Name: Summer Products Sales Performance
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Summer Products Sales Performance

E-commerce sales performance and ratings data for summer products

By Jeffrey Mvutu Mabilama [source]

About this dataset

The Summer Products and Sales Performance dataset is a comprehensive collection of product listings, ratings, and sales data from the Wish platform. The dataset aims to provide insights into the trends and patterns in e-commerce during the summer season. It contains valuable information such as product titles, prices, retail prices, currency used for pricing, units sold, whether ad boosts are used for product listings, average ratings for products, total ratings count for products, counts of five-star to one-star ratings for products.

Additionally, the dataset includes data on various aspects related to product quality and shipping options such as badges count (indicating special qualities), local product status (whether the product is sold locally), product quality rating badges (indicating the quality of the product), fast shipping availability badges (indicating whether fast shipping is available), tags associated with products (making them more discoverable), color variations of products available in inventory along with their count. It also provides information on different shipping options including option names and their corresponding prices.

Moreover,the dataset encompasses details about merchants selling these products including merchant title and name as well as information on merchant rating count (total number of ratings received by merchants) ,merchant profile picture availability,and subtitle which gives additional details about merchant's info.

The dataset further includes links to images of individual listed products along with links to respective online shop pages where these are found . In addition,currency buyer specifies currency type used by buyers throughout various transactions.Items flagged under urgency text have an associated urgency text rate indicating how urgently they are desired or needed.

This comprehensive dataset also allows users to analyze units sold per listed item as well as mean units sold per listed item across different categories/theme .Further evaluation can be done using totalunitsold variable which represents total volume sales from all listed items tied together across Wish platform.

Aiding further analysis around elasticity theory users can find marked down rates/percentage tagged describing discounts over retail price,ranging from 0-1 as well as average discount values for individual listed products.Further custom insights such as number of countries items can be delivered to, their origin country, if they possess an urgency banner or fast shipping and if the seller is famous/has a profile picture.

This comprehensive dataset served to build model helping sellers predict how well an item may sell so as to equip businesses with ability to make replenishment decisions guided by this model

How to use the dataset

Familiarize Yourself with the Columns:

Before diving into data analysis, it's important to understand the meaning of each column in the dataset. The columns contain information such as product titles, prices, ratings, inventory details, shipping options, merchant information, and more. Refer to the dataset documentation or use descriptive statistics methods to gain insights into different attributes.

Explore Product Categories:

The dataset includes a column named theme that represents the category or theme of each product listing. By analyzing this column's values and frequency distribution, you can identify top-selling categories during the summer season. This information can be beneficial for businesses looking to optimize their product offerings.

Analyze Pricing Data:

The columns like price, retail_price, and currency_buyer provide insights into pricing strategies employed by sellers on Wish platform.

Calculate various statistical measures like mean price using 'meanproductprices', highest priced items using 'price', average discount using averagediscount'

Investigate relationships between pricing factors such as discounted prices compared to original retail prices ('discounted price' = 'retail_price' - 'price').

Examine Ratings Data:
4a) Analyze Product Ratings:
To gauge customer satisfaction levels regarding products listed on Wish platform products rating features have been provided.
Available columns-
-> Number of ratings received per star rating
-> Total number of ratings received (rating_count)
-> Average rating (rating)
Perform analysis to find:
- Average rating at product level
- Ratings distribution (number of each star rating)
A higher average rating and a favorable ratings distribution can indicate the product's popularity and customer satisfaction.

4b) Ratings vs. Product Sales:
Analyze the correlation between product ratings and sales performance using columns like rating and units_sold. Evaluate whether highly-rated products tend to have higher sales volumes. This can help businesses understand the importance of quality in driving sales.

Explore Inventory Details:
Investigate columns related to inventory management such as 'product_variation_inventory' which gives

Research Ideas

Analyzing the relationship between product ratings and sales performance: This dataset provides information on product ratings and the number of units sold, which can be used to examine whether there is a correlation between higher ratings and higher sales. This analysis can help businesses understand the importance of product quality in driving sales.

Identifying top-selling categories: By analyzing the number of units sold for different product categories, businesses can identify which categories are most popular among customers during the summer season. This information can be used to guide marketing and inventory decisions, as well as inform future product development strategies.

Predicting sales performance: With the available data on various attributes such as price, shipping options, badges, and discounts, it is possible to build a predictive model to forecast the potential success of a product in terms of units sold. This could help businesses make informed decisions about stocking levels and optimize their inventory management processes

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: summer-products-with-rating-and-performance_2020-08.csv

Column name	Description
title	The title of the product. (Text)
title_orig	The original title of the product. (Text)
price	The price of the product. (Numeric)
retail_price	The original retail price of the product. (Numeric)
currency_buyer	The currency used for the price of the product. (Text)
units_sold	The number of units sold for the product. (Numeric)
uses_ad_boosts	Indicates whether the product listing uses ad boosts. (Boolean)
rating	The average rating of the product. (Numeric)
rating_count	The total number of ratings for the product. (Numeric)
rating_five_count	The number of five-star ratings for the product. (Numeric)
rating_four_count	The number of four-star ratings for the product. (Numeric)
rating_three_count	The number of three-star ratings for the product. (Numeric)
rating_two_count	The number of two-star ratings for the product. (Numeric)
rating_one_count	The number of one-star ratings for the product. (Numeric)
badges_count	The total number of badges for the product. (Numeric)
badge_local_product	Indicates whether the product is local or international. (Boolean)
badge_product_quality	Indicates the quality of the product. (Boolean)
badge_fast_shipping	Indicates if fast shipping is available for the product. (Boolean)
tags	Theme/category tags associated with each listing/product. (Text)
product_color	The color of the product. (Text)
product_variation_inventory	The number of variations of the product available in the inventory. (Numeric)
shipping_option_name	The name of the shipping option for the product. (Text)
shipping_option_price	The price of the shipping option for the product. (Numeric)
shipping_is_express	Indicates whether the shipping option is express or not. (Boolean)
countries_shipped_to	The countries to which the product can be shipped. (Text)
inventory_total	The total number of products available in the inventory. (Numeric)
has_urgency_banner	Indicates whether there is an urgency banner for the product. (Boolean)
urgency_text	The reason for the urgency banner. (Text)
origin_country	The country of origin for the product. (Text)
merchant_title	The title of the merchant selling the product. (Text)
merchant_name	The name of the merchant selling the product. (Text)
merchant_info_subtitle	The subtitle of the merchant's information. (Text)
merchant_rating_count	The total number of ratings for the merchant. (Numeric)
merchant_has_profile_picture	Indicates whether the merchant has a profile picture. (Boolean)
merchant_profile_picture	The URL of the merchant's profile picture. (Text)
product_url	The URL of the product. (Text)
product_picture	The URL of the product picture. (Text)
theme	The theme or category of the product. (Text)
crawl_month	The month in which the data was crawled. (Text)

File: Computed insight - Success of active sellers.csv

Column name	Description
rating	The average rating of the product. (Numeric)
listedproducts	The number of products listed by a merchant. (Numeric)
totalunitssold	The total number of units sold by a merchant. (Numeric)
meanunitssoldperproduct	The average number of units sold per product by a merchant. (Numeric)
merchantratingscount	The total number of ratings received by a merchant. (Numeric)
meanproductprices	The average price of the products listed by a merchant. (Numeric)
meanretailprices	The average original retail price of the products listed by a merchant. (Numeric)
averagediscount	The average discount offered by a merchant on their products. (Numeric)
meandiscount	The average percentage discount offered by a merchant on their products. (Numeric)
meanproductratingscount	The average number of ratings received by a product. (Numeric)
totalurgencycount	The total number of products with an urgency banner. (Numeric)
urgencytextrate	The rate of urgency banners displayed on products. (Numeric)

File: unique-categories.csv

Column name	Description
theme	The theme or category tags associated with the product. (Text)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Jeffrey Mvutu Mabilama.

Tables

Computed Insight Success Of Active Sellers

@kaggle.thedevastator_summer_products_sales_performance.computed_insight_success_of_active_sellers

62.53 kB
958 rows
14 columns

CREATE TABLE computed_insight_success_of_active_sellers (
  "index" BIGINT,
  "merchantid" VARCHAR,
  "listedproducts" BIGINT,
  "totalunitssold" BIGINT,
  "meanunitssoldperproduct" DOUBLE,
  "rating" DOUBLE,
  "merchantratingscount" DOUBLE,
  "meanproductprices" DOUBLE,
  "meanretailprices" DOUBLE,
  "averagediscount" DOUBLE,
  "meandiscount" DOUBLE,
  "meanproductratingscount" DOUBLE,
  "totalurgencycount" DOUBLE,
  "urgencytextrate" DOUBLE
);

Summer Products With Rating And Performance 2020–08

@kaggle.thedevastator_summer_products_sales_performance.summer_products_with_rating_and_performance_2020_08

489.93 kB
1,573 rows
44 columns

CREATE TABLE summer_products_with_rating_and_performance_2020_08 (
  "index" BIGINT,
  "title" VARCHAR,
  "title_orig" VARCHAR,
  "price" DOUBLE,
  "retail_price" BIGINT,
  "currency_buyer" VARCHAR,
  "units_sold" BIGINT,
  "uses_ad_boosts" BIGINT,
  "rating" DOUBLE,
  "rating_count" BIGINT,
  "rating_five_count" DOUBLE,
  "rating_four_count" DOUBLE,
  "rating_three_count" DOUBLE,
  "rating_two_count" DOUBLE,
  "rating_one_count" DOUBLE,
  "badges_count" BIGINT,
  "badge_local_product" BIGINT,
  "badge_product_quality" BIGINT,
  "badge_fast_shipping" BIGINT,
  "tags" VARCHAR,
  "product_color" VARCHAR,
  "product_variation_size_id" VARCHAR,
  "product_variation_inventory" BIGINT,
  "shipping_option_name" VARCHAR,
  "shipping_option_price" BIGINT,
  "shipping_is_express" BIGINT,
  "countries_shipped_to" BIGINT,
  "inventory_total" BIGINT,
  "has_urgency_banner" DOUBLE,
  "urgency_text" VARCHAR,
  "origin_country" VARCHAR,
  "merchant_title" VARCHAR,
  "merchant_name" VARCHAR,
  "merchant_info_subtitle" VARCHAR,
  "merchant_rating_count" BIGINT,
  "merchant_rating" DOUBLE,
  "merchant_id" VARCHAR,
  "merchant_has_profile_picture" BIGINT,
  "merchant_profile_picture" VARCHAR,
  "product_url" VARCHAR,
  "product_picture" VARCHAR,
  "product_id" VARCHAR,
  "theme" VARCHAR,
  "crawl_month" VARCHAR
);

Unique Categories

@kaggle.thedevastator_summer_products_sales_performance.unique_categories

42.55 kB
2,620 rows
2 columns

CREATE TABLE unique_categories (
  "index" BIGINT,
  "tag" VARCHAR
);

Unique Categories Sorted By Count

@kaggle.thedevastator_summer_products_sales_performance.unique_categories_sorted_by_count

45.01 kB
2,620 rows
3 columns

CREATE TABLE unique_categories_sorted_by_count (
  "index" BIGINT,
  "count" BIGINT,
  "tag" VARCHAR
);