Customer Sentiment Analysis using Artificial Intelligence

Business challenges

Inaccurate sentiment analysis: Difficulty in accurately interpreting customer emotions and sentiments from call data.

Time-consuming manual processes: Reliance on manual processes for sentiment analysis, leading to long processing times.

Limited call data analysis: Inability to analyze a large volume of call data to gain deeper insights into customer experience.

Lack of real-time analysis: Inability to process and analyze call data in real-time, leading to delayed service and response.

Poor customer engagement: Inadequate understanding of customer experience leading to poor engagement and suboptimal services.

High operational costs: High operational costs associated with manual processes and limited data analysis capabilities.

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Solution Approach

AI helps call centers and customer support firms overcome the challenge of assessing customer sentiment.

We have developed a model that can understand the mood of the customer and provide appropriate services for the customers.

This AI model uses a Speech-to-Text model and analyzes a huge volume of calls and segregates call feedback based on the analyses.

The initial step involves converting call audio to text format (Speech to Text) and pre-processing of audio text data.

Feature selection is then performed for selecting the most important features from call.

A classification model is built to predict whether the customer in the call is further serviceable or not.

Business Impact

Accurate sentiment analysis: The AI-based solution provides accurate analysis of customer emotions and sentiments, leading to better understanding of customer experience.

Efficient operations: Automated processing of call data reduces manual effort and time required for sentiment analysis, improving operational efficiency.

Increased call data analysis: The ability to analyze a large volume of call data provides deeper insights into customer experience.

Enhanced customer engagement: Improved understanding of customer experience leads to better engagement and optimized services.

Reduced operational costs: Automated processing reduces operational costs associated with manual processes and limited data analysis capabilities.

Competitive advantage: The use of advanced AI technology provides a competitive edge and differentiates the company from others in the market.

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Technology Stack

Trusted and Proven Engagement Model

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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