Improving customer satisfaction by using advanced speech-to-text conversion

Most organizations operate contact centers to resolve customer queries and record calls for audit purposes. Contact centers have prefixed call reasons to understand the topic of calls.

But most often customer queries reveal a lot more than the reason like the exact problem faced by the customer, the sentiment of the customer, additional needs of the customer, and future financial plans. Most of these are never summarised at this granular level and precious information is lost. Although representatives may jot down some key points from these calls, the information is rarely comprehensive.

Previously, much of this valuable information was overlooked due to the lack of technology to process it and the workforce required to carry out detailed call listening. Now, with AI/ML-driven solutions, accessing and utilizing this information has become much easier.

For customer-centric organizations, speech analytics has become an essential tool for better understanding and addressing customer needs.

This tool allows users to upload audio, which is transcribed into text using a locally deployed Speech-to-Text model. The transcribed text is then analyzed to:

1. Summarize and extract key topics/themes from calls
2. Assess sentiment from calls
3. Detect compliance issues in calls
4. Separates complaints from regular calls

These analyses are compiled into reports for use by management and operations teams.

This solution is for organizations that:

1. Are customer-centric in their strategy and have identified it as a key success metric for their business.
2. Are advanced in their customer service journeys, possess rich data on customer call recordings, and are willing to invest in it.
3. Aim to achieve self-service goals within a stipulated timeframe using a scientific approach and data science.
4. Seek to develop a holistic, end-to-end solution for their contact center.

Reducing complaints can lead to several benefits:

1. Increased revenue:
   Negative sentiment is a key indicator of potential attrition. By tracking and addressing customer sentiment, organizations can reduce attrition and increase revenue.
2. Improved operational efficiency:
   Customer calls often reveal hidden problems with products and communication. Addressing these issues can enhance operational efficiency and lower service costs.
3. Agent training:
   Management can use detailed call summaries to evaluate agent performance and conversation quality. Training agents with high negative sentiment interactions can improve overall customer experience.
4. NPS/VOC/CSAT & loyalty:
   Customers tend to favor organizations that help them maintain a positive experience, as measured through sentiment. This enhances brand image and boosts customer loyalty.

This solution necessitates call recordings.

1. Algorithm Uses:
   Neural network-based speech-to-text model, which can be deployed locally
2. Sentiment Analysis:
   Lexicon-based methods, classification, or transformers
3. Summarization:
   Large Language Models (LLMs) such as OpenAI API or locally deployed LLMs
4. Key Topics Extraction:
   Topic modeling using Latent Dirichlet Allocation (LDA) or LLMs through OpenAI API or locally deployed LLMs

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. Final outputs could include speech transcripts with their topics, sentiment at the interaction level, and summarized form.
2. Summaries can be shared through PowerPoint presentations or interactive dashboards.
3. Detailed outputs can be shared through API requests. By sending an API request with inputs, you can receive a response. This can be integrated into an existing application or a separate web app can be created.