HelpSocial makes it easier and faster to create AI automations for advanced CX.

Social media, messaging and, now, traditional communication channels are analyzed for message intent and context.

When you receive the customer activities, they have been cleaned up into a standardized format for simplified processing and enriched with attributes making it easier to build automations.
Here are some examples for helping more customers, in less time, at a lower cost.
Advanced Attribute Routing

Traditional, skill-based routing was a big improvement over round-robin, available/not available agent selection. Attribute routing allows for matching an agent or bot to the situation, not just making a selection from the general reason for the engagement.

Inbound messages are analyzed to determine the intent of the engagement. Internal systems are used to find relevant interactions from the past, as well as information that tells us more about who the customer is. The emotion in the message is gauged and a priority status is added.

These added attributes are delivered with the message to the routing engine so it can intelligently choose an agent. The agent selection decision is simplified by only having to deal with 5 decision points:

1

Message Intent

Why are they engaging us?

Available agents are narrowed down to only those who can help with this specific type of issue.

2

Historical Context

Who is this customer to us?

Prior interactions and social media behavior help determine whether this is a special situation or not.

3

Sentiment

Fuming or mild perturbed?

The difference matters. Agents are further narrowed down to those who are skilled for irate customers.

4

Priority Status

Is this an emergency issue?

This data point indicates with whether to escalate or not. It may also be a point where bots are ruled out.

5

Agent Status

Who is available to help?

Of the agents best suited to handle this situation, who is next in the queue to help with the interaction?

Bots & Conversational AI

Programming a computer to understand a human and respond like one is hard. The algorithms are intensely complex. The data they make decisions with to formulate responses has to be clean and in a standardized format.

Before a bot engineer can start the tough process of designing a response engine, they have to build all the systems up front needed for understanding everything about the inbound message and customer.

HelpSocial assists bots and automation systems by taking on all the work needed for cleaning, formatting and understanding inbound activities. With a large amount of the work taken off the table, the engineer can focus on generating responses and deliver the product sooner so the business can help more customers, faster.

Real-Time Agent Assistance

Our experience has shown when agents do not have to search systems for the answers to questions or look up prior interactions to understand situational context, handle times drop by an average of 30%.

With HelpSocial, every inbound message is analyzed in real-time as the conversation unfolds. Topic attribute tags applied to each message can be used to provide an agent the answer to the question, faster than the agent can find it themselves. Additionally, real-time analytics on agent conversations reveal KPIs showing how the customer sentiment improves or declines over the course of the engagement.

These improvements not only help the business reduce support costs by lowering handle times, they meet the customer's expectations of faster, lower-effort service.

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Omnichannel Intelligence

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Channel Integration

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