Repeat Purchase Cohort Analysis
This dataset provides a detailed view of customer retention and repeat purchase behavior by grouping customers into cohorts based on their initial purchase date. It tracks key metrics such as repeat purchase rates, monetary value, and customer activity status, enabling businesses to evaluate the effectiveness of retention strategies and loyalty programs. The structure supports granular analysis of cohort performance over time.
Sample rows
preview · 8 of 120 rows · all 12 columns| cohort_idstring | purchase_value_totalfloat | is_active_customerboolean | cohort_start_datedate | cohort_end_datedate | customer_idstring | first_purchase_datedate | total_purchasesinteger | repeat_purchasesinteger | last_purchase_datedate | cohort_periodinteger | repeat_purchase_valuefloat |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-01 | 47.2 | false | 2023-01-01 | 2023-01-31 | CUST10001 | 2023-01-15 | 1 | 0 | 2023-01-15 | 0 | 0 |
| 2023-01 | 109.85 | false | 2023-01-01 | 2023-01-31 | CUST10002 | 2023-01-29 | 2 | 1 | 2023-06-08 | 5 | 68 |
| 2023-01 | 212.4 | false | 2023-01-01 | 2023-01-31 | CUST10003 | 2023-01-02 | 4 | 3 | 2023-03-25 | 2 | 170.55 |
| 2023-01 | 378.75 | true | 2023-01-01 | 2023-01-31 | CUST10004 | 2023-01-11 | 5 | 4 | 2024-01-08 | 12 | 310.25 |
| 2023-02 | 25.99 | false | 2023-02-01 | 2023-02-28 | CUST10005 | 2023-02-14 | 1 | 0 | 2023-02-14 | 0 | 0 |
| 2023-02 | 68.5 | false | 2023-02-01 | 2023-02-28 | CUST10006 | 2023-02-05 | 2 | 1 | 2023-03-01 | 1 | 32 |
| 2023-02 | 152.45 | false | 2023-02-01 | 2023-02-28 | CUST10007 | 2023-02-27 | 3 | 2 | 2023-07-10 | 5 | 101 |
| 2023-02 | 498.9 | true | 2023-02-01 | 2023-02-28 | CUST10008 | 2023-02-10 | 6 | 5 | 2024-02-04 | 12 | 426.8 |
| 2023-02 | 76.9 | false | 2023-02-01 | 2023-02-28 | CUST10009 | 2023-02-21 | 2 | 1 | 2023-04-01 | 1 | 51 |
| 2023-03 | 41.3 | false | 2023-03-01 | 2023-03-31 | CUST10010 | 2023-03-07 | 1 | 0 | 2023-03-07 | 0 | 0 |
| 2023-03 | 122.95 | false | 2023-03-01 | 2023-03-31 | CUST10011 | 2023-03-12 | 3 | 2 | 2023-08-03 | 5 | 85.5 |
| 2023-03 | 59.79 | false | 2023-03-01 | 2023-03-31 | CUST10012 | 2023-03-21 | 2 | 1 | 2023-04-19 | 1 | 25 |
| 2023-03 | 211.55 | true | 2023-03-01 | 2023-03-31 | CUST10013 | 2023-03-02 | 4 | 3 | 2023-12-17 | 9 | 167.4 |
| 2023-03 | 67.45 | false | 2023-03-01 | 2023-03-31 | CUST10014 | 2023-03-27 | 2 | 1 | 2023-04-09 | 1 | 29.5 |
| 2023-04 | 38 | false | 2023-04-01 | 2023-04-30 | CUST10015 | 2023-04-06 | 1 | 0 | 2023-04-06 | 0 | 0 |
| 2023-04 | 62.7 | false | 2023-04-01 | 2023-04-30 | CUST10016 | 2023-04-19 | 2 | 1 | 2023-04-27 | 0 | 31.35 |
| 2023-04 | 167.85 | true | 2023-04-01 | 2023-04-30 | CUST10017 | 2023-04-02 | 3 | 2 | 2023-12-28 | 8 | 114.2 |
| 2023-04 | 81.1 | true | 2023-04-01 | 2023-04-30 | CUST10018 | 2023-04-25 | 2 | 1 | 2023-11-15 | 7 | 50.2 |
| 2023-04 | 412.7 | false | 2023-04-01 | 2023-04-30 | CUST10019 | 2023-04-10 | 6 | 5 | 2023-10-30 | 6 | 338.4 |
| 2023-05 | 35.99 | false | 2023-05-01 | 2023-05-31 | CUST10020 | 2023-05-18 | 1 | 0 | 2023-05-18 | 0 | 0 |
What the 120 rows show
from the 120-row sample- 38%is_
active_ customer = true - 70.4median purchase_
value_ total - 2median total_
purchases - 1median repeat_
purchases - 1median cohort_
period - 32.0median repeat_
purchase_ value
Median 70.4, from 18.7 to 621.8.
- string 2
- integer 3
- float 2
- date 4
- boolean 1
Columns
12 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 2 columns | |||
cohort_id | string | Unique identifier for the customer cohort, typically based on the cohort's start date or period.unique | 2023-01 |
customer_id | string | Unique identifier for the customer who belongs to this cohort. | CUST10001 |
| Numbers 5 columns | |||
total_purchases | integer | Total number of purchases made by the customer since their first purchase.1 or more | 1 |
repeat_purchases | integer | Number of purchases made by the customer after their initial purchase.0 or more | 0 |
cohort_period | integer | Number of periods (e.g., months) since the cohort's start date when the purchase occurred (0 = first period, 1 = second period, etc.).0 or more | 0 |
purchase_value_total | float | Total monetary value of all purchases made by the customer since their first purchase.0 or more | 47.2 |
repeat_purchase_value | float | Total monetary value of repeat purchases (excluding the first purchase).0 or more | 0 |
| Dates and times 4 columns | |||
cohort_start_date | date | The date representing when the cohort's first purchase period begins (e.g., first day of month). | 2023-01-01 |
cohort_end_date | date | The date representing when the cohort's first purchase period ends (e.g., last day of month). | 2023-01-31 |
first_purchase_date | date | The date of the customer's first purchase, used to assign them to a cohort. | 2023-01-15 |
last_purchase_date | date | The date of the customer's most recent purchase.optional | 2023-01-15 |
| True or false 1 column | |||
is_active_customer | boolean | Indicates if the customer has made a purchase within the most recent analysis period. | false |
Use it for
An e-commerce dashboard
The is_
active_ customer rate and purchase_ value_ total. Excel, Power BI or Tableau. Why do 45 of 120 rows have is_
active_ customer = true? A class exercise
Hand out the rows and one question. Everyone works from the same 120 rows.
- Cohorts1202023-0147.2false2023-01378.75true2023-02498.9true
A software demo
Believable cohorts with cohort_
start_ date, cohort_ end_ date and customer_ id to fill a screen in front of a buyer.
blueprint · repeat-purchase-cohort-analysis
Behind this dataset
Same schema. As many rows as you need.
These 120 rows came out of a blueprint — 12 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.
- Cohorts are grouped by first purchase month
- Repeat purchase flag updates on each transaction
- Exclude customers with only one order
- Calculate cumulative retention rate by cohort
- Highlight cohorts above 20% repeat rate
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
- repeat-purchase-cohort-analysis