How does conversational AI work?<\/h2>\n
How conversational AI works is by using natural language processing (NLP) and machine learning (ML) to understand human speech or text and derive intent. Then, respond naturally over voice or text.<\/p>\n
What is natural language processing?<\/h3>\n
Natural language processing is a branch of AI that enables computers to understand text and speech similarly to how humans do. It \u201ccombines computational linguistics<\/a> \u2014 rule-based modeling of human language \u2014 with statistical, machine learning, and deep learning models.\u201d NLP empowers computers to recognize words, derive intent, and respond in a way that\u2019s understood by humans.<\/p>\n NLP is made up of four processes:<\/p>\n NLP has a wide range of applications, including customer service. For example, when a virtual agent<\/a> asks, \u201cHow may I help you today?\u201d and the customer says, \u201cI\u2019m looking for pink cowboy boots in a women\u2019s size 9.5 wide,\u201d the virtual agent will use NLP to identify the words \u201clooking for\u201d as intent. It then identifies \u201cpink,\u201d \u201ccowboy boots,\u201d \u201cwomen\u2019s,\u201d and \u201csize 9.5 wide” as search criteria. It\u2019ll look in the company\u2019s inventory and respond to the customer with options that meet those requirements. Other common applications of NLP are GPS systems, voice assistants, and chatbots.<\/p>\n Machine learning<\/a> is another branch of AI that enables computers to automatically learn and improve through the use of data and algorithms. The goal is to continually expand the computer\u2019s understanding and knowledge so it can be more accurate or provide better results. Data scientists write algorithms that are trained to classify and mine data for key insights, which are then used to make predictions or provide a response.<\/p>\n In conversational AI, machine learning is essential to the computer\u2019s ability to improve its understanding of language and intent and its response accuracy.<\/p>\n With text and speech becoming the way users interface with companies and their products or services, the user experience now relies on how well the conversation is crafted. Conversational design (CxD) is the art of designing two-way interactions between computers and humans, based on how humans communicate. It is a subset of user experience (UX) design. The result of good conversational design is efficient and cooperative conversations that feel natural and flow smoothly.<\/p>\n Conversational design is incredibly important because customers have high expectations for every brand interaction they have. They expect fast and accurate answers to their questions, resolutions for their issues, and guidance for purchasing decisions. They don\u2019t want to repeat information or have slow, cumbersome conversations. A poorly designed conversation causes frustration and motivates customers to either abandon the conversation or ask for a human agent.<\/p>\n The best conversational design makes interactions with computers feel so human that people can hardly distinguish whether they\u2019re conversing with a machine or not. On the other hand, poor conversation is extremely apparent. There\u2019s repetition, the conversation is confusing and difficult to follow, and the language doesn\u2019t feel natural to humans.<\/p>\n Contact centers that use conversational AI provide numerous benefits to their customers that ultimately improve customer experience and customer satisfaction. These benefits include:<\/p>\n The benefits of conversational AI for businesses include reduced costs<\/a>, elasticity, and a better customer experience.<\/p>\n Conversational AI gives businesses the ability to serve more customers without having to hire additional agents. Conversational AI solutions cost significantly less than adding more agents or outsourcing. In fact, conversational AI can cost just 50% of a highly optimized business process outsourcing<\/a> (BPO) provider.<\/p>\n Businesses can also generate revenue by using conversational AI to upsell or cross-sell products. Conversational AI can automatically surface personalized offers or make product recommendations so companies can capture more revenue.<\/p>\n Conversational AI relieves humans of rote, routine work and frees them to concentrate on customer issues that require empathy and advanced problem-solving. Freeing up human agents from the bulk of routine customer service issues enables brands to refocus employees on work that delivers higher value to the business.<\/p>\n For example, agents can be trained to deliver proactive customer service \u2014 reaching out to customers to address issues before they become a problem, informing customers of discounts, or recommending relevant products or services. This adds an unexpected human touch to the customer experience, increases customer satisfaction<\/a>, and gives brands more opportunities to generate revenue and retain customers.<\/p>\n Even with the most advanced forecasting models, it\u2019s impossible for contact centers to accurately predict when and to what degree customers will engage with them. Traditionally, brands have scaled their customer service by adding more staff, increasing overtime, or outsourcing, which are all costly options. Scaling through these methods have drawbacks though. Overestimating results in wasted agent capacity that you still have to pay for. Underestimating results in long hold times and annoyed customers.<\/p>\n Conversational AI creates elasticity, which gives brands the ability to automatically scale customer service capacity<\/a> up and down. When your capacity always matches demand, customers never experience hold times and unexpected call spikes no longer overwhelm your agents.<\/p>\n Additionally, brands achieve elasticity in their costs, since they only pay for the capacity used. This allows them to avoid the cost of adding staff or outsourcing and the wasted expense of being overstaffed when lulls happen.<\/p>\n\n
What is machine learning?<\/h3>\n
Conversational design: The art of creating effective, efficient, and cooperative conversations<\/h2>\n
Benefits of conversational AI for customers<\/h2>\n
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Benefits of conversational AI for businesses<\/h2>\n
Reduced costs and increased revenue<\/h3>\n
Repurpose human agents to deliver higher-value customer engagements<\/h3>\n
Elastic capacity and cost<\/h3>\n
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