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Each sentence of an inbound message is broken down into language structure: verbs, adjectives, punctuation, etc. and scored to show where the emphasis is placed.
The message subject (reason for sending the message) is identified and scored based on relevance to the customer's desired outcome (intent). In cases where multiple requests exist in the same message, subject and outcome scores can be used to prioritize the order of responses.
The HelpSocial data bank has more than 5 years of human-marked messages for +/- sentiment. We use this to accelerate the learning curve of machine learning systems, increasing accuracy rates from a market average of 60-65% accurate to 80-90% accurate.
The overall sentiment of the message, as well as the variations in sentiment over each sentence in the message are scored to understand where inflection is placed and what the tone of the message is.
All messages are searched for unique words and phrases to identify the topic of the customer issue and tagged accordingly. This system is easily customized (seriously easy - point & click, zero dev) for business specificity.
When combinations of words, phrases and other data points are met, the alert system adds an Alert Status to the message making sure your systems prioritize the most important items first.
Prior conversation history and attributes associated with those interactions are provided, giving an ability to bring human-like empathy to bot conversations as they reply within the context of the whole situation.
Our CRM is designed for bots and systems to save and recall information about the customer for response personalization and advanced service automation. Connect your own CRM to bring relevant internal notes and data points together with each message.
The customer's Net Sentiment Score, based on conversation history, gives instant insight into their satisfaction level and the +/- trend direction. This gives valuable situational context to any agent or any system engaging with the customer.
Our natural language processing and machine learning systems are rated for high competency and accuracy with English, Spanish, French, German, Italian, Portuguese (Brazilian & Continental), Japanese, Chinese (Simplified), Chinese (Traditional).