A home page might host a “greeting” conversation, which starts off with a quick hello, and a general question like, “what brings you to our website today?” In this case, the goal is to learn more about a customer so you can point them in the right direction.
Or, you might use something like this on a page that highlights specific features. Again – you’re gathering the information needed to find a solution that fits with their needs.
The chatbot displays this information based on a set of rules set by the human operator who can route responses based on keyword matching.
Rule-based chatbots are not programmed to respond to changes in language, rather they have a structured dialog that answers specific questions by matching the user input to programmed answers. The questions our Driftbot example highlights above are tightly controlled. This means that the script is designed to give salespeople the qualifying details needed to inform their next sales script.
However, if a visitor arrives on the website and asks something the programmer didn’t think of, the chatbot won’t be able to produce an answer.
An AI chatbot is trained to operate more or less on its own, using a process known as Natural Language Processing, or NLP, combined with artificial intelligence and the annotation of human data.
AI chatbots get smarter over time.
How to Make an AI Chatbot
Building an artificial intelligent bot yourself requires some serious expertise. I won’t go into too much detail explaining the nuances of NLP, deep learning, and other algorithmic forms of intelligence.
At the base level, an AI chatbot is fed input data, which it interprets and translates into a relevant output – or the response the user receives after asking a question.
So the AI chatbot receives information from a programmer. And then over time, it’s “trained” to understand context through several algorithms that involve tagging parts of speech.
In this example, let’s assume the programmer is teaching the chatbot AI to answer the question, what is the capital of England?
These days, you don’t need to be a seasoned programmer (or even understand the inner workings of the algorithms) to set up your own AI-based chatbot.
How to Choose the Best AI Chatbot for Your Needs
The first thing you’ll need to do when selecting the best AI chatbot software is to assess your needs, along with your organization’s technical capabilities. Do you need a rules-based chatbot or an AI bot that helps you analyze massive datasets and continues to build on its existing knowledge?
If you have a use case for an AI bot and are beginning to explore your options, here are a few things you should think about as you begin doing research.
Programming Languages for Chatbot AI
Despite the fact that many AI chatbot builders come with drag-and-drop interfaces, the choice of language is still an important consideration. Java and C++ offer more speed than Python-powered bots, but Python is easier for those teams who don’t have a ton of experience building chatbots.
Ease of Use of Your AI Chatbot
How much work can your team reasonably handle when it comes to building a chatbot? Some platforms are easier than others, but the trade-off is that the drag-and-drop style programs don’t always make room for customization.
What Kind of AI Bot Do You Want?
You’ll want to find an ai chatbot for websites that comes with the ability to understand tone, sentiment, and customer personalities so that it can deliver the best possible experience. This includes analyzing a situation and making a decision to escalate to a live agent or present a solution automatically.
Does the AI Chatbot Connect with Your CRM?
If you’re using chatbots to gather insights, you want to pick a program that syncs up with your CRM – and other tools like your marketing software, email service provider, and so on. One of the key reasons brands seek out an AI tool with analytical capabilities is so they can quickly review data and make decisions.
Is the Artificial Intelligence Bot Pre-Trained?
Of course, the best AI chatbots are built on an existing AI data set. But, you’ll want to make sure you select an option that comes with some understanding of terms and knowledge specific to your industry. A general chatbot might not be ready “out of the box.” As such, you’ll want to account for the amount of time required to get your bot trained for the job.
Which Is Better – AI Chatbots or Rule-Based Chatbots?
Well, it all depends on the use case.
Rule-based chatbots, though not as flexible as their AI counterparts, do have advantages. For brands that want to create a predictable, tightly controlled experience for users, chatbots allow them to focus on guiding their audience toward a specific goal, be it speaking to a human or signing up for a guided demo.
If you are looking for a smarter bot that can handle complex queries or help you make sense of massive datasets, AI bots may be a better choice here. Common use cases include the following:
- Sentiment Analysis – AI bots can comb through massive data sets to identify customer complaints, reviews, and mentions across multiple touchpoints.
- Understanding Behavioral Patterns – Here, artificial intelligence bots can be used to help brands identify patterns humans might not detect.
- Learning and Adapting to User Preferences – For example, if you want to offer customers a personalized solution, such as saving preferences, mirroring language, delivering customized content or offers automatically with chatbot AI.
But, it’s worth pointing out that, sometimes, when AI bots have a “mind of their own,” they end up in hot water, delivering inappropriate responses or displaying algorithmic bias. Or, on a less damaging note, go off script from time to time.
The key advantage of an AI chatbot is that it is capable of learning a lot about its users and can apply knowledge effectively with little intervention.
AI chatbots are becoming an essential piece of the workplace technology stack, and brands that have already embraced them have an advantage over those that have yet to make moves toward automation, artificial intelligence, or both.
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