Do Live Chat Apps Work For Lead Generation?

Live Chat Apps Of Today 

With what started in the 1970s as a means for university students to internally communicate on campus, the very first live chat tool, known as PLATO, has evolved significantly into what we know as today’s live chat apps. 

An image that shows a student using PLATO, a first-generation live chat software in 1973.

Customers can expect any reputable brand to include a live chat service online when agent-assisted support is required. And these expectations have only increased since the pandemic began. Without face-to-face customer service, digital customer service tools such as live chat were relied upon and quickly became customers’ preference over speaking to operatives over the phone. 

Did you know, 79% of customers said their preferred contact channel was live chat? 

With demand for the quick and convenient contact channel skyrocketing,  the global live chat software market is projected to reach $997 million by 2023, growing at a CAGR of 7.5% from 2017 to 2023.

 A Must-Have For Customer Service 

Live chat can be considered as a must-have when it comes to customer service, not only does it help to provide a smooth customer journey, impacting on CX and CSAT ratings but it promotes efficiency within the contact centre. 

Some live chat app providers include features within the agent interface that contribute significantly to the reduction of average handling times (AHT) and overall efficiency, therefore bringing support costs down. 

For instance, AI-predictive suggestions is a feature that recommends relevant articles to the agent as they begin to enter a query. This takes place in real-time and updates on each key-press, revealing relevant knowledge articles with optimal efficiency. 

Similarly, vendors that include a live key-press feed function within the agent interface produce huge reductions in Average Handling Times (AHT). As the user types their query in the live chat app, the agent sees a preview on each key-press. This often results in the agent finding a resolution to the issue before the user has hit “send”.  

Further, live chat works seamlessly with other customer service tools such as a chatbot. A chatbot can automatically escalate to live chat when necessary, taking place inside the same window for great CX.  

For instance, a chatbot detects that the query in question is too complex by nature for AI to handle or sentiment analysis identifies a certain word that indicates urgency. Through automation, the chatbot escalates the user to live chat eliminating the need for query repetition or sourcing a support telephone number. This significantly improves CX and streamlines the user journey 

More recently live chat apps are being utilised as lead generation tools, converting visitors into leads alongside their traditional customer service function. 

It means that now, live chat apps help with the entire user journey rather than solely after-sale should a query arise to do with the product or service they have purchased.

So, How Do Live Chat Apps Work For Lead Generation?  

Live chat apps of today include sophisticated, AI-powered features that facilitate not only excellent customer service but even lead and revenue generation. Here’s how. 

Real-Time Help Boosts Lead Capture 

Live chat is far more accessible than other agent-assisted contact channels such as telephony or email. The time in which it takes to connect a user to an agent via live chat is considerably shorter than telephone or email which can take up to days – which by when the user might have chosen an alternative company or even changed their mind. 

But, by providing real-time help and advice to leads whilst they’re hot, the chances of conversion increases. 

Trigger Management Intercepts Critical Touchpoints 

Custom triggers can be configured that force your live chat app to launch when certain conditions are met. For instance when a certain amount of time is spent on one page or when a user lands on a specific page. By configuring such triggers and intercepting site visitors at critical touchpoints within the user journey, companies increase the chances of capturing leads that would otherwise be anonymous and lost. 

Similarly, companies can set up intelligent escalations from self-service to live chat based on the keywords that are entered. For instance keywords such as “subscribe”, “buy” or “new account” indicates a lead and potential new business. By forcing live chat at these moments, companies increase the likeliness of lead generation. 

Seamless Integrations Fasttrack Lead Nurturing 

Effective live chat apps include open RESTful APIs that enable the two-way sharing of data amongst 3rd party apps such as CRMs. Such integrations automatically send lead data to your CRM, populating the system ready for salespeople to begin the nurturing process. 

Other popular 3rd party business tools that live chat integrates with to help convert leads into customers are: 

  • Email marketing systems
  • Ecommerce software 
  • Analytics platforms 

Intelligent Forms Automatically Capture Lead Data 

In digital CX, when it comes to lead generation it is good practice to capture personal information at the earliest possible opportunity. This prevents valuable lead data from being lost if, for example, the connection is lost. 

By including a mandatory form when a live chat conversation is first initiated, companies not only capture lead data, but if something goes wrong, with information such as email address acquired, companies have a secondary method of contact whereby user queries can still be answered, contributing to good CX. 

Asynchronous Messaging Allows For 24/7 Support 

Some live chat apps offer asynchronous messaging. This gives the user control over when they message the agent, initiating and continuing the conversation without it timing out. It maintains the chat history for the user to look back over and ultimately means that they can ask a query 24/7, rather than having to wait a certain time to do so. 

This provides convenience and flexibility to the user, who is in control of when they submit a question or reply to an answer – particularly useful for those with busy lifestyles.

Live Chat Apps: Beyond Customer Service And Lead Gen 

What is next for live chat apps?

From customer service to lead generation and revenue generation, here are just some of the processes we can expect to see live chat handle today and in the future: 

  • Identification & Verification (ID&V) 
  • Quotations and purchases 
  • Augmented reality for fixing physical issues in real-time 

If you enjoyed this article and would like to learn more about live chat, you can read our guide here or for advice on which live chat app is right for your business, please get in touch. 

The Top 5 Benefits Of A Self-Service Contact Centre

The requirement for self-service contact centres has only increased since the pandemic. Periods of non-contact and lockdowns meant that consumers had to learn the self-serve – so much so, that for many who are now tech-savvy, it is their preference. 

Contact centres understand not only the benefits in which self-service has on their customers but also the benefits it has to overall business efficiency.  

This article explores the top 5 benefits of a self-service contact centre: 

  1. Contact centre costs are considerably reduced 
  1. Resolution times shrink for better cx 
  1. Improved ESAT positively influences CSAT 
  1. Overall agent efficiency skyrockets 
  1. Continual optimisation of knowledge through feedback 

1. Contact Centre Costs Are Considerably Reduced

Before self-service contact centres, getting a resolution to a customer query went something like this: 

  1. Customer visits website
  2. Customer navigates to the Support page
  3. Customer rings the support number provided 
  4. Customer waits on hold for 20 minutes   
  5. Customer explains their query to an operative, before being transferred to the correct department
  6. Customer explains their query once again to the specialist operative 

Not only are there multiple steps to this process, making the overall experience cumbersome for the customer, but each step that involves handling the query itself has overheads attached to it.  

When we consider just how many queries a company receives each day, these support costs accumulate and can prove particularly costly over time. 

However, with the introduction of AI-powered self-service tools, getting a resolution to a customer query looks like this: 

  1. Customer visits website
  2. A chatbot asks if they require help with anything, to which the customer enters their query
  3. Using sophisticated Natural Language Processing (NLP), the chatbot produces an article that is relevant to the customer’s query   

Not only is this a shorter and smoother journey for the customer but it involves zero agent involvement, removing those costs associated with handling the query. 

Through the automation of routine queries and even tasks such as quotes, payments and ID&V, support costs are significantly reduced for the contact centre. 

2. Resolution Times Shrink For Better CX

A critical factor affecting good customer service is resolution time, the total time in which it takes for a customer’s query to be resolved – from start to finish. 

As demonstrated above, there are far fewer steps in the customer journey that utilises self-service in comparison to the one that does not utilise self-service. This is because the latter relies on agent availability and depth of specialist knowledge – which can be limited.  

AI, on the other hand, is available 24/7, produces automatic responses and therefore will never need to put a customer on hold to find the correct article or to consult a manager. 

This slashes resolution times and is made possible through an AI derivative known as Natural Language Processing (NLP). NLP receives the customer query on behalf of the chatbot or self-service tool and by unpacking each of its components and analysing them, can understand intent. This allows the very best and relevant results to be produced quickly. 

Self-service is quick, convenient and has quickly become the now tech-savvy consumer’s preference. 

3. Improved ESAT Positively Influences CSAT 

In a recent survey, contact centre executives were asked about their experiences. It was revealed that 74% of those asked felt that agent experience had a significant impact on customer experience.  

This suggests that there is a correlation between empowered employees and satisfied customers; when agents have job satisfaction, customer service is better. 

But what does this have to do with the self-service contact centre? 

Through powerful AI and by harnessing NLP, self-service software independently handles 20% of all routine queries online. This means that significantly fewer routine queries and tasks end up reaching contact centre agents. 

Instead, with routine queries partially filtered out, the agent gets to interact and help customers with complex and serious issues. By reducing the mundane from their job roles, agents feel empowered – particularly by finding resolutions to complicated questions. It gives them job enrichment and purpose. 

In short, self-service enhances employee satisfaction, which in turn can improve CSAT.

4.  Overall Agent Efficiency Skyrockets 

Self-service tools such as chatbots or FAQ widgets are powered by one centralised, intelligent knowledge base. This is central to the company and not only utilises AI to fuel customer service tools but also powers the interface in which agents use to resolve customer queries in the contact centre. 


An image to show how Synthetix products connect

When a company adopts a self-service contact centre, they begin to reap the benefits of the agent-facing console, with productivity and efficiency skyrocketing. 

The agent interface includes an integrated mini knowledge base so that hundreds, maybe thousands of articles of knowledge are available at agents’ fingertips.  

For optimal efficiency, AI-predictive suggestions recommend relevant articles on every key-press which significantly reduces the time in which it takes to handle a query, minimising Average Handling Time (AHT) and promoting CX.



Further, for self-service contact centres that include live chat as a contact channel, the live key-press feed feature allows agents to preview what users are typing in real-time before they hit “send”. This allows agents to sometimes craft a response before the query has even been sent, reducing wait time considerably. 


5. Continual Optimisation of Knowledge Through Feedback

Self-service software not only reduces routine contact levels but also helps companies with optimising the knowledge that resides within their centralised knowledge base.  

By including customer feedback prompts within each knowledge article, companies can grasp an idea of whether the content is serving its intended purpose – that is, resolving customer queries or not.  


an image that shows how user feedback is left on self-service software.

The wider knowledge base analytics centre can tell companies which articles were effective or optimisable over time, making it transparent and easy to identify which require improving and which are working. 

Similarly, data will show if an article is optimisable by comparing the query with the position of the article. If the article did not show as a first-page result then its keyword matching and intent will need looking into. 

Here’s How Synthetix Self-Service Solves Contact Centre Problems 

Over 80% consider bots and AI robotic automation to be an important function of the contact centre, yet only 48% have deployed such technology into the contact centre. 

With a fast-changing, customer-centric landscape it is vital to the success of CX and general business that self-service is incorporated into the contact centre.

For expert advice as to how the self-service tools mentioned in this article can help your contact centre, 


The Best Self-Service Software For Customers

Self-Service Software for Customers: The Considerations 

Rather than recommending something that should be subjective to each company’s needs, we have put together some considerations that will lead you to the best self-service software for your customers: 

  • Does the self-service software utilise Natural Language Processing? 
  • Is the self-service software fuelled by one centralised knowledge base? 
  • Can the FAQ articles be found organically using search engines? 
  • Can the self-service software be optimised for different customer journeys? 
  • How easy is escalation between self-service and agent-assisted channels? 
  • Can the self-service software’s content be improved over time? 

Does The Self-Service Software Utilise Natural Language Processing?

Natural Language Processing (NLP), a branch of Artificial Intelligence (AI), is used to help analyse linguistics and by doing so, understand intent. 

For customer service and particularly in self-service software, this means unpicking customers’ queries, analysing components such as grammar, keywords, intent and popularity to identify user intent. From here intent keywords are matched with knowledge articles, serving users the most relevant results. 

Many companies consider NLP-powered self-service as essential. Not only does it provide customers with accurate resolutions 24/7/365, but it also helps to significantly reduce contact centre costs. 

Because AI is capable of handling large volumes of routine queries – queries that would otherwise reach contact centre agents – the overheads associated with query handling are significantly reduced, bringing down support costs. 

It can also remove the repetitive and mundane from agents’ day-to-day tasks. As AI is capable of dealing with routine queries and tasks, operators can help with more complex and ‘worthwhile’ issues, adding to job satisfaction, or ESAT (Employee Satisfaction). 

Is The Self-Service Software Fuelled By One Centralised Knowledge Base? 

A centralised knowledge base acts as your main source of information, powering all customer service channels including self-service software for your customers to use.  

The benefit of utilising a knowledge base here – and particularly an intelligent, AI-powered knowledge base – is that it ensures consistency of information, encouraging CSAT but also helping to prevent any problematic situations that could occur as a result of inaccurate information. 

The issue with having multiple channels and sources of information is that they have to be updated separately, increasing the risk of them displaying contradictory, inaccurate or misleading information on behalf of your company. 

Not only may your brand’s reputation come under fire, but customer loyalty may also be affected or in some cases, the consequences of showing incorrect information could lead to legal action or worse. 

Having self-service software that is powered by a knowledge base also saves significant time as knowledge articles only need updating once and changes are reflected wherever you have opted for a particular article to show. 

Can The FAQ Articles Be Found Organically Using Search Engines? 

There are a number of different ways in which a user might begin their journey. Whether it’s directly through your site or via a social media channel, it’s important to accommodate every user and their preference – including organic search engine results. 

Having self-service FAQ results findable via search engine results not online streamlines the customer journey significantly, trimming time off their session and improving CX but can also help to enhance brand visibility. 

So, how is this achieved? 

For your FAQ result to be visible in search engine results, your self-service software provider translates each knowledge article into its own crawlable, searchable URL

Can The Self-Service Software Be Optimised For Different Customer Journeys? 

Not every routine query or task has a cookie-cutter resolution or process, often there are variables and conditions that will affect the result.  

Just as agents carry out the troubleshooting process by asking follow-up questions to a user’s initial query, the best self-service software does this too. Fuelled by AI and by harnessing intelligent decision tree technology, multifaceted routine queries can be automated.  

By mapping out your users’ most common journeys, popular FAQs and additional questioning requirements, you can create smooth and effective routes to resolution – without having to involve human interception. All of which is achieved through an easy-to-use decision tree creation tool, knowledge articles can be dragged and dropped to fit common journeys. 

Anticipating and catering to popular user journeys not only enhances CX significantly but it improves CSAT scores as users are served resolutions without having to wait on hold or repeat themselves.  

How Easy Is Escalation Between Self-Service And Agent-Assisted Channels? 

There will always be occasions where queries are too complex by nature to be handled using AI alone. This is where seamless escalation between self-service software and agent-assisted channels such as live chat is critical to CX.  

Your users don’t want to be told: “Sorry no results found. Please contact support”, to then have to find a contact number,  wait on hold and then repeat themselves. For your customers, it’s a huge waste of time. 

Instead, Ensure your customer service tools are integrated, working together frictionlessly. When your self-service tools suspect that human intelligence is required, automatic escalation to live chat takes place, all within the same window to prevent repetition, improving CX. 

Can The Self-Service Software’s Content Be Improved Over Time? 

When it comes to self-service software for your customers, it’s important that knowledge article content is helpful and being optimised regularly. 

One of the most effective ways to ensure your FAQ content is serving its audience effectively is by encouraging interactive customer feedback options. Asking users if a knowledge article helped resolve their issue can help uncover areas for improvement and ultimately other users’ journeys. 

Self-service software that includes a prompt for query feedback is particularly effective when it comes to optimisation, by getting users’ take on why an FAQ did not help contributes to constant optimisation. 

If you enjoyed this article and would like to find out more about self-service software for your customers, check out our guide here or for help selecting software

6 Advantages Of Chatbots In Customer Service

With the global chatbot market expected to reach $102.29 billion by 2026, registering a CAGR of 34.75% over the forecast period, 2021 – 2026, it’s critical that companies keep up with such CX trends and customer demands.  

In this article we explore the advantages of chatbots in customer service: 

  1. Chatbots reduce support costs through automation 
  1. Chatbots enhance CX with seamless journeys  
  1. Chatbots boost CSAT through consistent, 24/7 support  
  1. Chatbots contribute to lead and revenue generation  
  1. Chatbots satisfy customers by offering smooth escalation 
  1. Chatbots channel your brand’s personality  

1. Chatbots Reduce Support Costs Through Automation 

Customer service chatbots are powered by AI and use sophisticated Natural Language Processing (NLP) in order to resolve users’ routine queries and tasks. Through harnessing NLP, chatbots understand user intent and can therefore serve accurate and relevant resolutions. In many ways, a chatbot takes on the role of a digital customer support agent – one that deals specifically with routine queries.


Chatbots can independently handle 20% of routine queries online.  

Some chatbots enable Conversational Process Automation (CPA). By integrating with your core business tools, entire processes can be automated online. For instance, policy changes in the Insurance industry or meter readings in Utilities can take place completely online.  

Now that these routine queries and tasks – that would otherwise reach the contact centre – can be handled through AI and totally online, support costs decrease. There is a myriad of contact centre costs associated with query handling that is reduced when a  chatbot automates routine queries. 

2. Chatbots Enhance CX With Seamless Journeys  

Chatbots play an important role in the wider customer service journey. Acting as a digital CX concierge, chatbots can guide users through their entire journey if necessary, resolving queries along the way and ultimately helping them reach their end goal in the smoothest, most efficient way. 

Some customer service chatbots utilise intelligent decision tree technology to provide additional support to your users.  

Not every user will have a query that warrants a simple, ‘one size fits all’ resolution. But not every problem must be escalated to the contact centre. This is where decision tree technology compliments customer service chatbots. They help with problem-solving common problems that often require follow-up questioning to resolve. 

Managers simply map these common user journeys out using simple drag and drop systems and the entire journey is automated, providing excellent CX. 

An image to show Synthetix Decisions interface

3. Chatbots Boost CSAT Through Consistent, 24/7 Support 

Customer service chatbots are fuelled by one main source of information – an intelligent knowledge base. This is a library of knowledge articles that powers not only your chatbot and self-service platform but also powers the interface that agents consult when dealing with customers via live chat or directly over the phone. Because your flow of information to the public is derived from one source, it means accurate and consistent resolutions are served, avoiding problems that arise when misleading or inconsistent answers are given. 

Issues for your customers will arise regardless of the time of day and for your CSAT ratings, it’s critical that support is available 24/7 for those out of hours queries. Chatbots do not need to be manned or take breaks, they are available 24/7 and rely on AI to answer queries as opposed to human intelligence. 

Using sophisticated NLP, Chatbots take each user query and by analysing each keyword, user intent, grammar and popularity, produce the very best, most relevant answers. Pair this will 24/7 support and chatbots are catalysts to customer satisfaction.

4. Chatbots Contribute To Lead And Revenue Generation  

Chatbots that specialise in customer service include a level of customisation that, when configured correctly can increase your lead and revenue generation. 

Using custom triggers, you can determine where your chatbot proactively offers users help, whether it be once they have hit a certain high-value page or when they have spent a certain amount of time on a page. 

Customer service chatbots utilise sentiment analysis that helps to determine user urgency based on the keywords they use, for example, ‘cancel’ or ‘order’. These signal that help is required, lead opportunity or even revenue generation. By proactively offering assistance at those critical user touchpoints, the chances of conversions taking place significantly increases. 

5. Chatbots Satisfy Customers By Offering Smooth Escalation 

There will always be occasions where customers have complex questions that cannot be solved using AI alone. This is where human intelligence and understanding is required. However, what happens all too often is users interact with a company’s self-service tools to find that their query cannot be solved there. This is when they have to source agent-assisted channels, repeat the process and their query and time is wasted. 

With an AI-powered customer service chatbot, users are automatically escalated to agent-assisted channels such as live chat when the chatbot detects that the query is too complex by nature for AI to solve. The escalation takes place within the same window and transcripts are transferred to avoid any user repetition taking place.  

By streamlining this often onerous process, CX is enhanced and this reflects positively in customer satisfaction feedback. 

6. Chatbots Channel Your Brand’s Personality  

Implementing a chatbot into your online offering is not only a means to improve CX and customer service but also a branding opportunity. 

Some customer chatbots include additional search layers that can be customised to fit your brand personality. This is where any quirks or idiosyncrasies that match your culture and character can be configured. 

It makes the customer journey more enjoyable, replacing the same boring responses whilst injecting a piece of your brand personality. 

If you enjoyed this article and would like to find out how your company could benefit from customer service chatbots, you can read our guide or 


An image showing xan on a phone

What are Chatbots?

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An Introduction to Chatbots

A chatbot is a software application primarily used by businesses to facilitate customer service. Unlike agent-assisted contact channels such as live chat or telephone, chatbots rely on AI to resolve customer queries. The tool that enables human and machine interactions has become increasingly popular over the last decade, often acting as customers’ first online port of call, but how did the chatbot become so sought after?

The first chatbot, ELIZA was created in the 1960s before the term ‘chatbot’, or its predecessor, ‘chatterbot’ was invented. ELIZA was an advance in computer science but was not made with the intention to assist customer service.

Following ELIZA, there were several failed attempts at producing an effective chatbot including the likes of Cleverbot and Tay AI. What these bots had in common was unregulated training data; the way in which they ‘learnt’ was from the information fed to them by their users – the public – opposed to being configured by professionals using benchmarking and objectives. The result of which led to Tay AI’s termination following a spurt of offensive views it shared on Twitter.

Today we are familiar with a different type of bot – the customer service chatbot. Unlike other chatbots whose purpose was simply novelty – to be played with and to provide entertainment – the chatbots we are used to today are there to provide help, automating customer queries, providing good CX and subsequently helping customer service teams.

How Do Customer Service Chatbots Work

Customer service chatbots are built on AI and through Natural Language Processing (NLP) and Machine Learning (ML) principles effectively ‘understand’ customer queries to provide adequate responses. Chatbots integrate with companies’ centralised knowledge so that depending on what query is entered, they can utilise smart algorithms to identify and serve the most relevant knowledge articles to customers.

NLP is the combination of Natural Language Understanding (NLU) and Natural Language Generation (NLG) and is constantly learning from each customer interaction to be as optimal as possible over time. NLP is responsible for unpicking the customer query, dissecting the sentence into intent-based keywords, subjects, grammatical quirks and keywords popularity to analyse, interpret and present an appropriate result.

An image to demonstrate the differences between basic and advanced AI chatbot software.

It’s important to note that not all chatbots offered today are powered by AI, some are built on rule-based automations. Such chatbots can only respond to queries that exactly match their pre-loaded responses, leaving no room for query variations or synonyms. The result of which is “I’m sorry, I don’t understand the question. Please try again” served to customers more often than a genuine result.

This is why AI-powered customer service chatbots are so valuable to business. They are programmed to understand that there are multiple ways that a question can be asked, an array of synonyms and even idiosyncrasies. NLP helps to eradicate unhelpful responses.

Some chatbot specialists include additional layers of search for chatbots to consult if query intent is unclear. Synthetix’s system, “Jabberwocky”, for instance, unpicks query sentence structure, analysing grammar and a range of word classes. Its utilisation of proprietary NLP enables brand personality to be portrayed, increases answer accuracy and ensures that the customer always receives a conversational response.

An image to show how Synthetix’s additional search layer, jabberwocky can compliment knowledge base articles for chatbots.

Why Use Chatbots In Customer Service?

According to IBM , chatbots in customer service can solve 80% of all routine queries – including questions such as:

  • What is your returns policy?
  • Where is my delivery?
  • Can I apply for a refund?

Such routine questions make up a large percentage of overall contact volume, so when they are automated through AI and solved at scale, operational costs are significantly reduced. Without chatbot solutions in place, customer service teams would otherwise deal with routine queries, creating a backlog of tickets and an accumulation of overheads and staffing costs.


Did you know?
Chatbots can save businesses up to 30% in customer service costs.

It isn’t just the bottom line that benefits from chatbots’ capabilities to automate routine queries, agents are given a greater bandwidth in which they can deal with customer issues that are complex or sensitive by nature, rather than the same queries time and time again.
A study
 revealed that 64% of agents with AI chatbots are able to spend most of their time solving complex problems, compared to 50% of agents without AI chatbots. This in turn contributes to positive employee morale – their perceived quality of work is richer when the repetitive, mundane element is removed, promoting job satisfaction and therefore productivity.

Customer service chatbots have a key role to play in customer satisfaction. Not only does NLP ensure that customers are served the very best results, encouraging improvements in First Contact Resolution (FCR) rates, but the channel itself is easy to use and convenient. Offering 24/7, real-time results, it’s a channel that customers can depend on, if for example, a chatbot provides customers critical information in an out of hours emergency, this of course is reflected in CSAT scores.

The adoption of chatbots also helps to optimise CX through Machine Learning (ML) principles and decision tree technology. ML principles enable chatbots capabilities to learn, store and utilise trends in customer behaviour, for example popular queries, colloquialisms or preferences are collected and then used for future interactions making the overall CX more enjoyable.

Effective customer service chatbots provide a smooth and enhanced customer journey through decision tree technology. To reach a successful result, an agent will usually ask customers a series of questions, but when there is no agent involved, how is this facilitated? Decision trees are configured so that chatbots can intervene at the problem-solving stage and ask such questions. This helps customers reach their destination result quickly and effectively, seamlessly transferring them to agent-assisted channels like live chat if necessary.

Types of Customer Service Chatbots

There are 3 types of chatbots that are commonly used in customer service, these are:

  • Menu-based chatbots
  • Keyword recognition-based chatbots
  • Contextual chatbots

Menu-based chatbots use clickable menu buttons to help customers reach the result they require. Similar to the rule-based chatbot discussed previously in this article, this type of chatbot relies on one specific set of data and therefore cannot offer results based on anything outside of this. Although menu-based chatbots might be suitable for small businesses with super basic requirements, for instance providing answers to FAQs, they are simply not capable of answering queries more advance than this. As a result, the precision and efficiency of answers served suffer.

Keyword recognition-based chatbots utilise AI, isolating and analysing each keyword to determine the best results. With each query that is entered, this type of chatbot will concentrate on the keywords that warrant action, for example “order”, “account”, “payment” and verb word classes such as “setup”, “cancel”, “refund” to piece together the most suitable response for the question at hand. Keyword recognition-based chatbots, however, are not effective at recognising multiple variations of questions.

Contextual chatbots utilise both AI and ML principles, proving particularly valuable to customer service amongst a multitude of companies. This chatbot type learns and stores useful information to utilise in future conversations. By remembering certain customer preferences and behaviours, conversations with the same customer during different sessions are of optimal efficiency and CX. A large portion of time can be saved if the bot simply asks the customer if their preferences are the same this time around, opposed to carrying out the same question process time and time again.

In customer service, chatbots are not only an expectation but they can assist teams quite significantly whilst complimenting companies’ overall customer service toolkits. As chatbots are more widely accepted and businesses understand the importance of AI-powered tools, we can only expect bigger, more impressive things for the bots of customer service.

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