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Imagine working hard for months to close the deal with a prospect, only for them to churn in less than a year. There could be several reasons, such as:
- Poor customer service
- Choosing a competitor's solution
- Users not achieving their KPIs
Reducing customer churn is vital for businesses because it ensures customer satisfaction and boosts profitability. The best way to avoid high churn rates is to predetermine the customers at a churn risk.
In this article, we'll detail how customer churn prediction is the key to reducing churn and keeping the cash flowing in 💸
What is Customer Churn Prediction?
Customer churn prediction involves analyzing data to detect customers likely to cancel subscriptions. SaaS businesses use this analysis to identify at-risk customers, leading to cost savings and improved customer lifetime value.
Analyzing churn through data-driven insights can help your business understand patterns and provide a roadmap for improvement. For example, if your surveys reveal that your platform has a complicated onboarding process – you can direct users to your onboarding specialist to assist them.
Why is Customer Churn Prediction important?
Losing customers is always costly. However, the costs involved go beyond the revenue lost from the customers who leave. It also includes the marketing expenses required to find new customers to replace the old ones. In many cases, the amount spent on acquiring a new customer is not covered by the amount paid during their time with the company. This means that customer acquisition is usually more expensive than customer retention.
Plus, unhappy customers share their experiences with others, impacting the company's reputation and customer acquisition budget. Businesses must predict churn and take action beforehand to prevent customers from leaving.
Once you know a customer is going to churn, you can take actions such as:
- Providing more targeted re-engagement campaigns
- Launching incentives such as loyalty programs that encourage them to stay
- Creating educational material that is tailored toward their specific needs
- Ensuring accessible and improved customer support
How to Build a Customer Churn Prediction Model
Creating a churn prediction model can help businesses retain customers and sustain growth. Using data analytics and machine learning, companies can identify which customers are likely to leave and take action to prevent it.
Here are the key steps to develop an effective churn prediction model ⬇️
- Data collection and review
Ensure that the data is accurate by handling missing values, removing duplicates, and converting it into a suitable format for analysis. Before moving on to calculations, reviewing the data for accuracy and validity is crucial. Every piece of customer info is valuable in the upcoming churn calculations, so it's worth ensuring accuracy.
- Model selection
Select an appropriate machine learning algorithm for churn prediction, such as logistic regression, decision trees, random forests, or gradient boosting machines. Split the data into training and testing sets, train the model, and tune hyperparameters to optimize performance. Evaluate the model using testing data and cross-validation. Deploy the model into production to make real-time predictions and prevent churn.
- Use an automated predictive model
Do people with lower NPS scores tend to leave more? Are they evaluating competitor solutions? You can predict who might leave by spotting these signs in the data. You must use machine learning with a dataset containing all the information you have about customers who stayed and those who left. The algorithm learns from this historical data to understand how different factors relate to churn. Then, it can predict if future customers with similar behaviors might leave or stay.
💡Factors can help you identify customers evaluating competitor solutions by helping you track when they visit their G2 pages.
- Establish retention strategy
Optimize your retention strategy by prioritizing actions based on the probability of customer churn. When customers first sign up, use checklists and personalized help to ensure they understand and use the product. As they keep using it, watch out for signs they might leave. For instance, if they're not using a feature they need, you can send them helpful tips to get them back on track.
- Track results regularly
Continuously monitor the churn prediction model's performance and update it with new data periodically to ensure it remains effective as customer behavior evolves. Before introducing any further changes to your plan, collect enough data to measure the real impact of your efforts.
Your churn model will provide probabilities for various customer segments. It's essential to keep testing new strategies and recording the impact on these segments. While creating a mathematical model of churn requires time and resources from your team, each test can help you create a better model for the future.
6 Customer churn prediction best practices
Now that you know how to build a churn prediction model, here are a few handy tips you must remember to prevent customer churn:
1. Segment Customers
After obtaining your data, it's time to shift your focus towards the users and begin segmenting them. Since users have distinct needs and usage patterns, they don't churn for the same reason. Hence, it's essential to categorize them into separate segments. You can segment them based on their:
- Demographics, such as location, region, company size, and the year they signed up for your company.
- Behavior and usage, such as how often they log in, whether they use a particular feature more or less, or whether they have completed the onboarding process.
- Contract terms include pricing plans and whether customers signed up for a monthly, quarterly, or yearly deal.
You can design retention strategies and marketing campaigns tailored to specific customer segments by segmenting customers based on their churn likelihood and characteristics. Domain knowledge or clustering techniques can help you define meaningful segments.
2. Monitor product usage data of existing customers
Product usage data captures how and when customers use your software. Important data points include feature usage, customer behavior, clicks, and other metrics such as time-to-value and product stickiness.
To build an effective model, it's important to consider some key product usage data points such as:
- Monitor feature usage data to show users' engagement with your product's features, indicating popularity and relevance.
- Observe users’ actions within your product, like when they use it, how long they use it, which features they engage with, and how they progress through it.
- Track the number of times a user clicks or interacts with a UI element, such as a button, checkbox, text area, or menu.
- Track other product usage data such as time-to-value, product stickiness, interactions, and more.
3. Keep an eye on customer success metrics
Understanding your users' success with your product is crucial in determining if they will continue using it. Surveys such as NPS and CSAT can be used to measure customer success. An NPS score of less than 7 or 8 means you may need a win-back campaign or value incentive to change their attitude towards your product. NPS measures loyalty and willingness to recommend, while CSAT measures customer satisfaction. Conduct these surveys periodically to track customer success and satisfaction.
4. Gather customer feedback regularly
Apart from gathering feedback through conventional ways, you can use various other forms of customer feedback to gain insights into their experience with your product or service. For example, in-app surveys can provide you with contextual input from users. You can use them to find out about your customer's overall satisfaction with your product, their experience with a particular feature, any issues they may have faced, or even the features they would like you to add or implement. This can be very helpful in understanding the general sentiment of users and identifying areas of improvement or strengths.
To promptly address issues and demonstrate responsiveness to user input, incorporate real-time feedback loops within your product. Acknowledge the feedback received through in-app surveys and communicate any actions taken to address user concerns.
5. Enhance customer experience
You can streamline the customer experience using automated onboarding, self-service options, and personalized support. Furthermore, you should use customer feedback to identify areas of improvement and proactively address any customer dissatisfaction rather than reacting after the fact.
6. Improve customer service
Respond promptly to inquiries and complaints, offer helpful advice, and measure performance using metrics like support tickets, call center response times, and social media interactions. Monitor these metrics to gain insights into customer service trends and effectiveness.
Wrapping up
Reducing customer churn is crucial for businesses as it directly impacts long-term revenue, customer loyalty, and overall business stability. However, understanding why customers leave requires analyzing data and tracking various metrics over time. Effective churn analysis involves monitoring overall customer turnover rates and identifying factors contributing to attrition.
Businesses can use advanced analytics and machine learning techniques to identify potential churners and implement targeted retention strategies.
