Customer service is an area that has a high number of repetitive queries which are possible to solve through Artificial Intelligence (AI). AI is used in customer service in a variety of ways to provide better customer support and decrease the workload of customer service agents.
Examples include chatbots and intelligent Virtual Customer Assistants capable of understanding natural language and providing answers through messaging apps and website chat; digital IVRs for better routing to agents over the phone; call center data transcription and analytics; voice and face authentication and digital verification.
Artificial Intelligence (or AI for short) is a way of machines reasoning on their own and providing helpful information to people. In the context of customer service AI is a way that machines provide useful information to customers. This means that the machines are able to understand what customers are asking and they are also able to provide helpful information for the customers.
Such support can happen in a variety of different ways. Since AI is something that can be embedded into text, voice and backend databases and processes, the ways in which AI can be embedded into customer service are many fold. This is important to keep in mind because there are several touch points in the customer journey and customer experience where AI can make a difference.
AI is not only used in the customer facing roles but increasingly is playing an important role in helping customer support agents. This means that the systems are capable of giving helpful information to agents, bringing up the relevant data and also organizing information so that agents do not have to do it manually.
Here we are highlighting places in the customer journey where AI can be helpful in understanding customer issues and solving those issues. AI can be applied in customer support in several ways. It can be used across the whole journey of the customer - from contacting agents to finding information on the website to authenticating oneself to providing information assistance to customer support agents. Building AI has become easier today and there are no code options available for knowledge workers to build their own AI in 15 minutes.
Below we will break down the examples of AI in customer service as there are various ways in which AI can help. First we will look at it from the point of view of messaging and chat-based channels. Then we will look at the way in which AI can help in voice channels. And finally we will be looking at the ways in which AI can be of assistance in providing a more efficient and streamlined support for customers.
The simplest form is a customer service ai chatbot. A chatbot is an interface through which the user can obtain information from the machine. The interface is usually in written form (chat) and in many cases the chatbot presents the user information with simple Yes/No type of options. These if-else statements are essentially decision trees where the user selects a certain answer. Upon the selection of that answer the user is given a follow-up question with a choice of answers again.
Such solutions are useful because they are usually operated through a clickable interface. Here we have listed the top 30 chatbot software platforms. The user is presented with buttons and by clicking on the buttons the user confirms its choices and receives information. It is a quick way of showing info to users. The challenge, however, is that while the user is directed towards the answer, the chatbots at times do not provide a specific answer to the question that the user is looking for.
An improvement upon a chatbot is what's called a Virtual Customer Assistant (VCA). These intelligent agents are not only capable of presenting a multiple choice selection of answers to the user but also understand user intent from free text. Understanding natural language and being able to interact in multiple languages is a significant leap.
Many times users are looking to articulate their specific concern to the machine in a similar manner they would do to a human. User has a question and asks that specific question from the machine e.g. “When will I receive my payment from Bank ABC?”. The main drive behind this is that users are looking for a quickest way to get an answer to their specific question.
In our example, the user is looking to understand in hours or minutes how long it takes for the payment to arrive from Bank ABC. Not Bank XYZ or GFD but Bank ABC. And the VCA has to provide this answer in the form of “It takes 6 hours during weekdays to receive a payment from Bank ABC”. In such a situation only the most relevant answer matters and for the users it does not matter if the answer comes from a machine or a human. As long as it is accurate and relevant.
One can see how a traditional chatbot falls short in this example. The chatbot might show an illustration of transfer times from other banks or give a link to a self-help article. However, the user needs a concrete answer. Hence, Virtual Customer Assistants with their ability to understand specific intent from free text, are helpful here. For more information on vendors, click here for our top 17 Conversational AI software platforms.
Using ai for customer service in the call center can be done in a variety of ways. It used to be that when calling customer support the main form for customers to reach human support was through pressing buttons on their phones. Now companies have deployed digital forms of IVRs where customers just speak and tell what their problem is.
The algorithms understand the phrase and are able to route the customer based on the content and intent of the phrase to the correct support agents. It is a way of bringing an analogue mode of support function to the digital realm.
Some companies take digital IVRs a step further. Instead of transferring customers to the correct agents, the customers ask a question over the phone for the AI to understand it and then answer it. From a technological perspective this is a challenging example as it requires for the AI to transcribe speech into text and then run the text through its own knowledge base to determine the intent.
Once the intent is found the algorithms can pull up an answer. Then the answer is read out by the AI, meaning that the answer is converted from text to speech. Such solutions are the ultimate form of real-time support. However, real-time dialogue with the Conversational AI for customer support is still something that requires algorithms to get smart and fast enough to understand customer intent and provide accurate answers.
An example of AI powered customer service are solutions make use of analyzing what customers have talked over the phone with customer support agents. Such technologies are capable of transcribing speech into text and then analyzing what customers actually wanted. This provides various information into what were customers' problems and what type of information they were looking for.
It also allows to understand how well the customer service agents responded to their customers and whether they provided good quality support. In addition, AI customer service statistics can show contextual information such as sentiment analysis, whether customers were angry or happy can provide an additional layer of information.
Also it is interesting that from such use cases information can be derived about whether customers were looking for a specific product. This provides helpful information to companies’ product and marketing teams to see what is the sentiment of customers towards the services and products that they offer. It also enables to see what kind of solutions customers are looking for and what new products should be created to meet customer demand.
AI also helps customer service agents provide better support. These so-called co-pilot modes assist agents when they are on the phone or chatting with customers. AI is analyzing information provided by the customer and from that they can provide answers that the customer service agent should tell the customers.
For example, if the customer is asking about new credit cards from a bank’s customer support representative then the algorithms can pull up relevant information on the service representatives dashboard about credit cards that he can offer for the customer.
In addition, such solutions can pull in customer specific information such as the type of device they are using or if they ever had service problems. All this information is something that provides more context for the agent that she can then decide to act upon.
Authentication in the context of customer service usually means authenticating through a combination of a sign-up ID and a password. It is also possible to use voice authentication. Here the algorithms are listening on how the customer is speaking. This means that the AI is looking at the tone, cadence and pitch of the voice of the customer. Based on those parameters the algorithms create a user profile and use it as a method of authenticating the customer.
This becomes even more powerful when combined with facial recognition i.e. analyzing the video of the customer in their webcam and their facial mimicry and the way they pronounce their name. A combination of such voice and facial authentication is a way in which voice analytics and images recognition are used to authenticate customers.
Identity verification is a topic where AI can help. When setting up new accounts especially with various online banking services it is important to verify a person's identity. In such cases AI is capable of reading information from the photo ID provided by the users.
It then matches it with the image that the algorithms see in the users’ webcam. Such solutions are taking advantage of image recognition and making sure that the person's face on the document ID matches the one on the webcam.
In some cases such solutions are used to fully automate identification and in some cases they serve as a pre-check for the customer support agents in flagging suspicious cases so that the service agents will do the final verification manually. The use of such image recognition speeds up customer ID identification processes.
AI is not necessarily a CRM but it is an intelligent layer on top of a CRM that provides helpful information. If you look at the way current CRMs are set up then it is usually a lot of information centered around the customer or the account. Examples of data stored can be last orders, services used, tier of support, payment history, their preference, detailed information on the products and services, their functioning etc.
However, such information is not static. From that point of view the information in the CRM needs to be constantly analyzed. So it's no longer just simple rules that if a customer has not paid invoices in a certain number of days then their credit rating is downgraded.
The issue with such rule based systems is that these rules are thought by humans. And humans can only think of a certain number of rules. However, what machine learning is capable of is looking at patterns from the data and finding those patterns itself.
So the AI can find correlations and causations in the data that is something that human analysts have never thought of. Algorithms are capable of going through vast amounts of data and spot trends and patters that humans are simply not capable of. So you can think of AI as an intelligent layer on top of the CRM database that teases out information that is vital for the product managers and customer service managers in providing better support.
Conversational AI is the way in which machines are built to understand and conduct conversations with people. It is a method for automating conversations to a degree where people get useful information from machines in a manner that resembles conversations between people.
It is used for a variety of cases such as virtual assistants for customer service automation and voice automated bots. Conversational intelligence gathers useful information from automated conversations such as topics discussed, things uttered, information requested.
Such items are useful for finding various types of data points to give more insight into the nature and capabilities of the conversation conducted. Chatbots and Virtual Customer Assistants that we talked about in the previous paragraphs are an example of conversational AI.
AI has the capability to improve customer service across several dimensions. As it can be applied in various domains as seen from what is described above, it is clear that the metrics used to measure the effectiveness of AI are manyfold. Below we have taken some of the metrics and described them in more detail.
With human support customers are bound to reach customer service only at times when customer support assistants are available. With AI it is possible to have conversations with virtual customer assistants 24/7. This means that the availability of services is around the clock and not dependent on the opening hours of call centers.
One of the most tedious things in customer support is waiting and being put on hold. This is especially true when calling call center agents but also when waiting in line to chat with customer service. Bots using AI are always available. This means that you are connected immediately to a chatbot or an intelligent Virtual Customer Assistant that gives you answers to your questions. Such immediate support speeds up the time to serve customers and avoid putting them on hold.
Much of the work in call centers is often spent on answering tedious questions that could be automated by an AI. So if you are operating a call center then an AI can provide information to the customers and in the case of chat provide deflection rates up to 50% and more. Further if you connect customer data to your call center software you can also measure the amount of customers that chatted with the bot but did not call you afterwards.
This means that your call center agents will have to deal less with tedious questions and can concentrate more on solving complex issues and doing sales. For a call center manager this means that your employees are doing intellectually more stimulating work and growing the business.
To build AI into your customer service it is important to pick the right tools. With a wide variety of products available, it can be overwhelming to decide which platforms are the best ones to use. We spent 25 hours going through dozens of products and put together a carefully curated list of top 10 AI Customer Service Software Companies. You can see the top 5 companies here and here you can see the full list of top 10 Customer Service AI software companies.
AlphaChat is a no-code end-to-end Conversational AI platform allowing anyone to build Natural Language Understanding Intelligent Virtual Assistants. The platform also offers advanced features for enterprise customers such as authentication, SSO, APIs, agent co-pilot mode and intelligent routing.
What’s special about this tool: Standard Package offers everything from to build your own AI. insights into customer conversations (topics discussed, customer satisfaction) and statistics on AI value performance. Enables to train the NLU chatbot in one language and have it automatically chat in any language. SLAs and AlphaOS with DIY custom code writing available for enterprise accounts.
Pros: Built-in no-code end-to-end chat automation with NLU chatbots, live chat product. Advanced authentication and integration capabilities with 3rd party systems for developers.
Cons: No phone product for agents and CRM.
Pricing: 10-day free trial. Paid plans from €399/month.
Bold360 is a customer service AI software company offering live chat and messaging automation for customer support.
What’s special about this tool: Bold360 offers comprehensive tools for automating live chat for agents and for customers.
Pros: Remote support products of other LogMeIn divisions.
Cons: Multi-lingual AI training.
Pricing: Price available on request.
Amelia (from IPSoft) is an AI software company that allows building job-based digital employees for external and internal customer support.
What’s special about this tool: Amelia is a no-code AI platform for creating digital assistants for a variety of use cases including internal IT and support.
Pros: NLU capabilities and integrations.
Cons: Enterprise customer focus only.
Pricing: Price available on request.
Twilio Autopilot is an AI platform from the communications software provider to build conversational IVRs and bots.
What’s special about this tool: Twilio Autopilot integrates with Twilio Flex contact center solutions and can be deployed across multiple channels.
Pros: Omnichannel integration.
Cons: Limited insight for training and conversation analysis; technical user interface.
Pricing: Messaging and chat $0.001/message, voice $0.04/minute.
Zendesk Answer Bot is a platform from the contact center software provider that allows building chatbots for support automation with the Flow Builder.
What’s special about this tool: Zendesk Answer Bot is a tool for building a quick bot to answer common questions and escalate complex queries to the agents.
Pros: Integrates into Zendesk products.
Cons: Limited training capability of chatbots.
Pricing: Paid plans from €49/agent/month includes up to 50 AI-powered automated answers.
Artificial intelligence makes customer service more efficient through the use of Natural Language Understanding virtual assistants. In addition, AI is applied to authentication and also voice data transcription for providing more insight into call center agents in offering better customer support.
If you are looking to try out AI for your own customer service, feel free to sign-up for an account with AlphaChat. In just 15 minutes you can get your own natural language understanding Intelligent Virtual Assistant that you can connect with your website.
Pre-trained in all languages and also with template answers you can easily modify. And if you have more specific enterprise needs, reach out and we can accommodate these as well.