First, what exactly is a chatbot?
It seems “chatbot” has become the all-encompassing term for automated online conversations. In reality, a chatbot is an elementary software program capable of carrying out online conversations or “chats” between business websites, mobile apps, and the people who want to talk to them.
What you may not know is that while some chatbots take advantage of AI, most are basic computer programs and options for more complex interactions are limited.
What are some examples of basic chatbots?
The most common chatbots live on websites, and interact with customers through a text window on the bottom right of the page. These chatbots are often rule or flow-based bots, meaning that potential conversations are already mapped out like a flow chart. These chatbots rely on predetermined questions and answers, making it impossible for conversations to deviate from certain topics.
Examples of these chatbots include FAQ bots or customer service chatbots for use with basic and routine interactions. A chatbot will guide customers through the conversation flow chart step by step, offering available options as buttons (i.e. new or existing customer) or simple inputs (i.e. age).
A basic chatbot is comparable to an automated phone or interactive voice response (IVR) menu. Customers can select from a series of options to find what they are looking for, but again, more complex options are limited.
What is conversational AI?
Conversational Artificial Intelligence, or conversational AI, refers to the AI technologies behind today’s digital interactions. These technologies are quickly advancing and allowing brands to have more intelligent conversations with customers. The main point of differentiation between conversational AI and basic chatbots is that conversational AI uses natural language processing (NLP) and machine learning (ML). Let’s explore this further.
The key differences between conversational AI and chatbots
- Natural Language Processing (NLP): NLP helps computers understand what humans say in their natural language. This includes slang, abbreviations, and common mispronunciations. NLP can be applied to both voice and text-based conversations. Chatbots with NLP can still understand a person’s intention — even if it is not the exact same syntax.
- Machine Learning (ML): Conversational AI can use machine learning to gain insight from every interaction. This allows the automation platform to optimize future interaction experiences based on customer data and history.
- Omnichannel capabilities: Conversational AI extends support beyond a company’s website to power brand interactions in other channels: instant messaging, social, and in-app, for example. This makes it easy for customers to self-serve in the platforms that serve them best. It can also drive repeat purchases by collecting customer data and communicating with new customers on their platforms of choice.
- Personalization: In today’s world, customers and employees want to do business with brands that share their values. That is why it is more important than ever to personalize the customer experience. Conversational AI surfaces relevant customer insights in real-time. This enables brands to deliver personalized interactions. Conversation prompts, response tone and answer flows can all be tailored to the customer's unique profile and needs.
Everyday examples of conversational AI
Many of today’s consumers are already interacting with AI-powered chatbots. Virtual assistants such as Siri, Google Assistant, and Amazon Alexa use machine learning to provide customers with a more seamless user experience.
However, businesses are better served with a solution that combines conversational AI with a live agent interface, automating the majority of interactions while offering a seamless handoff to human agents when things get more complex.
A brand interaction platform can route customer interactions to the correct agent for their issue and provide personalized experiences at every step of the customer journey. This is how companies can move beyond conversational AI to the next evolution in chatbot technology: automated brand interactions.
Moving beyond conversational AI with automated brand interactions
There is a growing gap between what brands promise their customers and how they actually interact with them. An interaction is any point of contact between a brand and a person, whether they are an employee or a current or potential customer.
Every interaction presents an opportunity to build trust, deliver value, and drive growth for a business. Customers send hundreds of billions of messages across the world every day and expect their needs to be met with the click of a button.
In the era of virtual assistants and same-day shipping, it is more valuable than ever to provide engaging interactions at scale. It is becoming more difficult for brands to differentiate on product, pricing, or even customer experience, making fast and friendly service with a brand interaction platform a competitive advantage.
What is a brand interaction platform?
While the terms chatbot and conversational AI are often used interchangeably, chatbots can use AI to understand language and automate meaningful interactions. This can help companies with basic brand interactions, but most lack the breadth and power to move beyond reactive support.
Most chatbots are designated to a particular department as they fulfill a certain use case. However, a brand interaction platform functions across all departments to offer a consistent customer experience across the entire lifecycle.
A brand interaction platform provides a consistent brand experience across all customer and employee touch points. It also makes capturing complete data insights easy — in a single location — to provide a personalized, VIP experience for every person, every step of the way.
With a brand interaction platform, companies can increase engagement, conversion, and revenue, accelerate lead qualification, improve customer retention and loyalty, and reduce their costs per interaction. Internal processes can be automated more efficiently, boosting employee productivity by automating repetitive tasks.
Companies that adopt a brand interaction platform can expect to see results quickly. By using AI technology to manage interactions better suited for self-service, internal teams can spend more time on high-value tasks.
Five priorities for automated brand interactions
- Be everywhere: Provide consistent, seamless interactions across any channel, for any stakeholder.
- Be proactive: Anticipate and initiate the conversations that matter, when they matter.
- Be personalized: Build and demonstrate a personal relationship at every step of the journey.
- Be effortless: Integrate across systems to enable instant action.
- Be self-improving: Get smarter, faster, at each stage of your program.
Comparing chatbots, conversational AI, and a brand interaction platform
|Features & Capabilities||Chatbots||Conversational AI||Brand Interaction Platform|
Natural Language Understanding
|Basic Keyword Recognition|
Multi-level intent recognition
3rd Party integrations
|Basic Integration Capabilities|
Dialog Management System
Integrated Processes and Workflows
Authentic Interaction at scale
Anticipating customer needs and intent
A single platform for all your brand interactions
Ada’s brand interaction platform powers a consistent, connected brand experience across each customer and employee touchpoint, including:
Teams are able to resolve up to 90% of all volume with automation that meets brand standards while providing personalized support that anticipates needs. Customer support organizations are able to focus on the customer topics that matter while manual tasks are automated. Customers are able to get the answers they need faster.
Use Case: Escalation
Increase adoption rate and onboarding speed with personalized proactive interactions.
Sales teams can instantly qualify and funnel warm leads to sales reps and avoid wasted time with unqualified opportunities. Valuable prospects are presented with the tailored offers and information that lead to interest in direct conversations.
Use Case: Lead Capture
Engage with prospective customers to capture more leads and bring them into your funnel.
Teams are able to engage prospective and current customers with the personalized offers, answers, and experiences necessary to improve conversion and engagement across each step of the customer journey. Insights from these interactions can be used to enable better interactions that better meet the needs and interests of prospective and current customers.
Use Case: Feedback
Capture real time feedback to drive insights on improving automated customers experiences.
Helpdesk teams can use chat automation to help employees self-serve 60%+ of their questions, while decreasing handle time for tickets from days to minutes or less.
Use Case: Troubleshooting
Walk customers through personalized troubleshooting steps to solve urgent problems.
Leaders are able to automate up to 60% of inquiries through automation, helping employees do their best work more quickly.
Use Case: Onboarding
Collect employee information and automatically create/update their profiles. Free up HR agents from manual repetitive inquiries.
Teams can extend automation across the enterprise with integrations and channels that take minutes to set up. Technical teams have a platform that can become the basis for long term investment in automation as an in-house core competency. Users are empowered to handle the majority of program needs, allowing technical teams to focus on priority tasks.
Use Case: Order Tracking
Provide accurate and speedy responses to order tracking to increase trust and loyalty.