## product_description_lenght.x product_photos_qty.x product_weight_g.x
## 1 598 4 650
## 2 239 2 30000
## 3 695 2 3050
## 4 480 1 200
## 5 409 1 3750
## 6 558 1 450
## product_length_cm.x product_height_cm.x product_width_cm.x
## 1 28 9 14
## 2 50 30 40
## 3 33 13 33
## 4 16 10 15
## 5 35 40 30
## 6 24 8 15
## product_category_name.y product_category_name_english.y
## 1 cool_stuff cool_stuff
## 2 pet_shop pet_shop
## 3 moveis_decoracao furniture_decor
## 4 perfumaria perfumery
## 5 ferramentas_jardim garden_tools
## 6 utilidades_domesticas housewares
## product_id.y product_name_lenght.y
## 1 4244733e06e7ecb4970a6e2683c13e61 58
## 2 e5f2d52b802189ee658865ca93d83a8f 56
## 3 c777355d18b72b67abbeef9df44fd0fd 59
## 4 7634da152a4610f1595efa32f14722fc 42
## 5 ac6c3623068f30de03045865e4e10089 59
## 6 ef92defde845ab8450f9d70c526ef70f 36
## product_description_lenght.y product_photos_qty.y product_weight_g.y
## 1 598 4 650
## 2 239 2 30000
## 3 695 2 3050
## 4 480 1 200
## 5 409 1 3750
## 6 558 1 450
## product_length_cm.y product_height_cm.y product_width_cm.y order_item_id.y
## 1 28 9 14 1
## 2 50 30 40 1
## 3 33 13 33 1
## 4 16 10 15 1
## 5 35 40 30 1
## 6 24 8 15 1
## seller_id.y shipping_limit_date.y price.y
## 1 48436dade18ac8b2bce089ec2a041202 2017-09-19 09:45:35 58.90
## 2 dd7ddc04e1b6c2c614352b383efe2d36 2017-05-03 11:05:13 239.90
## 3 5b51032eddd242adc84c38acab88f23d 2018-01-18 14:48:30 199.00
## 4 9d7a1d34a5052409006425275ba1c2b4 2018-08-15 10:10:18 12.99
## 5 df560393f3a51e74553ab94004ba5c87 2017-02-13 13:57:51 199.90
## 6 6426d21aca402a131fc0a5d0960a3c90 2017-05-23 03:55:27 21.90
## freight_value.y review_id review_score
## 1 13.29 97ca439bc427b48bc1cd7177abe71365 5
## 2 19.93 7b07bacd811c4117b742569b04ce3580 4
## 3 17.87 0c5b33dea94867d1ac402749e5438e8b 5
## 4 12.79 f4028d019cb58564807486a6aaf33817 4
## 5 18.14 940144190dcba6351888cafa43f3a3a5 5
## 6 12.69 5e4e50af3b7960b7a10d86ec869509e8 4
## review_comment_title
## 1
## 2
## 3
## 4
## 5
## 6
## review_comment_message
## 1 Perfeito, produto entregue antes do combinado.
## 2
## 3 Chegou antes do prazo previsto e o produto surpreendeu pela qualidade. Muito satisfatório.
## 4
## 5 Gostei pois veio no prazo determinado .
## 6
## review_creation_date review_answer_timestamp
## 1 2017-09-21 00:00:00 2017-09-22 10:57:03
## 2 2017-05-13 00:00:00 2017-05-15 11:34:13
## 3 2018-01-23 00:00:00 2018-01-23 16:06:31
## 4 2018-08-15 00:00:00 2018-08-15 16:39:01
## 5 2017-03-02 00:00:00 2017-03-03 10:54:59
## 6 2017-05-23 00:00:00 2017-05-24 19:00:09CustomerSellerProduct$shipping_limit_date.x <- format.Date(
CustomerSellerProduct$shipping_limit_date.x, "%Y-%m")
CustomerSellerProduct$shipping_limit_date.y <- format.Date(
CustomerSellerProduct$shipping_limit_date.y, "%Y-%m")
CustomerSellerProduct$review_creation_date <- format.Date(
CustomerSellerProduct$review_creation_date, "%Y-%m")
CustomerSellerProduct$review_answer_timestamp <- format.Date(
CustomerSellerProduct$review_answer_timestamp, "%Y-%m")
head(CustomerSellerProduct)## order_id order_item_id.x
## 1 00010242fe8c5a6d1ba2dd792cb16214 1
## 2 00018f77f2f0320c557190d7a144bdd3 1
## 3 000229ec398224ef6ca0657da4fc703e 1
## 4 00024acbcdf0a6daa1e931b038114c75 1
## 5 00042b26cf59d7ce69dfabb4e55b4fd9 1
## 6 00048cc3ae777c65dbb7d2a0634bc1ea 1
## product_id.x seller_id.x
## 1 4244733e06e7ecb4970a6e2683c13e61 48436dade18ac8b2bce089ec2a041202
## 2 e5f2d52b802189ee658865ca93d83a8f dd7ddc04e1b6c2c614352b383efe2d36
## 3 c777355d18b72b67abbeef9df44fd0fd 5b51032eddd242adc84c38acab88f23d
## 4 7634da152a4610f1595efa32f14722fc 9d7a1d34a5052409006425275ba1c2b4
## 5 ac6c3623068f30de03045865e4e10089 df560393f3a51e74553ab94004ba5c87
## 6 ef92defde845ab8450f9d70c526ef70f 6426d21aca402a131fc0a5d0960a3c90
## shipping_limit_date.x price.x freight_value.x product_category_name.x
## 1 2017-09 58.90 13.29 cool_stuff
## 2 2017-05 239.90 19.93 pet_shop
## 3 2018-01 199.00 17.87 moveis_decoracao
## 4 2018-08 12.99 12.79 perfumaria
## 5 2017-02 199.90 18.14 ferramentas_jardim
## 6 2017-05 21.90 12.69 utilidades_domesticas
## product_category_name_english.x product_name_lenght.x
## 1 cool_stuff 58
## 2 pet_shop 56
## 3 furniture_decor 59
## 4 perfumery 42
## 5 garden_tools 59
## 6 housewares 36
## product_description_lenght.x product_photos_qty.x product_weight_g.x
## 1 598 4 650
## 2 239 2 30000
## 3 695 2 3050
## 4 480 1 200
## 5 409 1 3750
## 6 558 1 450
## product_length_cm.x product_height_cm.x product_width_cm.x
## 1 28 9 14
## 2 50 30 40
## 3 33 13 33
## 4 16 10 15
## 5 35 40 30
## 6 24 8 15
## product_category_name.y product_category_name_english.y
## 1 cool_stuff cool_stuff
## 2 pet_shop pet_shop
## 3 moveis_decoracao furniture_decor
## 4 perfumaria perfumery
## 5 ferramentas_jardim garden_tools
## 6 utilidades_domesticas housewares
## product_id.y product_name_lenght.y
## 1 4244733e06e7ecb4970a6e2683c13e61 58
## 2 e5f2d52b802189ee658865ca93d83a8f 56
## 3 c777355d18b72b67abbeef9df44fd0fd 59
## 4 7634da152a4610f1595efa32f14722fc 42
## 5 ac6c3623068f30de03045865e4e10089 59
## 6 ef92defde845ab8450f9d70c526ef70f 36
## product_description_lenght.y product_photos_qty.y product_weight_g.y
## 1 598 4 650
## 2 239 2 30000
## 3 695 2 3050
## 4 480 1 200
## 5 409 1 3750
## 6 558 1 450
## product_length_cm.y product_height_cm.y product_width_cm.y order_item_id.y
## 1 28 9 14 1
## 2 50 30 40 1
## 3 33 13 33 1
## 4 16 10 15 1
## 5 35 40 30 1
## 6 24 8 15 1
## seller_id.y shipping_limit_date.y price.y
## 1 48436dade18ac8b2bce089ec2a041202 2017-09 58.90
## 2 dd7ddc04e1b6c2c614352b383efe2d36 2017-05 239.90
## 3 5b51032eddd242adc84c38acab88f23d 2018-01 199.00
## 4 9d7a1d34a5052409006425275ba1c2b4 2018-08 12.99
## 5 df560393f3a51e74553ab94004ba5c87 2017-02 199.90
## 6 6426d21aca402a131fc0a5d0960a3c90 2017-05 21.90
## freight_value.y review_id review_score
## 1 13.29 97ca439bc427b48bc1cd7177abe71365 5
## 2 19.93 7b07bacd811c4117b742569b04ce3580 4
## 3 17.87 0c5b33dea94867d1ac402749e5438e8b 5
## 4 12.79 f4028d019cb58564807486a6aaf33817 4
## 5 18.14 940144190dcba6351888cafa43f3a3a5 5
## 6 12.69 5e4e50af3b7960b7a10d86ec869509e8 4
## review_comment_title
## 1
## 2
## 3
## 4
## 5
## 6
## review_comment_message
## 1 Perfeito, produto entregue antes do combinado.
## 2
## 3 Chegou antes do prazo previsto e o produto surpreendeu pela qualidade. Muito satisfatório.
## 4
## 5 Gostei pois veio no prazo determinado .
## 6
## review_creation_date review_answer_timestamp
## 1 2017-09 2017-09
## 2 2017-05 2017-05
## 3 2018-01 2018-01
## 4 2018-08 2018-08
## 5 2017-03 2017-03
## 6 2017-05 2017-05# Seller average review score...
seller_scores <- aggregate(review_score ~ seller_id.x, data = CustomerSellerProduct, FUN = mean)
poorly_rated_sellers <- seller_scores[seller_scores$review_score < 3.0, ]Graph and modeling
Graphing
# total customer satisfaction
CustomerSat <- ggplot(CustomerSellerProduct, aes(review_score)) +
geom_bar(fill = "steelblue")
# Seller score averages
Seller_Score <- ggplot(data = seller_scores, aes(seller_id.x, review_score)) +
geom_jitter(aes(color = review_score), size = 1.5) +
scale_color_gradient(low = "blue4", high = "green2")
# All seller sales per month.
monthly_revenue <- CustomerSellerProduct %>%
mutate(month = shipping_limit_date.x) %>%
group_by(month) %>%
summarise(total_revenue = sum(price.x))
Seller_Gross_Rev <- ggplot(data = monthly_revenue, aes(x = month, y = total_revenue)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Month") +
ylab("Gross") +
ggtitle("Gross Seller Sales") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# All seller Modeling the data
# modeling the relationship between reviews and seller id
kruskal.test(review_score ~ seller_id.x, data = CustomerSellerProduct)##
## Kruskal-Wallis rank sum test
##
## data: review_score by seller_id.x
## Kruskal-Wallis chi-squared = 19972, df = 2988, p-value < 2.2e-16# modeling the relationship between month and gross estimated
monthly_revenue <- na.omit(monthly_revenue)
library(zoo)##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericmonthly_revenue <- CustomerSellerProduct %>%
mutate(month = as.yearmon(shipping_limit_date.x)) %>%
group_by(month) %>%
summarise(total_revenue = sum(price.x))
model <- lm(total_revenue ~ month, data = monthly_revenue)
summary(model)##
## Call:
## lm(formula = total_revenue ~ month, data = monthly_revenue)
##
## Residuals:
## Min 1Q Median 3Q Max
## -315410 -80512 -1936 92756 267522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.389e+09 1.450e+08 -9.577 1.73e-08 ***
## month 6.888e+05 7.188e+04 9.583 1.71e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 154500 on 18 degrees of freedom
## Multiple R-squared: 0.8361, Adjusted R-squared: 0.827
## F-statistic: 91.82 on 1 and 18 DF, p-value: 1.715e-08Analyze
I’ve developed multiple graphs showing valuable insights into the customers satisfaction and buisness gross growth.
customer satisfaction overall
Seller ratings on average
Estimated Gross Revenue
### Disclaimer
First I’d like to say that I chose not to sample the data set to keep the number of observations high for more precise analysis.
Second I’d like to say it’s possible for these results to be subject to human error.
Lastly, thanks for viewing my 3rd case study.
Conclusion
Total sales for all sellers grew exponentially until 2018, where growth appeared to be at near flat-line.
Total customer satisfaction is relatively high, though some sellers influenced the lower ratings.
Poor rated sellers are shown below;
## seller_id.x review_score
## 11 010da0602d7774602cd1b3f5fb7b709e 1.000000
## 29 02d35243ea2e497335cd0f076b45675d 2.782609
## 30 02dcd3e8e25bee036e32512bcf175493 2.760000
## 48 04aa0a1c5ce6b222003403a3e11c3cc0 2.926829
## 54 052577151711aec5c744fe12e66db3e1 2.250000
## 88 0791d9fc1e30678ecf03d3e55fa108d3 1.627907
## 91 07bf9669d84d1f11be443a9dd938f698 2.777778
## 92 07d75e33f2750d97d467fb57e4dfdd8a 2.500000
## 102 0873d9f8f36123f8d910f4760e788cfb 2.625000
## 105 08ad4ac1388e4420ca531c3edfc46198 2.400000
## 106 08cdbae123ff67ca4e36d9d641ce0119 1.769231
## 117 099095b050cfe8eb1ddff5317587e96e 2.576923
## 123 0aa124728afc1131dff5655f4c6f487b 1.000000
## 124 0aa2205ca24f113f4658a5c536667426 1.727273
## 157 0d83f8e03188682112cc0d93523705cc 1.444444
## 164 0dfbed20065e425d2eaefb101f9816c0 2.666667
## 167 0e982cff76cc0579f632cea8a0e38c9d 2.514286
## 185 101921376b577a4540dc30e9009133ca 2.000000
## 207 1284de4ae8aa26997e748c851557cf0e 2.666667
## 216 134285d1f41da5c13a756ee8142c8a4e 2.166667
## 220 1352e06ae67b410cdae0b2a22361167b 1.000000
## 232 1444c08e64d55fb3c25f0f09c07ffcf2 1.000000
## 237 14f2b3587172b9db894c9bad8dab520b 2.500000
## 239 154bdf805377afea75a3bd158e9eab10 1.000000
## 244 15ac3c501e2599e4917316fde5c5669a 1.000000
## 245 15aec03fe4cf30dfa574cf550f5ff5ff 1.000000
## 246 15b3b1b81484422eb41df68ac87f1f50 1.000000
## 270 17adeba047385fb0c67d8e90b4296d21 1.000000
## 285 191d55f7edb8524d525ed01b20fe32e8 2.333333
## 287 19484c79cef6c062cb177aa4ef2fcc3c 1.000000
## 288 1967a9e2ad6f51802b093147d861df58 1.000000
## 292 1992f8fb6b19fcccd97ca819811e7267 1.000000
## 298 1a932caad4f9d804097d7f8e615baed1 2.928571
## 303 1b4b28463457a256e9a784ebe2a8f630 1.000000
## 306 1b65c144b17e607c0f37f10bb7dfec8d 2.333333
## 322 1ca7077d890b907f89be8c954a02686a 2.670588
## 346 1dfe5347016252a7884b694d4f10f5c4 2.853659
## 348 1e47d8b6546eb3a77b1acddcc9406242 1.000000
## 358 1f7dfad2cb384ea4d2d7e1ffbd78c407 1.000000
## 362 1fbe10c70e30765ed1de86d9f7e98409 1.115385
## 364 1fddcb7b326905d3ad5efe82187db347 1.000000
## 374 2078fe5066350e7d220c0ad3a3bbc6c1 2.000000
## 380 20d53aad4fe5ee93a64f8839609d3586 2.500000
## 382 20fd2d2080ed85fa67fad3fcbb2c1813 1.000000
## 412 244b04680fdbded0acf5aebd9c92b44a 1.000000
## 416 249f0e9905a6e06ad6c6bea7547ab9f6 2.333333
## 420 2528744c5ef5d955adc318720a94d2e7 2.400000
## 430 25debeafbce801fdd479539350185eee 2.000000
## 450 2709af9587499e95e803a6498a5a56e9 1.158120
## 451 270d3ef21830ad0ecf3a6c53d08a533a 2.194444
## 457 278b6e0b20c4f61fefaa0577943d7a35 1.000000
## 474 299c89a525fee36c567f4fe72f038100 2.000000
## 477 2a1348e9addc1af5aaa619b1a3679d6b 2.617647
## 479 2a261b5b644fa05f4f2700eb93544f2c 2.607656
## 480 2a50b7ee5aebecc6fd0ff9784a4747d6 1.000000
## 494 2b3b9ce054da76a7428df143a567b156 1.842105
## 506 2c3d56f92f360fb89d849b84e8f50eae 2.857143
## 513 2cb4700db635baa1c0d4f90ed27b6669 2.000000
## 528 2dd1fb6f0e42a2243b8a01e40cd41216 2.142857
## 534 2e3be8a987a30d7544dbbda6861cc14e 2.857143
## 538 2eb70248d66e0e3ef83659f71b244378 2.781250
## 539 2ec6e37d992a95eaddb3369dcbcc76d9 1.666667
## 543 2f09f7d37a97956d11cc1a7cadd5262b 2.222222
## 561 30c7f28fd3a5897b2c82d152bb760c17 1.666667
## 562 312ba1d77e9c332ef21f9598b7f64cd7 2.583333
## 572 324583d68897f5ddbaf38f6248322176 1.000000
## 574 327b89b872c14d1c0be7235ef4871685 2.333333
## 579 32f1bb9dfa65bc7df5c0d9d95a616be4 2.714286
## 582 3338e7a0710a195872be80e0d2423867 1.000000
## 600 340636a908b13bbe0f4fb63fbe1d0c03 2.500000
## 603 344223b2a90784f64136a8a5da012e7f 2.000000
## 609 34aefe746cd81b7f3b23253ea28bef39 1.000000
## 614 353e21e8bf8de2722b4fae1636664b76 2.000000
## 618 35ef5aa32949680f627efec3c23e705c 2.571429
## 620 3606774ec64634822d78dc2db6d37045 1.000000
## 635 37ceb404da208418c9032f0674e57804 1.000000
## 645 38e6dada03429a47197d5d584d793b41 2.333333
## 647 391bbd13b6452244774beff1824006ed 1.000000
## 669 3a52d63a8f9daf5a28f3626d7eb9bd28 1.000000
## 711 3da38366e7bd9baf6369071f782ecdf0 1.000000
## 728 3f8034bfc6b8b4135534975d49f04c63 1.956522
## 738 3febca52652e7209509ccfe61cbde40e 2.000000
## 740 4003520d80d0bad1d5623f7aa3c63cda 1.000000
## 745 40536e7ca18e1bce252828e5876466cc 1.000000
## 748 4091f77d8c768fc3e81c20761471761d 2.750000
## 750 40db9e9aa57f7bb151bcda6b0f9bdbb7 2.500000
## 773 42d4b7e70819438f5ada61c965e92d7e 1.000000
## 784 4342d4b2ba6b161468c63a7e7cfce593 1.263158
## 791 43a5d4a57af536fddc96d08be3afdfcb 1.000000
## 796 443d880f15cbd3572885e1d44bf2c478 2.000000
## 802 44f091b5abab8018f682fce8124b85e5 2.500000
## 807 455f46ef09a9e45667e2981df84b5cc2 1.000000
## 809 458c6fa920810ce2492da71bc26d9eec 2.500000
## 826 48328ec9a579151d9cc6a86d4a59336c 2.000000
## 838 48fb026ed7455a42f5d3e9390ed5088e 2.500000
## 852 4a1917ef994878940e1fb03eeb787ad7 2.400000
## 863 4b1eaadf791bdbbad8c4a35b65236d52 1.666667
## 868 4bc3d93e4a5f88a598d3d8ce4ae510b3 1.000000
## 888 4ce8b55b4bfa4659184e7becc1fba3f2 2.333333
## 894 4d6fb82c629c7a5028bfd973c07b05e0 2.000000
## 901 4dc646b9b9e8e411ef48ba000ea3ac6f 2.000000
## 904 4deebb3199eeafb282ae33229144f94b 2.714286
## 908 4e2627090e6e5b9fabba883a37897683 1.000000
## 909 4e326052e5dbba8adcd512f3450a307e 1.285714
## 910 4e42581f08e8cfc7c090f930bac4552a 1.000000
## 916 4e8dacf3d38f281ae26c3e0321d92d88 2.074074
## 926 4f40d2ed38d1cc945364a7cd202a82c7 1.000000
## 932 5058e8c1e82653974541e83690655b4a 2.609023
## 938 50c361bcf670d16f6df4d52de3dff924 1.000000
## 944 5151aea44289d6c6b090ee31c2132508 1.833333
## 951 51a04a8a6bdcb23deccc82b0b80742cf 1.000000
## 971 52ac6f920b7280e4c6d30dd94de6259b 2.500000
## 975 52f0fe436a347ddad7ed5f9aa4e27eaa 2.588235
## 976 52f6f9f13ba19504c02781132a049e75 2.000000
## 988 538caafddff204241cecbf3a02e6b3cf 2.500000
## 1000 54965bbe3e4f07ae045b90b0b8541f52 2.750000
## 1005 553a440137d6e0cf776aee0bb83ac9f5 1.000000
## 1008 556a6409838156ea299c9d1f9e2dde43 2.666667
## 1011 559d56f3b8f0bc2e5a9a62fe2ce2a39f 2.500000
## 1015 55c96925041a14097b6a7825554f4ad5 2.461538
## 1022 5659d77e8d90378e39b960ad5956f64e 2.500000
## 1023 56642bcb79900e777d68e91915cb4267 2.811111
## 1025 56a8b450e802502ba53a9885ee38e29f 1.000000
## 1046 586a871d4f1221763fddb6ceefdeb95e 1.000000
## 1048 5882f40daf39bc2a465e0cb453ddc222 2.000000
## 1049 58b98ccb79873e04eac4357cacc590d9 1.000000
## 1051 58e4b302b54937e55a678c4d15111da4 2.333333
## 1074 5aaa890629f83706d8d9bfecd8377c1c 1.000000
## 1077 5acd070dd3fe441bbb2ec1f1ede515ee 1.666667
## 1083 5b35136197710e16ac5e7b7c2bd4ea85 2.538462
## 1084 5b51032eddd242adc84c38acab88f23d 2.875000
## 1095 5bc55dbe2f12b6af6d83ed46023e0dc8 2.108108
## 1101 5c603ffb38cbbf25a0e0698941324143 1.000000
## 1104 5ca5d579b5857ee8709479b1047cecc3 1.000000
## 1108 5d043cd5512d4bd2f88e5ccdd5736c38 2.000000
## 1116 5dc4bdaf869e8c77c648078cbe8d59c2 1.000000
## 1142 602044f2c16190c2c6e45eb35c2e21cb 2.666667
## 1149 60ad151920c4f6f3ebbe8cfdf6166779 1.000000
## 1159 61b893c57e33626afb104d4112b1be76 1.000000
## 1160 61c36f0fc4a47f9532e5512b66668e62 1.000000
## 1161 61f159ef6da2d441951d2c0efa719362 1.000000
## 1169 62c50c1af4dfdc4149d25c5222043d39 2.545455
## 1171 630008e0f062605a415d694489f6f82e 2.600000
## 1175 633ecdf879b94b5337cca303328e4a25 1.800000
## 1177 63704069d9bd3a75c1cf59babe56004a 1.000000
## 1182 63bc58679c44e8a998792119fd7ad23e 1.000000
## 1189 64b87978a11c1ea7501a89eabe8c2c1a 2.000000
## 1200 6576fd3e23c88f0e5d4d23f39bba0542 1.000000
## 1208 6614814a00d344b846ae209f95ee7e3f 1.000000
## 1211 6650fcccd8cd2f7e55ffa524f30c4c59 2.250000
## 1216 66d5ae34537110d3affc56174dad2b2f 1.000000
## 1217 66dc1b1632bd6a3e508510c5b3492107 2.983051
## 1219 66fd84ee7528c0c0de85d0f44a73d5ab 2.152174
## 1229 67e43d802fde8cfd3f9580124f8167d1 1.000000
## 1233 685b870da5b52a710782c9c0fefbeb5b 2.000000
## 1235 6861de0f40cacb0602e473c51639f980 1.650000
## 1273 6bef34c5be6f2bbd70aa5165f6df94b1 2.000000
## 1284 6cf476a4ca74498db55cbccdaa9dcfb6 2.000000
## 1285 6d04126aba80df143fd038e711b8fd96 1.000000
## 1293 6dc9bec584588412a6a338830946a3e4 1.800000
## 1300 6e2d3e2dc84fb30f6078f39dd3bddd6f 1.000000
## 1304 6e85dc5ecd97a61094b89b046a509d8e 1.000000
## 1310 6ee85be3693ed79a8e80718743d80655 1.363636
## 1311 6eeed17989b0ae47c9f11ece6f38ea90 2.333333
## 1312 6f1a1263039c76e68f40a8e536b1da6a 2.684211
## 1340 71271995e85f5b8530be99ed54a91b89 2.500000
## 1357 7210cd29727d674c00741e5e387b3ccd 2.592593
## 1364 725af026f869236a8dd0ceb98564d156 2.700000
## 1388 747c1cdcd1737dcfbd1547f6eb6f2cf3 1.375000
## 1393 74bafed391319577bce6f4d2acc0589a 2.666667
## 1399 7586919161935337bf6b6d7ff5779648 2.166667
## 1404 762ed976527cddf02787edba70fffa2a 2.166667
## 1412 76d336555bb25f3165d2f0d6da719c40 2.000000
## 1415 76de363de16dacc923eec8daf91df504 2.500000
## 1424 77dcbb67ccb3659bd58eaf5661560a30 1.000000
## 1429 781a66c0d89e30814d49fe8b5e14cd07 1.000000
## 1441 791cfcfe22fe4a771ece27f90017da92 1.500000
## 1445 7994081388d55b9cf976c050653dd359 1.153846
## 1451 79ee721b6f766066a80264402548d359 1.000000
## 1452 7a188843479798ca813108fa7b959286 1.000000
## 1453 7a241947449cc45dbfda4f9d0798d9d0 2.316901
## 1472 7b7f91e98f8c6def45a1407923304402 2.346154
## 1476 7bcd7c5f8631701474db233ccf1c094b 2.500000
## 1485 7c9130f5f3729edd32aa059141d92484 2.500000
## 1486 7caa63f175b1cecbfaadd8b5ab9998c7 1.666667
## 1506 7e3dde9ffba72d5340f9aaf09b4424be 1.000000
## 1510 7e959205cebc10adefcb331f934238a4 1.000000
## 1522 7fdb0720c8d7c9075538b365dc8c3a22 2.200000
## 1535 8132b9bd16876e1b0f8808d43825dd48 1.800000
## 1569 83d7070a0b9a8b4625fa8682b819fabb 1.000000
## 1573 8444e55c1f13cd5c179851e5ca5ebd00 2.552632
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## 1598 86bb7c4b535e49a541baf3266b1c95b1 2.916667
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## 1634 89de2d6f23e9746ff309705b23581faa 2.714286
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## 1644 8a87611c08849ffeeccab52aa798b6c7 2.529412
## 1655 8b87cd0d43d49b9bdf40f56229364d59 2.600000
## 1659 8bd0e3abda539b9479c4b44a691be1ec 1.325000
## 1665 8c3b533c63cca56240f94f1e3a6b18ef 1.363636
## 1675 8cf21434553b02cbd37c0bbd07e041b0 2.500000
## 1678 8d899e15a5925f097cca50faa49b15e3 1.547619
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## 1692 8e6d7754bc7e0f22c96d255ebda59eba 1.571861
## 1698 8f0fbe2cd4d472157dc1cdef6edecaa9 2.333333
## 1714 8ff38bc3969e67c36c48343a07090f66 2.500000
## 1720 90b36c5aeb728d5504a39f435bef0c5f 2.230769
## 1722 90d4125885ab6c86e8820a722be71974 1.000000
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## 1739 92992bec39dde5766f590d3b1f46d465 1.000000
## 1742 92d14edfb62ee5ae7fdff70b0f4e7233 1.891892
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## 1774 95f83f51203c626648c875dd41874c7f 2.448980
## 1787 96f7c797de9ca20efbe14545bed63eec 1.000000
## 1788 972d0f9cf61b499a4812cf0bfa3ad3c4 2.820000
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## 1800 984c273121e743dd14767befe6cb17f5 1.000000
## 1806 98e164d549402270247f3c96a1a158ef 2.500000
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## 1892 a0d7fbb07bb5ebc3b05980e750db56f3 1.571429
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## 1925 a36ac007a4d18f865c8d32c3b2402c2d 2.555556
## 1943 a49928bcdf77c55c6d6e05e09a9b4ca5 2.811024
## 1944 a4b6b9b992b46e9ef863637af96e04bc 1.800000
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## 1947 a4e634166243bbb3a13fa584463fa468 2.000000
## 2004 ab3e0c171fe84a7ba7de130f19cfb485 2.666667
## 2005 ab75b89cc49c9ab3160d0c91565a442a 1.333333
## 2013 ac1ed5fc15901fbc92920361eb4ab350 1.000000
## 2015 ac51cac5e20d69ff485a54746488ef48 1.000000
## 2021 ace601496e211a664100321f038f50af 2.000000
## 2025 ad273c9eb54ecb4532de4bf75bae9e4e 2.947368
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## 2031 ad8ada0ffa993cb1197d102d9ab21a3e 1.000000
## 2036 adcf50477d6a1f5d91bb9d54c4903cb5 2.989362
## 2038 aded58c8142dedc5494a81f948431fab 2.222222
## 2056 b0b346d3a89f5eb4c2968af3f083cd43 2.785714
## 2058 b1204cfda1d4948679db3cfc0d3cb95c 2.451613
## 2067 b19f3ca2ea475913750f25a5c37c8d8f 2.625000
## 2071 b1b3948701c5c72445495bd161b83a4c 1.722222
## 2092 b37c4c02bda3161a7546a4e6d222d5b2 1.094118
## 2099 b43d8b707f887a30f4675391098354a0 1.750000
## 2112 b56828a9f9027f3c918c04ca720d9e3c 2.666667
## 2114 b57e8460909fa137df7951b4a3b5ea84 2.000000
## 2116 b5abf4f36adc043117b4fca82c22984c 2.266667
## 2126 b6c6854d4d92a5f6f46be8869da3fa1a 1.000000
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## 2139 b7ed9fb14c8eadb37adb9c45d67ab0fb 2.476190
## 2178 bb135baca94c82fcb731335ad5b04a03 2.317460
## 2180 bb3dca718920a4d7ec3cc9ffb025108b 1.800000
## 2188 bbad7e518d7af88a0897397ffdca1979 2.547771
## 2190 bbd3acfe6de99892e73940d3fdceaf68 2.333333
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## 2243 c004e5ea15737026cecaee0447e00b75 1.000000
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