When to use AI chatbots to enhance user engagement

AI chatbots are designed to simulate human conversation and provide automated responses to user queries. Some think they are an excellent invention and great for business. Others believe that they are wrong and won’t replace humans, ever. 

No matter which side you are on, if your company deals with user support in any way, it’s hard not to admit that they are pretty useful. 

They take a huge load off of human support teams so they can actually deal with issues that require more creativity than simply answering questions someone could’ve read answers to in FAQs.

Anywho, let’s get into how these intelligent chat interfaces can enhance customer satisfaction and streamline operations for startups and business owners, along with non-developers.

Why AI chatbots drive user engagement

User engagement is one of the key metrics for any business. It shows how individuals interact with your website, application, or other digital product, how often they use it, and how long they stay on it. This is basically a crucial indicator of customer satisfaction and loyalty. Here’s how AI chatbots are known to contribute:

Always-on support

AI chatbots provide immediate answers around the clock. They are operating 24/7 regardless of traditional business hours or geographical time zones. This constant availability significantly reduces customer wait times, which is a common point of frustration. 

73% of users now expect websites to feature digital assistants for convenient interactions, and the fact that 69% of users specifically value quick reply times further underscores that the speed of service is a primary driver of a positive AI chatbot UX.  

Personalization

AI chatbots can analyze extensive user data, including browsing patterns, purchase history, and past interactions. Afterward, they adjust product recommendations, offers, and responses specifically to individual preferences. 

AI chatbots can also dynamically customize greeting messages and promotional offers based on specific customer demographics or interests. The ability to scale such interactions is almost virtually impossible through manual means, so it is a significant advantage, particularly when supported by robust LLM integration with existing customer data systems.

Efficiency and cost savings

AI chatbots automate a wide array of repetitive tasks, substantially reducing the manual workload for human support teams. This automation frees us, humans, to concentrate on more complex, nuanced, or sensitive customer issues that genuinely require more human empathy and problem-solving skills. 

The systems can handle a large volume of inquiries simultaneously, and that effectively buffers support teams from sudden spikes in ticket volume. Businesses have reported quite impressive results, including a 67% increase in sales through chatbot assistance, with 26% of all sales transactions originating from a bot interaction.

That said, success isn’t guaranteed by simply adding a bot. Users can tell when a bot is just “for show.” 

A well-designed chatbot that truly helps will encourage users to stick around and even increase conversion, but a clunky bot put there to try to prevent you from contacting actual support will make you want to leave. In other words, a chatbot’s value depends on how well it’s executed and integrated.

AI chatbot types and their business applications

Not all chatbots are created equal. There are several categories, from simple scripts to advanced AI. Knowing the differences will help you choose the right kind for your use case:

Rule-based chatbots

These are the simplest bots, following predefined rules or decision trees. Rule-based chatbots operate on “if-then” logic and can only handle scenarios anticipated by their designers. They don’t truly understand language – they might just look for keywords or require users to pick from options.

When to use: 

If you’re a small startup with a limited set of common questions or you need a quick solution for basic tasks, a rule-based bot can do the trick.

Rule-based bots are cost-effective and easy to implement for frequently asked questions and routine action.

Pros: Reliable and consistent answers for known questions, fast deployment, no fancy AI needed.

Cons: Very limited flexibility – poor at handling phrasing variations or anything it wasn’t explicitly programmed to address.

Conversational (NLP) chatbots

Next up are AI chatbots that use Natural Language Processing and machine learning to understand free-form text. These bots are more advanced than simple rule-based ones. They can parse a user’s sentence to detect intent and extract relevant information. They also can learn from examples to improve over time.

When to use: 

If your users tend to ask questions in their own words and you have a broader range of queries, an NLP chatbot is a good fit.

Compared to old rule bots, AI-powered chatbots can handle unpredictable phrasing. They won’t catch everything, but they can ask clarifying questions rather than hitting a dead end.

Pros: Understands natural language and synonyms, handles a wider range of queries, can improve with training data.

Cons: Requires more setup and training data, might still fail on very complex or ambiguous questions, needs maintenance to update intents and examples.

LLM-based bots

Instead of picking answers from a script or database, generative AI chatbots create responses on the fly by predicting what a helpful answer might be. They are fluent and flexible, often able to carry on open-ended conversations. A generative bot can handle unanticipated questions and even generate creative content (within limits).

When to use: 

Generative bots are ideal when you want a very conversational AI experience, and your users might ask anything under the sun. They’re great for broad knowledge inquiries, creative tasks, or complex dialogues.

Generative AI bots excel at understanding context and can generate entirely new responses rather than selecting from pre-written options.

Pros: Highly flexible, can handle diverse and unexpected inputs, very engaging and human-like, continuously learning from vast data.

Cons: Can produce incorrect or nonsensical answers if not monitored (they don’t truly “know” facts, they predict likely answers), requires more computing power (can be costly at scale), and may need careful prompt engineering or filters to align with your brand voice and policies.

Hybrid chatbots

Hybrid chatbots blend the reliability of rules with the intelligence of AI. In practice, a hybrid chatbot might follow a rule-based flow for everyday tasks but switch to an AI mode when the conversation gets complex or goes off script. 

Another aspect of “hybrid” is combining automation with human agents. Many experts recommend this approach: let the bot handle the easy stuff and smoothly hand it off to a human when needed.

When to use: Frankly, most businesses will benefit from a hybrid chatbot strategy. It offers the best of both worlds. If you have a set of common questions or menu options, a rule-based interface can get users quick answers (and ensure accuracy). For anything the bot can’t handle confidently, you have NLP/AI to attempt an answer or ask for clarification.

Hybrid bots require a bit more setup (you need to design the rule-based parts and configure the AI components and live agent handoff), but the payoff is big: users get fast answers to simple issues and competent help for complex ones.

Pros: High resolution rate - simple issues resolved instantly, complex issues handled appropriately. Flexible and scalable, yet controlled.

Cons: More complex to build and maintain, needs coordination between bot logic and human support workflow, and you must train both the rule-based flows and the AI parts. Also, involving humans means you need staff available for escalations (though far fewer than handling everything manually).

How to get a superior AI chatbot user experience

No matter which type of chatbot you choose, how you design the experience (“chatbot UX”) will determine if it actually engages users or drives them away.

AI chatbot implementation

Before anything, clarify who will use the chatbot and what they need. Are your users typically tech-savvy developers asking detailed questions, or non-technical customers looking for quick answers? 

1. Research their common pain points and queries. 

This will guide the chatbot’s tone, language, and content. Also, define what business goal is - e.g., faster support resolution, higher lead capture, or more user engagement on your site. A chatbot built to increase conversions might have a different approach (more proactive in offering help or promotions) than one built for pure support.

2. Design an intuitive conversation flow

A chatbot is a part of your product’s user interface. Map out the conversation like a user journey. Ensure the bot’s prompts and questions make sense and guide the user step-by-step. Keep interactions simple and focused, and don’t ask for too many things at once or bombard users with long messages. 

For a rule-based flow, provide clear buttons or quick replies so users aren’t forced to type awkwardly formatted answers. For AI-based bots, still consider adding hints or examples in the initial greeting.

3. Integrate with your systems

One big advantage of AI chatbots is integration with your existing business systems (CRM, databases, analytics). By connecting the chatbot to these, you get a personalized experience in your responses, which streamlines the user’s journey. 

As a guide, make sure the chatbot can access relevant user data (with proper consent), and that handoffs to other channels are fluid.

Integration also allows for automation beyond chatting: your bot could schedule meetings, create support tickets, or even trigger workflows (like a refund process) directly if you set it up.

If you’re not technical, consider using a partner (e.g., our Merge team) that offers LLM integration and business automation – they can connect the AI chatbot to your databases and tools without you writing all the code.

4. Train and test

User language and needs evolve, and your bot should too. Regularly update the chatbot’s knowledge base and training data. Add new FAQs when you launch new features or notice new customer queries coming in. 

Retrain NLP models if using machine learning, so accuracy improves. Monitor chatbot analytics: look at conversations to see where the bot failed or users got frustrated.

5. Measure and iterate

Monitor things like conversation completion rate, user satisfaction scores, conversion rate (did the user end up signing up or purchasing?), and containment rate (issues resolved by bot vs. needing human). 

If you’re trying to improve chatbot UX, look at how long users converse, how many messages on average, and at what point (if any) they drop off. This data can highlight UX issues, for example, if many users quit the chat after a certain question, that question might be confusing or too early. 

What's next?

The AI chatbot market is experiencing rapid expansion, with projections indicating it will reach $15.5 billion by 2028. Below are a few trends we anticipate are going to pop out soon:  

• More human-like. Continued advancements in AI and Natural Language Processing (NLP) will enable conversations to become even more natural, nuanced, and context-aware, blurring the lines between human and machine interaction.   
• Multimodal interactions. Future AI chatbots will seamlessly support communication across various modalities, including text, voice, gestures, and even the interpretation and generation of images or videos. This evolution means capabilities like speech-to-text and text-to-image will become standard features.   
• Proactive and predictive assistance. Chatbots will increasingly anticipate user needs and offer solutions or information before being explicitly asked, moving from reactive support to intelligent, predictive assistance.   
• Deeper integration. The trend of seamless integration with IoT devices and a wider array of business systems will continue to grow, creating a more interconnected and responsive digital environment.   
• AI avatars. The development of digital personas that can mimic human behaviors and understand emotions will further enhance user engagement by providing a more lifelike and relatable interaction experience.

If you focus on genuinely assisting users, you’ll naturally see more engagement: longer chats for the right reasons, higher click-through on suggestions, and more self-service success. 

And if you need a little help implementing a smart chatbot without a big dev team, consider partnering with services (like our Merge professionals) that specialize in LLM integration, business automation, and voice tech - they can jumpstart your efforts so you can focus on strategy.