How Rule-Based Chatbots Beat AI Chatbots in Business

Chatbots used to be seen as a novelty before rule-based bots entered the scene. Now they're gaining business' attention and reshaping online support.

These chatbots, which work on a decision tree “if/then” logic, perform basic tasks and can hold a conversation with customers — but within limits. That’s why some businesses consider advanced AI options that understand context and intents to emulate their agents to a T.

Each has their own strengths, which I’ll cover in the next section, but the truth is that rule-based chatbots are just better for most businesses. Keep reading to learn why.

  1. Rule-based vs. contextual chatbots
  2. How to use rule-based chatbots in business
  3. How to get started with a rule-based chatbot
  4. Create a rule-based chatbot with Userlike

1
Rule-based vs. contextual chatbots

When my mom bought a Camaro, my siblings and I were confused. Why was our mom, a nervous driver who only needed five minutes to get to work, buying a fast sports car? It sat low to the ground, created major blind spots and was so long it was difficult to park.

But it looked cool.

The thing is, the Camaro just wasn’t a good fit. The allure of owning it soon wore off and its impracticality started to show. This is what happens when you go big instead of being realistic.

That’s why it’s important to look past the “cool” factor of chatbots, and instead focus on its specs and what’s under the hood.

Rule-based chatbots use a series of defined rules to answer questions it identifies. Conversations are mapped out like a flowchart, which allows for few topic deviations but quicker resolutions. They’re often used as FAQ or knowledge base chatbots and can perform simple tasks such as creating tickets and scheduling calls.

screenshot of Elisa displaying button options
Lufthansa created “Elisa” using Cognigy to assist customers with flight cancellations, refunds and answering FAQs.

Contextual chatbots, meanwhile, can have free-flowing conversations and often learn from them, which later improves the quality of their replies. They can also follow mapped conversation flows, but they are often looser and allow for topic deviation.

For a business, both options sound attractive. Rule-based solutions are quicker to build, easier to maintain and fully capable of assisting customers. Contextual chatbots are great for recreating the feel of speaking to an agent and learning from and dissecting customer information into actionable insights.

Choosing a contextual chatbot option might be the Camaro of choices when it comes to your business. If you list the most common reasons why you may need a chatbot, such as to answer common questions, promote your products and help with lead generation, then rule-based chatbots are often the better choice.

They make automation possible for every business, start-up or enterprise. After all, not every company has the time, money and resources to build a customized intelligent system from scratch.

Meme showing the excitement of thinking about making a contextual chatbot versus the stress of actually making one

A chatbot is a tool first and foremost, so if your use cases are general, then investing time and money into an advanced chatbot may be a big unnecessary investment.

This kind of complex chatbot is more often used in healthcare, automotive, insurance and banking. These industries often process large amounts of sensitive and/or complex data, and customers have high expectations for the type of service they receive from these types of businesses considering their serious nature.

Of course, a rule-based chatbot may still be optimal if your business is a pharmacy, car dealer or bank.

Screenshot of chatbot Ebbie on ebase's website
Ebase, a European B2B Bank for Financial Services, uses a rule-based Userlike chatbot on its website to assist customers with FAQs.

It all depends on the extent of the service you wish to offer, plus your budget, team size and more.

2
How to use rule-based chatbots in business

Provide 24/7 assistance

Chatbots provide instant, real-time assistance 24/7. If your customers are active after business hours, chatbots can help by answering simple questions, creating tickets and forwarding the chat for agent follow-up.

Perform simple tasks

Chatbots make great assistants for taking over all those boring and time-consuming (but important) tasks. Besides answering common questions, rule-based chatbots can schedule calls, check statuses and update contact information, just to name a few.

Answer frequently asked questions

Customers value speed, and the fast nature of the internet amplifies this expectation for instant answers.

To take the pressure off your agents, you can use a rule-based FAQ chatbot to answer your customers’ most common questions. It can also help you track popular search terms so you’re aware of what troubles customers are often facing. This may benefit your marketing efforts and help with growing and revising your FAQ pages.

Collect customer data

Rule-based chatbots may not learn from their conversations, but they can help you grow your customer knowledge base. They can ask for the site visitor’s name and contact information at the beginning of the chat to begin building a profile in your customer messaging software and/or CRM.

Product image of stored contacts in Userlike

If the chat is forwarded to an agent, or if the customer contacts your business again, a profile helps you quickly familiarize yourself with the lead.

Generate and filter leads

You only have a few seconds to keep a site visitor’s attention. You can use a proactive lead generation chatbot to capture customer interest and keep them from leaving your site.

A rule-based chatbot can use a series of qualifying questions to determine if the visitor is a good candidate for agent follow-up. If the site visitor is still on the fence, your chatbot can lead them through your sales funnel by linking relevant articles, product pages and lead magnets.

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3
How to get started with a rule-based chatbot

You often read about “rule-based chatbot versus AI chatbots” but rule-based bots are AI bots. They use “fixed” AI, which means they don’t “learn” from their interactions but instead work from a predefined decision tree, unlike machine or deep learning bots.

A fixed chatbot may ask the customer questions and provide button options or keywords the customer can use in order to take action or answer their request, like checking the status of a shipment. It’s all based on the selected route.

Creating a rule-based chatbot can be broken down into these few steps:

  • Collect data. If you’ve determined your chatbot’s duties, gather all the data it needs to start mapping its conversation structure. This includes information from your Userlike knowledge base, past customer conversations and relevant business intelligence.
  • Create conversation flows. Diagram conversation paths for the topics your chatbot will cover. You can break this down into various elements, such as greetings and apologies, which I covered in detail in my post, “6 steps for creating a smooth chatbot conversation flow.”
  • Screenshot of an example flow in figma
    Try creating a conversation map in Figma to visualize your bot.
  • Convert flows into the bot’s language. Depending on the chatbot provider you choose, you may either need to convert your chatbot into html, use a drag and drop interface or create intents. Rule-based chatbot providers often offer no-code solutions, and most providers are generally user–friendly.
  • Upload or embed your chatbot into your preferred channels. Once you’ve built your chatbot, we recommend pairing it with customer messaging software, like Userlike. That way you can deploy it on your website and multiple messaging channels, such as WhatsApp. This helps you monitor your chatbot’s performance and allows you to keep your agents as a fallback.
  • Test, and test again. Before your customers meet your chatbot, test it among your team. If possible, ask a select number of “outsiders” like existing customers, friends or even family to chat with your bot and give their honest thoughts. Working on something so closely can cloud your perspective, so outside opinions will help you see anomalies you may have missed.
  • Deploy. Once you’re happy with your chatbot’s flows and look and have tested all integrations and functionalities, it’s go time. Consider using proactive chat to make your chatbot visible to site visitors, and promote it on your social channels.

4
Create a rule-based chatbot with Userlike

At Userlike, we make it easy to have a rule-based chatbot connected to your customer messaging software without having to build it elsewhere.

Our Logic Bot functions as an active member of your team: it can greet customers, answer questions using simple syntax and navigate visitors through your sales funnel.

You can easily experience the benefits mentioned in this post by using our Logic Bot. It’s an all-in-one solution that works as a proactive first contact for customers, as backup for your agents or for requests received outside of service hours.

Screenshot of KTR System's chatbot, Buddy
KTR Systems use Userlike’s Logic Bot to deploy “Buddy,” which automatically redirects users to the pages they’re looking for using button options.

If you do decide to build a contextual chatbot, we also offer a chatbot API so you can connect it to our software.

If you’re interested in learning more about our Logic Bot and chatbot services, start a chat on this screen to talk to a member of our team or sign up for a consultation.