Chatbots are essentially your (free) 24/7 customer service workforce. They don’t complain or take days off (unless you make them), and they are always nice to your customers, making them love your company more.
2. Artificial Intelligence-powered Chatbots
Artificial Intelligence-powered chatbots (or Machine Learning-chatbots) can reply to ambiguous questions and can produce a reply on their own based on Natural Language Processing technology.
- They learn from their past conversations
- They can produce an original reply from the scratch
- The more they chat the more they learn and the narrower their conversational limitations become
Hubspot’s research shows that 47% of consumers would buy items from a chatbot.
Chatbots Deliver 24/7 Customer Service
Remember that we talked about the change in customers’ habits and needs? Whatever customers want, they want it right now. Research shows that 82% of today’s buyers demand an immediate answer to their question. Chatbots can help you to satisfy such an attitude. They can free your support agents from repetitive queries and assist customers even when all the agents are offline.
Chatbots Create Sales Opportunities
Chatbots can boost your conversion rate by engaging 100% of a website’s visitors. How does it work? Let’s imagine that your returning customer just opened your website again – a chatbot such as our good old Tidus then prompts the message to the client and offers a discount voucher.
Chatbots Are Faster Than Humans
There are tasks that chatbots can complete much faster (and often better) than humans. Businesses can take advantage of chatbots’ speed and automate certain tasks such as, for instance, pizza order – the customer makes just a few clicks and the order is placed.
Chatbots Personalize Communication
Greet new visitors or send a “thank you” message – this makes things more personal, and customers love that. Or maybe you’d want to send a personalized offer or discount? You can do that as well. According to barilliance.com data, personalized offers convert 1.7x times better than the generic ones.
Chatbots Can Multitask
Live chat operators can handle ~2-3 chats simultaneously and remain fairly efficient. Chatbots, on the other hand, is not limited to the number of customers they can talk to. This means they can respond to customers’ queries without putting anyone on hold.
Chatbots Generate Leads
You ran out of stock and the customer wants to leave the website? No problem. A chatbot can ask them for an e-mail, and you can later inform your client that the product is available. Or perhaps you would like to invite the visitors to join your newsletter? A chatbot can do that too.
PS: Did I mention that statistically, 63% of consumers would consider messaging an online chatbot to communicate with a business or a brand? That means that they wouldn’t mind leaving their contact information.
Easy and efficient lead generation, what can be better than that?!
Chatbots Can Instantly React To Customers’ Behavior
Chatbots can see what you do not. For example, they can notice when the customer is about to leave the website. In such a situation they can intervene and ask the customer the reason for their leaving, or offer a discount to convince them to stay and continue their shopping.
Chatbots Are Cheap To Maintain
If you compare the cost of implementing a fully-working chatbot to the cost of developing a cross-platform app (or hiring extra support staff), you will realize that chatbots are a cost-effective solution. What’s also worth mentioning is that Tidio live chat and chatbots can be integrated with numerous web and store platforms (e.g. Wix, WordPress, Joomla, Shopify, PrestaShop), as well as marketing apps (e.g. MailChimp, HubSpot, GoogleAnalytics). Chatbots work on mobile devices, too.
Chatbots Are Cheap To Customize
Depending on the product or services you sell, your support staff may need to frequently update their knowledge to keep up with the regular changes. The same rule applies to chatbots. They also need to be kept up-to-date to deliver the best service to the customers.
Unlike software (website, mobile app), chatbots are not only easy to build but also update – no coding skills or software developer is required.
Chatbots Generate High Customer Engagement
It is very important to keep your customers engaged with your brand and that is why many businesses include chatbots in their social media marketing strategy. Additionally, chatbots are not limited to answering the queries – they can also initiate conversations with new visitors and turn some of them into followers.
Chatbots Bridge The Gap Between Technology And Humans
If you are a part of a non-profit organization, NGO, or any charitable foundation, then chatbot can be a solution for you as well.
How do Chatbots Work?
On a simple level, a human interacts with a chatbot.
If voice is used, the chatbot first turns the voice data input into text (using Automatic Speech Recognition (ASR) technology). Text only chatbots such as text-based messaging services skip this step.
The chatbot then analyses the text input, considers the best response and delivers that back to the user. The chatbot’s reply output may be delivered in any number of ways such as written text, voice via Text to Speech (TTS) tools, or perhaps by completing a task.
It’s worth noting that, understanding humans isn’t easy for a machine. The subtle and nuanced way humans communicate is a very complex task to recreate artificially, which is why chatbots use several natural language principles:
Natural Language Processing (NLP)
Natural Language Processing is used to split the user input into sentences and words. It also standardizes the text through a series of techniques, for example, converting it all to lowercase or correcting spelling mistakes before determining if the word is an adjective or verb – it’s at this stage where other factors such as sentiment are also considered.
Natural Language Understanding (NLU)
Natural Language Understanding helps the chatbot understand what the user said using both general and domain specific language objects such as lexicons, synonyms and themes. These are then used in conjunction with algorithms or rules to construct dialogue flows that tell the chatbot how to respond.
Natural Language Generation (NLG)
Delivering a meaningful, personalized experience beyond pre-scripted responses requires natural language generation. This enables the chatbot to interrogate data repositories, including integrated back-end systems and third-party databases, and to use that information in creating a response.
Conversational AI technology takes NLP and NLU to the next level. It allows enterprises to create advanced dialogue systems that utilize memory, personal preferences and contextual understanding to deliver a realistic and engaging natural language interface.
Types of Chatbot Technology
The majority of chatbot development tools today are based on two main types of chatbots, either linguistic (rule-based chatbots) or machine learning (AI chatbot) models.
Linguistic Based (Rule-Based Chatbots)
Linguistic based – sometimes referred to as ‘rules-based’, delivers the fine-tuned control and flexibility that is missing in machine learning chatbots. It’s possible to work out in advance what the correct answer to a question is, and design automated tests to check the quality and consistency of the system.
Rule-based chatbots use if/then logic to create conversational flows.
Language conditions can be created to look at the words, their order, synonyms, common ways to phrase a question and more, to ensure that questions with the same meaning receive the same answer. If something is not right in the understanding it’s possible for a human to fine-tune the conditions.
However, chatbots based on a purely linguistic model can be rigid and slow to develop, due to this highly labor-intensive approach.
Though these types of bots use Natural Language Processing, interactions with them are quite specific and structured. These type of chatbots tend to resemble interactive FAQs, and their capabilities are basic.
These are the most common type of bots, of which many of us have likely interacted with – either on a live chat, through an e-commerce website, or on Facebook messenger.
Machine learning (AI Chatbots)
Chatbots powered by AI Software are more complex than rule-based chatbots and tend to be more conversational, data-driven and predictive.
These types of chatbots are generally more sophisticated, interactive and personalized than task-oriented chatbots. Over time with data they are more contextually aware and leverage natural language understanding and apply predictive intelligence to personalize a user’s experience.
Conversational systems based on machine learning can be impressive if the problem at hand is well-matched to their capabilities. By its nature, it learns from patterns and previous experiences.
But, to perform even at the most rudimentary level, such systems often require staggering amounts of training data and highly trained skilled human specialists. In addition, a machine learning chatbot functions as a black box. If something goes wrong with the model it can be hard to intervene, let alone to optimize and improve.
The resources required, combined with the very narrow range of scenarios in which statistical algorithms are truly excellent, makes purely machine learning-based chatbots an impractical choice for many enterprises.
Hybrid Model – The Ultimate Chatbot Experience
While linguistic and machine learning models have a place in developing some types of conversational systems, taking a hybrid approach offers the best of both worlds, and offers the ability to deliver more complex conversational AI chatbot solutions.
A hybrid approach has several key advantages over both the alternatives. When considered against machine learning methods, it allows for conversational systems to be built even without data, provides transparency in how the system operates, enables business users to understand the application, and ensures that a consistent personality is maintained and that its behavior is in alignment with business expectations.
At the same time, it allows for machine learning integrations to go beyond the realm of linguistic rules, to make smart and complex inferences in areas where a linguistic only approach is difficult, or even impossible to create. When a hybrid approach is delivered at a native level this allows for statistical algorithms to be embedded alongside the linguistic conditioning, maintaining them in the same visual interface.
Building conversational applications using only linguistic or machine learning methods is hard, resource-intensive and frequently prohibitively expensive. By taking a hybrid approach, enterprises have the muscle, flexibility and speed required to develop business-relevant AI applications that can make a difference to the customer experience and the bottom line.