You’re looking at the clothes in your closet, wondering if you have enough decent things to wear for the fast-approaching holidays. Unfortunately, you realize, you donated most of it to charity because it no longer fits.

Now what? You go to your favorite clothing retailer’s website to look for a coat in your new, roomier size. But before you can enter a complex query (“Warm men’s wool black peacoat size XL…”), a conversational AI chatbot (a young descendant of the more rudimentary, rule-based chatbot) pops up in the lower right corner and says “Hi! Can I help you?”

Well, sure. You skip the search box mumbo jumbo and type right to the chatbot, describing the coat you need. Like a human salesperson, It helpfully rustles up some options. You start looking at details on the coats’ product detail pages, then veer off into social media to see what real-time influencers are wearing these days, then cut your research short and head to work, leaving the chatbot to snooze.

Later, back at your desk (or chilling on your mobile phone), you resume browsing the site. The same kindly chatbot doesn’t miss a beat: “Still looking for a coat? We have a few that might fit.”

This is one example of conversational artificial intelligence in action.

Defining conversational AI 

Conversational AI. Is that AI that can talk? That’s fairly accurate. The technology revolves around chatbots and other entities (e.g., Apple’s Siri, Amazon Alexa, Google Assistant, ChatGPT) that people can converse with as if they’re talking to a human. The AI in the background is able to process, make sense of, and respond remarkably well to human language in a natural conversational manner.

A growing impact

Conversational AI platforms are transforming the ways humans interact with retailers, among other use cases. As with the impact of generative AI’s large language models on the greater business world, shopper conversations with virtual assistants are providing a new dimension to the omnichannel customer experience.

Ecommerce sites are grabbing onto this technology to optimize the customer journey, providing online personal assistants to help shoppers get through the purchasing stage. Being able to talk to an online assistant is not only becoming the norm, it’s proving to be wildly embraced, with conversational experiences coming increasingly into the mainstream. The upshot: by 2025, this red-hot market is expected to top nearly $14 billion. 

How does conversational AI work?

A convergence of several new technologies, conversational AI relies on:

Machine learning 

A subset of artificial intelligence that empowers systems to learn and progressively improve by analyzing vast amounts of data, machine learning is a foundational element of conversational AI. Through  algorithms, conversational interfaces use tools such as sentiment analysis to refine their understanding of language, adapt to user preferences, and enhance their response-generation capabilities. By continuously learning from user interactions and refining their datasets, machine learning systems can ensure progressively greater accuracy and efficiency during conversing. 

Speech recognition 

Another fundamental component, human speech recognition technology, converts spoken language to text, allowing the system to process and comprehend the input.  

Speech recognition employs sophisticated algorithms that analyze audio signals, identify phonemes, and convert them into meaningful words and sentences. This technology has advanced significantly in recent years, enabling conversational AI systems to accurately transcribe spoken language and provide smart-sounding responses. Unlike humans, “What did you say?” isn’t part of its lexicon. 

Natural language processing 

When spoken material has been converted to text, NLP — natural language processing — takes over. Natural language processing is about just that: converting text into structured data by using algorithms. Through computational techniques such as removing stop words, segmenting words, and splitting compound words, the content is converted into segments that are more computer-friendly. NLP identifies keywords, parts of speech, and other important components in text.

Natural language understanding 

NLU — natural language understanding — a subset of NLP, goes a step further, leveraging AI to identify language attributes such as sentiment, semantics, context, and intent in order to understand what is meant, even if there are misspellings and other errors. It enables computers to understand language subtleties and variations. NLU algorithms analyze processed text, which could be generated from a query, request, or command, and identify the user intent. NLU allows computers to figure out whether people are saying the same things, for instance. By accurately pegging intent, conversational AI systems can provide contextually correct responses. NLU thereby allows computer software and applications to be more accurate in responding to spoken (as well as text) commands. 

Natural language generation

Based on the understanding gleaned through NLU, conversational AI systems employ natural language generation (NLG) techniques to produce responses that are coherent and contextually appropriate. NLG algorithms analyze the extracted information, combine it with predefined models and templates, and generate humanlike responses, whether they’re delivered as text or converted to voice using a text-to-speech tool. For voice delivery, this final part of the picture also ensures that replies are not only accurate but engaging and natural sounding to the shopper or customer.

Dialogue management 

Think of dialogue management as an invisible moderator, maintaining the conversational flow and keeping track of the context. It is responsible for managing the customer conversation history and ensuring coherence in the conversation as well. 

In addition, algorithms enable conversational AI systems to “remember” previous interactions, ensuring that the systems can handle multi-turn conversations and provide coherent responses throughout the entirety of the interaction.  

Response delivery

The final step of a conversational AI system is completing the interaction loop by delivering the generated response to the human companion. Depending on the platform and user preferences, the response is conveyed in text or speech (sadly, never by owls). Text-based responses are commonly used with bots and messaging applications, while speech-based responses are prevalent with virtual assistants and voice-enabled devices.

And then the process starts again.

Benefits of conversational AI 

Ever-developing conversational AI applications span use cases in a variety of industries and sectors, providing a personalized, efficient, proven user experience regardless of the context. Benefits include: 

Higher-quality customer support 

Conversational AI tools such as chatbots have become ubiquitous in the customer-service industry and been found to improve service automation. Virtual agents eliminate wait times and provide personalized support, efficiently resolving queries and juggling a large volume of self-service customer interactions, thereby freeing up the contact center’s human agents to address customers’ more-complex issues. 

By utilizing NLP and NLU, customer-service chatbots can comprehend customer inquiries, provide relevant solutions, and escalate complex issues to human agents if needed. This not only reduces costs for businesses but ensures round-the-clock availability and faster response times for customers. 

Enhanced ecommerce search

Major ecommerce platforms are a great example of arenas enjoying better support. Etailers typically field thousands if not millions of search requests every day, with an additional number of browsing expeditions. Shoppers have questions about things like which items are recommended, product specifications, order tracking, and processing returns. By letting shoppers carry on easy conversational experiences with AI-powered chatbots, retailers can efficiently handle inquiries and answer FAQs, improving customer-support workflow efficiency and customer satisfaction. 

Superior virtual assistants 

Conversational AI models have upgraded the abilities of virtual assistants, enabling them to perform a wider range of tasks and offer more-personalized recommendations. Modern virtual assistants can “understand” natural language input, interpret user intent, and respond or execute accordingly. They’re widely used in industries such as healthcare, travel, and financial services to simplify tasks and enhance the user experience. 

For example, in the healthcare industry, virtual assistants can schedule appointments, provide medication reminders, and answer various health-related questions. Patients can interact with this technology through plain-English speaking or typing, saving time and reducing frustration as well as the workloads of doctors’ office staff. 

Streamlined smart speakers

Personal-assistant functionality through smart speakers is ubiquitous: voice assistants Siri, Alexa, and Google Assistant have become literal household names whose popularity only continues to grow. And why wouldn’t it? With an assistant that can play music, answer general-knowledge questions, give weather updates, suggest restaurants, and even make dinner reservations based on your preferences, who could object?

Voice-activated systems do all of this well by utilizing conversational AI to understand voice commands, remember preferences, and provide personalized responses as if they’re participating in a human conversation. NLP and speech recognition (also known as automatic speech recognition, or ASR) allow for the accurate interpretation of customer intent. They leverage conversational AI to understand natural language input, learn user preferences over time, and generate appropriate responses, thereby creating rewarding customer engagement.

Need the right conversational AI solution?

Conversational AI technology with built-in scalability is unquestionably revolutionizing the ecommerce industry. As a leading search and discovery provider, Algolia is in the process of integrating the power of this technology by deploying a conversation option with a personalized interface.

If you’d like early access to what’s going on with this, sign up on our waiting list for the initial customer cohort.

Meanwhile, find out how NeuralSearch can offer your website searchers fantastic personalized experiences that could easily result in a bump in your success metrics.

Get in touch today!