Why CRM Projects in B2B Fail and How to Make Them More Successful
Many studies over the years have shown a very high failure rate for Customer Relationships Management (CRM) systems. Already in 2001 Gartner estimated this rate to be at about 50%.
Author: Qymatix
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.
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.
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.
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).