Retain Your Customers with Accuracy

Churn Prediction Model

Customer churn is the most serious challenge businesses encounter today. Losing customers affects both revenue and increases acquisition costs. With our churn prediction model, you can actively identify possible churners before they leave and use focused strategies to retain them.

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Express Analytics’ Approach

Struggling to retain your customers?
The churn prediction and prevention model is the latest data-based solution designed to inspect customer engagement rates, behavior, transaction details, and more. Identifying trends and symptoms that point out possible churn, allows companies to make the right decisions to hold customers and increase lifetime value.
How Does the Customer Churn Prediction Model Affect Your Businesses?

  • Revenue

    Churn can have a greater effect on the revenue of newer or smaller businesses because every customer describes a larger proportion of the overall revenue.

  • Product development

    Greater churn rates can indicate issues with a service or product. Companies can analyze the causes of customer churn to find regions that require improvement.

  • Brand reputation

    Repeated customer income can negatively impact the reputation of a business and discourage prospective customers.

  • Customer acquisition costs

    Replacing lost customers and acquiring fresh customers requires considerable sales and marketing efforts. These costs can proliferate, making profitability maintenance even more difficult.

Essential Features of Our Churn Prediction Model

  • Integration with CRM

    Consistently integrate with your current systems for streamlined workflows.

  • Personalized algorithms

    Customize the model to match your customer base and industry.

  • Predictions in real-time

    Get updated insights to solve possibilities of churn.

  • Behavioral analytics

    Identify changes in customer touchpoints.

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Major metrics to predict customer churn

Businesses can use the following metrics for churn prediction using machine learning:
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  • Information about a customer’s account :

    This dataset involves information regarding a customer’s preferred payment option, billing, how long they are staying with a company, monthly charges, and past transactions. It also contains account names, contact information, and billing addresses.

  • Services used by customers :

    This identifies the subscription plans or services the customers used and signed up for. Several subscription features and tiers specify the customers’ preferences.

  • Customers that departed :

    This will be the focus column in the ML model to foresee churn.
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How can Express Analytics help you in Customer Churn Prediction and Prevention?

Express Analytics’ ML platform lets you predict customer churn models before they occur. Our platform uses modern ML algorithms to examine retail data and find trends that point out possible churn. Our platform can provide a holistic view of every customer’s behavior by analyzing several data points, including website visits, social media activity, customer service touchpoints, and purchase history. Our best model for churn prediction can answer the following questions to determine whether a customer is likely to leave:
  • How long has the customer been with your business?
  • How regularly do they buy services/products from the business?
  • How much do they usually spend? What is the age and place of origin of an average buyer?
  • Do they have any sort of loyalty program or membership?

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