Churned docs


This section explains all the powerful Ecom metrics that Churned provides 💪

KPI dashboard

Most of the metrics shown in this dashboard are updated daily and are calculated based on the RFM running period. Meaning that you look at a period between today and RFM period days ago.
Next to each of the metrics you will often find a comparison:
  • vs last month: this is the percentage variation in the current value in comparison to last month. In practice, it is calculated as the comparison between the value of the last RFM period versus the value of the RFM period 30 days ago.


RFM is a method used in customer segmentation to divide customers into groups based on their purchase behavior. RFM stands for Recency, Frequency, and Monetary value. In an ecommerce setting, RFM can be used to identify the most valuable customers and target them with personalized marketing campaigns.
  • Recency refers to how recently a customer made a purchase. This is determined by looking at the latest order date of each customer.
  • Frequency refers to how often a customer makes a purchase over a set period of time.
  • Monetary value refers to the amount of money a customer spends over a period of time.
Based on these three criteria, customers can be divided into segments. For example, customers who have made a purchase recently, make frequent purchases, and spend a lot of money on each purchase would be considered the most valuable and placed in the "champions" segment. Customers who haven't made a purchase in a while, make infrequent purchases, and spend little money on each purchase would be considered the least valuable and placed in the "churned" segment.
Intuitive meaning of the RFM segments
RFM Segment
RFM Code
555, 554, 545, 544, 455, 454, 445
553, 552, 551, 543, 542, 541, 444, 435, 355, 354, 345,
Potential loyalist
532, 531, 452, 451, 442, 441, 431, 453, 433, 432, 423, 353, 352, 351, 524, 523, 522, 521, 422, 421, 425, 424, 525, 535, 534, 533
515, 514, 513, 512, 511, 415, 414, 412, 411, 313
Need attention
443, 434, 343, 334, 325, 324, 342, 341, 333, 323, 335, 315, 314
At Risk
255, 254, 245, 244, 253, 252, 243, 242, 235, 234, 225, 224, 153, 152, 144, 145, 143, 142, 135, 134, 133, 125, 124, 155, 154, 215, 214
332, 322, 233, 232, 223, 222, 132, 123, 122, 212, 211, 331, 321, 312, 221, 213, 231, 311
111, 112, 113, 114, 115, 121, 131, 141, 151

RFM segments

Champion Customers with highest recency, frequency, order value highest recency, order frequency, order value
Loyal Customers with high recency and high frequency High recency, order frequency, order value
Potential loyalists Almost loyal customers that should improve frequency High recency, medium order frequency
Need attention High value customers that should improve frequency High recency, medium order frequency, very high order value
New Customers that have only one recent purchase at any value High recency, low frequency
At risk High value customers not seen for a long time Very low recency, high order value
Hibernating sporadic customers not seen in a while low recency, low frequency, low order value
Lost Customers that are not expected to return Very low recency, very low order value


Customers that made a purchase in the last RFM period Active customer base
This is the latest number of active customers registered in your system. A customer is counted as an active customer whenever they have at least one purchase in the last RFM period.

Retention rate

Percentage of returning customers How loyal are your customers
RetentionRate=1ChurnedActiveRetention Rate = 1 - \frac{\textnormal{Churned}}{\textnormal{Active}}
The Retention Rate is the percentage of customers in the last month that made multiple orders within the RFM period. New customers are not taken into account.
The Retention Rate is a measure of the percentage of customers who remain with a company over a given period of time. It is often used as a key indicator of a company's success in retaining its customer base and can be an important factor in the overall health and growth of the business.

Average Order Value (AOV)

The average amount of money spend per order. The willingness to pay of each customer The AOV is calculated by dividing the total revenue generated by the total number of orders (n) over a giving time period. In the case of the dashboard, the period is equal to the period set for the RFM segmentation.
AOV=i=1nRevenueii=1nOrderiAOV = \frac{\sum_{i=1}^{n} \textnormal{Revenue}_i}{\sum_{i=1}^{n} \textnormal{Order}_i}
AOV is a useful metric for E-commerce businesses because it can help them understand how much money each customer is spending on average, which can drive pricing and marketing strategies such as optimizing retention.

Average Order Frequency (AOF)

The average number of orders per customer over a time period. How frequently are your customers buying at your shop.
The AOF is calculated by dividing the total number of orders (n) by the total number of customers (c) over a giving time period. In the case of the dashboard, the period is equal to the period set for the RFM segmentation.
AOF=i=1nOrderij=1cCustomerjAOF = \frac{\sum_{i=1}^{n} \mathrm{Order}_i}{\sum_{j=1}^{c} \mathrm{ Customer}_j}
AOF is a useful metric because it can help understand how often customers are making purchases, which can drive retention and loyalty programs.

Expected Average Customer Value (Expected ACV)

The expected average revenue value of active customers What are you customers worth
The Expected Average Customer Value is a weighted sum of the current ACV and the previous ACV.
ExpectedACVt=w1ACVt+w2ExpectedACVt1ExpectedACV_t=w_1*ACV_t + w_2*ExpectedACV_{t-1}

Cohort plots

The percentage returning customers per cohort. How loyal is your current customer base.
A cohort plot is a graphical representation of data that divides a population into groups, or "cohorts," based on common characteristic. In this case each cohort represents the number of unique new customers that have placed an order in the specific year-month period. The columns in each row in the plot represent the percentage of customers that have returned for a next order.
In the example below we can see that 3,534 new customers made a purchase in december 2020. From those 3,534 we see that 80% has returned to place a third order and 65% has made 5 orders or more.
Example of a cohort plot with the number of orders on in the columns.