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Web chat, chatbots, virtual agents & AI

Web chat, chatbots, virtual assistants, virtual agents, conversational AI - as with so much in the world of AI, there is disagreement about definitions. In this case, it’s better to focus on the functionality and ‘intelligence’ powering it, rather than the phrase in itself.

What's in a name? 

AI for customer contact is currently best known for chatbots, applications that run automated tasks and simulate conversation with the customers. It may be given a human avatar and personality characteristics, and includes natural language processing, dialogue control, access to knowledge bases and a visual appearance that can change depending on who it is talking to, and the subject of the conversation. Chatbots are often found in the web chat channel, but the functionality can be used in any other digital channel, such as social media, email or even voice self-service.

Chatbots are not always fully-automated or AI-enabled, and may in fact be a glorified FAQ interface, lacking ‘understanding’ and simply searching through keywords. However, some use natural language processing and can ask questions to understand customer intent and improve the accuracy of the output, and may also use machine learning to improve future outcomes.

Virtual assistants are not dedicated to a single task (such as customer service), and can assist in numerous ways such as taking notes, carrying out web research, setting alarms, communicating with smart devices, etc.

Both chatbots and virtual assistants are conversational interfaces, but the level of AI involved can differ greatly.

Web Chat Takes Too Long

Perhaps the most obvious potential use of AI in the customer contact environment is in handling digital enquiries, where web chats generally take far longer than phone calls (due to agent multitasking, and typing time) and some email response rates can still be measured in days.

As the cost of web chat is broadly similar to other channels such as email, voice and social media, there is considerable room for increasing efficiencies and lowering costs.

Web chat automation has grown in 2018, mainly as a result of initial handling by automated chat bots which may then hand off to live agents where appropriate. The channel is increasingly popular, especially with the younger generation of customers

Further comparing the experience of web chats with telephone calls, surveys find that 52% of web chats take longer than 3 minutes to complete fully, as agent multi-tasking and the time taken to type differs from the experience of handling a phone call.

Some customers may experience a shorter overall length of interaction over web chat: 24% of web chats are handled in less than 3 minutes, compared to only 16% of phone calls, almost certainly due to the average complexity of phone queries being greater than other channels. A lot will depend on the complexity of the query.


From Web Chat to Chatbots

The most sophisticated chatbots or virtual agents encourage the visitor to engage with them using natural language, rather than keywords. The virtual agent will parse, analyse and search for the answer which is deemed to be most suitable, returning this to the customer instantly. Many virtual agent applications will allow customers to give all sorts of information in any order, and either work with what it has been given, or ask the user for more detail about what they actually meant. Having been unconsciously trained over the years to provide their queries in a way which standard search functionality is more likely to be able to handle (for example, a couple of quite specific keywords), customers must be encouraged and educated to use natural language queries in order for virtual agents to be able to deliver to their full potential.


The virtual agent application is different from standard search functionality, ignoring bad punctuation or grammar, and using longer phrases rather than just searching on keywords. Sophisticated AI applications attempt to look for the actual intent behind the customer’s question, trying to deliver a single correct answer (or at least a relatively small number of possible answers), rather than a list of dozens of potential answers contained in documents which may happen to contain some of the keywords that the customer has used. The virtual agent application may also try to exceed its brief by providing a list of related questions and answers to the original question, as it is well known that one question can lead to another. Solution providers and users train the system to pattern-match the right words or association of words with the correct result: the application, unlike older forms of web search techniques, does not simply guess what the customer wants, or how they will express themselves. Through ‘listening’ to what the customers actually say - perhaps through a mixture of large quantities of audio and text – the initial set-up configuration can achieve a good accuracy rate, which really benefits over time as a positive feedback loop is established.

When the virtual agent application has low confidence that it has returned the correct result, it is able to escalate the customers query seamlessly to a live chat agent, who then has access to the self-service session history, enabling a greater chance of a successful resolution without repetition. (It is generally considered best practice that escalations to real agents are not hidden from customers). The eventual correct response can be fed back to the automated virtual agent (and the knowledge base underlying it), which will make it more likely that future similar requests can be handled successfully through automated agents.

It’s important to reiterate an earlier point: not all chatbots or virtual agents are powered through AI and machine learning – many use programmer-defined rules and scripting in order to retrieve answers from a knowledge base. While these types of chatbot have their place in tightly-defined situations where there are a relatively small number of options or answers, businesses should remember that not all chatbots work the same way.






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