Intelligent technologies in bpm’online products
Accelerate productivity with AI and machine learning. New machine learning capabilities and predictive algorithms are designed to offer users relevant information and automation within a specific context, based on the analysis of historical data, enabling users to make data-backed decisions. Cutting down on manual analysis frees up time for increased execution and output.
Intelligent data enrichment from open sources
Manage your CRM data faster and with minimal effort by leveraging automatic smart data enrichment. Bpm’online’s CRM data scientist will swiftly identify email addresses, phone numbers, social media profiles and other valuable information about a company from open sources. With this intelligent tool, bpm’online users get the most complete and up-to-date customer data with no additional effort.
Intelligent data enrichment through email
Leverage intelligent Natural Language Processing tools for enriching customer profile with the most accurate data from incoming emails. No need to manually search for customer data in incoming emails - the system will automatically analyze the email content and enrich contact information based on obtained data. This allows users to create a new contact or update an existing contact profile in just one click. You decide what information should be added to the system, and what should be excluded.
Predicting the values of data fields in the system
Leverage machine learning capabilities and predictive algorithms to simplify day-to-day operations and free employees from the routine tasks. Thanks to bpm’onlne’s intelligent capabilities, users can set-up their own custom learning models for predicting the values of drop-down lists based on a predefined set of characteristics. This means the platform is capable to make intelligent recommendations across a wide range of business applications, from automatically identifying customer need to specifying the most relevant opportunity category or sales rep to assign the opportunity. For instance, by analyzing the previous service request history and email content of the incoming request, the system can predict the group of case assignees and automatically assign the case to a corresponding support team.