Marketing Attribution Path Dataset
This dataset provides detailed synthetic logs of customer journeys across multiple marketing channels, capturing the sequence, timing, and types of interactions leading to conversions. It enables advanced analysis and machine learning for channel attribution, campaign optimization, and customer behavior modeling, making it ideal for marketing analytics and decision support.
Sample rows
preview · 8 of 200 rows · all 14 columns| journey_idstring | conversion_valuefloat | conversionboolean | geo_countrystring | customer_idstring | start_datetimedatetime | end_datetimedatetime | touch_countinteger | channels_sequencestring | touch_typesstring | device_typesstring | geo_citystring | campaign_idsstring | touch_timestampsstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| JRN10001 | 129.99 | true | US | CUST102 | 2024-03-05T14:12:22Z | 2024-03-05T14:20:05Z | 3 | email,search,social | impression,click,view | desktop,mobile,mobile | San Francisco | CMP1001,CMP203,CMP908 | 2024-03-05T14:12:22Z,2024-03-05T14:15:44Z,2024-03-05T14:20:05Z |
| JRN10002 | 0 | false | GB | CUST579 | 2024-02-19T09:34:10Z | 2024-02-19T10:02:41Z | 2 | search,display | impression,click | mobile,desktop | London | CMP908,CMP4452 | 2024-02-19T09:34:10Z,2024-02-19T10:02:41Z |
| JRN10003 | 2500 | true | JP | CUST8888 | 2024-01-11T18:00:00Z | 2024-01-13T08:30:00Z | 6 | display,search,social,email,affiliate,video | impression,click,view,impression,view,click | desktop,mobile,mobile,desktop,tablet,mobile | Tokyo | CMP9999,CMP1001,CMP908,CMP4452,CMP203,CMP100 | 2024-01-11T18:00:00Z,2024-01-11T19:45:00Z,2024-01-12T09:30:00Z,2024-01-12T12:00:00Z,2024-01-13T07:00:00Z,2024-01-13T08:30:00Z |
| JRN10004 | blank | false | blank | CUST1003 | 2024-04-02T10:15:12Z | 2024-04-02T10:16:05Z | 1 | affiliate | impression | desktop | blank | CMP203 | 2024-04-02T10:15:12Z |
| JRN10005 | 480.5 | true | DE | CUST459 | 2024-03-29T16:22:09Z | 2024-03-29T18:05:30Z | 4 | email,search,social,display | impression,click,click,view | desktop,mobile,tablet,desktop | Berlin | CMP203,CMP4452,CMP908,CMP1001 | 2024-03-29T16:22:09Z,2024-03-29T16:45:45Z,2024-03-29T17:13:10Z,2024-03-29T18:05:30Z |
| JRN10006 | blank | false | FR | CUST36 | 2024-02-14T20:05:00Z | 2024-02-15T01:20:00Z | 2 | search,email | impression,view | mobile,desktop | Paris | CMP908,CMP4452 | 2024-02-14T20:05:00Z,2024-02-15T01:20:00Z |
| JRN10007 | 59.99 | true | US | CUST800 | 2024-02-23T11:41:22Z | 2024-02-23T12:21:09Z | 3 | email,social,search | impression,view,click | desktop,mobile,mobile | Boston | CMP203,CMP4452,CMP1001 | 2024-02-23T11:41:22Z,2024-02-23T11:59:00Z,2024-02-23T12:21:09Z |
| JRN10008 | 99999.99 | true | AE | CUST459 | 2024-04-11T07:10:30Z | 2024-04-12T09:45:15Z | 5 | display,search,email,social,affiliate | impression,click,view,click,impression | desktop,tablet,mobile,desktop,tablet | Dubai | CMP9999,CMP4452,CMP203,CMP1001,CMP100 | 2024-04-11T07:10:30Z,2024-04-11T08:40:00Z,2024-04-11T12:10:00Z,2024-04-12T08:22:00Z,2024-04-12T09:45:15Z |
| JRN10009 | 0 | false | blank | CUST1003 | 2024-04-02T06:15:30Z | 2024-04-02T06:29:21Z | 1 | search | impression | tablet | blank | CMP203 | 2024-04-02T06:15:30Z |
| JRN10010 | 19.99 | true | US | CUST800 | 2024-01-18T05:40:00Z | 2024-01-18T06:05:00Z | 2 | email,social | impression,click | desktop,mobile | New York | CMP1001,CMP203 | 2024-01-18T05:40:00Z,2024-01-18T06:05:00Z |
| JRN10011 | 0 | false | CA | CUST36 | 2024-01-29T21:31:09Z | 2024-01-29T21:40:33Z | 1 | display | impression | mobile | Toronto | CMP908 | 2024-01-29T21:31:09Z |
| JRN10012 | 120 | true | FR | CUST579 | 2024-03-07T16:00:00Z | 2024-03-07T18:31:59Z | 4 | search,email,social,display | impression,click,view,view | desktop,mobile,mobile,tablet | Paris | CMP1001,CMP908,CMP203,CMP4452 | 2024-03-07T16:00:00Z,2024-03-07T16:18:00Z,2024-03-07T17:20:00Z,2024-03-07T18:31:59Z |
| JRN10013 | blank | false | blank | CUST459 | 2024-02-17T12:15:00Z | 2024-02-17T12:16:42Z | 1 | affiliate | impression | tablet | blank | CMP203 | 2024-02-17T12:15:00Z |
| JRN10014 | 80 | true | US | CUST1003 | 2024-02-01T17:22:40Z | 2024-02-01T18:06:02Z | 2 | email,search | impression,click | desktop,mobile | San Francisco | CMP4452,CMP908 | 2024-02-01T17:22:40Z,2024-02-01T18:06:02Z |
| JRN10015 | 99999.99 | true | SG | CUST36 | 2024-03-02T23:10:00Z | 2024-03-02T23:55:40Z | 5 | display,search,social,email,referral | impression,click,view,impression,click | desktop,mobile,mobile,desktop,tablet | Singapore | CMP9999,CMP4452,CMP1001,CMP203,CMP100 | 2024-03-02T23:10:00Z,2024-03-02T23:22:00Z,2024-03-02T23:34:12Z,2024-03-02T23:48:00Z,2024-03-02T23:55:40Z |
| JRN10016 | 0 | false | RU | CUST102 | 2024-02-25T08:44:00Z | 2024-02-25T09:19:00Z | 2 | search,display | impression,view | desktop,tablet | Vladivostok | CMP203,CMP908 | 2024-02-25T08:44:00Z,2024-02-25T09:19:00Z |
| JRN10017 | 59.99 | true | US | CUST579 | 2024-01-05T07:00:00Z | 2024-01-05T07:23:00Z | 2 | email,search | impression,click | desktop,mobile | Chicago | CMP4452,CMP908 | 2024-01-05T07:00:00Z,2024-01-05T07:23:00Z |
| JRN10018 | blank | false | IN | CUST8888 | 2024-04-01T12:11:20Z | 2024-04-01T13:44:10Z | 1 | video | impression | mobile | Mumbai | CMP163 | 2024-04-01T12:11:20Z |
| JRN10019 | 480.5 | true | SE | CUST36 | 2024-02-27T17:10:00Z | 2024-02-27T17:10:45Z | 3 | email,search,display | impression,view,click | desktop,mobile,tablet | Stockholm | CMP689,CMP1010,CMP882 | 2024-02-27T17:10:00Z,2024-02-27T17:10:22Z,2024-02-27T17:10:45Z |
| JRN10020 | 0 | false | blank | CUST800 | 2024-03-09T03:00:00Z | 2024-03-09T05:15:00Z | 2 | search,email | impression,view | tablet,desktop | blank | CMP203,CMP908 | 2024-03-09T03:00:00Z,2024-03-09T05:15:00Z |
What the 200 rows show
from the 200-row sample- 55%conversion = true
- 60.0median conversion_
value - 11customers
- 18geo cities
- 28device typeses
- 36touch typeses
Median 60.0, from 0.0 to 1,000,000.
- string 9
- integer 1
- float 1
- datetime 2
- boolean 1
Columns
14 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 9 columns | |||
journey_id | string | Unique identifier for each customer journey across marketing channels.unique | JRN10001 |
customer_id | string | Unique identifier for the customer associated with the journey.11 customers | CUST102 |
channels_sequence | string | Comma-separated sequence of marketing channels touched in order (e.g., 'email,search,social'). | email,search,social |
touch_timestamps | string | Comma-separated list of ISO 8601 timestamps for each channel touch, in journey order. | 2024-04-02T10:15:12Z |
touch_types | string | Comma-separated list of touch types (e.g., 'impression,click,view') corresponding to each channel touch. | impression,click,view |
device_types | string | Comma-separated list of device types used during each touch (e.g., 'mobile,desktop,tablet').optional | desktop,mobile,mobile |
geo_country | string | Country code (ISO 3166-1 alpha-2) representing the customer's location at conversion or journey end.optional | US |
geo_city | string | City name representing the customer's location at conversion or journey end.optional | San Francisco |
campaign_ids | string | Comma-separated list of campaign identifiers involved in the journey.optional | CMP1001,CMP203,CMP908 |
| Numbers 2 columns | |||
conversion_value | float | Monetary value of the conversion, if applicable. Zero or null if no conversion.0 or more · optional | 129.99 |
touch_count | integer | Total number of marketing channel touches in the journey.1 or more | 3 |
| Dates and times 2 columns | |||
start_datetime | datetime | Timestamp when the customer journey began. | 2024-03-05T14:12:22Z |
end_datetime | datetime | Timestamp when the customer journey ended or conversion occurred. | 2024-03-05T14:20:05Z |
| True or false 1 column | |||
conversion | boolean | Indicates whether the journey resulted in a conversion (e.g., purchase, signup). | true |
Use it for
A marketing dashboard
The conversion rate, conversion_
value and a breakdown of geo_ country. Excel, Power BI or Tableau. Why do 109 of 200 rows have conversion = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
- Journeys200JRN10001129.99trueJRN100020falseJRN100032500true
A software demo
Believable journeys with customer_
id, start_ datetime and end_ datetime to fill a screen in front of a buyer.
This dataset200 rows14 columns
Yours1,000 rows14 columnsgeo_country: UK only
blueprint · marketing-attribution-path-dataset
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 14 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
- Session and channel ID mapped
- Touchpoint events timestamped
- Conversion events labeled per journey
- Include simulated channel interactions
- User data anonymized
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- marketing-attribution-path-dataset