Reduction of customer attrition by implementing a churn prediction software in your sales reporting

Understanding and avoiding customer churn ( or attrition) in Business-to-Business(B2B) organisations can make the difference between a successful financial year or a miserable one.

Every experienced sales leader knows that some customers will eventually churn. Studies in the field of customer retention talk of a 5 to 25 % customer churn per year, depending on the industry. Customer attrition is revenue lost.

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With predictive analytics, big data becomes a big opportunity for B2B sales managers. This big opportunity requires, however, a profound understanding of the sales situation, coupled with an understanding of big data mining models available.

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How the sales analytics tools you use impact your sales controlling.

Controlling sales in B2B is increasingly becoming a high-tech game. Since selling cycles in business-to-business are getting longer and sales is getting more expensive, controlling need to look further into the future.

Machine learning, a well-known example of weak artificial intelligence, represents a fantastic opportunity for improvement in B2B sales controlling and business intelligence. It enriches the world of sales analytics with a substantial competitive advantage.

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Getting an edge in today’s competitive market place is vital. For B2B companies, possessing the tools to explore customer behaviour and prioritise leads not only aids productivity but helps boost that all-important bottom line.

Predictive sales analytics has come a long way in the last decade. It is now far more accessible to businesses in traditional industries. For many, it’s a powerful tool in helping to support sales and operations planning.

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B2B companies can, on average, approximately expect a yearly 11 % customer churn rate, found a recent study that.

This cancellation rate fluctuates between countries and industries. Customer attrition can represent a 24 % average in office supplies, 16 % in the insurance industry and 13 % in banking.

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