Introduction to Churn Prediction in SaaS
Customer churn prediction determines which customers are about to cancel their subscription or quit your product. It helps you understand who might leave and why.
Predicting churn helps Saas firms tackle customer loss early and boost retention. By spotting signs of potential churn, companies can focus on retaining subscribers.
Predictive analytics and machine learning allow SaaS businesses to analyze data like usage, behavior, and engagement to predict churn accurately.
Why is Customer Churn Prediction Important?
Customer churn is expensive. Getting new customers can be 5 to 25 times more expensive than retaining existing ones, especially for SaaS companies offering subscription products. Predicting churn is critical to business success, allowing teams to:
- Target at-risk customers with re-engagement campaigns.
- Create better education content to maximize the Customer Lifetime Value (CLV).
- Stop customers from leaving.
- Use churn data to improve marketing strategies and attract more customers.
Understanding the Data: The Foundation of Churn Prediction
There are three main steps to predict customer churn.
- Data Preparation: Collecting and readying data for modeling. It's often noted that this step consumes 80% of data scientists' time.
- Churn Data Analysis: Understanding your data and uncovering reasons for customer churn.
- Predictions: Building a predictive model to flag high-risk customers before they leave.
To start making your churn prediction model, gather the correct data first. Having more data means better results.
Analyzing The Right Data Sets
Collecting and analyzing data is the cornerstone of churn prediction in SaaS. Businesses can identify patterns or trends that point to potential churn by tracking customer interactions with the software.
The correct data sets in this scenario may include:
- Feature Usage: How users interact with different parts of your product, highlighting popular features.
- Customer Behavior: Everything users do within your product, like when they use it, how long they use it, which features they use, and how they navigate through it.
- Clicks: Records the number of times users interact with UI elements like buttons, checkboxes, and menus.
- Other Metrics: Additional data like time-to-value, product stickiness, and interactions.
Qualitative and Quantitative Data
Qualitative data provides insights into the "why" behind customer behavior. This includes feedback from customer surveys, support tickets, and user interviews.
Understanding the reasons behind churn helps to develop the right retention strategies and improve the customer experience.
Quantitative data, on the other hand, focuses on measurable metrics like user activity, subscription duration, and feature usage. It allows businesses to identify specific triggers or indicators of churn, such as declining engagement levels or decreased usage frequency.
You need both qualitative and quantitative data to make a good prediction model. Instead of guessing, data scientists use machine learning to build these models and produce accurate predictions.
Key Metrics and KPIs for Churn Prediction
In churn prediction for SaaS businesses, several KPIs are vital in identifying at-risk customers. Understanding and keeping a close eye on these metrics can help you proactively address churn.
Customer Lifetime Value (CLV)
CLV helps predict churn by estimating the total revenue a customer will produce throughout their relationship with your company. It identifies high-value customers worth retaining so you can prioritize efforts to keep them engaged.
CLV = (Average Revenue per User) x (Average Customer Lifespan)
Net Promoter Score (NPS)
NPS measures customer loyalty and satisfaction, with detractors being at a higher risk of churn. By identifying detractors, businesses can focus on resolving their issues and improving satisfaction levels, finding out how to reduce customer churn.
NPS = % of Promoters - % of Detractors
Customer Satisfaction Score (CSAT)
CSAT gauges customer satisfaction levels based on feedback. Low CSAT scores indicate dissatisfied customers who may be prone to churn. Addressing their concerns promptly can prevent churn.
CSAT = (Number of Satisfied Customers / Total Number of Surveyed Customers) x 100
Customer Effort Score (CES)
CES measures how hard it is for a customer to interact with your product or service. High effort scores may indicate frustration, leading to churn. Streamlining processes to reduce customer effort can mitigate churn.
Average CES = (Sum of Responses / Total Number of Responses)
Customer Retention Rate
Retention rate indicates the percentage of customers retained over a specific period. A decreasing retention rate signals potential churn. Enhancing product features or providing personalized support can mitigate churn in this scenario.
Retention Rate = ((Number of Customers at End of Period - Number of New Customers Acquired) / Number of Customers at Start of Period) x 100
Customer Engagement Levels
Engagement metrics indicate customer satisfaction and interest, including:
- Login frequency
- Feature usage
- Time spent within the application
Decreased engagement may precede churn. By analyzing and improving engagement levels, SaaS companies can retain customers and cause churn reduction.
Machine Learning Models: The Engine of Prediction
SaaS businesses use churn prediction models instead of depending solely on human decision-making. These models can analyze large amounts of data simultaneously without the risk of bias or human error. Plus, they’re a lot easier to scale.
Churn prediction models rely on datasets, which comprise of two aspects: target variables and features.
The target variable is the feature you’d like to predict – in this case, it’s churn. It would be the indicator of whether or not a customer will churn in a simple binary format (yes/no equals 1/0). You’ll need tons of historical data to obtain this variable.
Meanwhile, the features are data points that describe your customers. You can source this from feedback surveys, analytic tools, CRM systems, including data points like:
- Feature usage
- Customer success metrics
- Engagement metrics
Commonly used machine learning algorithms include logistic regression, decision trees, and random forests. Here's how each algorithm applies to churn prediction.
Logistic Regression
Logistic regression is a statistical learning method used for binary classification, which is simple yes or no tasks. That includes predicting whether a customer will churn or not.
It measures the probability of a binary outcome based on one or more independent variables. In this case, the variables are different factors or behaviors exhibited by customers. These models can analyze patterns in this customer data that may be associated with churn.
But keep in mind that logistic regression is not known for its accuracy. It’s a yes-or-no model, so you won’t find out how likely it is for churn to occur.
Decision Trees
Decision trees are used for classification and regression tasks. In churn prediction, they break down customer data into subsets based on key traits like billing amount or contract length. Then, it determines which of those subsets are likely to churn and which aren’t.
Decision trees are easy to understand and offer more complex insights into usage patterns and churn. There’s a lot more data to interpret and turn into actionable insights for your churn prediction strategy. Instead of just finding out a binary answer, you’ll learn how likely a customer segment is to churn.
As long as there's no limit set, decision trees can be 100% accurate.
Random Forests
Random forests combine multiple decision trees to make the predictions even more accurate and robust. In churn prediction, they can collect the predictions of various decision trees to reduce overfitting (inability to predict churn reliably).
Random forests can handle large sets of data points. Plus, they're resistant to noise and outliers, meaning they can still provide accurate results if the data is somewhat inconsistent or fluctuating.
Steps to Improving Predictive Model Accuracy
Here's the process of building, training, and refining predictive models for churn prediction broken down into steps:
- First, gather historical data about customer traits, behaviors, and churn outcomes from surveys and analysis tools. This data should be organized and free of errors or inconsistencies.
- Then, produce the relevant data points that can help predict churn, like feature usage or engagement metrics. You can even combine existing variables to create new features. For example, combining “monthly bill amount” and “number of existing customer service calls” will create a new feature, “average cost per call.”
- Choose the most suitable machine learning model and train it to learn patterns in features and churn outcomes. The most suitable ones include logistic regression, decision trees, and random forests.
- Assess the performance of the trained models with metrics like accuracy, precision, recall, and F1-score. It'll show how well the models can predict churn based on the given dataset.
- Keep fine-tuning the learning models to improve their accuracy over time. You'll need to adjust the hyperparameters (settings that control model behavior) and weed out valueless features.
Real-World Applications: Churn Prediction Across Industries
Churn isn’t just limited to SaaS companies, where the term refers to the number of subscriptions canceled over time. It’s simply the likelihood of customers quitting your services, which you can predict with certain variables. However, these variables will not be the same as SaaS churn prediction functions such as engagement or usage rates.
Telecommunication Services
Businesses use churn prediction models to stop customers from moving to a rival's telecom network. Tracking churn helps these firms know where to focus their technology for better customer service.
In this case study, AppsTek developed a customized churn prediction model for a telecom company in India. The model was able to cut down the customer churn rate from 12% to 1.67%.
SaaS Companies
In churn prediction models for SaaS, the list of features or data points has to be more extensive than a traditional model. It’s not enough to learn whether or not a customer will churn – a well-trained model will also tell you why and when they’ll churn.
In this Escalent case study, they helped a leading SaaS business audit its current churn prediction model. Its accuracy was low, especially when it came to outlining the timing and root causes of churn. The company really needed more insights into early indicators of churn.
So, Escalent partnered with the company’s data science team to expand the list of data variables in the “event” part of the churn prediction model. This way, they were able to replace it with a more reliable model that:
- Allowed their executives to implement customer success strategies before churn occurs.
- Included multiple time horizons and customer account types in the churn prediction “features.”
- Provided a single, data-backed metric of churn likelihood.
Retailers
Retail companies collect customer behavior data such as their purchase history and website interactions. This allows them to curate targeted customer retention strategies.
You've probably seen ads and suggestions tailored to you on social media or online shops. Predicting churn helps retailers understand why people stop buying and how to keep them interested in different products and deals.
Here's a great example of how churn prediction applies in the retail business. With dotData's Managed Predictive Analytics program, a supplier company was able to:
- Build an analytics process in 14 days.
- Identify more than 50 churn predictors.
- Build a model that saves over $40 million annually.
Overcoming Challenges in Churn Prediction
Churn prediction, like other business forecasts, relies on accurate data and consistency. Keeping track of customer interactions generates a lot of data. Turning this data into valuable insights can be challenging for SaaS companies if they're not ready to deal with the following issues:
- Data Quality and Quantity: Churn prediction works well when it analyzes how different customer factors relate. However, if you use incorrect or incomplete data, your predictions won't be as helpful for retaining customers. The right step is to make sure your data is clean and updated with the help of data validation checks.
- Time Constraints: You need to train, test, and validate churn prediction models before using them. This involves collecting, cleaning, and organizing customer data. Companies should plan ahead and set realistic timelines for these tasks to avoid delays in retaining customers.
- Model Complexity: Using advanced AI technology requires a lot of knowledge and resources to work fast. Companies in challenging or specialized industries might need extra techniques to train their models for better predictions. This can take a lot of time, people, and money for technology.
As a smaller business, it’s best to start with simpler models. Pre-trained models and libraries can also reduce the need for extensive training resources.
- Limited Customer Feedback: To predict customer behavior accurately, your churn prediction model needs to have enough customer data. But sometimes, companies struggle if not many customers fill out surveys or share why they're leaving. Heat maps can help product teams gather valuable customer behavior data without having to wait for direct feedback.
- Business Integration: If your churn prediction tools don't fit well with your team's current work, you'll waste time fixing problems. Your data tools should blend smoothly with your product and make things easier for your team. Get feedbacks from the stakeholders of each department to make sure your tools are suitable.
- False Positives and False Negatives: No tech is flawless. Sometimes, churn prediction models say customers will leave when they won't or miss when they will. To reduce mistakes, teams can mix up their approach and keep a close eye on how customers use products and interact with support and educational materials.
The Future of Churn Prediction in SaaS
Development of machine learning is improving churn predictions, but what does its future look like?
Well, you can expect AI to take over major aspects of churn prediction models. These technologies can deal with large sets of data with better accuracy and efficiency than humans. By doing so, SaaS businesses can identify even the subtlest patterns and predictors of churn.
Other than that, big data analytics tools and platforms like Chart Mogul and Power BI will allow SaaS businesses to help track things like user journeys or UX issues.
Lastly, the integration of predictive analytics techniques may become more significant. This type of analysis uses machine learning, AI, and deep learning to process even the most complex datasets like firmographic, transactional, and customer support data. SaaS companies will most likely integrate predictive analysis in CRM systems and automation platforms.
Conclusion
Predicting churn is only the beginning of customer retention – the data isn't helpful unless you use it to keep customers from churning.
Different companies will have different strategies for re-engaging inactive or at-risk customers. It all depends on what you sell and who your customers are. Overall, investing in churn prediction techniques is crucial for SaaS companies to:
- Drive growth,
- Sustain competitive advantage,
- Retain valuable customers.
With data-driven insights and analytics tools, you can predict churn and keep long-term customer relationships.