12 Feb
9 Min
min to read

Customer Churn Prediction: How To Identify Early Warning Signs?

Customer Experience

A customer with a high satisfaction rate can churn. On the contrary, an unhappy customer can renew. When it comes to churn prevention and prediction, there is one truth—always expect the unexpected.

Regardless of the challenges, it’s still possible to stay armed and ready to minimize customer churn. And since the cost of getting new customers is from 5 up to 25 times higher than the cost of keeping existing customers, the spent resources are worth the outcome. 

In this article, we’ve tried our best to give you only tested tips so you can identify early warning signs. Also, we’ll discuss what DOESN’ T work in reality when it comes to predicting customer churn and discuss the most popular churn prediction models.

FYI: While the tips and models are useful, managing and implementing these strategies can be overwhelming for internal teams.  EverHelp's AI-powered customer support outsourcing leverages advanced technology and a team of expert support professionals to proactively engage at-risk customers, address their concerns, and ultimately prevent churn.

What Doesn’t Help in Churn Prediction

Let’s start with the things you can’t rely on for churn prediction.

💡What Is Churn Prediction? 
Churn is the practice of using data analysis and machine learning to identify customers who are likely to leave (either cancel a subscription or stop using a service). This empowers businesses to proactively woo them back, saving money and boosting customer loyalty.

Churn detection with customer satisfaction rate (CSAT)

For sure, customer satisfaction metrics can give you some worrying signs. However, CSAT is a transactional score and is usually short-lived. A high CSAT doesn’t mean that customers are going to stay. And vice versa—a low customer satisfaction rate can’t say for sure that customers are here to stay. That’s why, while CSAT is a valuable metric for gauging customer happiness, it does not really help with churn detection.

Relying on the number of support tickets for a recent period

You should be very careful when reading this metric. Support tickets count ≠ churn since not every ticket is a complaint. Besides, sometimes a decreasing trend in the number of support tickets can indicate that the customer is losing hope. Many of our highly engaged and satisfied customers steadily produce many support tickets just because they have a lot of users working their way through the software.

Overreliance on Quantitative Data for churn detection

While quantitative data is invaluable for identifying trends and patterns, it lacks the context that qualitative insights can provide. Customer interviews, surveys, and feedback can uncover the reasons behind the numbers, offering a deeper understanding of potential churn drivers. There should be a balance between qualitative and quantitative data when predicting churn.

Internal focus only for churn prediction

Focusing solely on internal data and ignoring external factors such as economic trends, industry shifts, and competitive actions can lead to incomplete churn predictions. External factors can significantly influence customer decisions and behaviors, impacting churn in ways that internal data alone cannot predict.

For example: Imagine a SaaS product X, music streaming platform, that noticed a drastic increase in churn. They analyze viewer behavior patterns for any signs. However, the internal data does not reveal significant anomalies. But one of the main competitors has launched a massive promotional campaign offering deep discounts for annual subscriptions, and a recent economic downturn has led to widespread budget tightening. By focusing solely on internal data, the company missed these critical external cues.

Tips That Actually Assist in Churn Detection

Although the question “how to predict customer churn”  is a constant challenge, there are some reliable tips that will help businesses handle it professionally.

Start with a unified customer dataset

Break down the data and create a holistic view of each customer, with all the interactions, purchases, and behavioral patterns. You need to have a unified customer dataset in one place before you move on to the next steps. Integrate this data from multiple touchpoints and departments into one database, and then your support team (or AI tools) can track any changes in customer behavior.

Analyze customer journey data by segments

Analyze customer journey by segments. Segment customers based on demographics, behavior, or product usage and analyze churn patterns within each group for targeted interventions. Map out the touchpoints in your customer journey and analyze behavior at each stage. Identify drop-off points or areas of dissatisfaction that might lead to churn.

Set up alerts for significant drops

Monitor key metrics like engagement (emails, product usage) and set up alerts for concerning declines, prompting proactive outreach. These alerts will ensure your support team can proactively reach out to disengaged customers with personalized offers or support on time.

Get the support from the analytics team

Collaborate with a professional outsourced or in-house analytics team to come up with reliable metrics and uncover hidden patterns and customer segments at higher churn risk. This professional approach helps to identify the metrics that work specifically for your company. 

Test the customer engagement with messages from support team

If customers started to respond rarely to your messages and demonstrates low engagement with support team through different channels for a long time, it can be a warning sign.

Delayed responses or ignoring communication altogether is an alarming sign of frustration or disinterest.

Develop a customer health score

Create a scoring system that combines various metrics (engagement, support tickets, product usage, etc.) to assess each customer's overall health and churn risk. This score can guide targeted outreach and support efforts.

Define and analyze tenure categories

First, define the tenure categories based on your business context. You can segment your customer data based on their tenure, calculated from their first purchase date or subscription start date. This is important because each segment might have different churn drivers, and separate models can capture these nuances.

 

Common examples include:

New customers: First 3 months, First year

Existing customers: 1-2 years, 3-5 years

Long-term customers: 5+ years, Lifetime customers.

Plot the percentage of customers churning against their time with your company (tenure). This can be a line graph, stacked bar chart, or other suitable visualization. This can be a line graph, stacked bar chart, or other suitable visualization. Analyze the curve to identify periods with significant increases in the churn rate.

Customer Churn Rate by Tenure example

Define rule-based indicators

Define specific events or behaviors that suggest a high risk of churn within a defined timeframe. Examples include:Assign a score or probability of churn to each event based on its historical association with actual churn. Identify customers who trigger multiple rules or have a high overall score, indicating they are at high risk of churning.

Churn probability by event example

 

Involve different teams for reliable churn prevention

Encourage collaboration between marketing, sales, and customer support to create a holistic churn prevention strategy. Provide transparent access to key churn data across teams, fostering informed decision-making and coordinated interventions. Also, make sure the team shares the same objectives with a unified churn reduction goal, incentivizing collaborative efforts and shared success.

Conduct exit interviews

Understand the reasons behind churn through exit interviews to improve future customer experiences. Address common pain points identified in exit interviews to improve product/service offerings and prevent future churn.

AI-Driven Churn Prediction Models

These churn prediction models leverage advanced algorithms to analyze customer data, predict behaviors, and provide actionable insights for retention strategies.

Linear Discriminant Analysis (LDA) Model

Overview: LDA is a classification method that finds the linear combination of features that best separates two or more classes of objects or events. It uses Bayes' theorem. In simple words, imagine you have a grocery store and want to predict which customers are likely to churn. LDA is like a sorting hat, considering various customer features like purchase history, preferred brands, and location. It combines these features in a special way to "sort" customers into two groups: those likely to keep shopping (loyal) and those at risk of churning.

Practical Tips:
1. Ensure data normality. Think of features like income or purchase frequency. These "scores" should ideally be spread out evenly (like a bell curve) for LDA to work best.
2. Use techniques like combining similar features or keeping only the most important ones to simplify the model.
3. Regularly evaluate the model against new data to maintain its predictive power and adjust as needed for changing customer dynamics.

Logistic Regression Churn Prediction Model

Overview: Logistic regression is a staple machine learning technique used for binary classification tasks. It establishes a predictive relationship between multiple factors and a binary outcome, estimating the likelihood of customer churn.  For instance, imagine you're trying to predict which customers might cancel their gym membership (churn). Logistic analyzes things like how often they visit, what classes they take, and how long they've been members. This helps it estimate the chance of them quitting.

 

Practical Tips:
1. Collect and preprocess customer data, focusing on variables known to influence churn. Missing or messy data can mess up the predictions.
2. Combine existing data in smart ways, like grouping members by activity level or analyzing their workout patterns.
3. Keep it fresh: Regularly update the calculator with new data so it doesn't get outdated and makes less accurate predictions.

Decision Trees Model

Overview: Decision trees generate a hierarchical structure of decisions, which makes them intuitive and powerful for classification tasks. Imagine you're trying to figure out which customers might leave your restaurant (churn). A decision tree helps you do that step-by-step, like a flowchart. It asks questions based on customer data (e.g., "Do they order often?" or "Do they leave good reviews?"). Based on the answers, it predicts if they're likely to return or not.

Practical Tips:
1. Ask questions directly linked to their habits and how happy they are (e.g., "Do they order online regularly?" or "Did they complain about anything?").
2. Keep the tree simple to avoid getting "confused" by too many options.
3. Test it on different sets of customers to make sure it works well in general (tip applies for all churn models)

Bottom Line: So Which Approach to Churn Prediction Is the Most Sustainable?

Remember, each approach to customer churn (even AI-driven churn prediction models!) isn't perfect. There might be other reasons people quit, like moving or getting injured. Use these tips along with other insights to understand your customers better. Continuously monitor, analyze, and adapt your churn prediction and prevention strategies for long-term success.

Also, don’t hesitate to sign up for a call and see how exactly EverHelp can simplify your life (including customer churn prediction & prevention). We integrate seamlessly with your existing systems to collect and analyze customer data, leveraging AI models to identify churn risks with unparalleled accuracy and beyond.

Remember, at EverHelp, we don't just predict customers leaving with churn models, we prevent it.

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