Predictive Sales Analytics

Vertriebscontrolling-Kunstliche-Intelligenz

Artificial Intelligence Example to Rock Sales Controlling in B2B

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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B2B-Sales-Controlling

Predictive Sales Analytics – How to use it in B2B Sales Controlling

 
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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sales-ai-analytics

5 Ways Predictive Analytics Can Power Your Sales to Success in 2019

 
Five practical examples of Predictive Analytics that will make your sales team successful in 2019.

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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customer-attrition

Define and reduce customer attrition in the subscription industry

 
Customer churn definition in the subscription industry

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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big-data-small-data

Predictive analytics – how much data do you really need?

 
How Much Data Do You Need For Predictive Analytics?

Predictive analytics is one of the technologies with the highest financial impact in B2B sales. Several popular applications of predictive analytics are becoming “must-have” nowadays.

Sales leaders rely on lead scoring, customer attrition modelling, cross-selling analytics and pricing analytics to prioritise their sales activities and increase their customer lifetime value.

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