Customer Succes for SaaS

This section explains all the powerful metrics that Churned provides 💪

KPI dashboard

All the metrics show 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.

Example:

In the snapshot below, the current number of churners is 29. This figure reflects the number of customers 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.

Active customers

The current number of customers with an active subscription. How many customer are active at this moment.

The number of "active customers". 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 contract that is currently active.

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Churners

The number of customers that have churned in the last 30 days. How many customers have left you recently.

"Churners" is defined as the number of customers that have churned in the last 30 days. A customer 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.

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Churn rate

The percentage of customers that have churned in the last 30 days. How much churn are you experiencing.

The "churn rate" indicates the percentage of customers which have churned in the last 30 day. This number is calculated relative to the number of currently active customers. A high-churn rate indicates a substantial number of customers canceling their subscriptions/contracts among the currently active customers.

Specifically, the churn rate is calculated as follows:

$$churn \ rate_t \ = \ \dfrac{ {churners} _t } {active \ customers_t \ + \ churners_t}$$

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ARR

The current annual recurring revenue (ARR). 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.

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Churn loss

The total loss in yearly revenue due to customers that have churned in the last 30 days. How much churn has cost you recently.

The churn loss indicates the annual revenue that has been lost due to the customers that have churned during the last 30 days. This is calculated by the simple formula:

$$churn \ loss_t = \sum_{i} churner_{i,t} \times ARR_{i,t}$$

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Remaining CLV

The average remaining life-time value of your customers. How valuable your active customer base is.

The "remaining CLV" (Customer Lifetime Value) represents the average remaining monetary value of the current active customers. In other words, this is the expected present value of all predicted future subscription/contract fees paid by the average customer.

Our estimate of remaining CLV is calculated based on the historical data of all your customers. Our AI models take into account how long the subscription of each customer is predicted to last, and how much each customer is predicted to spend.

At the end of the day, this metric is predicted by our state-of-the-art AI engine, so we spare you the details.

The formula for remaining CLV is as follows:

$$Remaining \ CLV_t \ = \ \frac{1}{N} \sum_{i} \sum_{\tau > t} P(active_{i,t}) \times \mathbb{E}(fee_{i,t})$$

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Remaining CLT

The average remaining lifetime of all your customers. How loyal your active customer base is.

The remaining CLT (Customer Lifetime) represents the average remaining lifetime of your active customers. Just like the CLV, this metric is predicted by our powerful AI engine and predicts how loyal your customer base is.

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AI health score

An all-in-one measure of the health of your customers. How healthy your customer base is.

The Churned AI health score gives you an all-in-one measure of the health of your customers. This metric includes short-run and long-run trends in customer churn risk, customer value, customer engagement, effective churn rates, and much more.

The Churned AI health score is probably one of the most powerful metrics about your business. Unlike other Customer Success tools, this health score is actually driven by state-of-the-art AI rather than being based on simplistic rule-based KPIs.

Our AI health score ranges from 1 to 10. Our score translates to "sick" when ranging between 1-4, "concerning" between 4-6, "healthy" when ranging between 7-8, and "excellent" for a score of 9-10.

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Risk levels

A segmentation of your customers into three risk levels. How many customers and corresponding value are in each risk group.

The risk levels metric divides your current active customers into the Churned risk levels: High Risk, Medium Risk and Low risk. Based on their churn prediction, each customer receives one of these labels. The total value (sum of the ARR of these customers) 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 customers within each risk level is revealed as well as the change in the value of that risk level compared to 30 days ago.

Since we want as many customers in the Low Risk group, an increase compared to the previous period turns green, while an increase in the High Risk group turns red.

Risk level metrics

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Net Revenue Retention (NRR)

The recurring revenue generated from existing customers over a period. How sustainable is your business for revenue growth.

Net revenue retention is a measure of the total revenue a company earns from its existing customers during a given period, taking into account any cancellations, downgrades, or contract expirations that occurred during the same time frame. This measure is commonly referred to as "net dollar retention" in the United States and "cash retention" in other parts of the world. It is used to assess the effectiveness of a company's retention and growth strategies, as well as to identify potential areas for improvement.

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Formula for Net Revenue Retention

To calculate net revenue retention, you will need the total revenue earned from your existing customers during a given period, as well as any cancellations, downgrades, or contract expirations that occurred during the same time frame. With that information it's quite simple to calculate.

ARR: The recurring revenue receiving from customers at the end of the previous month.

Expansion: How much additional recurring revenue was generated from that same customer group from upsell and cross-sell.

Contraction: How much recurring revenue was lost in that same group from downgrades.

Churn: How much recurring revenue was lost from that same group due to churn.

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CSM cockpit

All the metrics show in this dashboard are updated daily and are calculated based on the latest available information at the customer level. Only active customers are presented here.

You can "zoom in" on each customer by clicking on the desired customer on the table. This will highlight and reveal additional information on that customer, including the Top Risk Drivers.

Some of the metrics (e.g. engagement metrics) are calculated based on the last 30 days of information. Meaning that you look at a period between today and 30 days ago. Additionally, some of the metrics will show recent changes in some of the metrics.

• 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 preceeded it.

• vs last year: this is the percentage variation of the current value in comparison to last year. In practice, it is calculated as the comparison between the year-to-date (YTD) value and the previous year during the same period.

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ARR

The annual recurring revenue (ARR) from each customer. How much yearly revenue can you expect based on the customer's current subscription/contract.

The annual recurring revenue (ARR) is a simple but essential business metric which indicates how much recurring revenue you can expect from a given customer, based on currently active subscription/contract. ARR is also the annualized version of monthly recurring revenue (MRR) representing revenue in the calendar year.

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CLV

The average remaining life-time value of your customer. How valuable each customer is.

The "remaining CLV" (Customer Lifetime Value) represents the average remaining monetary value of each customer. In other words, this is the expected present value of all predicted future subscription/contract fees paid by each given customer.

Our estimate of remaining CLV is calculated based on all the historical data you have available. Our AI models take into account how long the subscription of each customer is predicted to last, and how much each customer is predicted to spend.

At the end of the day, this metric is predicted by our state-of-the-art AI engine, so we spare you the details.

The formula for remaining CLV of customer i at time t, is calculated as follows:

$$Remaining \ CLV_it \ = \ \sum_{\tau > t} P(active_{i,t}) \times \mathbb{E}(fee_{i,t})$$

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Risk Score

The ultimate measure of churn risk for each one of your customers. Advanced indicator of churn risk leveraging the full power of the Churned AI engine.

The Churned "risk score" ranges from 1 to 10 and gives you an all-in-one measure of churn risk for each customer. A high risk score indicates a high chance that a customer will churn soon. A low risk score reveals that a customer is less likely to churn in the near future.

This metric looks into all the information available on your customer, including recent engagement, and indicates how likely is a customer to churn. The Churned risk score is a state-of-the-art metric in churn analytics. Unlike other Customer Success tools, this risk score is actually driven by advanced proprietary AI rather than being based on simplistic rule-based KPIs.

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Risk Level

A classification of each customer into three churn risk levels. Quick overview of the churn risk for any given customer.

The risk level metric uses the Churned risk score to classify each one of your customers into three risk levels: High Risk, Medium Risk and Low risk. This provides you with a quick-and-easy intuitive way of grouping your customers. You are thus able to focus attention on the right customer groups.

Based on their risk score, each customer receives one of the risk level labels. A customer with Low risk level is a customer which is less likely to churn in the near future. A customer with Medium risk level has an average churn risk. A customer with a High risk level has a relatively high chance of churning soon.

The Low, Medium and High risk levels are adapted to the unique realities of your business. Our AI engine automatically identifies the relevant risk range of your company and adjusts to the specific risk levels which are relevant for your business.

A customer with a High risk level is a customer with a substantially higher risk of churning than most of your other customers. Similarly, a customer with a Medium risk level is a customer that has an average chance of churning relative to the churn risk of your customer base.

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Risk Change

Keep track of recent changes on the risk score of your customers. Quick view on how your customer's risk score is evolving.

The risk change gives a simple but effective view on the recent changes in your customer's risk score. The risk change will trend upwards when the risk of churn is increasing, and downwards when the risk is decreasing.

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Top 3 risk drivers

Explanation for the churn risk by our AI engine. The top three drivers of your customer's churn risk.

At Churned, we go beyond predicting the churn risk for each customer. Our AI engine actually uncovers the drivers behind the predicted churn risk. This means that, for every given customer, 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 customer,

• which drivers help decrease or increase churn risk,

• if the driver's value is low or high for that customer.

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.

Example:

Consider a customer with high churn risk. Suppose that the top 3 risk drivers for this customer are:

1. Low number of logins

2. High number of products

3. Low NPS score

We can see here that the main driver behind the "high risk" prediction for this customer is the Low number of logins. This is colored red because the Low number of logins contributes to a higher churn risk. This is similar to the Low NPS score which also leads to higher churn risk. In contrast, the High number of products appears in green since it helps attenuate that churn risk according to our AI engine.

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Risk Change Drivers

Explanation for the churn risk score variations as determined by our AI engine. The top drivers of your customer's churn risk score variation.

Our AI engine predicted churn risk for a given customer is dynamic, fluctuating over time according. The primary factors instigating this variation are itemised under the Risk Change Drivers section.

The Risk Change Drivers reveal the following key insights:

• what are the main drivers of risk change for that customer

• how the recent change in the churn drivers value affects the risk score

• what recent changes in customer behaviour affect the churn risk, and how

Example:

A customer can have a High number of logins, which helps maintain the Risk Level low.

However, on a scenario where the number of logins starts to drop, then the Risk Level might increase. On this case the Decreasing Number of Logins will be shown as a driver of Risk change.

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Next Best Action

The best way of preventing churn and retaining your customer. See which action should you take next!

The Churned AI engine is unique uncovering which actions are most likely to be effective in preventing a customer from churning. Our engine analyzes all your customer data to measure the impact of each action and identify which action is most indicated for each customer. It takes all customer characteristics into account, as well as the most recent customer behavior and engagement indicators.

The Next best action gives you a view of the actions that are most effective in preventing a customer from churning. This list of proposed actions also takes into account if an action was already taken recently. This means that after you take a certain action, you will not be advised to take that same action again, at least for a while.

Overall, the Next best action goes well beyond predicting the churn risk for each customer. It is all about producing actionable insights, giving you the power to actively reduce churn.

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Engagement Score

An all-in-one indicator of the engagement level of your customer. How engaged is your customer: a unique indicator produced by our AI engine.

The Churned engagement score gives you an all-in-one measure of the engagement level of each of your customers. This metric is automatically adjusted to the unique realities of your business and the distinct behavior of your customers.

The Churned engagement score is another powerful metric that you can use to optimize your business. Unlike other Customer Success tools, this engagement score is driven by advanced AI. It is not a rule-based indicator.

Our engagement score ranges from 1 to 10. Values ranging between 1-4 reveal low engagement relative to other customers. Values between 5-6 indicate normal levels of engagement and 7-8 are reserved for relatively high levels of engagement. An engagement score of 9-10 is excellent.

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User segments

In this section it is possible to view more information about your users. This can be achieved by creating custom segments by hitting the create segment button where you can customize the segment in any way you want.

Aside from the custom segment, product usage based segments are created by Churned AI. The model that is used is the RFV (Recency, Frequency & Volume) model.

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RFV Model

RFV is a customer segmentation method used to categorize customers based on their product usage behavior. RFV stands for Recency, Frequency, and Volume. In an SaaS or product-centric setting, RFV helps identify the most engaged and valuable customers by analyzing how they use the product, allowing for targeted and personalized engagement strategies.

Components of RFV

1. Recency (R)

   • Definition: How recently a customer has used the product.

   • Measurement: Determined by the date of the latest product usage or interaction.

   Indicates the current level of engagement. More recent usage suggests higher engagement.

2. Frequency (F)

   • Definition: How often a customer uses the product within a specific time frame.

   • Measurement: The number of product interactions or sessions over a set period.

   Higher frequency reflects greater reliance and habitual use of the product.

3. Volume (V)

   • Definition: The depth or extent of product usage.

   • Measurement: Quantitative metrics such as the number of features used, time spent on the product, number of transactions, or amount of data consumed.

   Higher volume indicates deeper engagement and more comprehensive use of the product’s capabilities.

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RFV Segments

Based on the three RFV criteria, users can be segmented into distinct groups. For each of the criteria, a user receives a score ranging from 1 (most weak) to 5 (most strong). Together they form a RFV code. Below is a breakdown of RFV segments, their corresponding RFV codes, and intuitive meanings:

Intuitive Meaning of the RFV Segments

RFV Segment	RFV Code Examples	Description
Champions	555, 554, 545, 544, 455, 454, 445	Users with the highest recency, frequency, and volume of usage. Highly engaged and loyal.
Loyal Users	553, 552, 551, 543, 542, 541, 444, 435, 355, 354, 345	Users with high recency and frequency, and substantial volume. Consistently engaged.
Potential Loyalists	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	Users showing strong engagement but with room to increase usage volume or frequency.
New Users	515, 514, 513, 512, 511, 415, 414, 412, 411, 313	Recently started using the product, with low frequency or volume. Potential for growth.
Need Attention	443, 434, 343, 334, 325, 324, 342, 341, 333, 323, 335, 315, 314	Users with high usage volume but declining frequency or recency. May need re-engagement strategies.
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	Users who previously had high usage but have not engaged recently. Risk of churn.
Hibernating	332, 322, 233, 232, 223, 222, 132, 123, 122, 212, 211, 331, 321, 312, 221, 213, 231, 311	Sporadic users with low recency, frequency, and volume. Minimal engagement.
Lost	111, 112, 113, 114, 115, 121, 131, 141, 151	Users with very low recency, frequency, and volume. Unlikely to return without intervention.

Detailed Segment Descriptions

1. Champions

   • Criteria: Highest Recency, Frequency, and Volume.

   • Description: These users are highly engaged, frequently using the product recently and extensively. They are your most valuable users and should be rewarded with exclusive offers and recognition to maintain their loyalty.

2. Loyal Users

   • Criteria: High Recency and Frequency, substantial Volume.

   • Description: Regularly engaged users who use the product frequently and with significant depth. Focus on maintaining their engagement through consistent value delivery and occasional incentives.

3. Potential Loyalists

   • Criteria: High Recency and either medium Frequency or Volume.

   • Description: Users showing strong engagement but not yet at the level of Champions or Loyal Users. Encourage them to increase their usage through targeted campaigns and feature highlights.

4. New Users

   • Criteria: High Recency, low Frequency or Volume.

   • Description: Recently acquired users who have just started using the product. Focus on onboarding, education, and encouraging deeper engagement to convert them into loyal users.

5. Need Attention

   • Criteria: High Volume but declining Recency or Frequency.

   • Description: Previously engaged users who are showing signs of reduced interaction. Implement re-engagement strategies such as personalized offers or updates on new features to regain their interest.

6. At Risk

   • Criteria: Previously high Volume but very low Recency.

   • Description: Users who were once highly engaged but have not used the product recently. They are at risk of churning and may require targeted retention efforts to win them back.

7. Hibernating

   • Criteria: Low Recency, Frequency, and Volume.

   • Description: Infrequently engaged users with minimal usage. While not entirely inactive, their engagement is low. Consider strategies to increase their interaction, such as reactivation campaigns or highlighting underused features.

8. Lost

   • Criteria: Very low Recency, Frequency, and Volume.

   • Description: Customers who have not engaged with the product for a long time and show minimal usage. These users are unlikely to return without significant intervention, such as win-back campaigns or surveys to understand their disengagement.

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Segment analysis

Engaged Customers

The total number of engaged customers of the active customers in the selected segment. How engaged this segment is compared to the other active customer base.

This metric is based on the Engagement score.

Non-Engaged Customers

The total number of non-engaged customers of the active customers in the selected segment. How non-engaged this segment is compared to the other active customer base.

This metric is based on the Engagement score.

High-Value Customers

The total number of valuable customers of the active customers in the selected segment. How valuable this segment is compared to the other active customer base.

The number of customers within this group that has an ARR above the median ARR of the active customer base.

Low-Value Customers

The total number of less valuable customers of the active customers in the selected segment. How valuable this segment is compared to the other active customer base.

The number of customers within this group that has an ARR below the median ARR of the active customer base.

Top Three Risk Drivers

What are the biggest drivers of the churn risk. The top three drivers of your customer'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 customers within this group. How the risk drivers are computed is explained here.

Per Risk Level

How (un)healthy the selected segment really is. The distribution of the active customers in the selected segment over the three risk levels.

This distribution represents how the number of active customers within the selected segment are spread over the different risk level groups.

Example:

Consider the situation where you have selected 2 segments:

1. Customers in onboarding

2. Loyal customers

Looking at the distribution in the graph we now see that 4.8% of the total active customers in the segment onboarding is at 'High risk' while this percentage for loyal customers is only 0.8%

Cohort analysis

In our cohort plots, we group customers by their signup period (e.g., month of acquisition) and track their retention rates over subsequent months. These plots clearly show how customer retention evolves over time for each cohort, revealing trends such as improving or declining customer loyalty. By analyzing cohort plots, we can measure the impact of product updates, onboarding improvements, or pricing changes on customer retention, enabling targeted strategies to boost long-term user engagement and reduce churn.

Example of the cohort plot

In the cohort plot, each row shows the initial number of new customers who started their first subscription in that month and year, followed by the retention percentage.

Avg. retention This number represents the average retention rate for the complete column (specific month)

Avg. dropoff This number represents the average percentage of customer that churns moving from month X to month Y in their subscription.