Using Predictive Analytics for Customer Churn

It's generally known that retaining an existing customer is easier and 5 to 25 times less costly than acquiring a new one. It's also usually true that saving a departing customer is easier than trying to win them back later. 

But how do you know when a customer is about to leave? And what can you do to retain them?

In today's guide, we discuss how you can use predictive analytics for customer churn to foretell when a customer is looking to leave. We'll also explore some simple strategies for retaining them successfully. 

What is Predictive Analytics?

Predictive analytics is the process of identifying patterns and forecasting future trends, events, or outcomes based on historical and current data. 

In the context of a contact center, predictive analytics involves using historical and current communication data, statistical models, artificial intelligence (AI), and machine learning, among other techniques. 

Our aim is to analyze agent-customer interactions and customer behavior to predict future events and outcomes, such as agent performance, customer behavior, and interaction volumes. 

Key Benefits of Using Predictive Analytics for Churn

Based on the explanation above, let's note that predictive analytics both considers and predicts customer behavior. 

One of the most critical aspects of customer behavior forecasted through this process is customer churn, which refers to the likelihood of a customer leaving the interaction or making a purchase. 

But why is predicting customer churn important for contact and call centers? Let's review a few key reasons:

• Reduced Customer Churn: By identifying at-risk customers and addressing their issues early, you can retain more customers.
• Improved Customer Satisfaction: Solving problems proactively and personalizing interactions can lead to a better overall customer experience. You can achieve this easily because you already have a better understanding of customer preferences and behavior.
• Increased Efficiency: Improved forecasting enables you to manage your resources more effectively and streamline operations, ultimately making your contact centre more efficient.
• Improved Decision-Making: Data-informed insights empower you to make strategic and forward-looking decisions, rather than merely reacting to events and outcomes as they occur.

Data Sources for Customer Churn Prediction

By combining diverse datasets, you gain a comprehensive view of customer behavior and experience, which enables you to leverage the benefits of predictive analytics for churn prevention. 

Below are the key data sources you can use in customer churn prediction and prevention. 

• CRM Data: Your Customer Relationship Management (CRM) software can provide customer demographics, contact details, and their overall history with the company.
• Contact Center Logs: Your call or contact center logs can show call records, support tickets, detailed agent-customer interactions, chat transcripts, common issues, and the resolutions achieved. 
• Service Requests: These include details on customer-reported complaints, issues, and whether they were resolved efficiently. 
• Customer Feedback: You can get data from feedback forms and surveys, Net Promoter Score systems, and direct customer comments regarding your services or products. 
• Product or Service Usage Data: Your contact center can track how frequently and how customers use a certain product or service. Usage reviews are ideal for showing customer engagement and potential disinterest. 
• Other Peripheral Sources: Your customer churn data can come from other sources like web analytics, mobile app analytics, and social media signals. For example, you can check browsing history, app usage patterns, and prevailing customer sentiment on social media.

Predictive Analytics Techniques for Churn

Having these data sources alone isn't enough. You’ll need to apply the right techniques to excel at customer churn predictive analytics. 

Let’s check out some artificial intelligence and manual methods you can try.

1. Artificial Intelligence

You can use AI for customer churn prediction through various ways that may include:

Rule-Based Expert Systems

In a rule-based system, you use predefined company rules created by industry experts to infer potential churn risks. The rules are typically structured as “IF-THEN” logic. 

For example, IF a customer calls more than three times a week and expresses dissatisfaction, THEN you can flag them as a potential churn risk. 

Sentiment Analysis Through Natural Language Processing (NLP)

NLP algorithms for sentiment analysis review agent-customer interactions to detect negative sentiments, frustration, or disengagement. 

Under this option, you can flag a customer for proactive retention if they frequently use negatives like “cancel” or “switching vendors”. 

Knowledge Graphs for Behavior and Relationship Analysis

Contact centers can utilize knowledge graphs to connect customer data points, including interactions, billing issues, and product or service usage, to identify patterns and relationships that indicate potential churn risk.

If a customer has a history of frequent complaint tickets, billing disputes, and recent downgrades in service tier, you can note them as a churn risk. 

Intelligent Process Automation (IPA)

Your contact center can use AI-enabled automation to monitor event sequences and trigger churn alerts. 

For example, a missed payment → reduced usage → call surge sequence can indicate a high risk of churn and the need for you to follow up. 

Customer Journey Analytics (CJA)

You can use AI-driven Customer Journey Analytics for a comprehensive view of entire customer journeys, rather than relying solely on the last interaction. 

Identifying hidden patterns and reasons for potential churn is easier when you consider multiple touchpoints across the entire journey. 

Machine Learning

For customer churn prediction using machine learning (part of AI), you can try these four methods:

1. Logistic Regression: As a statistical method, logistic regression is ideal for predicting the probability of a binary outcome. For example, whether a churn will occur or not. 
2. Decision Trees: Create a tree-like customer churn prediction model that outlines various decisions and their likely consequences. You can use this method to identify factors that lead to churn. 
3. Random Forest: This is an integrated method that combines multiple decision trees to enhance the accuracy and reliability of churn prediction. 
4. Neural Networks: These are complex models that can master intricate patterns from massive datasets. They are typically effective when a deep learning approach is required for more accurate predictions. 

As an AI-powered conversation intelligence platform for contact centers, Hear can:

• Automate and use predictive analytics to review agent performance and customer interactions to forecast churn.
• Automate and conduct sentiment analysis to detect negative sentiment and other churn signals.
• Send timely alerts for customer frustration, negative language, and other indicators of churn risk.
• Analyze 100% of your customer interactions at scale across chat, voice, and email for comprehensive churn prediction. 
• Surface product or service feedback directly from conversations as part of the data needed to predict churn. 

Discover why top contact centers use Hear for churn detection.

2. Manual Techniques 

Instead of using artificial intelligence for predictive customer analytics, you might want to use manual methods that rely on predefined metrics, human analysis, and basic data reporting. 

Such methods are ideal for small contact centers with limited data and may include:

• Customer Surveys and Feedback Analysis: Ask your customers directly about their satisfaction and likelihood of staying with your business. Negative comments or low scores for customer satisfaction (CSAT) or Net Promoter Score (NPS) are early indicators of churn. 
• Agent Observations and Escalation Logs: Your agents can proactively identify frustrated or at-risk customers and provide notes or escalation flags that you can review manually to detect potential churn risks.
• Historical Trend Analysis: You can manually analyze past churn cases to identify common customer behaviors like reduced call frequency and frequent complaints. Simple spreadsheets can help you track these trends over time.
• Churn Rate Monitoring by Segments: Tracking churn rates based on customer type, service plan, region, and other relevant segments can be helpful. A spike in a certain segment can indicate issues that require your attention. 

For faster results, it's best to use modern contact center conversation analytics software to leverage diverse artificial intelligence capabilities. 

Steps to Implement Predictive Analytics for Churn

These techniques are easy to implement if you follow the right steps. Here's what to do:

1. Define Churn Early: Establish what churn means for your contact center. Is it a service downgrade, prolonged customer inactivity, or account closure? 
2. Gather and Sync Data: Collect data from all relevant sources, including call logs, CRM, customer feedback, support tickets, and more. The data should be clean, accurate, and consistent. 
3. Identify Key Indicators: Determine which customer behaviors or signals you want to associate with churn, such as negative sentiment, short wait times, and high complaint frequency. 
4. Adopt Conversation Intelligence Software: Select and deploy the best conversation intelligence platform tailored to your specific needs. For example, if you want to analyze 100% of interactions across multiple channels, you can use Hear. 
5. Monitor and Improve Continuously: Track how the platform performs, update what churn means for your center, and redefine or add key indicators. Refine the process based on outcomes and new customer behavior trends. 

How to Act on Churn Predictions

You don't stop at generating churn predictions. You must take action to ensure you use them as learning and improvement insights. 

Acting on churn predictions can take many forms, including:

• Proactive and Personalized Engagement: You can reduce the risk of churning using contact center intelligence software by segmenting at-risk customers and delivering personalized messages. These can be targeted special offers, exclusive loyalty rewards, or product recommendations to make them feel valued. 
• Coach Your Agents: Use the predictions to identify coachable moments. Train your agents to implement churn prevention tactics before, during, and after every customer interaction. 
• Integrate Feedback into Strategies: Use the feedback and outcomes of your retention efforts to update and refine your intervention strategies and the predictive churn process itself. 
• Close the Feedback Loop: Once the right retention measures have been applied, follow up to confirm the customer’s satisfaction or monitor if the risk of churn has reduced. 

Challenges in Predictive Churn Analytics

The entire process of learning how to predict customer churn, implementing, and acting on insights doesn't always go smoothly. 

You are likely to encounter the following challenges:

• Data-Related Issues: Your data quality may be subpar, resulting in unreliable predictions. Combining data from different sources into a single view can be difficult. You'll want to use a customer data platform to make it easier to collect and synchronize data. 
• Customer Behavior Issues: Customer behavior and preferences evolve over time, necessitating proactive measures to capture and adapt to changes in real-time. 
• Operational Issues: Identifying at-risk customers while they are still active can be tricky. Translating churn predictions into concrete, actionable retention strategies that prevent churn proactively is also tricky. 

Ensure you apply proper measures to mitigate these challenges. For example, use the right software to identify at-risk customers while they are still active. 

You can also collaborate with various stakeholders and domain experts to redefine churn, understand customer journeys, and effectively act on the insights gained from these predictions. 

Frequently Asked Questions (FAQs)

Let's wrap up with a few questions about predictive churn analytics. 

What is the Difference Between Customer Churn and Customer Retention?

Customer churn is the loss of customers over a specified period, whereas customer retention refers to the ability to retain customers during the same period. 

For a contact center, churn can also indicate the percentage or number of customers who quit interactions midway. Retention marks the number of customers who remain engaged in an interaction until their issue is resolved. 

What Tools Are Available for Churn Prediction?

There are many tools available on the market that have churn prediction and prevention abilities. 

For instance, Hear is one of the best AI-powered solutions for boosting customer retention by detecting sentiment and churn signals. 

How Does Predictive Analytics Differ From Descriptive Analytics?

Predictive analytics uses data to forecast what might happen in the future, while descriptive analytics focuses on understanding past events and identifying patterns, trends, or relationships within the data. 

Descriptive analytics answers the question, “What happened?”. Predictive analytics provides answers to the question, “What is likely to happen?”. 

What Metrics Indicate High Churn Risk?

The metrics below can tell you if your customers are at a high risk of churning:

• Low Customer Satisfaction Scores (CSAT)
• Low Net Promoter Scores 
• Influx of support tickets 
• Influx of negative reviews and feedback
• Declining product or service usage 
• Slow response to communication (a customer who is slow to respond)
• Decreased customer activity, such as reduced interactions
• High first response time. 

Conclusion

Through predictive analytics for customer churn, your contact center can identify at-risk customers, why they are about to leave, and how you can retain them. 

The best way to predict customer churn is to use modern contact center conversation intelligence software with AI capabilities. 

Hear fits the bill nicely as a robust contact center intelligence platform with diverse AI capabilities for predicting and preventing customer churn. 

With Hear, you can analyze 100% of agent-customer interactions at scale across multiple channels to uncover issues and insights to curb churn proactively. 

See how predictive analytics can transform your churn prevention efforts - Try Hear today.