A customer sends you a message. HelpSocial unpacks it and wraps it with data so
your systems, bots and agents have the exact information they need to help the customer faster.
Message Structure

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.

Message Intent

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.

Accelerated Training

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.

Sentiment Analysis

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.

Keyword Analysis

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.

Priority Alerts

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.

Contextual Relevance

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.

Customer Identification

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.

Net Sentiment Score

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.

Languages Supported

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).


 

HelpSocial uses built-in natural language processing and machine learning systems to put AI at the core of customer engagement.

Contact Us and we’ll send you all the geeky details.


 

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One Source For Enriched Mobile, Social & Messaging

Inbound customer messages turn into instructions that tell your systems bots and agents how to help the customer faster.

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