How Transformers Revolutionize Customer Support in the Digital Age with SearchUnify

How Transformers Revolutionize Customer Support in the Digital Age with SearchUnify

In our previous blog post, “Transformer Models: The Ultimate Game Changer in Language Modeling,” we explored the features that set transformers apart from traditional language models.

Much like powerful robots in the “Transformer” movies, the world of AI has its own counterparts known as Transformer models, bearing the same name and extraordinary capabilities. But the burning question is: how can these formidable models be harnessed to revolutionize the customer support function expeditiously?

At SearchUnify, we are already leading the pack by leveraging the untapped potential of transformers to help businesses drive more relevant, faster, and hyper-personalized customer support. Wondering how? Give this blog post a read to learn!

Elevating Support Efficiency: The SearchUnify Approach to Harnessing Transformers

Transformer models are pre-trained on a large corpus of text data to understand and generate human-like responses and perform several Natural Language Processing (NLP) tasks.

SearchUnify, a leading unified cognitive platform, leverages transformers to empower enterprises to refine their customer support function in three ways:

1. Sentiment Analysis
2. Generative Question Answering
3. Title Generation

Let’s get into the details!

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1. Sentiment Analysis

Sentiment analysis is an automated process of analyzing and interpreting the text or expression as positive, negative, or neutral.

Transformer models go the extra mile in capturing the context and nuances of customer interactions, allowing for more accurate sentiment analysis. How you may ask? By harnessing their unique architecture and pre-training on vast amounts of text data, transformers delve into the semantics of words and phrases while examining their relationships and overall tone. This not only helps in identifying positive or negative sentiment but also the sentiment strength and polarity.

For example, a customer might express satisfaction with a product by saying, “This software is not as bad as I expected.” Transformers can recognize the negation (“not”) and accurately interpret the underlying sentiment as positive, despite the presence of negative words like “bad” and “expected.” What’s more?

  • Boost Customer Satisfaction with Personalized Recommendations

    A granular understanding of customer sentiments helps support enterprises better anticipate customer needs and preferences. They can utilize this information to recommend more suitable and personalized knowledge base articles, FAQs, or troubleshooting guides that address specific issues.

  • Enhance Relevancy of Search with Re-Ranking

    After analyzing the sentiment of customer queries, transformers can assign sentiment scores to potential responses or solutions. This allows enterprises to push query-resolving content to the top of search results, ensuring that customers are equipped with the most relevant and helpful answers in real time.

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2. Generative Question Answering

Generative Question Answering, as the name suggests, generates abstractive or direct answers to customer queries.

Transformers greatly assist in providing direct answers by employing a sequence-to-sequence generation approach.

Let me give you a brief! Transformers encode the user’s query and decode it into a concise and contextually relevant response. By understanding the query’s semantics and utilizing the encoded representation, transformers generate grammatically correct direct answers that precisely answer the user’s question.

For example, if a user asks, “What is the tallest mountain in the world?” a direct answer would be “Mount Everest.” The answer is concise, precise, and directly provides the requested information without requiring the user to sift through additional search results or read a lengthy article.

Although transformer models work incredibly well in delivering direct answers but struggle with more niche or specific questions. At SearchUnify, we use two innovative approaches to enhance their understanding and accuracy. These include:

  • Fine-tuning the model on domain-specific textual data to improve its performance.
  • Utilizing retrieval-augmented generation to capture relevant information and feed it into the model as a secondary source of information.

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3. Title Generation

Knowledge workers often spend hours racking their brains to come up with captivating titles and descriptions for support articles. What if I told you that this time-consuming task can be automated with the help of transformers?

Fortunately, it’s true!

Transformers have impressive language and contextual comprehension skills. They can understand the essence of your knowledge articles, extracting important themes, details, and points

Let’s say a knowledge worker has to write an article about troubleshooting network connectivity issues. The transformer-based model analyzes the content and automatically generates a title like “Mastering Network Connectivity: Troubleshooting Guide.” The title captures the essence of the content and entices readers to explore further.

At SearchUnify, we are leading the charge with our cutting-edge knowledge-centered customer service product—Knowbler. Powered by LLMs, it is capable of auto-generating titles and summaries for knowledge articles, thus expediting knowledge creation. This takes a huge load off support agents and empowers them to deliver faster and more relevant resolutions.

Let’s Get the Transformation Rolling!

Harnessing the power of transformers can be a game-changer in a competitive landscape. From anticipating your customer intent to streamlining operations and more, they can transform your customer support function for good.

If you truly want to experience the power of transformers or leverage their capabilities in your workflows, request a live demo now!