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FreeBot doesn't understand

The truth about free chatbots for customer service

Sarah Fox
Content Producer

Free chatbots are all over the internet. Some CRMs even offer built-in chatbots. It’s an easy choice, but it’s also the surest way to deliver a half-decent customer experience.

Unfortunately, free chatbots can’t meet a modern customer’s expectations — they can’t offer personalized recommendations, among other things. Hate to say it, but this poor experience will translate to lost revenue.

35% of customers report spending less with a business after a poor customer experience.

15% report they stopped spending money with the company altogether.

- Qualtrics XM Institute

In this guide, we dive deep into why free chatbots are, for lack of a better expression, bad news. Let’s discuss what to look for when selecting a chatbot for your business.

What are free chatbots?

Free chatbots are chatbots that, at least on the surface, look like they cost nothing. You’ll find two types of free chatbots on the market:

  • Chatbots built into CRM or other platforms: These chatbots lack advanced functionality and often limit your ability to fully utilize the power of large language models (LLMs), conversational AI, and machine learning. In most cases, built-in chatbots are scripted chatbots. This means they lack AI reasoning capabilities and generative AI technology (, holding you back from delivering the type of customer experience a modern customer expects.
  • Free chatbots with paid upgrades to access AI: We’ll admit it, there are some decent chatbots in this category. The problem? All the useful features like AI reasoning are not available on the free plan, if they’re available at all.

In both situations, you lose because you won’t have access to tools necessary to deliver an extraordinary customer experience.

If you do find a chatbot service that’s completely free and promises to be your customer’s best friend — take a step back, turn around, and run. Chatbot software is expensive to build. The average salary of a chatbot designer is $67,249 and that of a chatbot developer is $78,909 . There are various other specialists needed to build a decent chatbot offering, like a data scientist and an NLP engineer.

Nobody would spend all that time and money to offer a chatbot for free — if something’s free, you’re the product. Companies offering free chatbot software might collect customer data to train their model, or in worst cases, sell it.

Why a free chatbot doesn’t add value: No AI and limited integrations

Free chatbots are digital puppets that can’t quite pull off the tricks you need. The reason? Reliance on simple keyword matching and lack of integrations. Let’s talk about why using free chatbots can lead to disappointment and the capabilities you miss out on by not choosing an AI-powered solution.

Reliance on simple keyword matching

40% of leaders said they expect to realize substantial AI value to improve customer experience — but why exactly are these leaders so keen on investing in AI for customer service? Because free chatbots lack technologies like natural language processing (NLP) and machine learning. The lack of these technologies handicaps free chatbots — they can’t handle complex queries or generate insightful reports based on customer interactions.

40% of leaders said they expect to realize substantial AI value to improve customer experience.

- PwC

Below is a summary of letdowns to expect when using a free chatbot. We also discuss capabilities AI agents offer to explain what you miss out on by opting for a free chatbot:

  1. Failure to understand intent leads to a frustrating experience

50% of customers will switch to a competitor after just one bad experience. Are you willing to risk half your clientele (and revenue) with a free chatbot? For most businesses, that’s a hard pass. That’s why you need an AI agent that truly understands intent and natural language.

Free chatbots often rely on basic keyword matching to find the right script for a customer’s issue. Here’s the problem: searching for keywords often leads to more confusion. For example, if a customer says, “I’m unable to login to my account and when I try resetting my password, I get an error”, a free chatbot is likely to search for the keywords “reset password” and link to the same password reset page the customer’s already trying, and failing to use.

2. Inability to understand semantics translates to inferior response quality

Free chatbots are incapable of understanding semantics. They can’t fully grasp a message’s context and underlying meaning. It’s like trying to teach robots Shakespeare — they might understand the text, but the poetry is lost on them.

Suppose your product is a food delivery app. A customer sends a message, “Can you recommend a good Italian restaurant nearby?” A free chatbot might just share a list of Italian restaurants nearby. But a semantics-savvy AI agent might say something like, “Sure! How about trying Rubirosa’s just two blocks away? The pasta is divine, and the ambiance is molto bene!”

AI agents use various technologies to understand semantics:

  • NLP: NLP helps AI agents understand meaning. While scripted chatbots look for specific keywords in the customer’s query, NLP-powered AI agents decipher the meaning of each word. If a customer sends a message, “secure my account,” NLP will offer customers help with resetting their password or setting up 2FA. But a scripted chatbot might draw a blank because the keyword “password” is missing.
  • Contextual word embeddings: Embeddings help AI agents understand the meaning of each word within the context of a sentence. It’s what helps the AI agent understand the difference between a “bank” as a financial institution or a river bank.
  • Semantic role labeling (SRL): SLR assigns specific roles to each word in a sentence. The assigned roles help the AI agent identify who is performing an action, what the action is, and who’s the action being done to. For example, the customer’s message might be, “I need a refund for a defective product I purchased last week.” However, SLR:
    • Recognizes the customer as the Experiencer performing the action.
    • Identifies “need” as the Predicate representing the action.
    • Labels “refund” as the Theme receiving the action.
    • Recognizes “for a defective product I purchased last week” as the Reason that provides context for the need.

The SLR helps the AI agent understand that the user is experiencing a need (for a refund), specifying what they need (a refund), and providing a reason (a defective product).

  • Pragmatic inference: A combination of AI technologies like NLP and contextual word embeddings enable AI agents to understand implied meanings, humor, and intentions. They help the AI agent pick on the subtle undertones of human communication.

For example, when a customer says, “I’ve been waiting for ages,” the AI agent understands that the customer is frustrated. The AI agent considers this information and responds, “I’m sorry to hear that! How can I assist you promptly?” A free chatbot might send a generic response like “Thank you for letting me know.”

Test Alt Text in Hero

3. Poor context retention forces customers to repeat themselves

Customers don’t have to explain their problem every few seconds when talking to a support agent because humans can retain context. Unfortunately, free chatbots can’t. When the time comes to deal with a multi-part query, free chatbots fumble.

AI agents are generally equipped with technologies required for context retention. To retain context, AI agents use a combination of advanced technologies like:

  • Database management systems (DBMS): DBMS stores and retrieves data faster than you can say “context.” A DBMS organizes data into tables and uses a language called SQL (Structured Query Language) to quickly find the information needed during a conversation.
  • Tokenization and NLP: Tokenization breaks down text into smaller chunks — words, phrases, or even individual characters — called tokens. NLP uses various techniques, including part-of-speech tagging, named entity recognition, and sentiment analysis to understand context, sentiments, and the relationship between words (or tokens).
  • Machine learning algorithms: ML algorithms help your AI agent learn with every customer interaction. It learns what answers were most helpful, which queries customers ask more often, and the best responses for a customer based on their mood.
  • Graph databases: Graph databases map the intricate connections and relationships between pieces of information using nodes (entities like users and products), edges (relationships between nodes like “purchased” or “belongs to”), and graph structure (which enables the AI agent to navigate and retrieve information).

4. Inability to learn limits the potential to improve customer experience

AI agents use machine learning algorithms and generative AI to reason through the best solutions to any query. Free chatbots? They’re scripted. They don’t generate dynamic responses or learn from interactions — they’re trapped in a script they can’t escape. This digital deficiency limits the potential for improving customer experience because they can’t:

  • Predict behavior: AI agents use machine learning to predict behavior. Free chatbots can’t analyze customer historical data, pick up on customer patterns and behaviors, or refine their responses because they lack machine learning algorithms.
  • Personalize interactions: You need customer data to personalize interactions. AI agents collect customer data during interactions for insights into customer behavior and preferences and generate personalized responses using generative AI. They use this data to deliver personalized experiences , while free chatbots lack the technologies to offer personalization.

“While generative AI harbors vast potential across industries and applications, in CX it will drive hyper-personalization, and it will help businesses offer more humanized and personable interactions. That’s exactly what customers expect and welcome — they’ve made it increasingly clear that they want businesses to use the mountains of personal data they possess to offer warm, personalized experiences.”

- CX Trends 2024 Report, Zendesk

  • Generate data-driven insights: AI agents learn customer preferences and their most pressing questions based on interactions. They convert these into insightful reports and learn from them. Free chatbots live in data silos and can’t deliver tailored insights.

Lack of integrations

Integrations allow information exchange between platforms. Let’s say you’ve launched a new feature. Your chatbot is receiving queries about your new feature multiple times a day. An AI agent that integrates into your knowledge base software can scan through knowledge base content and FAQs to find answers to specific queries about the new feature. On the other hand, free chatbots would just link customers to an article that talks about the feature in general.

Most free chatbots offer limited integrations, if any. This means fewer options to automate tasks. Here’s the problem with free chatbots when it comes to integrations:

  • Platform-specific constraints: Even when free chatbots do offer integration options, most favor a handful of platforms. For example, a free chatbot might play nice when integrating with Messenger but turn into a digital hermit when you try to integrate it with WhatsApp or other popular platforms.
  • Limited API support: Application programming interfaces (APIs) help build custom integrations so you can connect the free chatbot with other tools. But free chatbots lack robust API support. This means even if you’re willing to spend time and money on building custom integrations, a free chatbot still won’t help you automatically update the records in your CRM, update knowledge base content, or pull invoice data on-demand when customers ask for it.
  • Security risks: Some free chatbots are susceptible to various security risks when deployed across multiple platforms. Inconsistent encryption standards and vulnerabilities in any one platform can create a domino effect, jeopardizing the security of customer data.

Free chatbots: are they at least good enough to get started?

You could argue that a free chatbot is good enough when you’re starting your business and strapped for cash.

That’s a flawed argument.

Startups might do fine without scalable solutions. Without heavy traffic, you don’t need a solution capable of handling a large volume of queries. You do need a solution that can provide accurate answers and immersive experiences. Free chatbots are not the way to do that.

In fact, free chatbots can be even more expensive than paid solutions.

70% of survey respondents say they'd advise friends and colleagues against buying a product or service after a negative customer experience.

18% would take the issue to social media.

- Hiver

Plug in the numbers for your company and see what losing 70% of customers seeking support could do to your revenue, bottom line, and cash flow.

The AI Agent: What to look for instead

If free chatbots can’t deliver good customer experiences, what’s a good alternative? We recommend an AI Agent.

"The average person today has still had a mostly negative experience with a chatbot. I think that’s changing very quickly. Our customers are deploying what we call AI agents, and those agents are starting to become as capable, and in some cases, more capable than human agents because they can learn, take action, and are highly coachable."

Mike Murchison
CEO & Co-founder

Let’s take a closer look at some advanced capabilities that make a chatbot a fully functional AI agent and discuss how these capabilities help reduce costs.

What is the difference between a chatbot and an AI Agent?

AI Agents are as different from chatbots as landlines are to cell phones.

Chatbots follow scripted conversation workflows that need to be built manually, while AI Agents use generative AI, large language models (LLMs) and natural language processing (NLP) to understand, respond and action customer queries. In short, chatbots regurgitate predefined information, while AI Agents reason.

Conversational AI

Conversational AI is a group of AI technologies that enable an AI agent to interact with customers using natural language. Technologies like NLP, generative AI, and machine learning make your AI agent’s responses human-like, fast, and accurate.

If you thought free chatbots saved you money, here’s a fun fact: Conversational AI can reduce labor costs in contact centers by $80 billion according to Gartner. Most of these savings come from using the AI agent as the first line of defense. The AI agent fields most customer queries so fewer queries reach the support desk. The fewer the queries, the fewer support agents you need.

Integration and onboarding

You get what you put into a chatbot, literally. With a scripted chatbot, you need to provide anywhere from 10 to 500+ examples of how a customer might phrase the question, so the chatbot can learn to recognize the question and serve your customer the correct reply. Manually scripting and auditing the ever-expanding branches of your chatbot’s conversation workflows is time-consuming and isn’t scalable.

In contrast, onboarding an AI Agent is similar to onboarding a new employee, one with unlimited potential. An AI Agent connects to sources of information you already have — like your help center, knowledge base, and technical documentation — and learns from it in seconds.

A chatbot might not be able to resolve an issue this complex, instead providing a link to an article. Or if it was able to solve the problem, it would have required enormously complex decision trees full of complicated logic to handle all the variables and possible scenarios. This requires extensive upkeep: manually scripting and auditing your chatbot’s conversation workflows. It’s time consuming and isn’t scalable.

And customers will notice the differences on the other end.

Chatbots can help resolve common inquiries, but its limitations are evident to the customer, with unnatural and robotic answers to complex questions. Where an interaction with an AI Agent feels like a conversation with an intelligent customer service representative, an interaction with a scripted chatbot can feel like choosing a preset response from a menu.

Customization and coachability

You can train AI agents to fine-tune their responses and behavior according to your needs. Here’s how you can customize AI agents:

  • Conversation flow control: Controlling the flow of conversations is vital to driving conversations in the right direction. AI agents allow you to define fallback scenarios, preserve context, and personalize responses so you can design the structure and progression of interactions between the AI agent and customers.
  • Branding and tone: You can train AI agents to adapt your brand’s voice and personality and consider your audience’s cultural context. You can insert your logo and customize colors, fonts, and overall design to provide a cohesive experience.
  • API integrations: AI agents that support external APIs and integrate with third-party services help fetch real-time data from backend systems. For example, if your customer wants to refer to a previous support ticket during a chat, the API helps the AI agent access previous support tickets in the CRM and provide relevant answers.

Premium AI agents have a negative cost

Free chatbots cost nothing upfront. They do cost you a ton in the long run because they fail to deliver immersive customer experiences and tarnish your reputation.

AI agents come with an upfront cost. But the benefits of AI agents more than make up for their cost — Wealthsimple increased their Automation Resolution Rate (ARR) by 2x and increased CSAT by 10% after moving from a scripted chatbot to an AI agent. Fast and accurate responses and immersive experiences reduce churn and contribute to your top line. At the same time, improved efficiency in your support process helps expand your margins.

Ada’s AI agent helps you deliver impactful customer service through hyper-personalized and accurate responses at scale. Our mission is to make customer service extraordinary for everyone.

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