predictive analytics in construction

Predictive Analytics in B2B E-commerce: A customer acquisition example

Predictive Analytics - Opportunities for new customer acquisition in B2B e-commerce using the example of an Austrian construction supplier.Mr Haunschmid, please introduce yourself to our readers.My name is Thomas Haunschmid, and I live in Lower Austria.

As "Business Manager E-commerce" I work daily with new trends and strategies to improve and optimise the buying experience of our B2B customers.

There is one trend that Business Managers cannot avoid in E-Commerce these days: the systematic use of data to optimise product and sales strategies.

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Vertriebsanalysetools

Predictive Sales Analytics Software | Qymatix Predictive Sales New Release

 

With the valuable input from partners and customers, Qymatix has released an improved version of its predictive sales analytics product.

 
The new version of the SaaS brings together extensive research in the field of predictive sales analytics and front-end usability. Sales leaders in Business-to-Business will benefit with an easy-to-use and easy to understand self-service predictive sales analytics software. Users do not need their IT department to perform advanced sales analytics and will no longer suffer “spreadsheet misery” during sales reporting.
 
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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel

Top 5 Predictive Analytics Books To Move The Needle in B2B Sales

 

Predictive Sales Analytics is a game-changing technology in B2B sales. Any organisation trying to rein in erratic sales funnel, to improve the performance of their sales force or to avoid customers from churning, should have a clear understanding of predictive analytics.

Sales leaders need to grasp the applications, the benefits and the limitations of predictive analytics. They should be able to define the scope of a predictive analytics project and to calculate its return on investment (ROI). Sales executives do not need to become data scientists; they need to understand the possibilities and limitations of the data science methodology for predictive analytics.

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