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Will chatbots replace search engines?

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Advances in chatbot technology could bring enormous change when it comes to searching for answers. But we’re creatures of habit—we still use search engines, such as Google, Bing, DuckDuckGo, etc. to answer our immediate questions.

Whether you’re checking how to spell a difficult word or figuring out an easy way to fix a zipper, search engines have been consumers' go-to source for years. However, the way we search online has evolved considerably since search engines rolled out in the 1990s.

Going back more than 30 years, the internet was still relatively new which also meant that search engines were also in their infancy. If you typed in a specific question, chances are few results, if any, came up. Flash forward to present day, and search engines are capable of processing hundreds of thousands of queries.

Because of the popularity of search engines, content providers have made significant efforts to tailor their content to match what consumers are searching for, increasing the odds that you’ll return to their websites. This assures that the search engines have the answers for almost all your questions because the content providers made your search experience seamless.

While search engines and chatbots aren’t a direct apples to apples comparison, there’s overlapping similarities in both their capabilities. Plus advances in artificial intelligence (AI) and natural language processing (NLP) have enabled chatbots to become more human-like and operate more akin to a search engine. Which begs the question: Could chatbots one day replace the need for search engines and mobile apps?

The rise of chatbots

Despite the vast differences in the size and structure of businesses, the need to respond to customers accurately, quickly, and with consistent answers is common across the board. While you likely have an FAQ page with commonly asked questions or a phone number customers can call for support, chatbots likely play a big role in your communication stack.

The benefits of AI-enabled chatbots are clear: They have 24/7 availability, offer a more personal direct interaction, and can save time for organizations and end-users. By 2024, it is estimated that consumer retail spend via chatbots worldwide will reach $142 billion—up from $2.8 billion in 2019. While chatbots continue to prove their value, one challenge they continue to face is the ability to understand the complexities of human language.

A misspelled or misused word can greatly impact a chatbot conversation, and potentially cause a customer to exit the conversation altogether if their question isn’t answered accurately. However, if chatbots can authentically understand human language, then communications with customers would completely transform.

Understanding human language

Search engines have taken steps to understand human language by establishing a process for better understanding words within the context of search queries. For example, in 2018, Google  launched the Bidirectional Encoder Representations from Transformers (BERT) model. After recycling an architecture typically used for machine translation, Google made it possible for the model to learn the meaning of a word in relation to its context in a sentence, giving it the ability to complete a wide range of language tasks. Last year, Google doubled down on past assertions that “the future of search is a conversation,” and unveiled its LaMDA conversation technology—a chatbot designed to converse on any topic.

Recent language models such as BERT and Lamda demonstrate the value of NLP and machine learning (ML). ML algorithms have proven to be essential, especially in AI applications in customer services. They have the capability to process information and automate conversations, increasing the ability of businesses to have conversations with their customers at any time regardless of location.

But, despite overwhelming consumer interest in initiating and replying to two-way conversations with brands, most businesses aren’t equipped for such messaging capabilities, resulting in unhappy customers. While these algorithms will play an important role in the customer journey, it will be critical for organizations to gain a deeper understanding of human language as they look to improve customer interactions.

Chatbots versus search engines

Interactions with chatbots are growing with 76% of shoppers having interacted with one in 2021—up from 51% in 2020. Of these, over half appreciate the immediate availability of an automated response. Chatbots equipped with NLP can help better understand customer inquiries and respond accordingly. With NLP technology, chatbots are capable of learning from previous customer interactions and handle large volumes of conversations efficiently— all while reducing human errors.

By contrast, the majority of search engines work only with specific keywords, rather than a true understanding of what a user is searching for. However, intelligent search applications enabled by NLP allow users to ask any question, and the engine will be directed to a knowledge base where it will be able to find the answer you are looking for.

The value of intelligent search applications

Sometimes the best experience for customers is a single place to ask a question and fetch the desired content easily, rather than looking into a deep and complex navigation menu. That is why an intelligent question-answering search engine may be the best choice for your business.

While intelligent search applications allow you to have a search box on your website to search your FAQs, it can also search any document or website. Using ML algorithms, these applications can also easily be integrated into your existing technology, like a chatbot. For example, intelligent search can handle the questions your chatbot doesn't know and create a better bot experience for your customers. This way, you can make your chatbot more intelligent and avoid the need to have multiple sources of truth. Some intelligent search applications can even suggest responses, route messages, and assist first and second-line agents by searching your knowledge database. By reducing the number of common questions in the call center, agents can focus on solving more challenging issues?.

You may be wondering if intelligent search applications always fetch the right answer. The answer is no. An intelligent search application operates similar to a search engine: It will return search results and rank them the best it can. However, just as with other search engines, you might not find exactly what you are looking for.

Looking ahead

While there continues to be breakthroughs in NLP, more work needs to be done for this technology to reach its full potential. Many of these systems still lack common sense, which can lead to inaccurate conversations and general frustration among customers. However, the way chatbot technology is headed, I suspect that it will eventually outperform search engines.

There will never be a powerhouse chatbot that can do everything all at once, but investing in an intelligent search application can improve the overall functionality of your chatbot. Combining these two technologies enables your chatbot to function more similar to a search engine, and can make a huge difference in the way you communicate with your customers. With more brands focusing on two-way conversations, it’s more important than ever to ensure you’re answering customers' questions accurately and efficiently.

Utilizing chatbots in this fashion may or may not have crossed your mind—until now. With further advancements in AI and NLP, it deserves heavy consideration as they can help streamline your business and boost overall customer satisfaction. Customers want more human-like interactions, and the best way to provide this experience is to equip chatbots with intelligent search. The technology already exists, now businesses just need to prioritize it.

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