Can ChatGPT reliably perform call center sentiment analysis? The short answer is yes, with some caveats.
This article covers the key steps involved in using large language models (LLMs) like ChatGPT to gain insights into customer emotions and satisfaction levels from call transcripts. You’ll learn how to transcribe your calls, prepare your data, and utilize ChatGPT for analysis.
Why AI pairs well with call center sentiment analysis
Call center sentiment analysis is about understanding how your customers feel when they talk to your team. This smart approach looks at what’s said (and sometimes how it’s said) to figure out if a customer is happy, frustrated, or somewhere in between.
By doing this, businesses can get a real sense of their service quality, tweak things to make customers happier, and even train their staff better based on real feedback.
But can AI really understand and report on human sentiments?
Yes. Using AI and machine learning (ML) in call center sentiment analysis is nothing new. This tech has been supporting sentiment analysis long before ChatGPT became a household name.
AI and ML use technology like automatic speech recognition (ASR) to turn spoken words into text and use natural language processing (NLP) to dig into those words to identify feelings and attitudes. From this data, AI tools can uncover trends that provide valuable insights into how you can better serve your customers.
Traditionally, these AI tools were only available to the largest organizations because of their cost and time to deploy.
With the arrival of ChatGPT and other large language models (LLMs), many more companies will be able to start using AI tools to enhance call center sentiment analysis.
Compared to previous tools, ChatGPT has the processing power to analyze conversations on a much larger scale and has a better understanding of nuance. This means it can pick up on subtle hints of customer satisfaction or dissatisfaction, even when a conversation is particularly complex.
Using ChatGPT to analyze customer feelings helps call centers understand their customers more fully. Organizations can make smarter decisions about how to help customers, talk to them more personally, and make customers happier overall.
Never used an LLM before? Check out our guide to using ChatGPT.
Call center sentiment analysis software vs ChatGPT
Call center sentiment analysis software is specifically built for analyzing calls, using finely tuned algorithms for this environment. Operationally, it’s been designed for integration with phone systems, VoIP, IVR, and other related call center technology.
Though it wasn’t built specifically for the task, ChatGPT can provide much richer feedback from voice data than traditional call center sentiment analysis software. More than just tagging calls as positive, neutral, or negative sentiment, ChatGPT can grasp the subtleties of human conversation, allowing it to provide more in-depth analysis.
Using call center sentiment analysis software
Sentiment analysis tools use algorithms to evaluate the tone, language, and context of customer phone interactions. It’s offered as a standalone tool or as an add-on feature with premium call center software.
This technology scans voice recordings or transcriptions of calls to identify and categorize emotions such as happiness, frustration, or anger. It helps call centers understand how customers feel about their service or products.
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Using ChatGPT for call center sentiment analysis
Using ChatGPT for call center sentiment analysis involves uploading bulk call center transcription data to the large language model for processing. From there, ChatGPT can be prompted to analyze the language and context of these conversations, surfacing insights into customer mood and sentiment at scale.
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How to run call center sentiment analysis with ChatGPT
Running call center sentiment analysis with ChatGPT involves several key steps and considerations to ensure you’re effectively capturing and understanding customer sentiment.
Here’s a general guide to get you started.
Transcribe calls
Transcribing calls is the first step for performing sentiment analysis using tools like ChatGPT. Automating the transcription process is essential for handling the volume of calls faced by even a small call center.
Here’s how you can transcribe calls using automatic speech recognition technology:
- Select an ASR tool: Start by choosing an ASR tool that suits your needs. There are many available, from free tools suitable for a small volume of calls to more advanced, subscription-based services that offer higher accuracy and additional features like speaker identification.
- Prepare your audio files: Before transcribing them, make sure your audio files are in the correct format for your chosen ASR tool. Some tools might require specific formats like WAV or MP3.
- Break down large files: If you have very long audio files, consider breaking them down into smaller segments. This makes the transcription process more manageable and may improve the accuracy of the ASR tool by reducing the processing load.
- Upload and transcribe: Upload your audio files to the ASR tool. This process can usually be done in bulk for efficiency. Once uploaded, the tool will process the audio and generate transcripts.
Clean the data
Cleaning your data involves reviewing your transcriptions to ensure they are accurate, error-free, and formatted consistently.
Most ASR and speech-to-text software will include tools to help you clean voice data, which is important because traditional data cleansing tools are not necessarily built for this. Typical tasks include:
- Remove background noise: ASR tools can mistakenly transcribe background noise or cross-talk from other conversations. You’ll want to remove or correct these so there isn’t any confusion.
- Correct misheard words: Automatic transcriptions can sometimes misinterpret words, especially if they’re industry-specific terms or spoken with heavy accents. Review and correct these errors for more accurate analysis.
- Remove filler words: Words like “um,” “uh,” and other conversational fillers can clutter your data without adding meaningful context. You can remove these for clearer sentiment analysis.
- Use consistent formatting: Ensure all your transcripts follow a consistent format for speaker labels, timestamps, and punctuation. This helps maintain a uniform dataset for a more accurate analysis.
Annotate data
Annotating your transcript data can greatly improve the accuracy and usefulness of your sentiment analysis results. Annotations provide additional context and metadata to help AI tools like ChatGPT better understand the nuances of each conversation. This annotation process can be done manually by human reviewers or by using automated annotation tools.
Here are helpful annotations to consider adding to your call transcriptions:
- Identify speakers: If your ASR tool doesn’t automatically differentiate between speakers, manually tag the agent and customer in the transcript. This is especially important for analyzing customer versus employee sentiment separately.
- Tag emotions: Flag sections of transcripts where you detect strong emotions like anger, confusion, or satisfaction. This makes it easy to identify the most emotionally charged moments of a conversation.
- Segment topics: Split transcripts into distinct sections based on the topic being discussed. This way, you can analyze the sentiment for each core issue individually.
- Identify silence and overtalk: Mark places where there are awkward silences or incidents of the agent and customer talking over each other.
- Add timestamps: Adding timestamps to the transcript makes it easier to locate and analyze critical moments in the conversation.
Integrate with ChatGPT
Integrating your transcribed and annotated call data with ChatGPT for sentiment analysis requires some additional setup.
One option is to interact with ChatGPT programmatically via an automatic programming interface (API) like OpenAI’s API. This means writing code to send your transcription data to the API so it can perform the analysis. This programmatic approach offers more flexibility to customize the integration and potentially faster performance. However, it does require some coding skills.
The other option is to use a pre-integrated platform or service that has already built ChatGPT’s language models into its software. These platforms hide the complex technical details of integrating with ChatGPT behind the scenes, allowing you to access its capabilities through a simple, user-friendly interface.
These platforms are less flexible than the API method but can make it much easier for non-technical teams to utilize ChatGPT’s robust language understanding without complex coding.
Whichever route you choose, you’ll need to purchase a subscription or service plan. Many vendors offer free trials or starter pricing tiers to help you evaluate their AI capabilities and determine if they meet your call volume and analysis needs.
Train and refine ChatGPT
While ChatGPT’s base language model is incredibly powerful, you will have to perform some custom training or fine-tuning to get optimal results for your specific use case. By fine-tuning the model on transcripts from your call center, you can teach it industry-specific language, product names, and common phrases. This specialization helps ChatGPT accurately understand the context and nuances of your conversations.
It’s also a good practice to analyze your initial sentiment analysis results, and then use that feedback to improve accuracy. If you notice the model is struggling with certain linguistic patterns or topics, additional training data focused on those areas can help strengthen its capabilities.
Analyze and implement insights
With your data prepared and integration set up, you’re ready to start using ChatGPT for large-scale sentiment analysis on your call transcripts. Run the model over your full dataset of transcribed and annotated conversations to extract insights around customer sentiment trends.
As you review the analysis results, look for common issues or pain points that negatively impact sentiment. Identifying the types of interactions and agent behaviors that lead to positive customer emotions is equally important.
Follow data-driven decision-making best practices. The insights you uncover should then inform concrete actions and strategies. This could mean updating training materials for agents, refining call scripts and procedures, or even driving product direction based on recurring customer frustrations. Regularly analyzing customer sentiment after making improvements can help confirm if those changes had the desired positive effect.