Updated on January 9, 2025
When Klarna told the world that it had automated 2/3rds of its customer support queries with a chatbot, it was a clarion call for all executives operating in the industry.
Live chatbots were not new, but Large Language Models (LLMs) armed with better Natural Language Understanding (NLU) were more suited to customer support than ever. Now, the biggest question became that of scale. How much scalability did Chatbots add to the human-operated customer support system?
How Much Scale Can Be Enabled by a Customer Support Chatbot?
ChatGPT, the world’s most famous chatbot, handles around 10 million queries a day, while a human agent, on average, handles only 29.
This works because live chatbots are algorithmic, and their scale depends entirely on your technological infrastructure.
What does this mean in terms of numbers?
According to Gartner, around 17 million contact center personnel are around the planet. Labor costs are central to the cost of these contact centers and can account for around 95% of the spending.
With chatbots, these businesses can save up to $80 billion annually by 2026.
This is not owing to full automation; these cost savings will be achieved even if only 1 in 10 customer queries are automated through chatbots. This ROI might be significantly higher since customer support chatbots can automate 80% of customer queries without human involvement.
Even a marginal reduction in conversation length (where a customer support chatbot gathers important data around customer problems before passing it on) can reduce your Average Resolution Time by 33%.
Thus, a live chatbot can add massive scale to your customer support operations. But what, how does it do so? Let’s understand chatbot-assisted customer support operations.
How Do Chatbots Build Scalable Customer Support?
Recent advances in Generative AI and Conversational AI have enabled chatbots to manage conversations at scale. Right now, a chatbot on your website can help your customer support function by:
1. Automating Data Collection
In 2023, NPR released an essay describing a frustrating customer service experience. The thread went like this.
- You call a customer service numbers
- You dial numbers on the keypad to flag your issue
- You’re transferred to a human agent
- You re-describe your problem
- The human agent transfers you to another department
- You re-describe your problems
- He transfers you again
- Rinse and repeat
Data collection is the first step in any customer support experience. But, due to disconnected systems, the entire experience is broken.
AI is good at this. It can understand phrases, parse data, and directly add details to your Customer Support system. This means that the first time a human agent receives a query, they already have all the information to solve their problems.
2. Reducing First Response Time (FRT)
The two golden metrics in Customer Support are CSAT (Customer Satisfaction Score) and First Response Time (FRT).
Among these, a live chatbot can immediately reduce FRT while keeping CSAT at par with human agents. The Klarna chatbot reduced resolution time by 80%, keeping CSAT scores stable.
A generative AI chatbot can directly train itself on your pre-existent documents and start solving problems within minutes of implementation. Remember that these chatbots are uniquely suited to increased scale so that they can reduce FRT and customer support queries significantly.
3. Handling Repetitive Queries
Most of the customer support queries an agent receives are repetitive. According to estimates, 60-80% of customer queries fall into this bracket.
Customer Support Chatbots trained to recognize these queries can handle these by themselves.
This boosts the productivity of your existing human support agents, enabling them to handle more critical queries. On the other hand, your customers remain satisfied because their queries are solved promptly without a gap in the process.
4. Omnichannel Support
90% of customers expect a consistent omnichannel experience across channels. However, scaling omnichannel support with human agents becomes complex, with multiple messages across multiple platforms.
Live chatbots offer a viable alternative. Not only can they automate interactions across multiple target channels, but they can also collate these messages in a single space so that bot-to-human handovers are simple.
5. 24/7/365 Availability
Chatbots can provide round-the-clock availability to your customers. They are primed to provide basic customer support even when your customer service representatives are offline. This allows you to service more timezones while scaling your product globally.
This provides your customers ready access to support, and also helps your customer service representatives with the history of the conversations when they log in. So, they’re empowered to solve critical issues asynchronously.
6. Reducing the Average Handling Time
Quicker resolutions are the holy grail of customer service. However, they’re hard to reach and depend on the complexities of an agent’s problems.
Since chatbots can take care of repetitive tasks, they can empower agents zto focus on the critical tasks that take longer to resolve. This improves resolution times and helps you improve your CSAT scores as well.
Chatbots can heavily impact your customer support metrics and scale your operations. However, the integration and preparation of chatbots is a sticky issue for most institutions. We’ve prepared a primer so that any company can use live chatbots immediately.
How to Introduce Chatbots to Your Customer Support System?
While chatbots can introduce scale to the customer support function, managing integrations can be complex. Most AI companies provide vanilla integrations through APIs, but these models need further training for customer interactions.
Therefore, most businesses prefer to use third-party partners to integrate live chatbots. People
1. Identify & Answer Common Questions
Live chatbots depend on “intents” and “entities.” “Intents” refers to the customer’s intent, and entity relates to the data that can be parsed from the intent.
So, the first step in implementing a chatbot is to identify common customer questions or intentions. If you write the answers to these questions into your systems, they can learn them and give them directly to your customers.
Once you’ve used intents to answer common questions, you can take data using entities. Entities store customer data and can fill it into your customer support system. Even if your customer support chatbot can’t answer questions, the entities will tell your agents about their problems.
2. Deploy the Chatbot to a Small Audience
The second step in your chatbot journey requires actual human interactions. Start by integrating the chatbot into a smaller channel and tracking its perforce. With the questions you receive, you can understand what your chatbot can and cannot do.
This will help you understand common questions you might have missed and also help you gauge the reaction of your audience. Once you’re sure, reiterate that you want to make the chatbot more robust by adding more “intents” to your chatbot’s training data.
The chatbot will also progressively learn about your audience and improve with time.
3. Fill in Knowledge Gaps and Re-Train
Sometimes, the documents in our database aren’t enough for a chatbot to answer every relevant question. In this case, understanding unanswered questions and adding different knowledge articles will help your chatbots give better and more pointed answers to your prospects. Keep re-training your chatbot whenever you add new data to your websites and keep it up to date.
While this might seem complex, most of this process can be done with a few clicks using a platform like Kommunicate.
However, it is also necessary to understand that chatbots aren’t silver bullets for scale. There are some limitations to this system.
What are the Limitations of Live Chatbots?
While chatbots can increase ROI and scale, understanding some limitations of these systems is significant. The fundamental limitations are:
- Inability to Replace Humans – Customer Support is an inherently human-to-human system. So, while customer support chatbots can automate a substantial portion of the function, humans are needed to solve complex problems.
- Technical Expertise Required – If you’re building your own chatbot directly, you need significant technical expertise. Everything from development to training requires substantial investment. However, this can be bypassed with third-party chatbot platforms.
- Compliance Issues – While many chatbot providers exist, only a few maintain cybersecurity compliances. Choosing providers that can protect your PII data and save you from possible data leakages is essential.
- Hallucinations – If you’re using a Generative AI chatbot, there’s a chance that it will hallucinate. Several providers use RAG (Retrieval Augmented Generation) systems to bypass this.
Even with these limitations, the scale that customer support chatbots can provide is simply unparalleled. So, when deciding how to achieve scale, chatbots will be a critical strategy adopted by everyone from startups to enterprises.
In A Nutshell
The new research and development in AI products have brought us live chatbots that can massively scale any customer support system. This massive scale can quickly multiply your business’ capability to reply to customer queries.
Live chatbots manage this scale by being consistently available across all channels and by automating the handling of repetitive requests. They can be implemented at scale using a simple iterative re-training and deployment strategy, and the opportunity becomes clear.
However, chatbots are not made to replace humans and still carry risks around technical requirements, cybersecurity, and hallucinations. These limitations can be bypassed through dedicated Customer Support chatbot building platforms like Kommunicate.
Given chatbots’ sheer scale and ROI potential and their efficacy in scaling customer support systems, prudent executives will allocate a budget for their implementation.
CEO & Co-Founder of Kommunicate, with 15+ years of experience in building exceptional AI and chat-based products. Believes the future is human + bot working together and complementing each other.
