According to a recent report from Acquire, 1.4 billion people are willing to use chatbots – on a global scale, that’s about a quarter of the human population.
Chatbots have become increasingly popular over the last few years. In fact, most major websites and apps you visit now have a little chat icon pop up in the bottom-right corner, allowing you to connect to a chatbot to assist you with your customer journey.
With that in mind, Apadmi wants to help support our clients and core sectors in staying ahead of the trend, and ensure that they not only have the option to incorporate chatbots into their mobile platforms, but also that they can offer industry-leading chatbot experiences to their users.
We’ve created this guide to help support you and your business in chatbot creation and implementation.
What is a chatbot?
To the user, a chatbot is an opportunity to receive instant communication from a company to help them solve problems or complete tasks. From the business side, a chatbot is a service powered by rules and, in some cases, artificial intelligence, which allows interaction with customers without the need for human resources.
Chatbots have become very popular in many industries, from weather bots to news bots to finance bots, and they can now provide complex responses to customer queries without ever having to connect to a human.
Chabot AI vs rule-based chatbots
There are two different types of chatbots – rule-based chatbots, and AI chatbots.
A rule-based chatbot is limited in what it can do. It relies on specific commands being entered, as each input and response needs to be programmed into the chatbot so it knows what to expect from the customer, and how to respond.
This can sometimes lead to a disappointing experience for customers; if the bot receives information it isn’t expecting, or doesn’t have a rule for a question that’s asked, the customer will be met with errors. Despite these limitations, these chatbots can be incredibly useful for some companies if they want their customers to be led down a defined pathway.
An AI chatbot uses machine learning to interact with customers. It has artificial intelligence (AI) that allows it to understand language and patterns so it can handle situations that it may not have encountered before. Machine learning also allows it to continuously adapt and learn from the interactions, so if it finds a situation it’s not familiar with, it will learn how to best respond the next time it happens.
Whether an AI or a rule-based chatbot is right for your business depends on the use case of the bot:
- Rule-based chatbots provide a tightly controlled experience for users, which may fit best in situations where you want the user to be guided down only a few select paths, or to simply gather important information before being connected to a support team.
- AI bots offer more flexibility for the user, as they can handle complex queries and suit situations with large datasets. However, there’s potential for the AI to go off-script and lead a user down the wrong path.
How chatbots use AI
AI in chatbots works through machine learning. A bot must first be trained on how to respond to queries, typically by using historical datasets, such as conversation logs from human chats or customer service logs.
The chatbot will examine these datasets and store them in memory, creating an algorithm that it uses for customer interactions. When a customer then interacts with the chatbot, it will calculate a response to the query by weighing up the connections and context in the stored data using this algorithm.
The bot will take each sentence and match these against the historical data set to formulate a response. This allows chatbots to be able to respond to customers in the same conversational style and offer the same solutions as the historic data, without ever having to have interacted with a customer before.
Rule-based chatbots using python
With a rule-based chatbot, you can specifically programme expected inputs from the user, and the specific responses you want the chatbot to provide using a programming language like Python. There’s no limit to the number of inputs and responses you can programme in Python, but they must all be explicitly specified for the chatbot to be able to respond appropriately to a situation.
When programming rule-based chatbots, it’s helpful to map the customer flow so you can see how users are likely to interact with the chatbot, and what specific points in the flow may cause errors, so you can handle these before they occur.
For example, you may want to programme your chatbot to understand common misspellings, or be able to respond based on a certain key term. The chatbot will then still be able to handle and respond to these queries, even if there’s a spelling error or a query is worded in a slightly different way to what the chatbot is expecting.
Can chatbots be hacked?
Unfortunately, chatbots are particularly susceptible to phishing and hacking attacks – but there are methods you can put in place to minimise the risk of this occurring.
By utilising encryption, multi-factor authentication and limiting the information the chatbot can send if the data is sensitive, the risks of hacking can be minimized.
The most common hack with a chatbot is a ‘man in the middle’ attack, where the hacker builds a chatbot that mimics the official original. The user assumes they’re communicating with the official chatbot – but in reality, it’s the hacker they’re talking to.
This allows the hacker to view the conversation, including any confidential information, and manipulate the user. Generally, this attack is used to steal confidential data such as passwords, email addresses and financial information.
Another vulnerability comes from the use of AI chatbots, who can be trained to respond to interactions in certain ways. This creates an opportunity for hackers to re-train these chatbots to respond with hateful, hurtful or even just completely irrelevant responses, damaging the customer service experience for the user.
Website and mobile app chatbot security
As mentioned above, chatbots do have some vulnerabilities with regard to hacking, but there are several effective ways to minimise and eliminate these risks.
The first is end-to-end encryption. Enabling encryption on the chatbot prevents anyone other than the user and the chatbot from seeing the content of the messages. It’s proven to be an effective way to secure personal data and prevent its misuse.
Next is authentication. There are many different types of authentication that you can use, and their use cases differ slightly for websites and mobile apps. The most familiar method of authentication is using logins and user IDs. By asking a user to create secure login credentials, before providing any confidential information, you can ensure the chatbot is only sharing information with the authenticated member.
In addition to a login user ID, you can improve the authentication process further by implementing two-factor authentication, where a user has to confirm their identity with a second device – email or text message, for example. For mobile apps, you can make use of the built-in technologies within the phone and implement biometric authentication (such as a fingerprint scan or face recognition) to add a further layer of protection for users.
Another useful tool to ensure security is by using timeouts on messages that contain confidential information. When confidential information is sent via a message, you can implement protocols that will delete the message after a specified period. This allows the chatbot and user to access this confidential data, but importantly, prevents personal data from being stored anywhere that may be accessed by a hacker at a later date.
Which chatbot is right for your business?
Both the rule-based chatbot and AI chatbot offer great opportunities for businesses. The choice depends on the customer journey for the specific business and what the business wants the chatbot to be used for.
A rule-based chatbot is perfect if there are a few defined paths that a customer conversation can go down, such as a frequently asked question. A rule-based chatbot can adequately handle these interactions and provide exactly the response the customer is looking for.
AI chatbots allow for more dynamic interaction, so are better suited for customer journeys with less structure and fewer defined conversation paths. An AI chatbot technology does require training, so would best fit a business that has historic conversation logs and previous customer interactions that the chatbot can analyse and use to inform the future interactions it has with customers.
Whether you choose a rule-based or AI chatbot, they can provide huge opportunities for businesses. Chatbots can be available 24/7, are proactive with customer interaction, gain greater insight into the customer experience, and save costs by reducing the need for human interventions in customer service.
What’s the difference between chatbots and conversational AI?
Conversational AI is the next step towards replicating a human-to-human customer service experience using technology and chatbots. Conversational AI has a greater ability to remember customers and use that to adapt to the way they communicate with a user.
Conversational AI has the added ability to function in both a text and voice capacity. While chatbots are confined to a text-only conversation, conversational AI can be utilised to understand and respond to voice commands. This technology unlocks huge potential for use in call centres, as well as offering greater accessibility for users who may not be able to type and read a text-only conversation.
In the coming years, we see conversational AI overtaking regular chatbots and forming the backbone of customer service for many companies.
If you would like to learn more about how chatbots and conversational AI can support your business, please get in contact below.