Can Enterprise Chatbots Deliver Hyper-Personalized Experiences?

Enterprise chatbots are brain dead. They have no cognition, no depth, and no ability to understand real-time concepts and context. However, enterprise chatbots, at their most effective, are allies to the most pertinent business objectives rather than enemies of progress and time. In 2022, enterprise chatbots should be allowing most white-collar workers to interact daily with conversational platforms, but enterprises still fail to provide the optimal conversation experiences needed to drive value. Most chatbots lack the ability to deliver the necessary personalization to optimize customer experiences.

Considering how much businesses, marketers, and customers prioritize personalization, a failure to provide extensive and dynamic personalization for customers who interact with AI-powered chatbots will have both financial and reputable costs. For enterprises to thrive with advanced technology and meet customer needs at different points of the buyer journey, they need impactful chatbots that are fast learners, constantly adaptive in real-time, and are not restricted by training data.

Chatbot Shortcomings

Even with increased adoption throughout the pandemic, chatbots have continuously failed due to:

• Statistical inference without a deep understanding
• Static model without dynamic learning and reasoning
• Stochastic parroting without hyper-personalization

Beyond these issues, enterprise chatbots also struggle with implementation because their common sense knowledge base is too shallow. Chatbots that lack the necessary capabilities are hard-coded, possess read-only solutions with little leeway for dynamism, and struggle with real-time adaptability. Learning for these chatbots isn’t autonomous or interactive enough, which leads to confusion and frustration for those engaging with them. More than half of customers shift to competing brands after a singular bad customer experience, necessitating ready-made technology that immediately understands the vernacular and context being presented in any scenario.

Because chatbots severely lack understanding and the ability to adapt in real-time (leveraging natural language), they lead to multiple siloes that can cause financial headaches for enterprise leaders trying to advance their objectives. They also lack the reasoning ability necessary to take conversations far beyond the introductory stage. Without deeper understanding and real-time adaptability, enterprises cannot scale their businesses the way they want, letting repetitive tasks slow down their journey toward optimal productivity.

A Dynamic Approach to Chatbot Learning

Chatbots should be fully scrutable solutions (not the Blackbox chatbots that are prevalent) with strong reasoning skills, including disambiguation. General learning should be ‘one-shot learning,' meaning there’s no need for employees or customers to constantly repeat themselves to get tasks done or answer questions. With real-time learning through natural language, a chatbot with a brain processes information and can understand not only direct requests but also the sentiments behind them.

A chatbot with a brain prioritizes high-value, hyper-personalized customer experiences that can be used across various verticals and use cases. These chatbots also have deep contextual comprehension, so they process what’s said in real-time with integrated short and long-term memory. The hyper-personalization aligns well with enterprise goals, objectives, and usage so that users don’t go back to stage one when interacting with these AI-powered conversational tools.

Cognitive and autonomous chatbots also include dynamic conversation management so users can pick up from a previous point in the conversation, adding value to future conversations since people don’t have to repeat themselves. With an integrated cognitive architecture, a chatbot with a brain also includes seamless language generation, parsing, and inference to create the type of hyper-personalization required to create meaningful, interactive customer experiences.

Using ontology, chatbots easily process particular terms and their meanings, manually gathering static data such as attributes and understanding synonyms. With common sense instilled when formulating a chatbot, a chatbot with a brain can successfully gather and implement business rules while gathering the necessary information to interpret how products or services are referred to in the real world. With rigorous testing and consistent tuning, as well as a comprehensive regression test system, chatbots can readily handle requests without the need to be taught repeatedly.

Hyper-Personalized Use Cases

Hyper-personalized chatbots are integral for call centers, helping enterprises maintain more customer relationships and accelerating brand growth along the way. When you have a chatbot with a brain, businesses greatly reduce the need for customers to speak with human agents, as the contextually astute digital assistant provides hyper-personalized experiences for all customers and reduces the cost to serve them. Call centers can also deflect a high amount of calls from the call center and scale customer service to millions of people instantly.

Hyper-personalized chatbots can also help employees by being formidable assistants for IT help desks and human resources assistants while also substantiating enterprise and mobile apps. Additionally, enterprises can engage in highly-thoughtful conversations about various business areas, including policies, healthcare, onboarding, salary/benefits, and more.

Optimized Interactions

For enterprise customers, a chatbot with a brain builds brand loyalty. With a working brain and an appetite for deep understanding and common sense knowledge, chatbots can scale enterprise services and create new pathways to maximize conversational value and increase takeaways to optimize future interactions. Chatbots should no longer be a liability for forward-thinking businesses who want to become more relatable and reliable in the eyes of their customer bases. Chatbots should have dynamic knowledge capabilities to address customer queries or pain points and allow enterprises to focus on other value-added tasks to maximize productivity.