Predict Churn

In the digital age, billions of data points are available and ripe for analysis to boost all types of operations. Organizations now can gather, store, and process customer data to increase their marketing endeavors and overall operations. 

Machine learning (ML) algorithms can help identify which sales leads will convert, which customers need more attention to retain and to predict churn: which are more likely to cancel or exit the sales funnel.

That is customer churn prevention, which is crucial for businesses using the subscription business model. 

If they want to remain successful and retain customers, they need a churn prediction dataset to understand why their customers leave and what they can do to prevent it. 

Organizations know it is significantly more expensive to find new leads and convert them than to retain existing or current customers. The difference in costs could reach as much as 700%, and keeping even only 5% of your current customers could increase profits by 25%. 

Predict Churn: What It Is and Why It Matters

Customer churn refers to those that leave a company for one reason or another. Businesses must know when customers are likely to leave their brand, so the marketing team can engage and retain them as much as possible. 

However, many companies do not know how to determine the actual cause of customer churn. They need to create a churn prediction model to help them identify the relevant factors and predict which customers are more likely to "churn."

Churn prediction is vital because the more precise it is, the more businesses will learn how to deal with customer churn and take the necessary actions to prevent it. 

Datasets You Need to Predict Churn

Datasets for customer churn are crucial to predict behavior that will retain customers or identify them as possible churn. Companies and their marketing teams must analyze the data and develop retention plans and programs for these customers. 

These are the following datasets needed for customer churn prediction for ML. The dataset includes customer attributes such as 

• their type of subscription, 
• how long they have been with the brand, 
• industry, 
• and geographical location. 

These attributes will be placed in columns, with one row per customer. 

Customers Who Left

Also called the "churn," is the target column in the ML model and includes the algorithm's prediction. Usually, this is a column with "true/false" or "0/1" values. 

Services Customers Used

This identifies the services or subscription plans the customers signed up for and used. The different subscription tiers and features often indicate the customer's preferences and what they found most relevant to their daily lives. 

Customer Account Information

This dataset includes information about a customer's preferred payment method, billing, how long they have been with a company or tenure, monthly charges, and transaction history. This also includes account names, billing addresses, and contact information.

Demographic Information

This includes information about a customer's gender, age, job title, company, location, and civil status. Demographics affect churn prediction because they help teams identify customer segments that are likely not to respond or are not satisfied with the brand or service. 

Creating the Customer Churn Prediction Model

Machine learning can build systems and algorithms that find patterns and trends in data. These trends are analyzed and organized to predict future behavior potentially. 

In churn prediction models, the ML model observes the characteristics and features that may cause a customer's enjoyment and satisfaction to decrease. The algorithm will identify and reveal customer behavior patterns in the initial training phase. It compares those that have left a brand and those that have remained. 

Once the ML algorithm is adequately "trained," it can review all customer behavior and identify which ones will potentially leave. 

Such a prediction can help brands design and implement proactive programs to identify and eradicate pain points for their customers. Customer success teams can then engage and interact meaningfully with customers on the verge of leaving and ideally prevent churn before it occurs.  

When companies use no-code machine learning software like Graphite Note to create churn prediction ML models, teams must first import the dataset. 

Every dataset has a summary that allows users to review feature columns, values, and other statistical measures.  

A column labeled "Churn" should be included, with a binary value system that indicates when a customer has left "Yes" (1) or "No" (0). This column is essential in all predictive analytics ML models.

Graphite Note columns using the binary classification model require the following:

• Target column (churn), which we want to predict in the future. 
• Set of Attributes or Features that can impact the target column.

Once the ML model is built and trained, companies could "ask" the model to predict which customers are likely to become churners. 

This will identify them and drive the marketing team to make reengagement and retention part of their marketing strategies. 

Graphite Note will ensure that the steps to achieve the best results will be handled automatically. The software will sample 80% of the data and train several ML models. The remaining 20% will also be tested using the same models and calculate the relevant accuracy scores. 

Graphite Note will automatically choose the best-performing machine model based on the result. 

Summary - Predict Churn and Retain More Customers

The core of every business is customer service, and it's vital to ensure that leads and subscribers alike find brand interactions meaningful and engaging. This can be challenging because of vast differences in customer preference.

Knowing and identifying which customers find your customer service engagements lacking is vital to maintaining the relationship. 

Customer churn prediction helps organizations find which activities lead to less retention and which ones are more likely to develop a reasonable customer lifetime value rate. 

Using ML for forecasting and data analytics will go a long way in understanding customer behavior and creating engaging marketing strategies that connect with the target market. It can help brands determine pain points and improve the customer experience. 

Know your customers; you can retain them and lead your organization to a better ROI and long-term success.

You can explore all other Graphite Models here. This page may be helpful if you are interested in different machine learning use cases. Feel free to train your machine learning model on any dataset with the same ease or schedule a demo if you need help or have any questions.