Amazon Brands And Exclusives by Kaggle | Finance and Economics

About this Dataset

Amazon Brands And Exclusives

Amazon Brands and Exclusives

Dataset from "Amazon Puts Its Own 'Brands' First Above Better-Rated Products"

About this dataset

This dataset contains information on the quality and sales of products on Amazon, as well as on the percentage of time that Amazon's own products are featured in the Buy Box. It also includes data on the top searches and generic searches on Amazon, as well as on the percentage of panelists who ranked each trait as very important or somewhat important when choosing a product on Amazon

Research Ideas

Determine which features are most important to customers when they are shopping on Amazon.

Understand how Amazon's own products compare to other products in terms of quality and sales.

Study how Amazon's marketing and ranking algorithms work, in order to optimize product listings on the site

Acknowledgements

Acknowledgements
The datasets used in this article were provided by The Markup

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: combined_queries_with_source.csv

Column name	Description
search_term	The search term used on Amazon. (String)
source	The source of the search term. (String)

File: quality_and_sales_comparisons.csv

Column name	Description
search_term	The search term used on Amazon. (String)
position_first_amazon	The position of the first Amazon product in the search results. (Integer)
position_first_non_amazon	The position of the first non-Amazon product in the search results. (Integer)
position_first_wholly_non_amazon	The position of the first wholly non-Amazon product in the search results. (Integer)
amazon_stars	The average star rating for Amazon products in the search results. (Float)
amazon_reviews	The average number of reviews for Amazon products in the search results. (Integer)
non_amazon_stars	The average star rating for non-Amazon products in the search results. (Float)
non_amazon_reviews	The average number of reviews for non-Amazon products in the search results. (Integer)
wnon_amazon_stars	The average star rating for wholly non-Amazon products in the search results. (Float)
wnon_amazon_reviews	The average number of reviews for wholly non-Amazon products in the search results. (Integer)

File: amazon_trademarked_brands.csv

Column name	Description
Word Mark	The word mark of the product. (String)
Goods and Services	The goods and services associated with the product. (String)
Filing Date	The date on which the product was filed. (Date)

File: fig2-scatter.csv

Column name	Description
****
Category	The category of the product. (String)
Perc Products	The percentage of products in the category that are sponsored. (Float)
Perc #1 spot	The percentage of products in the category that are in the #1 spot in the search results. (Float)
Perc first row	The percentage of products in the category that are in the first row of the search results. (Float)

File: fig3a-heatmap_amzn.csv

Column name	Description
****

File: fig3b-heatmap-unaffilated.csv

Column name	Description
****

File: fig3c-heatmap-sponsored.csv

Column name	Description
****

File: fig4-panel-brands.csv

Column name	Description
****
Brand	The brand of the product. (String)
Is an Amazon product	Whether the product is an Amazon product or not. (Boolean)
Is not an Amazon product	Whether the product is not an Amazon product or not. (Boolean)
Not sure	Whether it is not sure if the product is an Amazon product or not. (Boolean)

File: fig5-panel_ranking.csv

File: fig6-RF-feature_importance.csv

Column name	Description
****
0

File: fig7-1vN.csv

File: appendix-RF_ablation_feature_permutations.csv

File: appendix-RF_singlefeature.csv

File: table1-not_always_labelled.csv

Column name	Description
****
percentage	The percentage of products that are sponsored. (Numeric)

File: table2-RF_ablationstudy.csv

File: table2-panel_ranking.csv

File: table3-appendix-generic.csv

Column name	Description
****

The

Tables

Amazon Trademarked Brands

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.amazon_trademarked_brands

118.1 KB
158 rows
4 columns


CREATE TABLE amazon_trademarked_brands (
  "word_mark" VARCHAR,
  "goods_and_services" VARCHAR,
  "filing_date" VARCHAR,
  "owner" VARCHAR
);

Appendix Rf Ablation Feature Permutations

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.appendix_rf_ablation_feature_permutations

4.4 KB
127 rows
2 columns


CREATE TABLE appendix_rf_ablation_feature_permutations (
  "cols" VARCHAR,
  "accuracy" DOUBLE
);

Appendix Rf Singlefeature

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.appendix_rf_singlefeature

2.32 KB
7 rows
2 columns


CREATE TABLE appendix_rf_singlefeature (
  "feature" VARCHAR,
  "accuracy" DOUBLE
);

Combined Queries With Source

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.combined_queries_with_source

169.14 KB
15802 rows
2 columns


CREATE TABLE combined_queries_with_source (
  "search_term" VARCHAR,
  "source" VARCHAR
);

Fig2 Scatter

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig2_scatter

4.33 KB
4 rows
5 columns


CREATE TABLE fig2_scatter (
  "unnamed_0" BIGINT,
  "category" VARCHAR,
  "perc_products" DOUBLE,
  "perc_1_spot" DOUBLE,
  "perc_first_row" DOUBLE
);

Fig3a Heatmap Amzn

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig3a_heatmap_amzn

3.01 KB
60 rows
2 columns


CREATE TABLE fig3a_heatmap_amzn (
  "unnamed_0" BIGINT,
  "product_order" DOUBLE
);

Fig3b Heatmap Unaffilated

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig3b_heatmap_unaffilated

3 KB
60 rows
2 columns


CREATE TABLE fig3b_heatmap_unaffilated (
  "unnamed_0" BIGINT,
  "product_order" DOUBLE
);

Fig3c Heatmap Sponsored

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig3c_heatmap_sponsored

3 KB
60 rows
2 columns


CREATE TABLE fig3c_heatmap_sponsored (
  "unnamed_0" BIGINT,
  "product_order" DOUBLE
);

Fig4 Panel Brands

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig4_panel_brands

4.53 KB
9 rows
5 columns


CREATE TABLE fig4_panel_brands (
  "unnamed_0" BIGINT,
  "brand" VARCHAR,
  "is_an_amazon_product" BIGINT,
  "is_not_an_amazon_product" BIGINT,
  "not_sure" BIGINT
);

Fig5 Panel Ranking

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig5_panel_ranking

2.42 KB
5 rows
2 columns


CREATE TABLE fig5_panel_ranking (
  "trait" VARCHAR,
  "n__of_panelists" BIGINT
);

Fig6 Rf Feature Importance

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig6_rf_feature_importance

2.22 KB
7 rows
2 columns


CREATE TABLE fig6_rf_feature_importance (
  "unnamed_0" VARCHAR,
  "n_0" DOUBLE
);

Fig7–1vn

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.fig7_1vn

10.98 KB
47 rows
9 columns


CREATE TABLE fig7_1vn (
  "all_features" DOUBLE,
  "without_stars_delta" DOUBLE,
  "without_reviews_delta" DOUBLE,
  "without_is_amazon" DOUBLE,
  "without_is_shipped_by_amazon" DOUBLE,
  "without_is_sold_by_amazon" DOUBLE,
  "without_is_top_clicked" DOUBLE,
  "without_random_noise" DOUBLE,
  "up_to_product" BIGINT
);

Quality And Sales Comparisons

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.quality_and_sales_comparisons

101.83 KB
3492 rows
10 columns


CREATE TABLE quality_and_sales_comparisons (
  "search_term" VARCHAR,
  "position_first_amazon" DOUBLE,
  "position_first_non_amazon" BIGINT,
  "position_first_wholly_non_amazon" DOUBLE,
  "amazon_stars" DOUBLE,
  "amazon_reviews" DOUBLE,
  "non_amazon_stars" DOUBLE,
  "non_amazon_reviews" DOUBLE,
  "wnon_amazon_stars" DOUBLE,
  "wnon_amazon_reviews" DOUBLE
);

Table1 Not Always Labelled

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.table1_not_always_labelled

2.35 KB
4 rows
2 columns


CREATE TABLE table1_not_always_labelled (
  "unnamed_0" VARCHAR,
  "percentage" DOUBLE
);

Table2 Panel Ranking

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.table2_panel_ranking

2.42 KB
5 rows
2 columns


CREATE TABLE table2_panel_ranking (
  "trait" VARCHAR,
  "n__of_panelists" BIGINT
);

Table2 Rf Ablationstudy

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.table2_rf_ablationstudy

3.07 KB
8 rows
3 columns


CREATE TABLE table2_rf_ablationstudy (
  "feature" VARCHAR,
  "accuracy" DOUBLE,
  "change_of_accuracy" DOUBLE
);

Table3 Appendix Generic

@kaggle.thedevastator_amazon_s_dominance_in_e_commerce_why_you_should.table3_appendix_generic

4.09 KB
5 rows
5 columns


CREATE TABLE table3_appendix_generic (
  "unnamed_0" VARCHAR,
  "top_searches" VARCHAR,
  "unnamed_2" VARCHAR,
  "generic_searches" VARCHAR,
  "unnamed_4" VARCHAR
);