Drive Product, Service And Marketing Innovation Using Quantiful's Machine Learning Capabilities


Posted June 1, 2016 by quantiful

Quantiful's Pinpoint, an easy-to-use and powerful tool, allows you to build and deploy measured predictive models directly into your core business processes to identify trends, understand customers, improve your business, predict customer behaviour.

 
Machine Learning is a method of analysis that automates analytical modelling to classify an event or predict an outcome. It is immensely helpful for businesses in that it gives them access to a goldmine of information to improve their customer loyalty and engagement programmes.

Machine Learning helps businesses to understand consumers’ preferences and behaviours in real time. Quantiful’s Machine Learning-based solutions enable businesses to drive product, service and marketing innovation. Quantiful designs and builds automated and adaptable precision marketing solutions that result in more targeted and relevant customer interactions.

Headed by international branding expert and former CEO of Telecom Retail, Alan Gourdie and leading data scientist and algorithm architect Saby Upadhyay, Quantiful's Machine Learning driven products offer increased sales, conversion, customer loyalty and retention.

To contact Quantiful to find out how you can benefit from their Machine Learning services, please feel free to visit Quantiful’s website or contact them using the address below.

Please find the contact details of the company below.

Quantiful
LEVEL 1, 36 LORNE STREET
PO BOX 1249, SHORTLAND STREET
AUCKLAND 1140
NEW ZEALAND
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Contact Email [email protected]
Issued By Theresa Clifford
Website Improve Customer Loyalty Using Our Customer Engagement Programs
Phone +642102617779
Business Address Level 1, 36 Lorne Street
PO BOX 1249, Shortland Street, Auckland 1140
Country New Zealand
Categories Business , Services , Technology
Tags customer loyalty , Customer Engagement , Social Analytics , Precision Marketing , Machine Learning , data analytics , Pinpoint
Last Updated June 1, 2016