Sentiment Analyzer in Customer Experience
An ultimate Sentiment Analyzer should combine both the acoustic characteristics of a speaker’s voice and the context of the conversation into a single score. This call score can be used to measure relative sentiments or emotions across various cross-sections of calls, agent groups, customer profiles, and time frames. Sentiment Analysis can also provide critical insight into rapidly growing customer service issues, and the handling capabilities of the agents.
In many industries such as automotive, retail, banking, insurance, and healthcare, call centers are critical to on-going post-purchase success and support. Since call centers already practice recording and storing the agent-customer conversation, performing sentiment analysis will help in knowing the customer satisfaction, brand insights, agent performance, etc…
The well known benefits of sentiment analysis report on historic call records,
We could easily realize what’s working with customers by examining positive sentiments and emphasizing those features or aspects.
We shall drive greater sales by identifying up-sell and cross-sell opportunities to clients who express positive feelings and opinions.
Improve call center representative scripts and effectiveness by analyzing the sentiments associated with specific phrases.
Will improve the call center effectiveness, provide business insights for strategic change, increase revenue, and drive overall ROI.
The unresolved challenges
Language is complex, and as a process for quantifying and scoring, sentiment analysis is equally complex. This becomes more complex while we use code-mixed languages (e.g. a speaker switching language and words between Hindi and English in single conversation) in the conversation. Accounting for sarcasm is essential for realistic sentiment analysis but it cannot be achieved without both context and tone.
We have solutions in our AISS
With the help of VoiceProfiler, TextProfiler, VoiceSegmenter and VoiceTranscriber APIs from our AISS, one could easily get a realistic sentiment report. Our APIs account for the rate of speech, the stress in a caller’s voice, context of the conversation, aspects associated with the conversation, and changes in stress signals.
Unlike the traditional way of identifying business objectives and then developing complete products around those business requirements, primary techs of AISS can be seamlessly integrated with each other in various sequences to create a complete solution which can achieve specific business requirements thereby greatly reducing associated time and costs.
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