AI Sentiment & QA Analyzing customer emotions and monitoring service quality with AI.
Every day, contact centers handle a large number of customers from multiple channels, including phone calls, chat, emails, and social media messages. Traditional quality control, which involves randomly listening to only a portion of call logs, is insufficient for accurately assessing service quality and improving customer experience.
AI Sentiment & QA is a function developed to address this problem by using AI to analyze conversations between customers and agents across all channels, instead of randomly listening to calls from only one channel. This allows for systematic and timely assessment of customer sentiment, service quality, and the identification of areas for improvement.
AI Sentiment (Customer Emotion Analysis)
What is?
AI Sentiment uses AI to analyze speech, text, and conversational context, considering factors such as:
Tone and Pitch:
Detects speech volume, speed, and rhythm to identify anger or urgency.
Keyword and Phrases Context:
Detect positive words (e.g., thank you, impressed), negative words (e.g., terrible, dissatisfied, change suppliers), or critical warnings (e.g., threatening to sue, Consumer Protection Board, request to speak to supervisor).
Sentiment Flow Analysis:
Analyze whether emotions shift from negative to positive (successful problem solving) or positive to negative (errors during service) in order to use that information to assess customer sentiment trends. For example:
● Positive — The customer is satisfied, feels helped, or accepts the solution.
● Neutral — The customer is in a normal state, providing information or asking questions without showing any strong emotion.
● Negative — The customer is dissatisfied, anxious, disappointed, confused, or likely to complain.
AI QA (AI-powered cable quality assessment)
What is?
AI QA, or AI Quality Assurance, is the use of AI to help monitor and evaluate the quality of service provided by staff according to criteria defined by the organization, such as:
Greeting & Closing:
A script for greeting and closing the sale.
Verification & Compliance:
Customer identity verification and notification of legal requirements.
Empathy & Professionalism:
Showing empathy and using polite language.
First Contact Resolution (FCR)
refers to resolving customer issues on the first call received.
Traditional QA vs. AI QA
| Comparison topics | Traditional QA (random inspection by people) | AI QA |
| Inspection quantity | Only 1-2% of all lines are randomly inspected. | 100% verifiable on all lines and channels. |
| Accuracy | There may be bias depending on the perspective of each QA. | The same standards , 100% impartial. |
| Assessment speed | It took several days after the call ended. | Get results instantly in real-time , or within minutes of ending the call. |
| Handling crisis situations. | Knowing about it after it has already happened means it’s too late to stop it. | A real-time alert notifies the supervisor to intervene immediately. |
| Staff skill development | Reviewing past results on a monthly basis, and using time to identify topics for skill development. | Development is targeted and on a daily basis. |
AI Sentiment & QA work?
Collect conversations from all channels. Phone, email, and social messaging are used to view the customer experience from an omnichannel perspective.
Convert speech to text. Transcribe the conversation, separating the speaker between the customer and the staff member.
Analyze language and context. Identify the reasons for contacting us, the problems, needs, complaint risk, competitors, and compliance risk.
Analyze Sentiment Assess positive, neutral, and negative trends, as well as emotional direction throughout the conversation.
Evaluated according to the QA Scorecard. Review according to the topics, scoring weights, and criteria of each department, such as Greeting, Verification, Empathy, and Script Compliance.
The dashboard and reports system can display data at the staff, team, campaign, queue, and time period levels.
Business benefits for the organization.
Reducing operating costs meansthe QA team doesn't have to spend time listening to full calls; they can instead focus their time on developing agents' skills in real-world problem cases.
Improve customer satisfaction scoresby detecting dissatisfied customers and making timely follow-up calls (win-back) before external complaints arise.
Enhance employee developmentby leveraging best practices (high scores) from key departments to create training materials for new agents, and identify areas for improvement to ensure targeted training.
Managing riskensures 100% compliance with the PDPA law, including full notification of insurance terms and conditions or financial institution requirements.
Turn every conversation into an opportunity to improve customer satisfaction.
AI Sentiment & QA helps businesses understand customer sentiment, service quality, and staff performance from real-time conversations.
With AI’s capabilities, customer conversations can be transformed into actionable insights, enabling
organizations to identify problems faster, resolve issues more effectively, improve staff capabilities, and design more efficient customer experiences.
Why choose Protollcall?
Over 20 years of expertise.
In the telecommunications industry, understanding the specific needs of each business and delivering practical solutions is crucial.
Committed to quality service.
Continuously improving to ensure your business communication is always the best it can be.
The support team is ready to help.
Answer questions and resolve problems quickly.
Transform every customer conversation into insightful data to enhance the customer experience.
Contact our team for a consultation, QA Scorecard design, and an assessment of the most suitable AI Sentiment & QA implementation for your organization’s Contact Center.
Frequently Asked Questions about AI Sentiment & QA
Does AI Sentiment analyze tone of voice or speech?
AI analyzes words, phrases, context, and speech characteristics, depending on the technology, language, and models used.
Can AI QA replace entire QA teams?
AI cannot replace the entire QA team. It is suitable for assisting with initial screening and evaluation, while the QA team and supervisors play a role in reviewing context, assessing standards, and providing guidance and training to staff.
Can AI Sentiment & QA evaluate all lines of communication?
AI can enhance monitoring beyond traditional random sampling, but the actual number of samples analyzed depends on the AI package, channels, and resources the organization chooses to use.
Does AI Sentiment support the Thai language?
Speech-to-Text and Sentiment Models should be tested with the Thai language, especially regional dialects and accents, specific vocabulary, and real-life situations, before being used in full-scale applications.
Can Sentiment scores be used in place of CSAT scores?
Both datasets represent customer satisfaction assessment scores in different formats. Sentiment scores are generated by AI analysis of conversations, while CSAT scores are based on direct customer feedback. Both datasets should be analyzed together.
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