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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Machine learning (ML) and artificial intelligence (AI) hold the key to a tremendous improvement in sales controlling efficiency. A software is intelligent if it can solve problems independently and efficiently.

The degree of intelligence depends on the level of autonomy, the extent of complexity of the problem, and the efficiency of the problem-solving process. In artificial intelligence terms, researchers speak of “strong” versus “weak” AI – “general” and “narrow” AI, respectively.
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Sales processes, salespeople and sales management should adapt to the brave new world of artificial intelligence. Artificial intelligence is taking over the administrative tasks that consume much of managers’ time. It is doing it faster, better, and at a lower cost. It has pros and contras. AI will redefine management.

In B2B Sales, artificial intelligence (AI) refers to Enterprise Resource Planning (ERP) and Customer Relationships Management (CRM) Systems equipped with machine learning and automatized data mining features.

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ERP Data Mining: what we learnt from analysing 100 million of B2B sales transactions.

Data mining is the application of a varied assortment of statistical techniques to ERP datasets. Companies nowadays use data mining to predict outcomes, identify sales trends, prevent customer churn, and dynamically adjust pricing strategies.

Mining enterprise resource planning (ERP) sales data is critical in Business-To-Business, where small improvements in sales efficiency can have a significant impact on results. Mining ERP sales data helps customers to unlock a significant amount of value, discover quick-wins, and to prioritise their sales activities. Automatizing this process is possible today with the help of artificial intelligence (AI).

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