# Customer Success for NGOs NGO donors consist of structural and non-structural donors. ## Structural Donors ### KPI dashboard {% hint style="info" %} All the metrics shown in this dashboard are updated daily and are calculated based on a 30 days running period. Meaning that you look at a period between today and 30 days ago. Next to each of the metrics you will always find two comparisons: * *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 30 days versus the value of the 30 days that preceded it. * *vs last year:* this is the percentage variation in the value of the last 365 compared to the value of the 365 days that preceded that period. {% endhint %} {% hint style="info" %} **Example:** In the snapshot below, the current number of *churners* is 29. This figure reflects the number of donors which have ended their subscription/contract during the last 30 days. One can also observe that the change in the number of churners relative to last month is +0.4% and that the change relative to last year is -5.8%. This shows a scenario where the number of *churners* has recently increased in the last month, but overall it is still decreasing relative to one year ago. ![](https://246234927-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FkSyCQUyE0u7ya9ULaprd%2Fuploads%2F1KB6HIwdcIopXFbn9Az6%2FScreenshot%202022-08-03%20at%2021.07.43.png?alt=media\&token=6ec6af9c-1d9e-467e-9268-babb67a013a2) {% endhint %} #### **Active donors** *The current number of donors with an active subscription.*\ \&#xNAN;*How many donors are active at this moment.* The number of *"active donors".* This is the latest number of active donors registered in your system. A donor is counted as an active donor whenever they have at least one contract that is currently active. #### **Churners** *The number of* donors *that have churned in the last 30 days.*\ \&#xNAN;*How many donors have left you recently.* *"Churners"* is defined as the number of donors that have churned in the last 30 days. A donor has churned whenever they have canceled or ended an active contract, have no other active contract and do not have a new contract starting within one week after their latest contract has ended. #### **Churn rate** *The percentage of donors that have churned in the last 30 days.*\ \&#xNAN;*How much churn are you experiencing.* The *"churn rate"* indicates the percentage of donors which have churned in the last 30 day. This number is calculated relative to the number of currently active donors. A high-churn rate indicates a substantial number of donors canceling their subscriptions/contracts among the currently active donors. Specifically, the churn rate is calculated as follows: $$ churn \ rate\_t \ = \ \dfrac{ {churners} \_t } {active \ donors\_t \ + \ churners\_t} $$ #### #### **ARR** *The current annual recurring revenue (ARR).*\ \&#xNAN;*How much yearly revenue can you expect based on current subscriptions/contracts.* The *annual recurring revenue* (ARR) is a simple but essential business metric which indicates how much recurring revenue you can expect, based on currently active subscriptions/contracts. ARR is also the annualized version of *monthly recurring revenue* (MRR) representing revenue in the calendar year. #### **Churn loss** *The total loss in yearly revenue due to donors that have churned in the last 30 days.*\ \&#xNAN;*How much churn has cost you recently.* The churn loss indicates the annual revenue that has been lost due to the donors that have churned during the last 30 days. This is calculated by the simple formula: $$ churn \ loss\_t = \sum\_{i} churner\_{i,t} \times ARR\_{i,t} $$ #### **AI health score** *An all-in-one measure of the health of your donors.*\ \&#xNAN;*How healthy your donors base is.* The Churned AI Health Score gives you a single, powerful metric that captures how healthy your donors truly are; today and in the future. It combines advanced AI predictions with behavioral, transactional, and engagement data to give a holistic view of donor retention and value. **How it works** At Churned, we’ve analyzed the behavioral patterns of thousands of historical donors — including those who eventually churned to understand what truly signals risk, disengagement, or loyalty. Our system continuously learns from these historical patterns, identifying early indicators that precede donor churn such as reduced engagement, missed recurring donations, or changes in communication response. We train and evaluate multiple AI models (e.g., gradient boosting, random forests, deep neural networks) on each client’s dataset to find the most accurate and robust predictor of donor outcomes. The best-performing model is then deployed to continuously predict donor health, adapting automatically as new data flows in. **What it captures** The AI Health Score blends short-term and long-term dynamics across key dimensions such as: * Churn risk: The probability that a donor will stop giving. * Donor value: Projected lifetime value and contribution trends. * Engagement: How actively the donor interacts across campaigns, communications, and channels. * Retention trends: Both immediate and long-term retention probabilities. * Behavioral signals: Donation frequency, amounts, preferences, and changes in giving patterns. This fusion of predictive analytics and behavioral insights means that your health score is not just descriptive, it’s prescriptive. It highlights who is at risk, who is likely to grow, and where your efforts can have the highest impact. **How to interpret it** The Churned AI Health Score ranges from 1 to 10: * 1–4: 🩸 *Sick* — high risk of churn; immediate action recommended. * 4–6: ⚠️ *Concerning* — engagement or value decline detected; monitor closely. * 7–8: 💪 *Healthy* — stable and active donors; maintain engagement. * 9–10: 🌟 *Excellent* — loyal, high-value donors with strong long-term potential. #### **Risk levels** *A segmentation of your donors into three risk levels.*\ \&#xNAN;*How many donors and corresponding value are in each risk group.* The risk levels metric divides your current active donors into the Churned risk levels: *High Risk, Medium Risk* and *Low risk.* Based on their churn prediction, each donors receives one of these labels. The total value (sum of the ARR of these donors) is shown as well as the change in the value of that risk level compared to 30 days ago. Under the value the total number of donors within each risk level is revealed as well as the change in the value of that risk level compared to 30 days ago. {% hint style="info" %} Since we want as many donors in the Low Risk group, an increase compared to the previous period turns green, while an increase in the High Risk group turns red. {% endhint %} ![Risk level metrics](https://246234927-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FkSyCQUyE0u7ya9ULaprd%2Fuploads%2FtDp9KHVsOgypIG1wvd1k%2FScreenshot%202022-07-31%20at%2021.51.34.png?alt=media\&token=32a1619c-f280-4be4-818f-73a94d76f72d) ### Technical dashboard {% hint style="info" %} All the metrics show in this dashboard are updated daily and are calculated based on the latest available information at the donor level. Only active donors are presented here. {% endhint %} #### Top 3 risk drivers *Explanation for the churn risk by our AI engine.*\ \&#xNAN;*The top three drivers of your donors' churn risk.* At Churned, we go beyond predicting the churn risk for each donor. Our AI engine actually uncovers the drivers behind the predicted churn risk. This means that, for every given donor, you can see why the churn risk prediction is high or low. The *top 3 risk drivers* reveal the following key insights: * what are the three main *drivers* of risk for that donor, * which drivers help decrease or increase churn risk, * if the driver's value is low or high for that donor. {% hint style="info" %} Drivers that help reduce churn risk are always shown in **green**. These are "good" drivers.\ Drivers that increase churn risk are always shown in **red**. These are "negative" drivers. {% endhint %} {% hint style="info" %} **Example:** Consider a donor with *high churn risk.* Suppose that the *top 3 risk drivers* for this donor are: 1. **Low number of emails clicked** 2. **Multiple non-structural donations** 3. **Low NPS score** We can see here that the main driver behind the "high risk" prediction for this donor is the *Low number of emails clicked*. This is colored red because the *Low number of emails clicked* contributes to a higher churn risk*.* This is similar to the *Low NPS score* which also leads to higher churn risk. In contrast, the *Multiple non-structural donations* appears in green since it helps attenuate that churn risk according to our AI engine. {% endhint %} #### Engagement Score *An all-in-one indicator of the engagement level of your* donor*.*\ \&#xNAN;*How engaged is your donor: a unique indicator produced by our AI engine.* The Churned Engagement Score provides a focused view of how engaged your donors are — and how that engagement (or lack thereof) contributes to their churn risk. While the AI Health Score takes into account all dimensions of donor health, the Engagement Score isolates just one: behavioral engagement. **How it works** Through years of analyzing churned donors and their behavior leading up to that moment, we’ve learned that engagement patterns tell a powerful story. Donors rarely stop giving suddenly — they disengage first. Our AI models are trained specifically to understand those behavioral signals: declining interactions, skipped communications, irregular donation activity, or reduced participation in campaigns. By focusing purely on engagement data, the model quantifies how connected and active a donor truly is. The Engagement Score is powered by a dedicated AI model that uses only engagement-related features — such as communication opens, click behavior, campaign interactions and support tickets that are raised to predict a donor’s churn likelihood. This makes it a pure measure of engagement-driven risk. **How to interpret it** The Churned Engagement Score also ranges from 1 to 10: * 1–4: 💤 *Disengaged* — donor is at high risk of churn due to low or declining engagement. * 4–6: ⚠️ *At risk* — engagement is inconsistent or trending downward. * 7–8: 💬 *Engaged* — donor shows healthy interaction levels across touchpoints. * 9–10: 🔥 *Highly engaged* — donor is consistently active, responsive, and loyal. #### Engaged Donors *The total number of engaged donors of the active customers in the selected segment.*\ \&#xNAN;*How engaged this segment is compared to the other active donor base.* This metric is based on the [Engagement score](#engagement-score). #### Non-Engaged Donors *The total number of non-engaged donors of the active donors in the selected segment.*\ \&#xNAN;*How non-engaged this segment is compared to the other active donor base.* This metric is based on the [Engagement score](#engagement-score). #### High-Value Donors *The total number of valuable donors of the active donors in the selected segment.*\ \&#xNAN;*How valuable this segment is compared to the other active donor base.* The number of donors within this group that has an ARR above the median ARR of the active donor base. #### Low-Value Donor *The total number of less valuable donors of the active donors in the selected segment.*\ \&#xNAN;*How valuable this segment is compared to the other active donor base.* The number of donors within this group that has an ARR below the median ARR of the active donor base. #### Top Three Risk Drivers *What are the biggest drivers of the churn risk.* \ \&#xNAN;*The top three drivers of your donor's churn risk for the selected segment.* The top 3 risk drivers are calculated by counting how much each risk driver has occurred for the active donors within this group. How the risk drivers are computed is explained [here](#top-3-risk-drivers). #### Per Risk Level *How (un)healthy the selected segment really is.*\ \&#xNAN;*The distribution of the active donors in the selected segment over the three risk levels.* This distribution represents how the number of active donors within the selected segment are spread over the different risk level groups. {% hint style="info" %} **Example:** Consider the situation where you have selected 2 segments: 1. Donors in onboarding 2. Loyal donors \ Looking at the distribution in the graph we now see that 4.8% of the total active donors in the segment *onboarding* is at 'High risk' while this percentage for loyal donors is only 0.8% ![](https://246234927-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FkSyCQUyE0u7ya9ULaprd%2Fuploads%2FYVgB9ZxS7pEa9pHXx2vu%2FScreenshot%202022-12-12%20at%2012.03.50.png?alt=media\&token=74eba835-a36e-47ed-9644-a252ccbec4c8) {% endhint %} ## Non-Structural Donors ### KPI dashboard {% hint style="info" %} 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. {% endhint %} #### RFM RFM is a method used in customer segmentation to divide customers (donors) into groups based on their purchase behavior. RFM stands for Recency, Frequency, and Monetary value. In an donation setting, RFM can be used to identify the most valuable donors and target them with personalized campaigns. * Recency refers to how recently a donors made a donation. This is determined by looking at the latest order date of each donors. * Frequency refers to how often a donor makes a donation over a set period of time. * Monetary value refers to the amount of money a donor spends over a period of time. Based on these three criteria, donors can be divided into segments. For example, donors who have made a donation recently, make frequent donations, and spend a lot of money on each donation would be considered the most valuable and placed in the "champions" segment. Donors who haven't made a donation in a while, make infrequent donations, and spend little money on each donation would be considered the least valuable and placed in the "churned" segment.

Intuitive meaning of the RFM segments

| RFM Segment | RFM Code | | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------- | | Champion | 555, 554, 545, 544, 455, 454, 445 | | Loyal | 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 | | New | 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 | | Hibernating | 332, 322, 233, 232, 223, 222, 132, 123, 122, 212, 211, 331, 321, 312, 221, 213, 231, 311 | | Lost | 111, 112, 113, 114, 115, 121, 131, 141, 151 | ## **RFM segments** **Champion**\ \&#xNAN;*Donors with highest recency, frequency, order value*\ \&#xNAN;*highest recency, order frequency, order value* **Loyal**\ \&#xNAN;*Donors with high recency and high frequency*\ \&#xNAN;*High recency, order frequency, order value* **Potential loyalists**\ \&#xNAN;*Almost loyal donors that should improve frequency*\ \&#xNAN;*High recency, medium order frequency* **Need attention**\ \&#xNAN;*High value donors that should improve frequency*\ \&#xNAN;*High recency, medium order frequency, very high order value* ***New***\ \&#xNAN;*Donors that have only one recent purchase at any value*\ \&#xNAN;*High recency, low frequency* **At risk**\ \&#xNAN;*High value donors not seen for a long time*\ \&#xNAN;*Very low recency, high order value* **Hibernating**\ \&#xNAN;*sporadic donors not seen in a while*\ \&#xNAN;*low recency, low frequency, low order value* **Lost**\ \&#xNAN;*Donors that are not expected to return*\ \&#xNAN;*Very low recency, very low order value* #### Donors *Donors that made a purchase in the last RFM period*\ \&#xNAN;*Active donor base* This is the latest number of active donors registered in your system. A donors is counted as active whenever they have at least one donation in the last RFM period. #### Retention rate *Percentage of returning donors*\ \&#xNAN;*How loyal are your donors* $$ Retention Rate = 1 - \frac{\textnormal{Churned}}{\textnormal{Active}} $$ The *Retention Rate* is the percentage of donors in the last month that made multiple orders within the RFM period. New donors are not taken into account. The *Retention Rate* is a measure of the percentage of donors 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 donor 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.*\ \&#xNAN;*The willingness to pay of each donor*\ \ 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. $$ AOV = \frac{\sum\_{i=1}^{n} \textnormal{Revenue}*i}{\sum*{i=1}^{n} \textnormal{Order}\_i} $$ AOV is a useful metric for E-commerce businesses because it can help them understand how much money each donor is spending on average, which can drive pricing and marketing strategies such as optimizing retention. #### Average Order Frequency (AOF) *The average number of donations per donors over a time period.*\ \&#xNAN;*How frequently are your donors donating to you.* The AOF is calculated by dividing the total number of orders (n) by the total number of donors (c) over a giving time period. In the case of the dashboard, the period is equal to the period set for the RFM segmentation. $$ AOF = \frac{\sum\_{i=1}^{n} \mathrm{Order}*i}{\sum*{j=1}^{c} \mathrm{ Donor}\_j} $$ AOF is a useful metric because it can help understand how often donors are making purchases, which can drive retention and loyalty programs. #### Expected Average Customer Value (Expected ACV) *The expected average revenue value of active customers (donors)*\ \&#xNAN;*What are you donors worth* The Expected Average Customer Value is a weighted sum of the current ACV and the previous ACV. $$ ExpectedACV\_t=w\_1*ACV\_t + w\_2*ExpectedACV\_{t-1} $$ $$ ACV\_0=AOV \* AOF $$ #### Cohort plots *The percentage returning donors per cohort.*\ \&#xNAN;*How loyal is your current donors 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 donors** that have placed an order in the specific year-month period. The columns in each row in the plot represent the percentage of donors that have returned for a next order. In the example below we can see that 3,534 new donors made a donation 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.