We are excited to introduce Syncly's Dynamic sentiment analysis, an innovative way to capture sentiment trend throughout each conversation, and across multiple communication channels. It enables you to identify urgent feedback that requires immediate response, and conversations that customers leave happy or unhappy. Hence, it enables you to proactively identify urgent issues or at-risk customer / customer groups before they churn.

1. What is Sentiment analysis?

2. What is Dynamic sentiment analysis?

3. How Syncly’s Dynamic sentiment analysis is different from the conventional sentiment analysis?

4. Dynamic sentiment analysis use cases

What is Sentiment Analysis?

Sentiment analysis is the process of analyzing customer feedback or communication using natural language processing to determine if the emotional tone is positive or negative.

Typically, it can be classified into five categories : Very negative, negative, neutral, positive, and very positive, measured by the tone, keywords, intensity of emotional expression, and others.

What is 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.

Sentiment trend analysis within a conversation:

Customer sentiment is different in the beginning, the middle, and the end of conversation. Dynamic sentiment analysis shows how customer sentiment evolves within a communcation, allowing users to identify customers leave unhappy or happy.

Sentiment trend analysis across multiple conversations:

In many cases, communications between companies and customers happen multiple times. Customers easily lose the visibility if customer sentiment is improving or worsening across multiple conversations. Dynamic sentiment analysis shows the trend of customer sentiment across multiple conversation with reps, that enables teams to easily identify at-risk customer.

How Syncly’s Dynamic sentiment analysis is different from the conventional sentiment analysis?

Static chat analysis vs Syncly’s trend chat analysis

In chat conversations, dialogues between customers and reps contain multiple topics and sentiment. Conventional sentiment analysis could not capture the context and the flow of communication, while Syncly's dynamic sentiment analysis flagged sentiment based on the overall understanding of the flow of the conversation.

Easy to identify customer comes in angry or neutral tone, but leaves happy or unhappy.

Understanding the latest customer sentiment as well as trends of their sentiments are leading indicators that can predict potential churn risk. Dynamic sentiment analysis is the optimal method because it shows how the customer sentiment changes over time.

Dynamic sentiment analysis use cases

Issue prioritization by negative sentiment

It’s clear that as the customer base grows, it becomes harder to identify driving factors that causes negative customer experience. Dynamic sentiment analysis helps teams to identify main reasons that deteriorates a customer experience (i.e. onboarding friction, issues that drives low NPS), so the teams can build the solution on a daily, weekly, and monthly basis that keeps their customers happy and retained.

Identify at-risk customers and customer segments

With Dynamic sentiment analysis, customers can get the visibility on how much of their customers are in good, medium, or bad conditions, and who they are.

Customer service quality assurance

Dynamic sentiment analysis provides better customer experience by reviewing successful CS cases that flips customer sentiment from negative to positive and share the best practice across the organization.

Want to identify key issues that causes negative customer experience?

If you’re interested in learning more about Dynamic sentiment analysis, let us know. Book a meeting with co-founders of Syncly, and get free 1,000 credits now.