We are excited to launch Syncly’s Customer AI, a prescriptive and proactive customer management feature that dynamically tracks and interprets customer sentiments across various data channels. This empowers businesses to understand and respond proactively to customer sentiment, feedback, and experiences real time, holistically at the account level, and specifically at the individual customer level.

Syncly’s Customer AI goes beyond simply analyzing static customer conversations through transcription and categorization. Now, users are able to capture customer sentiment trends throughout each interaction, allowing easy identification of happy and unhappy customers and also effective management of at-risk customers. Users can also identify emerging trends, potential issues, or opportunities for improvement, enabling businesses to proactively address customer concerns and enhance their overall customer experience.

With Syncly’s Customer AI, businesses are able to stay informed about their customers' perceptions, adapt quickly to changing sentiments, and make data-driven decisions to drive customer satisfaction and loyalty.

The technology behind Customer AI: Dynamic Sentiment Analysis

The engine behind Customer AI is Syncly’s dynamic sentiment analysis. Dynamic sentiment analysis is the new way to analyze customer sentiment that reflects the nature of evolving customer sentiment with every experience. 

It is an advanced feature designed to analyze and interpret customer sentiments in real time across a variety of data sources. It employs our proprietary natural language processing (NLP) algorithms to understand the emotions, opinions, and attitudes expressed by customers in their interactions. It can accurately detect and categorize sentiment, including positive, negative, or neutral tones, as well as the intensity of emotions expressed.

Sentiment trend analysis within a conversation:

Customer sentiment varies throughout the entirety of a conversation. Dynamic sentiment analysis shows how customer sentiment evolves within a dialogue, allowing users to identify customers who leave happy or unhappy.

Sentiment trend analysis across multiple conversations:

Most customers interact with businesses multiple times. These businesses often struggle to have the full visibility of customer sentiment development across multiple conversations. Dynamic sentiment analysis shows customer sentiment trends throughout collective interactions, empowering effective real-time identification of loyal and at-risk customers. 

You can learn more about Syncly’s dynamic sentiment analysis in this blog post. 

Deep-dive of Customer AI functionality

1. Customer AI Dashboard

Check the real-time sentiment (happy vs. unhappy) status of all registered customers and track sentiment trends throughout all conversations by each customer within the set time frame. Use the newest filter to see the list of customers who still remain unhappy today. You can also track by accounts on a higher level and hone in on individual customers within selected accounts.

2. Customer/Account experience trend history and details

Check the real-time sentiment trend, the breakdown of positive and negative experiences, and details of individual conversations on the customer/account details page. Simply click on each conversation to get more detailed information.

3. Unhappy customer interaction details

The red exclamation triangle icon notifies the user that there is an issue to be addressed. Click on this icon to closely monitor and manage at-risk customers who have had the latest unhappy experience with unsolved issues. You can access the full conversation history to thoroughly understand the details of customer sentiment, experience, and feedback. 

With this data, businesses can proactively address customer concerns and enhance their overall customer experience.

4. Best practice tracking by agent

Do you want to share customer engagement best practice amongst your agents? You can filter customer conversations by agents to track performance of each agent real-time. By easily tracking agents with happy customers or those who were able to turn around customer experience from negative to positive through the conversation, you can use detailed information and history to develop best practice cases for other agents to learn from.

▶️ Watch Interactive Demo