How Semantic Vector Search Transforms Customer Service Interactions

Did you know that around 99.5% of the data collected by companies remains unchanged? In fact, it is not even used or analyzed. This shows a huge gap that can only be bridged by an advanced enterprise search platform.

In recent years, the search landscape has changed dramatically. It has moved from using full-text and keyword matching mechanisms to more sophisticated techniques such as semantic (understanding the meaning behind words) and visual (image search) platforms that define today's digital landscape.

This is in response to increasing customer expectations: people today not only want generalized search results, but they also want them to be personalized and relevant to a T. This is where semantic vector search comes into play. .

It seamlessly uses deep metric learning to train a semantic model that helps organize queries in a vector space so that similar things are combined and different things are separated.

Explore this blog post to learn the basics of semantic vector search and much more.

First things first: What is semantic vector search?

Traditional search engines only focus on exact keywords and return results focused on them. In contrast, an advanced ai system goes beyond keyword mapping to understand the context and intent behind the query.

For example, an agitated customer contacted customer service and complained: “Wow, thanks for sending my luggage to Washington and flying me to Los Angeles at the same time. Brilliant service!” In this scenario, a traditional search engine would focus on words like 'Wow' and 'Brilliant service.' However, a semantic vector search engine digs deeper into the problem and understands the underlying sarcasm and customer frustration. It's like having a support agent who not only listens to keywords but also captures their meaning and context, offering more effective assistance.

McKinsey research found that companies that excel at personalization generate 40% more revenue from those activities than average players. Across all US industries, moving into the top quartile of personalization performance would generate more than $1 trillion in value.

But how does it work?

Let's imagine each word as a resident of a building that rises to the sky. Residents with addresses such as 45, 46, and 47 reside on the same block, while residents of 55, 56, and 57 reside on the next block. The distance and direction between these residents represent relationships between words where words with similar meanings end up closer to each other.

Now that you know the basic meaning, let's get down to it.

When a user enters a query, the system translates it into a vector. Thereafter, look for other vectors that have a similar meaning. This ensures that the system retrieves not only exact matches but also semantically related information.

But what role does semantics play in vector search?

The importance of semantics lies in the ability of the artificial intelligence system to understand and capture the exact meaning behind words considering synonyms and context, among other things.

The result? Accurate, relevant and personalized answers that drive problem resolution and leave customers delighted.

But to ensure everything runs smoothly, there is a complex mechanism, also a subset of artificial intelligence, working under the hood known as machine learning.

ML algorithms deeply understand the subtle similarities and differences between words. To do this, they see them as pieces of a large puzzle instead of understanding the meaning of a single word.

artificial intelligence (ai) algorithms analyze the complexities of language. Analyze the context, patterns and correlation within the data. They demystify the meaning of words by going beyond superficial interpretation.

For example, a customer approaches your company with a concern. They want to decorate their kitchen with the vibrant shades of cherry and plum. However, his virtual assistant mistook the reference for a fruit. This dual use highlights how the same term can have different meanings and contexts.

This is where an advanced ai system can interpret the inherent meaning of the terms and their correlation with each other.

Types of Semantic Vector Search Techniques

This search applies powerful word embedding techniques to understand the nuances of language:

How does Vector Search transform customer service interactions?

Two domains where the search for semantic vectors enhances customer service to improve the experience are:

1. Understand natural language

Natural language is ambiguous. It becomes an uphill battle for machines to understand the way humans communicate. This is where natural language processing (NLP) to be able to help. It is a multidisciplinary process that combines machine learning and natural language generation to make interactions between humans and machines possible. Semantic vector search applies NLP to catalyze sentiment analysis.

Analysis of feelings classifies data as positive, negative or neutral. By incorporating sentiment analysis into their existing systems, organizations can gain valuable insights into their customers' needs and problems. Doing so allows organizations to deliver proactive and personalized experiences.

2. Facilitate information retrieval

Because semantic vector search can capture the underlying meaning, intent, and context of the query, it ensures that the right help articles reach the right audience at the right time. Leverage ai to get a 360-degree view of your user's journey. This way, organizations can deliver support at full speed by hyper-personalizing it based on user browsing history, customer reviews, etc. As an added bonus, CSAT scores skyrocket too. Everyone wins, right?

But wait! Where there is brilliance, there are challenges.

Challenges on the path to effective vector search

Although the future of semantic vector search promises profound benefits, there are obstacles in the way of achieving them. They are:

1. Handling ambiguity in semantics

Since natural or human language is ambiguous, words and sentences can have different interpretations for different individuals. For this reason, it is incredibly difficult to correctly decipher the context and intent of user queries. Implementing advanced machine learning algorithms can help. They learn from user interactions to improve their accuracy over time.

2. Ethical considerations

As semantic vector search systems are trained on a massive data set, it is inevitable that some ethical considerations related to privacy, bias and transparency will arise. Let's treat these privacy concerns as stepping stones rather than seeing them as obstacles. To avoid discriminatory results, it is imperative to take steps to mitigate bias by regularly auditing and updating data.

Looking for a force multiplier to speed up your enterprise search engine?

There are a multitude of techniques such as neural, vector and semantic search circulating in the market. Here neural networks add depth, vector search improves precision, and semantic understanding reveals nuanced layers of meaning.

Using them in isolation would leave their capabilities underutilized. This is where a unified cognitive platform can appear as the silver lining. Seamlessly integrates different search techniques to ensure that users can enjoy a personalized, contextual and fast search experience.

taranjeet is a data scientist with a strong interest in behavioral sciences, which gradually focused on relevance. Delivered production-ready solutions on problems such as LLM, escalation prediction, intent detection, semantic search, and recommender systems, while leveraging cutting-edge ai and NLP technologies.