Predict Sentiment in Customer Service Chats

Technology Marketing / Sales Improve Customer Experience Customer Segmentation Executive Summary
Predicting the positive/negative sentiment associated with a given customer support chat conversation (applicable to both chatbots and human support representatives).
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Overview

Business Problem

According to Forbes, over 90% of customers prefer live chat customer support over any other customer support channel. 50% of consumers even said that having access to a live chat to ask questions during a purchasing decision is one of the most important features a brand can offer. With the vast majority of industries reporting over 80% of customer service chat feedback as positive, it’s evident that healthy customer satisfaction rates are achievable through chatbots. Though live chat has mass consumer appeal and already has a strong foundation, the post COVID-19 world will rely even more heavily on a business’s ability to provide strong virtual customer service as vendors continue to prioritize their digital businesses. The high bar set by the largely reliable user experience of existing live support chat applications makes ensuring successful chat interactions extremely important. While live chat may not be a deal-maker alone, it can certainly be a deal-breaker if it frustrates or confuses customers.

Intelligent Solution

AI empowers your chat agents, physical or virtual, to predict a customer’s existing sentiment towards customer support chat and can identify the critical drivers of positive and negative change from previous interactions. This will allow you to respond to the right customer with the right message at the right time. AI can also analyze customers at the aggregate level and determine the kinds of the responses that tend to drive, or dampen, sentiment most. Most automated chatbots use a constrained set of canned responses depending on the query; machine learning algorithms, particularly supervised machine algorithms, can help attribute these canned responses to sentiment levels in subsequent messages.

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