Brianna Van Tuinen at CallMiner explains that generative AI isn’t the only answer and takes a look at the role of specialized AI techniques in conversation intelligence.
In an era where customer data is gold, conversation intelligence stands as a cornerstone of customer-centric strategies embraced by businesses across the globe.
As organizations strive to gain a competitive edge through data-driven decision-making, the ability to tap into the rich tapestry of customer data, analyze it, and extract invaluable insights has become paramount.
The information revealed in each customer conversation is growing in significance, especially as survey fatigue continues to rise and customers opt out of engaging with businesses more than ever.
Conversation intelligence platforms are powered by artificial intelligence (AI) to analyze conversations at scale and offer insights for various purposes.
When considering AI for understanding customer conversations, organizations increasingly turn towards large language models (LLMs) and wonder if they can cover all their requirements.
While LLMs (and other generative AI) have transformed AI and natural language understanding (NLU), are revered for their creativity, and undoubtedly have a place within these solutions, it’s important to recognize that they are not the answer for all conversation intelligence needs.
So why shouldn’t you rely solely on LLMs to unveil insights in customer conversations? They are not always the optimal choice for highly specialized tasks; often a combination of many AI techniques (including LLMs) can provide a more comprehensive solution for complex conversation analysis.
Depending on the task, other AI techniques have advantages more suitable than generative AI:
Automatic Speech Recognition (ASR):
While LLMs can detect what’s said in a conversation and generate information from it, specialized speech recognition models are crucial for transcribing customer interactions accurately.
Voice Print Identifiers:
A voice print identifier uses a complex, mathematical representation that captures the unique anatomical and acoustic characteristics that define each person’s distinct voice.
It is used to verify the person calling is who they say they are, helping prevent fraud. Generative AI excels at generating voices rather than speaker identification.
While LLMs can grasp the general tone of a conversation, sentiment analysis or opinion mining can provide a more nuanced understanding by identifying positive, negative, or neutral sentiments.
Organizations can use sentiment analysis to gauge customer satisfaction, identify potential issues, and assess brand perception.
Embeddings enable similarity analysis between words or sentences. This can be helpful in finding similar user queries, identifying related topics, or recommending relevant content or responses based on the similarity between text inputs.
It also enables semantic search in conversation intelligence platforms. Embedding models are generally smaller and less resource intensive than LLMs, making them a more practical choice in many applications.
Vector databases store text embeddings in a new way. They are designed to efficiently search and retrieve similar vectors, which is crucial for tasks where searching for contextual meaning outweighs searching for exact matches, similar documents, or relevant responses in conversation intelligence applications.
By storing vector representations of text data, these databases enable quick and accurate similarity analysis, content recommendation, and anomaly detection.
Named Entity Recognition (NER):
NER is specialized in identifying and extracting named entities (e.g., names of people, organizations, locations, dates) from text.
LLMs may not always excel at precise entity recognition (and are prone to hallucinations), especially in cases where accurate entity recognition is critical, such as in legal or medical documents.
NER can provide structured information from unstructured text, which is valuable for information retrieval and database population.
Rules-based algorithms allow for highly specific and deterministic processing of text. They are ideal when you need to enforce specific business rules or regulations in text analysis.
In situations where precise control and interpretability of the analysis process are essential, rules-based approaches provide clear, explicit rules that can be audited and validated.
Rules-based systems can provide more explicit control over the processing of data, making them suitable for situations where accuracy and compliance are paramount.
When handling sensitive or regulated data, like personally identifiable information (PII) or healthcare records, LLMs may pose privacy and compliance challenges. NER and rules-based approaches can be more controlled and compliant in these scenarios.
Topic modeling techniques are designed explicitly for uncovering thematic structures in text data. They are valuable for tasks such as uncovering latent topics, document categorization, content recommendations, and summarization, where the goal is to organize and summarize large amounts of text based on thematic content. LLMs may not always provide the same level of thematic organization as topic modeling.
Structured Data Extraction:
LLMs are excellent for understanding context and generating text, but for structured data extraction from documents, forms, surveys, or meta data, custom data extraction tools may be more suitable.
Task-Specific NLP Models:
Natural language processing (NLP) models can be fine-tuned or designed for specific tasks, such as agitation and silence detection, machine translation, etc. LLMs, while versatile, may not outperform task-specific NLP models in certain applications.
LLMs have brought about a significant breakthrough in AI, creating new possibilities and elevating conversation intelligence platforms.
But that doesn’t mean LLMs are always the optimal choice for highly specialized tasks that require precise entity recognition, strict rule enforcement, thematic analysis, or structured data extraction.
Choosing the right AI tool or technique depends on the specific requirements and objectives of the analysis task at hand. Frequently for organizations, the use of many of these methods can offer a more holistic, comprehensive solution that better meets critical business needs.
Ultimately, organizations need to articulate the objectives for conversation intelligence and their business needs, and then pinpoint the AI techniques that align most effectively with their path to achieving those goals.This blog post has been re-published by kind permission of CallMiner – View the Original Article
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