Every interaction between a customer and a contact center agent includes a narrative.
Digital innovations like our text analytics and customer sentiment analysis tool are a means of connecting data points with the human experience to better understand the “why” behind consumer preference and behavior. The raw data is the words used in a conversation, and this tool focuses on the key moments that define every chat or call to determine if our agents delivered a “wow” experience or missed an opportunity.
The tool analyzes tens of thousands of transcripts each month, digging into the weeds of conversations to assess tone, highlight specific words that convey action or emotion, and explain customer thought processes of the product or service that we support. The goal is understand users at an elemental level by analyzing terms that connect to user sentiment.
How it works
The tool considers the impact of certain words within a sentence and the grammatical elements that are in play. For example, let’s consider the word “like” in various uses:
Negators:
these reverse the word – I do not like it.
Amplifiers:
increase the impact of the word – I really like it.
De-amplifiers:
have the opposite effect – I hardly like it.
Adversative conjunction:
overrules the previous clause – I like it, but it’s not worth it
The tool classifies transcripts as positive, negative, or neutral and assigns a specific score based on the weight of the words in each sentence. This has the effect of giving sentiment a number.
This works by blending the concepts of text analytics, such as text mining and N-gram analysis with classical improvement concepts like Pareto, Root Cause Analysis, and others to drive a more gratifying customer experience.
Improved agent performance
Integrating technologies/ systems
Impacting customer retention
Maintaining a culture of innovation
Facilitates more proactive selling
Optimizing the use of available resources
Reducing staff turnover
The idea is to learn what customers think, and that’s where the tool’s value differs from Quality Assurance. The QA team is more focused on agent performance and reviews calls and chats accordingly. Also, the quality function includes a sample of interactions; the tool goes through all conversations, word by word, for a deep dive review and results that can provide insight into decision-making.
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