Dataset: Dunnhumby - The Complete Journey

About this Dataset

Dunnhumby - The Complete Journey

This dataset contains household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer. It contains all of each household’s purchases, not just those from a limited number of categories. For certain households, demographic information as well as direct marketing contact history are included.

Due to the number of tables and the overall complexity of The Complete Journey, it is suggested that this database be used in more advanced classroom settings. Further, The Complete Journey would be ideal for academic research as it should enable one to study the effects of direct marketing to customers.

The following are examples of questions that could be submitted to students or considered for academic research:


How many customers are spending more over time? Less over time? Describe these customers.
 - Of those customers who are spending more over time, which categories are growing at a faster rate?
 - Of those customers who are spending less over time, with which categories are they becoming less engaged?
 - Which demographic factors (e.g. household size, presence of children, income) appear to affect customer spend? -Engagement with certain categories?
 - Is there evidence to suggest that direct marketing improves overall engagement?

Tables

Campaign Desc

@kaggle.frtgnn_dunnhumby_the_complete_journey.campaign_desc

3.81 KB
30 rows
4 columns


CREATE TABLE campaign_desc (
  "description" VARCHAR,
  "campaign" BIGINT,
  "start_day" BIGINT,
  "end_day" BIGINT
);

Campaign Table

@kaggle.frtgnn_dunnhumby_the_complete_journey.campaign_table

18.95 KB
7208 rows
3 columns


CREATE TABLE campaign_table (
  "description" VARCHAR,
  "household_key" BIGINT,
  "campaign" BIGINT
);

Causal Data

@kaggle.frtgnn_dunnhumby_the_complete_journey.causal_data

42.65 MB
36786524 rows
5 columns


CREATE TABLE causal_data (
  "product_id" BIGINT,
  "store_id" BIGINT,
  "week_no" BIGINT,
  "display" VARCHAR,
  "mailer" VARCHAR
);

Coupon

@kaggle.frtgnn_dunnhumby_the_complete_journey.coupon

543 KB
124548 rows
3 columns


CREATE TABLE coupon (
  "coupon_upc" BIGINT,
  "product_id" BIGINT,
  "campaign" BIGINT
);

Coupon Redempt

@kaggle.frtgnn_dunnhumby_the_complete_journey.coupon_redempt

17.85 KB
2318 rows
4 columns


CREATE TABLE coupon_redempt (
  "household_key" BIGINT,
  "day" BIGINT,
  "coupon_upc" BIGINT,
  "campaign" BIGINT
);

Hh Demographic

@kaggle.frtgnn_dunnhumby_the_complete_journey.hh_demographic

12.4 KB
801 rows
8 columns


CREATE TABLE hh_demographic (
  "age_desc" VARCHAR,
  "marital_status_code" VARCHAR,
  "income_desc" VARCHAR,
  "homeowner_desc" VARCHAR,
  "hh_comp_desc" VARCHAR,
  "household_size_desc" VARCHAR,
  "kid_category_desc" VARCHAR,
  "household_key" BIGINT
);

Product

@kaggle.frtgnn_dunnhumby_the_complete_journey.product

1.21 MB
92353 rows
7 columns


CREATE TABLE product (
  "product_id" BIGINT,
  "manufacturer" BIGINT,
  "department" VARCHAR,
  "brand" VARCHAR,
  "commodity_desc" VARCHAR,
  "sub_commodity_desc" VARCHAR,
  "curr_size_of_product" VARCHAR
);

Transaction Data

@kaggle.frtgnn_dunnhumby_the_complete_journey.transaction_data

19.64 MB
2595732 rows
12 columns


CREATE TABLE transaction_data (
  "household_key" BIGINT,
  "basket_id" BIGINT,
  "day" BIGINT,
  "product_id" BIGINT,
  "quantity" BIGINT,
  "sales_value" DOUBLE,
  "store_id" BIGINT,
  "retail_disc" DOUBLE,
  "trans_time" BIGINT,
  "week_no" BIGINT,
  "coupon_disc" DOUBLE,
  "coupon_match_disc" DOUBLE
);