Introduction

You are probably familiar with the implementation of chatbots. They're bots designed to chat (as the name suggests), and that's why people often use the term interchangeably with conversational AI. 

But at a closer look, there's much more to conversational AI than meets the eye. So, what is a key differentiator of conversational AI? Keep reading to find out.

What Is A Key Differentiator Of Conversational AI?

Conversational AI uses machine learning algorithms to analyse massive data (Image source: Needpix). 

Machine Learning

Regular chatbots lack this. Conversational AI uses machine learning algorithms to analyse massive amounts of text data from human conversations, such as emails, chats, social media interactions with customers, etc.

Sifting through this data allows AI to learn patterns in human language quickly. As a result, it starts to recognise human-exclusive language units like sentence structure, word choice, and even sarcasm!

Contextual Understanding

From our experience, chatbots struggle with context and might misinterpret a question if it's phrased differently than what they're programmed for.

On the other hand, conversational AI has no trouble analysing the flow of conversation. It always considers previous human interactions and the overall situation to understand the intent behind your words.

Adaptability

Thanks to machine learning and context awareness (as we just mentioned), conversational AI can adapt its responses on the fly. Specifically, it can rephrase questions, offer clarifications, or even apologise for misunderstandings. That explains why the interaction feels much more natural and engaging!

Continual Learning

Unlike rule-based chatbots with static programming, conversational AI gets smarter with every interaction. It refines its own understanding of language and improves its response quality/accuracy whenever a new prompt or question occurs.

How Does Conversational AI Work?

The conversational AI keeps learning and improving with every interaction (Image source: Needpix). 

Step 1.

You interact with the conversational AI, either through text or speech.

Step 2. 

If the human input is speech, Automatic Speech Recognition (ASR) will kick in first. It works with acoustic modelling to analyse the sound waves into phonemes (basic human speech units). Next, based on the recognized phonemes, it uses language modelling to predict the most likely sequence of words.

A decoder combines these models to generate the most probable word sequence matching your speech.

Step 3.

Once you have the text (from ASR or directly typed), NLP (Natural Language Processing) takes over to understand the structure and meaning of the sentence. It breaks the sentence down into smaller units called tokens. These can be words, punctuation marks, or even emojis. 

After each token is assigned a grammatical function (noun, verb, adjective, etc.), NLP reduces words to their base form (e.g., "running" becomes "run") to group similar words together. It also identifies and classifies named entities like people, places, or organisations (e.g., "Paris" in "weather in Paris").

Step 4.

NLU (Natural Language Understanding) builds on NLP's analysis to grasp the deeper meaning of your message and figure out the overall goal of your question or statement. For example, is it a request (booking a flight), a question (checking the weather), or something else?

Next, NLU identifies specific details (slots) within your message that are relevant to the intent. Suppose your purpose is "booking a flight;" the slots might be "destination city" and "travel date."

Step 5.

Throughout this process, machine learning algorithms play a crucial role.

They are trained on massive amounts of ongoing conversation data, allowing them to identify patterns in language usage. 

Over time, the AI becomes better at recognising and using these patterns to understand the meaning behind your messages. As a result, with the combined insights from ASR, NLP, NLU, and reinforcement learning, the conversation AI engine can now generate relevant human-like responses based on customer intent:

  • It might access and process information from the real world through APIs (application programming interfaces) to provide a comprehensive answer.
  • The coherent response is designed to be informative, helpful, and tailored to your specific request.

Step 6.

As said earlier, the dialogue doesn't end here. The conversational AI keeps learning and improving with every interaction! If you provide feedback or ask follow-up questions, the AI can use that information to refine its general understanding even further.

Benefits Of Conversational AI Solutions To Your Business

Conversational AI can now engage your customers in natural, human-like conversations (Image source: Needpix). 

Optimised Data Collection 

Traditional methods (like surveys) often have low response rates and limited data capture. But no more worry: conversational AI can now engage your customers in natural conversations and gather richer data seamlessly throughout the customer journey!

For instance, after a purchase, AI might use open-ended questions to gather valuable insights on the product or purchase experience (e.g., What features would make this product even better for you?") 

Similarly, it can also ask follow-up questions based on the customer's initial inquiry. Let's say your customer has successfully resolved their technical issue. Once everything's wrapped up, the AI might ask, "On a scale of 1 to 5, how satisfied are you with the troubleshooting steps?" to identify areas where support can be improved.

Better, conversational AI is easily integrated into mobile apps or websites, serving as virtual agents that passively collect data through user interactions. 

Suppose your AI is designed to recommend products based on a user's browsing/purchase history. In that case, your company also gains valuable data on user preferences by assessing those past purchases!  

Needless to say, all this data is a goldmine for your customer service team. You can use it to make informed decisions to improve product features and functionalities. Targeted marketing efforts (thus increasing campaign effectiveness) are within reach as well. 

Better Operational Efficiency & Productivity

We all know that repetitive customer service tasks (like answering basic customer queries, scheduling appointments, or processing data) can consume significant agent time. Fortunately, conversational AI now automates those tasks to free up your human agents for more complex issues or strategic areas. 

FAQ chat software is a common example; potential customers can get immediate answers to frequently asked questions, reducing the burden on call centres. 

Likewise, virtual/intelligent assistants handle appointment scheduling for various departments, like sales or customer service. That means customers only need 1-2 minutes of interaction to book appointments at their convenience. No more back-and-forth emails or phone calls!

Let's not forget about data entry tasks, which are usually prone to manual errors. Great news: you can easily train conversational AI to process customer invoices or update inventory levels automatically based on incoming data, saving everyone from headaches. 

Lower Customer Service Costs

Traditional customer service channels like phone support require significant personnel costs and infrastructure investment. Yet, as said earlier, conversational AI deflects user queries to automated solutions, which eliminates the need for human intervention and reduces call centre workload.

As a result, your centre will handle much higher customer volumes without any proportional increase in staff. No wonder conversational AI has been considered a cost-effective lifesaver for millions of startups and growing businesses.

Factors To Consider When Choosing Your Conversational AI Platform

Management

Consider the platform's user interface and how easy it is for your team to create, edit, and manage your chatbot or virtual assistant. Assess the following questions: 

  • Does this software program offer cloud-based deployment, on-premise options, or a hybrid approach? 
  • Will the platform be able to handle your current volume and potential future growth in customer interactions?
  • Does the platform integrate with your existing CRM, marketing automation, or other pre-built knowledge bases for a seamless workflow?

Privacy

Ensure your preferred platform has robust security measures in place to protect customer data and comply with regulations like GDPR and CCPA. 

You need to understand who owns the data collected through your AI interactions (full ownership and control over your customer data will be ideal, from our observations). Most importantly, this platform space must be transparent about how customer data is used and introduce diverse options to customers, helping them access and control their information.

Commercial Terms

NDAs (Non-Disclosure Agreements) are common for most conversational interfaces and can help protect your confidential business information during selection. 

However, ensure these NDA terms are fair and don't restrict your ability to discuss your needs with other vendors. Don't forget to investigate how the platform handles intellectual property (IP) ownership, either.

Price

Virtual call centres like StringeeX have various pricing models for different business sizes, making it one of the market's most popular call centre solutions. Other conversational AI systems, on the other hand, categorise pricing models based on features (e.g., per-user fees, transaction fees, or tiered pricing). 

Choose a dynamic automation platform that works best with your budget and usage needs! Also, be sure to factor in potential hidden costs (if any), like additional fees for training time, integrations, custom development work, etc.

Can You Create Your Own Conversational AI Tools?

Yes. Even with limited coding experience, no-code or low-code platforms allow you to build basic chatbots with a user-friendly interface. All you need is a simple drag-and-drop functionality and pre-built templates!

For more sophisticated conversational AI, you'll need to step into the world of programming languages and frameworks. Popular choices include Python with libraries like TensorFlow or PyTorch (for machine learning techniques) and frameworks like Rasa or Dialog Flow (for building AI-powered chatbots).

Conclusion

What is a key differentiator of conversational AI? Unlike traditional chatbots, deep learning allows it to constantly learn and adapt; your customer service can easily reach new heights! 

Just be sure to choose your platform wisely, and feel free to contact us if you have additional questions about business strategy.