More than 80% of routine business interactions between customers, employees and processes drive their overall experiences.
A day in a typical enterprise is primarily composed of interactions between one or more of these key stakeholders: customers, employees, suppliers, vendors and systems (or “machines”) which drive the overall experiences for various stakeholders. According to a survey done by our team, only 25% of interactions are strategic or “high value” conversations, which means that the majority are routine or repetitive. There is a huge opportunity to automate and optimize such interactions and experiences.
Kore.ai defines Experience Optimization (XO) as the process of automating and optimizing routine business interactions by leveraging innovative conversational AI technology. Such automation preserves current investment, reduces operational cost and helps you deliver personalized, contextual, omnichannel and multi-lingual conversational experiences for your stakeholders.
In an increasingly digital world, organizations find it difficult to effectively manage the explosion of interactions across voice and digital channels. The biggest challenge is to ensure an optimal experience while preserving the current tech stack and minimizing cost.
Most prevailing technologies cannot understand context nor respond conversationally. Technology solutions with limitations such as these force customers and employees to converse in a robotic or machine-like language. They then try to skip these options and speak to humans directly for service or support requests, increasing operational costs.
Advanced AI-first technology, when deployed correctly, enables businesses to harness conversational interactions with greater efficiency and at reduced costs while ensuring the highest customer, employee, agent and partner experiences. Ultimately, companies can have complete control over the user experience with real-time information, nudges and alerts. As one of the pioneers in the field of Experience Optimization and Conversational AI, here are a few things that Kore.ai offers:
“By 2023, more than 60% of all customer service engagements will be delivered via digital and self-serve channels, up from 23% in 2019”
“By 2025, customer service organizations that embed AI in their multichannel customer engagement platforms will elevate operational efficiency by 25%.”
Intelligence that speeds up efficiency and innovation
Understand, process and respond to complex requests in natural language
Handle non-linear conversations and understand the context
Empower business users to build AI solutions with no code
Manage conversations across voice and digital channels
Proactively assist agents to drive efficient customer outcomes
Glean insights and build better models with conversational data
Integrate with back-end enterprise systems for workflow execution
Devices are getting smaller while menus, systems and apps are growing increasingly complex, which people often don’t know how to navigate. However, they know what they want to do and they know how to chat and text. By replacing traditional UIs with human-like conversations, companies can make customer and employee experiences more straightforward and intuitive.
Recent advances in language technologies have also made complex linguistic decision-making methods beyond linear scripts and crude yes/no trees possible. Such advances have made virtual assistants a mature solution that enterprises across many industries are taking seriously.
Provides timely, accurate and tailored experiences on your customer’s terms
Reduces the need for tickets, callbacks and queues. Available 24/7
Provides self-service across popular channels, endpoints and IVRs
Generates new sources of data on customer behavior, language and engagement
Highly scalable, available in a variety of languages and integrates seamlessly
Requires minimal upfront investment, deploys rapidly and quickly reduces support costs
Delivers responses in seconds and eliminates wait times
Experience Optimization (XO) uses a combination of conversational AI, natural language processing (NLP), machine learning (ML), speech recognition, natural language understanding (NLU) and other language technologies to process and contextualize the spoken or written word, as well as figure out the best way to handle and respond to user input. Overall, it helps optimize the interaction experiences on various channels, languages, and contexts.
Conversational AI works by breaking sentences down to their root level, handling the many quirks of human language and acknowledging that there is information or a command to be parsed. The process by which a computer can understand human language is known as NLP. It does so by pulling out intents and entities, looking for statistically significant patterns that it has been trained to identify and considering factors such as synonyms, canonical word forms, grammar, slang and more.
Machine learning and other forms of training models allow computers to recognize the combinations of words that typically indicate intent and learn from experience without being explicitly programmed by a human. Most platforms and frameworks provide only one of these types of training engines.
Within the world of machine learning, there are two main types of learning methods. Supervised ML refers to analyzing a training dataset and using some form of a learning algorithm to make predictions, compare its output with the correct, intended outcomes and identify errors. This output is then used to modify the model accordingly – making it more accurate over time. On the other hand, unsupervised ML refers to analyzing a set of data that isn’t explicitly classified or labeled and it is typically used after the bot has been deployed for internal testing or to the field. Unsupervised ML usually involves automatically expanding a virtual assistant’s language model for chatbots by adding all successfully identified utterances to its model.
Fundamental Meaning is a deterministic process. Here, a piece of given input information will always produce the same output information. The method uses semantic rules, such as grammar, word match, word coverage, word position, sentence structure and language context to match a user utterance to an intent.
Knowledge Graph is another training model that enables developers to create an ontological structure, a method of grouping according to similarities and differences, of key domain terms. The model then associates them with context-specific questions and their alternatives, synonyms and ML-enabled classes.
Omnichannel refers to a multi-channel approach to marketing, selling and serving customers in a way that creates a consistent and cohesive experience across channels. While most businesses today are multi-channel, most lack a way to effectively integrate all these channels while providing consistent messaging at every point of contact. Conversational AI allows customers to start a conversation in one channel and complete the same thread in another – without losing conversation context or continuity.
Human language consists of many complex rules and nuances, including multiple word meanings, interruptions, slang, clarifications and more – all of which can fundamentally change the meaning and importance of an utterance. To account for this complexity, conversational AI provides several capabilities that allow you to go beyond predefined and linear resolution paths:
Conversations often consist of many twists and turns. Conversational AI allows developers to create complex, fluid and dynamic dialogs. Examples include pausing, starting and resuming tasks, processing multiple intents in a single utterance and amending entities at any point in a conversation.
Conversations between people can differ significantly based on relationships and past knowledge. Conversational AI allows virtual assistant’s to remember key details from past dialogs, user information, preferences and more, so you can personalize your virtual assistants message and sales pitch and apply rules and standards whenever needed.
Emotion and tone can significantly change a word’s meaning. Conversational AI allows virtual assistants to identify key triggers, including connotation and word placement, that signal the type and intensity of emotion. Virtual assistants can use these triggers to assess user inputs, initiate custom flows, bring in human agents and steer conversations.
Modern business is global. Conversational AI supports nearly every major language, allowing you to expand more easily into new markets and serve more customers. It means you can work with the language of your choice when defining intents, entities, training data and more.
Kore.ai increases the speed of business by automating customer and employee interactions through digital virtual assistants built on its market-leading conversational AI platform. Companies who prioritize customer and employee experiences use Kore.ai’s no-code conversational AI platform to raise NPS and lower operational costs. The Kore.ai Experience Optimization Platform offers a unique blend of conversational AI virtual assistants, process assistants and digital apps, all in one platform. The no-code platform is a secure, scalable and superior end-to-end solution to design, build, test and deploy AI-powered virtual assistants. Its ready-to-use, highly customizable, domain-trained solutions help accelerate your interaction automation journey.