In this rapidly evolving world of technological innovation,conversational AI applications and systems are quickly becoming the preferred solution for optimized customer engagement. For a high-quality conversation to occur between a customer and a machine, computer-generated responses must be intelligent, fast and natural.
Also known as conversational artificial intelligence, these technologies enable machines or applications to communicate effectively and accurately with humans in a clear, concise and easy-to-understand vocabulary.
These features eliminate the need for customers to fill out complicated forms or engage in tedious phone conversations with customer service agents.
The two most popular examples of conversational AI are Callbots and Chatbots. A Callbot or Chatbot is a program that interacts with the customer and mimics the human voice and/or conversation using artificial intelligence. Callbots can perform a wide variety of tasks related to customer service, marketing, sales and even IT support. Some services like Google Assistant and Amazon Alexa use AI to ensure their platforms and services work more efficiently for end users. See article on Callbots.
In addition to Callbots and Chatbots, conversational AI is also useful in voice applications via the phone or Internet. For example, customers can conduct transactions with automated call centers by speaking directly with a callbot rather than a call centre agent.
In addition, some AI-enhanced bots interact with customers by simply asking them to press numbers on their smartphones in response to pre-recorded questions and comments from an automated system. This technology has been around for decades, you probably know it, it's called IVR. Indeed, we've all experienced the "press 1 to report a claim..."
How does conversational AI work?
What is conversational AI and how do these applications and systems translate human language into something a machine can easily understand?
At first glance, the process seems deceptively simple. A customer interacts with a Callbot or Chatbot, which quickly provides an appropriate response.
However, when we dig deeper, there are several different technologies working together behind the scenes to make this virtual dialogue possible. Some of the individual components of a successful implementation of conversational AI involve the following technologies:
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- Natural Language Processing (NLP) engines that understand complex linguistic structures and grammatical rules, while making direct associations between specific words and phrases.
- Natural Language Understanding (NLU) engines that understand exactly what is being said in a text message and convert it into a machine-readable format for processing by other applications.
- Text-to-Speech (TTS) systems that take conversational AI applications a step further by converting written text into synthesized speech for user interaction.
- Advanced Speech Recognition Systems (ASR ) that convert spoken words into text with up to 99% accuracy.
Let's take the simple example of a customer asking a corporate Chatbot for its opening hours. The customer's voice is transmitted through the NLP technology technology, which cleans and deciphers the customer's language to determine exactly what they are saying. In text-based interactions, NLP technologies can correct grammar and spelling errors, identify synonyms, and break down the textual query into programming code that is easier for the virtual agent to understand.
Once the NLP technology has successfully translated the original message NLU technologies technologies take over and clarify the customer's main intention behind the question. NLU technologies can also perform sentiment analysis, useful for identifying any emotional triggers of frustration or anger in the customer's voice.
Now that the system correctly understands the customer's question, the conversational AI solution must formulate an appropriate response by comparing the information acquired via NLU with the company's database of pre-formulated potential solution streams. Then, the system quickly submits the appropriate response to the customer. See the article on the differences between NLP NLU and NLG
Depending on the AI system used, these pre-formulated responses can take the form of text or virtualized speech. For visually or hearing impaired customers who prefer voice applications, TTS technologies technologies can convert pre-captured and pre-formulated text responses into computer-generated audio. For people with limited mobility, ASR technologies technologies allow customers to ask questions verbally rather than through manual input. Asconversational AI technologies continue to advance, the possibilities simply seem endless.
What are the benefits of conversational AI?
It is often very difficult for companies to provide personalized support to large groups of people simultaneously. Conversational Artificial Intelligence aims to solve these problems by providing customers with a natural and efficient way to interact.
With the help of conversational AI, customers can receive quick and accurate information 24/7 without waiting for a telephonist to be available.
Many of the most advanced conversational AI solutions involve personalized systems enhanced with machine learning (ML) technologies that collect critical data about each individual shopper, such as their preferred product preferences.
ML technologies can also help companies identify typical consumer buying patterns. For example, ML can help sales and marketing teams identify how many times a customer typically visits their website before purchasing a product or service. See full article on the benefits of a Callbot
Reduce operational costs with conversational AI solutions
Conversational AI is beneficial for any company looking to dramatically improve customer service while avoiding massive financial investments and the constant need to train and renew new and existing staff members.
Gain insight into the client's decision-making processes
By leveraging modern conversational artificial intelligence technologies, companies can track consumers' online shopping habits and better understand why certain products and services are more popular than others.
Marketing teams can determine how many products a typical customer evaluates before making a final purchase.
They can also identify how much time a customer spends reading each product's web page. Callbots, Chatbots and other solutions can then use this information to provide more appropriate responses to customer inquiries. Thanks to ML technology, companies now have access to invaluable feedback that would otherwise only be available by speaking directly with a call agent.
Enhance self-service functionality
Self-service features, such as automatic bill pay and other services, are becoming increasingly popular among customers who may or may not want to interact with live customer service agents.
Conversational AI can also help companies streamline internal sales processes by providing automatic updates to product catalogs, marketing materials and promotional content. Customers can even use chatbots configured to help them perform specific tasks, such as online purchases via the company's website or mobile app.
Better return on investment (ROI)
Developers can custom design conversational AI solutions to provide businesses with multi-channel capabilities that go far beyond conventional chat or messaging services.
For example, many AI-enhanced systems are able to process data from social media sites, such as Facebook and Twitter, when responding to customer requests. As these applications become more prevalent across multiple channels, the company experiences a significant increase in ROI.
Reduce the risk of data leakage
One of the most cited concerns about AI and ML technologies is that too much reliance on automated systems could lead to data leakage through malicious hacking incidents or mismanagement of potentially sensitive personal information.
Conversational AI systems are designed to avoid potential security risks because the information they process is generally not classified as critical.
Analyze feelings with conversational AI
Conversational AI will also help companies identify emotional triggers that cause undue stress or frustration to their customer base, which can negatively impact the company's bottom line.
Automating sentiment analysis eliminates the need for customer service agents to manually sift through thousands of pieces of data, saving the company even more time and money.
Optimized scalability and reliability
The scalability and reliability of conversational AI helps companies achieve higher execution rates that increase their long-term ROI.
This capability is crucial for large enterprises that want to provide high levels of customer support without experiencing downtime during peak hours, when customer traffic tends to increase. See article on the future of customer relationship 3.0
Instantly forward customer service calls
Instead of searching for an available customer service agent while putting the angry or worried customer on hold, companies can configure their AI-powered Callbots or Chatbots to instantly return phone calls instead, saving time, money and frustration for everyone involved.
In some cases, contacts should not be automated, as the call center agents will handle them more efficiently. AI can prioritize these contacts so that angry people are not waiting on the phone line.
"Human factor" and sales opportunities
Modern conversational AI systems can be specifically designed to learn from customer interactions, allowing companies to improve their customer relationships, consumer satisfaction levels and even their online ratings.
Conversational AI can detect sales opportunities and prioritize them. This is especially important, as a portion of calls are dropped due to long hold times.
Improving brand loyalty with conversational AI
Modern conversational artificial intelligence technologies allow companies to make the most of their existing resources by using automated features to communicate with existing and potential customers.
Direct engagement with these systems provides a more personalized experience for consumers who also want customer support. With its ability to learn from specific customer interactions, conversational AI helps companies improve their brand loyalty rates while increasing operational efficiency.
Conclusion
Conversational AI solutions and systems improve customer loyalty by providing a smooth and convenient customer service experience. By using AI to respond to consumer requests, companies optimize their existing resources by increasing operational efficiency and reliability while improving return on investment.
In addition, conversational AI creates less work for employees, which improves compliance efforts within regulated industries, such as healthcare providers and financial institutions.
These capabilities alone make AI-enhanced solutions an invaluable tool for today's most competitive organizations, with the primary goal of providing the best customer experience.
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