Synthetic Retail Promotion Uplift
This dataset provides granular sales transaction records enriched with detailed promotion and campaign information, enabling precise uplift analysis for retail marketing teams. It supports measurement of promotional effectiveness across products, stores, channels, and customer segments, making it ideal for optimizing campaign strategies and maximizing ROI.
Sample rows
preview · 8 of 200 rows · all 18 columns| transaction_idstring | uplift_groupstring | unit_pricefloat | promotion_appliedboolean | store_regionstring | transaction_datedate | store_idstring | customer_idstring | product_idstring | product_categorystring | quantity_soldinteger | total_sales_amountfloat | promotion_idstring | promotion_typestring | promotion_discount_amountfloat | promotion_discount_percentfloat | campaign_idstring | sales_channelstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TX000001 | control | 349.99 | false | Pacific | 2021-01-01 | S-101 | C-1001 | P-2001 | Electronics | 2 | 699.98 | blank | blank | 0 | 0 | blank | in-store |
| TX000002 | treatment | 1.99 | true | South | 2021-07-04 | S-003 | C-1002 | P-2002 | Groceries | 120 | 214.32 | PR-3001 | discount | 24.48 | 10 | CA-4001 | in-store |
| TX000003 | treatment | 29.99 | false | Central | 2022-11-24 | S-014 | C-1003 | P-2003 | Clothing | 3 | 89.97 | blank | blank | 0 | 0 | blank | online |
| TX000004 | control | 39.99 | true | Midwest | 2023-02-14 | S-202 | C-1004 | P-2004 | Home & Garden | 1 | 33.99 | PR-3002 | rebate | 6 | 15 | CA-4002 | in-store |
| TX000005 | treatment | 17.5 | false | Northeast | 2022-12-25 | S-049 | C-1005 | P-2005 | Toys | 4 | 70 | blank | blank | 0 | 0 | blank | in-store |
| TX000006 | control | 13.5 | false | Pacific | 2024-02-29 | S-101 | C-1006 | P-2006 | Beauty | 5 | 67.5 | blank | blank | 0 | 0 | blank | mobile |
| TX000007 | treatment | 2.29 | true | South | 2024-03-17 | S-003 | C-1007 | P-2007 | Groceries | 500 | 1000 | PR-3003 | coupon | 145 | 12.7 | CA-4003 | in-store |
| TX000008 | control | 24.99 | true | Central | 2023-10-31 | S-014 | blank | P-2008 | Toys | 2 | 44.99 | PR-3004 | coupon | 5 | 10 | CA-4001 | mobile |
| TX000009 | treatment | 14.99 | true | Central | 2022-05-28 | S-020 | C-1008 | P-2009 | Clothing | 1 | 11.99 | PR-3005 | discount | 3 | 20 | blank | online |
| TX000010 | control | 799.99 | false | Midwest | 2022-09-15 | S-111 | C-1009 | P-2010 | Furniture | 1 | 799.99 | blank | blank | 0 | 0 | blank | in-store |
| TX000011 | treatment | 425 | true | Pacific | 2021-12-31 | S-005 | C-1010 | P-2011 | Electronics | 3 | 1200 | PR-3006 | bogo | 75 | 6.25 | CA-4004 | online |
| TX000012 | control | 2.5 | false | Pacific | 2023-08-09 | S-101 | C-1011 | P-2012 | Groceries | 60 | 120 | blank | blank | 0 | 0 | blank | in-store |
| TX000013 | treatment | 15 | false | Central | 2022-04-17 | S-014 | C-1012 | P-2013 | Toys | 8 | 120 | blank | blank | 0 | 0 | blank | online |
| TX000014 | control | 39.95 | true | South | 2024-01-20 | S-201 | C-1013 | P-2014 | Clothing | 2 | 63.92 | PR-3007 | rebate | 15.98 | 20 | CA-4005 | mobile |
| TX000015 | control | 6.99 | false | South | 2023-12-25 | S-003 | blank | P-2015 | Beauty | 3 | 20.97 | blank | blank | 0 | 0 | blank | mobile |
| TX000016 | treatment | 499.99 | true | Pacific | 2021-11-26 | S-009 | C-1014 | P-2016 | Electronics | 1 | 409.99 | PR-3008 | discount | 90 | 18 | CA-4006 | in-store |
| TX000017 | control | 32.99 | false | Central | 2022-07-04 | S-212 | C-1015 | P-2017 | Home & Garden | 2 | 65.98 | blank | blank | 0 | 0 | blank | in-store |
| TX000018 | treatment | 0.89 | true | South | 2021-03-01 | S-222 | C-1016 | P-2018 | Groceries | 1000 | 760 | PR-3009 | discount | 130 | 12.8 | blank | in-store |
| TX000019 | treatment | 15.5 | false | Central | 2023-05-05 | S-014 | C-1017 | P-2019 | Clothing | 5 | 77.5 | blank | blank | 0 | 0 | blank | online |
| TX000020 | control | 1850 | true | blank | 2021-06-15 | S-099 | C-1018 | P-2020 | Automotive | 1 | 1700 | PR-3010 | rebate | 150 | 8.1 | CA-4007 | in-store |
What the 200 rows show
from the 200-row sample- 43%promotion_
applied = true - 20.0median unit_
price - 3sales channels
- 4promotion types
- 9product categories
- 53stores
Median 20.0, from 0.0 to 50,000.
- string 11
- integer 1
- float 4
- date 1
- boolean 1
Columns
18 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 11 columns | |||
transaction_id | string | Unique identifier for each sales transaction record.unique | TX000001 |
store_id | string | Unique identifier for the retail store where the transaction took place. | S-101 |
store_region | string | Geographical region or market segment of the store.8 regions · optional | Pacific |
customer_id | string | Unique identifier for the customer making the purchase.optional | C-1001 |
product_id | string | Unique identifier for the product sold. | P-2001 |
product_category | string | Category or type of the product sold.9 categories · optional | Electronics |
promotion_id | string | Unique identifier for the promotion applied (if any).optional | PR-3001 |
promotion_type | string | Type of promotion used (e.g., discount, BOGO, coupon).discount · bogo · coupon · rebate · none · optional | discount |
campaign_id | string | Identifier for the marketing campaign associated with the promotion.optional | CA-4001 |
uplift_group | string | Experimental group assignment for uplift analysis (e.g., treatment, control).treatment · control | control |
sales_channel | string | Channel through which the sale was made (e.g., in-store, online, mobile).in-store · online · mobile · optional | in-store |
| Numbers 5 columns | |||
quantity_sold | integer | Number of units of the product sold in the transaction.1 or more | 2 |
unit_price | float | Price per unit of the product at the time of sale.0 or more | 349.99 |
total_sales_amount | float | Total sales amount for the transaction (quantity_sold * unit_price).0 or more | 699.98 |
promotion_discount_amount | float | Absolute discount amount applied to the transaction due to promotion.0 or more · optional | 0 |
promotion_discount_percent | float | Percentage discount applied to the transaction due to promotion.0 to 100 · optional | 0 |
| Dates and times 1 column | |||
transaction_date | date | Date when the sales transaction occurred. | 2021-01-01 |
| True or false 1 column | |||
promotion_applied | boolean | Indicates whether a promotion was applied to the transaction. | false |
Use it for
A marketing dashboard
The promotion_
applied rate, unit_ price by uplift_ group and a breakdown of store_ region. Excel, Power BI or Tableau. Why do 86 of 200 rows have promotion_
applied = true? A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
- Transactions200TX000001349.99controlTX0000021.99treatmentTX00000439.99control
A software demo
Believable transactions with transaction_
date, store_ id and store_ region to fill a screen in front of a buyer.
This dataset200 rows18 columns
Yours1,000 rows18 columnsstore_region: UK only
blueprint · synthetic-retail-promotion-uplift
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Baseline and promotional sales tracked
- Multiple channels (in-store, online) simulated
- Customer segments respond differently
- Seasonal effects modeled
- Outliers flagged for audit
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- synthetic-retail-promotion-uplift