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Six Steps to Optimize Customer Retention Forecasting

March 9, 2023
February 1, 2023
February 1, 2023

You don't need me to tell you how important retention forecasting is to your customer success strategy.

But, if you have made your way to this post, chances are that you are struggling with your retention forecasting.

Worry not!

With the right approach and tools, you can accurately predict and mitigate customer churn, drive customer loyalty, and improve business performance.

But how do you get started?

How do you confidently forecast customer retention and take customer success efforts to the next level?

This post will provide the ultimate six steps for improving retention forecasting.

We'll cover everything from utilizing predictive analytics and data science to artificial intelligence (AI) and machine learning models.

By implementing these steps, you can boost customer satisfaction, reduce churn rates, and create loyal customers that stick around for the long haul.

So if you're looking to unlock the power of predictive analytics and machine learning to drive your customer success efforts forward, let's jump right in!

Step 1: Define Retention Metrics

When it comes to customer retention, the first step is to define the key metrics you’ll be tracking.

The most commonly used metrics are churn rate, Net Revenue Retention (NRR), and Net Promoter Score (NPS).

Churn Rate

Churn rate is a measure of how many customers are leaving your service in a given period. It’s usually calculated as the number of customers that have left over a set period of time divided by the total number of customers at the beginning of that period.

This metric helps you understand how quickly customers are leaving and gives insights into what factors might be causing them to leave.

Net Revenue Retention (NRR)

Net revenue retention measures how much revenue you retain from existing customers over time.

It’s usually calculated as the revenue from existing customers in a given period divided by the total amount of revenue from those same customers in the previous period.

NRR gives you an understanding of whether your current efforts are helping keep revenue from existing customers or if revenues are declining due to customer attrition.

Net Promoter Score (NPS)

The net promoter score is a measure of customer satisfaction and loyalty. It’s usually calculated by asking customers to rate their experience with your product or service on a scale from 0-10 and then grouping responses into three categories: promoters, passives, and detractors.

Promoters are highly satisfied customers who would recommend your product or service; passives are moderately satisfied but unlikely to recommend it; detractors are unhappy customers who would not.

By tracking NPS over time, you can measure customer satisfaction and loyalty and identify areas for improvement.

Product Behavior and Customer Fit Scoring

Parative's Dynamic Scoring Engine

Once you have identified the key metrics to track, the next step is to improve upon those metrics by considering other factors, such as product behavior and customer-fit.

Scores in this category include Expansion Qualification Score, Churn Risk Score, Customer Health Score, and Expansion Account Fit Score.

Expansion Qualification Score

The expansion qualification score is a metric that assesses whether or not a customer is likely to be an ideal target for upsells or expansions.

It looks at usage behavior, including average spend, product usage rate, total active users, and more.

It can help you identify customers likely to benefit from expanding their current subscriptions or purchasing additional products and services. By focusing on these customers first, you can boost both revenue retention and loyalty over time.

Churn Risk Score

The churn risk score is a metric that helps measure how likely a customer will churn in a given period.

It looks at historical data such as past usage patterns, support tickets created, and other indicators of customer engagement over time to calculate the risk level of each customer.

By tracking this score over time and assessing changes in activity levels or usage rates, you can identify customers who might be at risk of leaving your service so that you can take action before they do.

Customer Health Score

A customer health score measures how healthy your relationship with each customer is based on metrics like lifetime value (LTV), average usage rate, total purchases made, etc.

This score gives you an overall picture of each customer's activity within your ecosystem and provides insights into which ones are most engaged with your service offerings.

By tracking health scores over time, you can better understand which customers are more engaged than others so that you know where to focus your attention when upselling or expansion opportunities.

Expansion Account Fit Score

This score indicates which customers are best suited for an upsell/expansion opportunity based on how their firmographic data matches your ideal customer.

By tracking this metric over time – along with churn risk scores – you can identify which customers are more open to an upsell opportunity while also minimizing the risk associated with any potential losses due to a failed attempt at expanding their subscription package.

Combining Metrics for Comprehensive View

Combining these three metrics will give you a comprehensive view of customer health and help you track progress toward your 95% annual customer retention goal.

Having an accurate picture of customer health will allow you to take proactive steps to prevent churn and ensure that your current efforts successfully retain existing customers while attracting new ones.

Step 2: Enhance Data Collection

At this stage of the game, it’s time to get serious about collecting data that will help you create an accurate retention forecasting model.

To do this, you’ll need to ensure that your existing tools are correctly integrated and consider adding additional data sources.

Using Your Existing Tools

Most SaaS companies have many analytics and customer experience tools – such as Mixpanel, CRM, Pendo, etc.

Leverage these tools to gather key customer usage metrics and build a comprehensive view of their behavior and usage patterns.

Ensuring these tools are properly implemented and integrated will be crucial for achieving an accurate retention forecast.

Employ Additional Tools

Don’t rely on those existing tools for collecting customer data – there are plenty of other ways to gain insights into your customer’s needs and motivations. For example, consider implementing customer surveys regularly to measure how satisfied customers are with your product or service.

Look into social listening platforms or third-party review sites to get feedback from customers (or potential customers).

Access to more detailed and accurate customer data is essential to generate meaningful predictions about future churn rates.

Ensure you’re collecting as much relevant customer data as possible so that your predictions have some substance.

Parative Scoring Engine

Parative is a Revenue Scoring Engine that helps businesses understand and predict customer likelihood to buy more or churn.

With the increasing number of data sources for various teams - from customer-facing teams like customer success and sales to RevOps, Product Management, and Data and Engineering - it can be difficult to make sense of all the data and identify key revenue signals.

Parative solves this problem by monitoring customer behavior, usage, intent, contract consumption, feedback, and market conditions to score each customer's outcome readiness in real-time.

In addition, Parative:

  • Automates action in other tools by proactively alerting teams and triggering workflows when scoring indicates opportunity.
  • Brings all customer data together and merges it into truly unified records.
  • Allows for customer segmentation and identifying signals that predict revenue opportunities
  • Delivers scores directly to your CRM while alerting reps in Slack or over email via notification triggers.

Parative provides an invaluable toolkit for businesses of all sizes looking at enhancing their predictive analytics capabilities when it comes understanding customers better.

Parative Revenue Expansion Offer

Step 3: Develop a Segmentation Model

Parative's Customer Segment Builder

Customer data is valuable in predicting customer retention and informing customer success efforts.

The third step in crushing your retention forecasting is to develop a segmentation model that can be used to analyze usage patterns, contract size, and other relevant factors to identify potential risks for attrition.

The Benefits of Segmentation

Segmentation can provide valuable insight into customer behavior, allowing you to identify patterns that could inform your customer retention strategy.

By segmenting customers into different cohorts, it becomes easier to spot trends that indicate an increased churn risk and prioritize resources accordingly.

Additionally, segmentation provides the opportunity to understand better what types of customers are most likely to remain loyal—and which ones need more help from your team.

Common Segments for Retention Analysis

When building a segmentation model for retention analysis, there are several common segments you should consider:

  • Usage Patterns: Look at how often customers use your product or service, as well as any changes in usage over time. This will give you an idea of which customers are actively engaging with your product—and those who may be at risk of churning due to decreased engagement.
  • Contract Size: Analyze the size of contracts signed by customers and look for any correlations between contract size and churn rate. This will help you understand if larger contracts are associated with greater loyalty or if more minor contracts pose more risk of churning.
  • Customer Type: Consider whether certain types of customers—such as enterprise or SMB—are more likely to churn than others. This can help you determine which types of customers should receive additional attention from your team and which ones may require different approaches when it comes to retention efforts.
  • Demographics: Analyze customer demographics such as age, gender, location, etc., and look for correlations between these factors and a customer’s likelihood to remain loyal or churn.

Applying Your Segmentation Model

Once you have identified the segments that best fit your particular customer base, you can apply the model to understand better how each segment behaves differently regarding loyalty and attrition rates.

You can use this information to create targeted strategies that address the needs of each segment separately—ensuring that each one receives the right level of attention and support from your team.

Additionally, this data can be used to keep track of performance over time so that you can continually adjust your strategies based on new insights gained from analyzing customer behavior within each segment.

By developing a segmentation model based on usage patterns, contract size, customer type, demographics, and other relevant factors, you will gain valuable insight into how best to approach customer retention efforts to maximize success rates across all segments in your target market.

Step 4: Implementing a Retention Model

Developing an effective model is one of the most critical steps when forecasting customer retention.

This model should be able to predict customer behavior and usage patterns to provide more accurate and actionable insights into customer health.

In this step of retention forecasting, we’ll break down how to develop a statistical model that will help you better understand your customers and their needs.

Understanding Customer Behavior

The first step in creating an effective retention model is understanding customer behavior.

It’s vital to identify key drivers of customer behavior, such as purchase history, product usage, and engagement levels.

Analyzing this data can provide valuable insights into why customers stick around or churn away from your company.

By understanding the factors influencing customer decisions, you can create targeted strategies for improving retention rates.

For example, suppose you find that customers who purchase more expensive products are more likely to stay with your business. In that case, you can focus on offering more expensive products to high-value customers to increase their loyalty.

Using Segmentation Models

Once you have identified the key drivers of customer behavior, it’s time to refine the retention model using segmentation models.

Segmentation models allow you to group customers based on similar characteristics and behaviors so that you can target them with tailored strategies for increasing loyalty and reducing churn rate.

For example, you may find that customers who use your product frequently are more likely to stay with your business than those who don’t use it as often.

By grouping these customers into a “frequent user” segment, you can target them with incentives such as discounts or free trials to encourage them to continue using your product.

Using Predictive Analytics

Finally, once you have identified key drivers of customer behavior and created segmentation models for targeting different groups of customers, it’s time to use predictive analytics tools such as machine learning and logistic regression algorithms to develop an accurate predictive model for predicting customer churn risk.

By analyzing historical data about existing customers, these algorithms can be trained to accurately predict which new customers are likely to stay loyal and which ones are at risk of leaving.

With an effective predictive model, businesses can better understand their customer base and take proactive steps to improve retention rates by targeting at-risk segments with tailored strategies for increasing loyalty and reducing churn rates.

Step 5: Monitor and Alert System

It’s not enough to build a retention model.

To truly make the most of predictive analytics, you must set up a real-time monitoring and alert system that notifies customer success teams of potential churn risks.

This system must be integrated with your retention model and other customer data sources to provide actionable insights into customer health.

Monitoring and alert systems are key for the timely identification of customers who may be at risk for churning.

These systems can also help you identify patterns in customer behavior that could lead to potential churn, such as a decrease in usage or an increase in support tickets.

By proactively addressing these issues, you can mitigate customer churn before it even happens.

Here are some key components you need to consider when setting up a monitoring and alert system:

  1. Data Sources: The first step is to identify all the relevant data sources that you can use to monitor customer health. This could include product usage data, support ticket logs, NPS surveys, customer feedback, etc. It’s essential to collect as much data as possible so that your system has comprehensive visibility into the customer journey with your product or service.
  2. Analytical Tools: Once the relevant data sources have been identified, it’s time to decide which analytical tools will be used for the analysis. Common options include machine learning models (e.g., logistic regression), predictive analytics (e.g., predicting churn probability or identifying loyal customers), artificial intelligence (AI) algorithms (e.g., natural language processing for sentiment analysis), etc. Access to all these tools means you can easily tailor your solution to specific customer needs and keep track of their journey from acquisition to loyalty over time.
  3. Dashboards: A monitoring and alert system should have an intuitive dashboard where all the relevant customer data is displayed in one place, and easy-to-read charts show any changes over time. This dashboard should also include automated alerts that notify teams when certain thresholds are met, such as when a customer’s usage drops below a certain level or when they receive too many negative reviews/comments on social media platforms like Twitter and Facebook).
  4. Action Plans: Finally, once an issue has been identified and an alert triggered, teams need to have action plans ready to quickly implement to take action on any issue before it escalates into churn risk territory. These action plans should be tailored according to the individual customer's needs and preferences — what works for one may not work for another — so having access to detailed customer profiles is also essential here.

Implementing a real-time monitoring and alert system is no small task, but it is critical if you want your retention forecasting efforts to succeed long-term.

By proactively identifying potential risks early on, teams can take quick action before those risks turn into actual churn scenarios — saving time and money in the process.

Plus, having access to detailed analytics makes it much easier for teams to assess their current retention strategies against others in their industry — giving them valuable insights into what works best for them so they can adjust accordingly.

Step 6: Foster a Culture of Customer Success

To drive customer success and retention, fostering a culture of customer success within your organization is crucial.

This includes providing training and resources to the customer success team, encouraging cross-functional collaboration, and ensuring customer success is a priority for everyone.

Training and Resources

Provide your customer success team with the necessary training and resources to help them develop the skills needed to drive customer success.

This includes everything from understanding customer needs and behaviors to using predictive analytics and machine learning models to monitor churn risk, identify opportunities for improvement, and develop personalized strategies.

It also means educating your team on using data-driven insights to build better customer relationships and create experiences that keep them returning.

Cross-Functional Collaboration

Encourage collaboration across departments, so that customer success is a shared responsibility.

This means involving product teams in understanding customer feedback, marketing teams in creating personalized messaging, sales teams in developing tailored plans for upselling existing customers, and engineering teams in ensuring product reliability.

By working together towards the same goal - creating an outstanding experience for every single customer - you can ensure that everyone is focused on driving long-term loyalty.

Prioritizing Customer Success

Finally, ensure everyone - from the C-suite down - understands that customer success should be a top priority.

Encouraging the entire organization to adopt a customer-centric approach will ensure that all decisions are made to maximize customer value.

By taking these steps, you can foster a culture of customer success within your organization that will help you crush your retention forecasting goals.

With effective training and resources in place, cross-functional collaboration encouraged, and everyone prioritizing customer success as a key priority, you’ll be well-positioned to achieve long-term loyalty from existing customers while continuing to acquire new ones.


Creating an effective retention forecasting strategy is essential to understanding and mitigating customer churn, driving customer loyalty, and improving overall business performance.

To accomplish this, you need to take a data-driven approach that involves collecting and analyzing customer data, cross-functional collaboration, and a customer-centric approach.

Following the six steps outlined in this blog post can create a strong foundation for your retention forecasting strategy.

You can use predictive analytics, machine learning models, logistic regression analysis, and more to identify trends in customer behavior and predict churn risk.

Additionally, you can use customer feedback metrics like net promoter score (NPS) to measure customer satisfaction and loyalty.

Finally, continuously monitoring and evaluating your efforts with product analytics tools ensures that your retention efforts are successful.

Remember: every company has different needs when optimizing its retention forecasting strategy.

But by considering these six steps when crafting your own strategy, you'll be well on your way.

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Mark Lerner

Head of Marketing @ Parative, the Customer Behavior Platform. SaaS enthusiast, B2B Marketing Specialist, Startup Survivalist. Dad x2.

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