Conversational AI :
The Ultimate Guide

Thousands of organizations around the world are implementing or planning to implement chatbots and conversational AI, but why? Explore the technologies that are helping all kinds of brands grasp what their consumers really want and fulfill their needs in real-time.

Conversational AI

Abstract

We know a company’s success is largely based on its ability to connect with customers and employees. In a fully digital world, human and emotional connections have become essential to growing your customer base, increasing loyalty towards your brand, and boosting employee retention and motivation.

However, building meaningful relationships at scale through a screen can be challenging: there’s the lack of resources that damages the outcome and the UX, suboptimal flows that become confusing or senseless…

Amidst this context, conversational AI has become the ultimate tool to help transform the way you build rock-solid customer relationships and help you get ahead of the competition. In this guide, we cover the essentials to get you started with it.

What is Conversational AI?

Conversational AI (Artificial Intelligence) refers to a set of technologies, such as chatbots and voice assistants that can deliver automated messaging and speech-enabled applications. With Conversational AI, computers can understand, process and respond to voice or text inputs, offering natural, human-like interactions in multiple languages between computers and humans. These interactions can be used to get opinions, recommendations, assistance, or to execute transactions or other objectives through conversation. 

Conversational AI uses vast volumes of data, machine learning and natural language processing to recognize interactions and learn from them in order to provide accurate information. 

What is conversational Artificial Intelligence ?

What is it used for?

Conversational AI bridges the gap between human and computer language to make communication between the two more natural. The set of technologies that comprise it allow computers to recognize and decipher different human languages and understand what is being said. Proficient Conversational AI platforms recognize intent, comprehend the tone and context of what is being and determine the right response accordingly. 

Conversational AI is efficient for automating processes to reduce workloads in overworked staff or save resources. A clear goal is usually to improve customer engagement and customer experience as this conditions brand loyalty and revenues. If the Conversational AI platform is efficient, it can help people interact with systems swiftly and easily in their preferred channel, be it social media, WhatsApp, online or a smart device, while helping businesses deliver personalized engagements and support at scale and 24/7.

How can Conversational AI help your organization?

Having seen the main benefits of Conversational AI, the next question is, how can Conversational AI help your business? 

Conversational AI comes with features that are renowned for making AI applications so efficient. Analytics, Big Data and automation are key elements that can help businesses leverage technology to their advantage. However, Conversational AI also provides further capabilities to help business leaders serve their customers and stakeholders. 

There are multiple benefits in using Conversational A that affect different departments and sectors of a business and improve ROIs. We have highlighted some of them:

Enhance productivity and employee experience: freeing up many automatic, low-skilled activities, human agents have more time to increase productivity and hone their skills. But there are also many benefits Conversational AI can provide to enterprises. 

Expand engagement possibilities and brand loyalty: Conversational AI opens a wide range of engagement possibilities for brands – whether it is in sales, customer support or in marketing- to bring customers closer to a brand. At the same time, the brand gets more insights about their customers, which lets them improve sales and provide better offers, products and services. 

Deliver swift responses to an increasingly demanding customer base: Customers demand quick and efficient responses to their queries. A study from McKinsey showed that 75% of customers expect their services to be tended to within five minutes of making contact online. Conversational AI technologies allow customers to have their demands met regardless of the time and channel and without the need to queue to receive any service.

Boost revenue, decrease costs:Conversational AI platforms can leverage data analytics to suggest better deals and remember customer preferences to drive sales conversions and revenue. Personalization features in Conversational AI also allow chatbots to provide recommendations and cross-sell products that customers may have not initially thought about. Additionally,the ability to efficiently service more customers without the need to deploy more human agents also reduces costs. 

Deliver actionable insights to understand your customer:Conversational data can deliver valuable insights on trends and customer sentiment that can be used to improve product and service development, as well as to optimize marketing approaches to boost engagement and revenues.

Reach new customers:Conversational AI bots help customers contact businesses via a wide array of channels, so that they do not have to be limited to contacting them via an email or a call. There are constantly new channels being made available to be able to engage with new customers. 

How do the best Conversational AI platforms overcome challenges?

Conversational AI platforms still face some challenges when delivering the best customer service. It is important to choose a platform that can overcome these challenges and still guarantee high-quality solutions. Some of these challenges are: 

Constantly changing communication: The way that humans communicate is always evolving. Whether it is through dialects, sarcasm, emojis, or slang, technology needs to keep up with these changes in order to constantly improve communication between humans and machines.

Security and Privacy: A lot of information can be taken from conversations, but there are also ethical standards that must be respected. The information shared between a customer and a voice assistant or chatbot must be treated securely and safely. Recent GDPR regulations have furthered the need to ensure that customers are guaranteed to have their data treated within privacy regulations. When choosing a Conversational AI platform, it is important to know that the data being used is done so safely and securely. 

Multilanguage capabilities: While a vast section of the world population speak English, when it comes to native languages English alone doesn’t cut it. It can be a challenge for some voice assistants and chatbots to interact in other languages. Businesses that can engage with their customers in their mother tongues are more likely to boost rapport and brand loyalty. The best chatbots also bear in mind regional and cultural differences to deliver localized experiences to their customers. 

Adopting new technologies: The use of Conversational AI is becoming a key figure in many corporate digital transformation plans. However, there are still difficulties to adopt technologies for different use cases and industry verticals. Ease of deployment, scalability and adaptability are important elements to consider when choosing a Conversational AI platform for your business that can meet the demands of both Developers and C-suite executives and project managers. 

What are the benefits of Conversational AI?

The benefits of Conversational AI are notable. With businesses increasingly seeking ways to increase revenues, boost productivity and increase brand loyalty, Conversational AI has achieved more and more recognition as an asset to achieve these KPIs. 

Some of the main benefits of Conversational AI include the following: 

Engagement: Conversational AI and chatbots deliver services that are focused on optimizing interactions and engagement with customers. They do this by ensuring that their services are accurate, personalized to each customer’s needs and on-point.

Scalability: Conversational AI stands out for its easy scalability. Adding infrastructure to support growing projects is cheap and fast and does not require rebuilding projects. Conversational projects can grow and seamlessly integrate with projects, which is useful when these expand to new markets or during unexpected short-term spikes in demand as, for example, the surge in requests for customer service in some sectors during emergency scenarios such as Covid-19 or during holiday seasons. 

Quality data collection: Conversational AI is an excellent source for collecting data to better know your customer’s preferences and dislikes. What better way is there to understand a customer than talking to them? The feedback from these interactions can be used to improve sales and marketing practices as well as delivering a better digital customer experience by determining better ways to engage with customers. 

Better, more consistent customer service: Conversational bots can assist customers with all sorts of end-to-end requests, from finding information to carrying out purchases and processes. They also excel in providing assistance to human agents, initiating processes and gathering data and information and handing them over to qualified agents, saving time and resources, while automating more straightforward and collecting valuable data and conversational AI analytics to ensure that the service is tailored to a customer’s needs. Importantly, these services can be carried out 24/7 and in multiple languages, ensuring that customers are never left waiting and increasing satisfaction levels and brand loyalty. With humans often providing inconsistent answers, chatbots can ensure better comprehensiveness and consistency in replies to informative queries. 

Asynchronous, omnichannel capabilities: As mentioned, chatbots do not need breaks or holidays. They can work at any time of the day and respond to customers regardless of how many people are using a service at a specific time. Their capabilities also go beyond 24/7 services, as they can also cater to customer demands on multiple channels and languages, increasing self-service capabilities on the programs, apps, or devices that customers feel most comfortable using and without having to introduce the same data if and when a customer needs to switch devices. 

Promptness and Cost-efficiency: Conversational AI platforms require minimal upfront investment and can grow at the same rate as your projects. They can be deployed rapidly, so that customer support costs are quickly reduced.

How does Conversational AI work?

Conversational AI combines natural language processing (NLP), Machine Learning (ML), natural language understanding (NLU) and other language and speech recognition technologies to interpret, process and contextualize written or spoken words and respond to each input accordingly. 

Conversational AI uses algorithms and workflows the moment an interaction commences when a human makes a request. AI parses the meaning of the words by using NLP, and the Conversational AI platform further processes the words by using NLU to understand the intent of the customer’s question or request. 

Conversational AI also then uses Machine Learning to ensure that responses to customer requests improve over time by learning with each human interaction. The use of data is an asset, as the best Conversational Platforms can also leverage the content and data gathered from each interaction to better understand what people want when they communicate with the platform. 

With this, proficient Conversational AI works by delivering contextualized, personalized and relevant interactions between humans and computers.

Conversational AI technology and applications

Technology that powers Conversational AI

Deterministic Approach vs Mathematical Approach to AI

Artificial Intelligence requires a lot of focus on the nature of algorithms of data. However, Symbolic AI and Machine Learning are also key approaches upon which Artificial Intelligence is founded on. These approaches are also described as deterministic and mathematical, they differ in the outcomes they expect and in their processes. 

Symbolic AI was the dominant form of AI research in its first decades up until the late 1980s, while Machine Learning has gradually become the prevalent approach to the discipline. But what is the difference between the two and how do they differ when applied to Natural Language Processing?

What is Symbolic AI?

Symbolic Artificial Intelligence, also known as Good Old-Fashioned AI (GOFAI), uses human-readable symbols and representations of problems, entities, concepts or logic in order to create ‘rules’ for the manipulation of these symbols, leading to a rule-based system. 

Basing itself on the assumption that many aspects of intelligence can be achieved via the manipulation of symbols, symbolic AI involved the explicit embedding of human knowledge and behavior rules into computer programs.

The symbolic approach applied to NLP

One of the many uses of symbolic AI is linked to Natural Language Processing for conversational chatbots. This approach is also known as the “deterministic approach”, and it is based on the need to teach machines to understand languages,  in the same way that humans learn how to read and write.

Just as humans have had to go to school to learn how to structure language by abiding by rules, grammar, conjugation and vocabulary, computational linguistics do the same. In this case, they use rules, lexicon and semantics to teach the bot’s engine how to understand a language.

How and when is Symbolic AI efficient?

Symbolic AI and the deterministic approach rely on these concepts: humans think using symbols; computers operate using symbols; computers can be trained to think. 

We use symbols to define even the simplest things and to describe what we don’t. For this reason, Symbolic AI can be successful when solving problems that need to satisfy certain constraints or conditions, and that are quite specific and not random. 

When conversational aspects of NLP are rule-based and follow logical inferences, Symbolic AI works as it makes sense of inputs and generates conclusions based on rules and evidence. 

With symbolic AI, everything is visible, understandable, and explainable, leading to what is called a “transparent box” as opposed to the “black box” created by machine learning. 

As a result of this, bot developers have a completely different approach when using Symbolic AI technology than with Machine-Learning based technology, as they need to focus on writing new content for the knowledge base rather than small segments of existing content. Developers also have full transparency on how to fine-tune the engine when it doesn’t work properly as they can understand why a specific decision has been made and have all the tools available to make amendments. 

What is machine learning?

Machine learning is an application of artificial intelligence that focuses on the use of data, algorithms and statistical models by computer systems to perform specific tasks without the need to use explicit instructions to perform a task. The model imitates the way that humans learn to gradually improve its accuracy. Instead of using instructions, machine learning algorithms build mathematical models based on sample data, known as “training data,” to make predictions or decisions.

The algorithms in machine learning technology teach computers to solve problems and gain insights from these processes. That way, computers earn automatically, without human intervention or assistance.  Machines look for patterns in data and use feedback loops to monitor and improve predictions. Computers are not overwhelmed by mass amounts of data, but actually improve by using data to keep learning and make better decisions in the future. 

Deep Learning, Neural Networks and Machine Learning

Often, when talking about Artificial Intelligence, Machine Learning, the term is also used interchangeably with Neural Networks and Machine Learning. However, they have their differences. 

While linked to one another, each one is a component or subset of another. Machine learning is a subfield of Artificial Intelligence. Deep learning is a subfield of machine learning, and neural is a subfield that constitutes the backbone of deep learning. 

The neural networks that are a subfield of deep learning mimic the human brain through a series of algorithms. They are designed to recognize patterns and interpret data through machine perception, where they label or cluster inputs as numerical vectors. 

What is the difference between Neural Networks and Deep Learning?

Deep learning refers to the depth of layers in a neural network. When a neural network consists of more than three layers, this can be considered a deep learning algorithm. These neural networks tend to flow in one direction but can be trained to backpropagate and analyze errors in order to ensure that they can adjust and fit correctly in the algorithm.

What is the difference between Deep Learning and Machine Learning?

Deep learning is a subset of machine learning. A key element that differentiates the two is how each algorithm learns and how much data is used in each process. Deep learning requires less human intervention as it is heavily automated. 

Machine learning depends more on human intervention to learn, as the latter establishes the hierarchy of features to categorize data inputs and ultimately require more structured data than in the case of deep learning. 

Machine learning applied to NLP

Machine learning can be applied to many disciplines, and Natural Language Processing is one of them, as are AI-powered conversational chatbots

When applied to NLP and chatbots, machine learning works ensuring that the conversational bot is fed as much relevant data as possible by somebody who oversees the chatbot. The bot then gets asked questions by users and decides which answer to push for in each specific intent it is queried for. Botmasters, the people who oversee the bot, then need to review these responses and manually tell the engine which answers are correct, and which answers are incorrect. This way, the machine learns how to deliver the correct answer to an intent. 

This can be quite time-consuming, as there are many ways of asking or formulating a question. Also, if you bear in mind that knowledge bases tend to hold an average of 300 intents, using machine learning to maintain a knowledge base can be a repetitive task. 

This doesn’t mean that machine learning is not an efficient tool. It has proven to be just that when carrying out tasks such as image and voice recognition, but it can have its limits when it comes to NLP.

When is machine learning an efficient approach? 

Machine learning is an excellent tool that has its drawbacks. While symbolic AI makes things more visible and is more transparent, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens. In machine learning, the algorithm learns rules as it establishes correlations between inputs and outputs. In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program. 

Machine learning can be used for projects that require predicting outputs or uncovering trends. The use of data can help machines learn patterns that they can later use to make decisions on new data inputs. However, its lack of transparency and large amounts of required data means that it can be quite inconvenient to use. 

Companies now realize how important it is to have transparent AI, not only for ethical reasons but also for operational ones, and the deterministic approach is gaining credence.

NLP Technology

Natural Language Processing (NLP) is a part of computer science and artificial intelligence that focuses on how computers can understand text and spoken words in the same way that humans can. 

This can be quite complex. People use natural language daily to communicate with one another. During these interactions, we interpret, understand, process and use words. Additionally, these words can be delivered in different languages, all of which have their own syntax and grammar, along with unique rules and structures.

Additionally, human language includes text and voice inputs that can easily be misinterpreted such as sarcasm, metaphors, typos, variations in sentence structure or strong accents. Programmers must teach natural language applications to recognize and understand these variations. 

The differences between languages and how they have evolved vary from artificially created languages, also known as constructed languages, because they have different rules between them. Computer programming languages follow much stricter and yet simpler rules. 

Machine Learning and Natural Language Processing conversational Artificial intelligence

Helping computers understand humans

For computers, formal languages such as mathematical notations in PHP, SQL and XML, are used to transfer information with little ambiguity. Under this premise, computers can work very efficiently. However, enabling computers to understand natural language is a bigger challenge. This is where artificial intelligence plays a key role in computer science in establishing the interactions between computers and natural human language. 

NLP combines rule-based modeling of human language with machine learning and deep learning models. These technologies let computers process human language in the form of text or voice data and comprehend the meaning, intent and sentiment behind the message. 

Computer programs that use NLP can translate texts in multiple languages and in real-time and have become more present with the growing use of digital assistants, dictation software, chatbots and voice assistants. It isn’t only customers who are increasingly benefiting from NLP. Enterprises are also using NLP to streamline their business operations, boosting productivity, revenues and resources while automating and simplifying processes.

NLP can help in many tasks, such as: 

  • Speech recognition: reliably converting voice data into text data to follow commands or answer questions. These technologies must be able to overcome challenges such as accents and incorrect grammar or cacophonies to work efficiently. 
  • Making sense of ambiguous words: Similar words can have very different meanings depending on their semantic analysis. Words can be nouns or verbs or have very different meanings depending on its context. Proficient NLP technology must be able to detect these differences. 
  • Named entity recognition: In a similar way to differentiating ambiguous words, NLP can identify words as useful entities such as locations, brands or people’s names. 
  • Natural language generation: In the same way that NLP can recognize speech and transform it into text, it can also transform structured information into human language. 
  • Sentiment analysis: NLP doesn’t just analyze semantics, it also extracts subjective qualities such as emotions like anger, frustration, satisfaction, sarcasm or confusion and can act accordingly. 

Natural Language Processing is quickly being used by enterprises for multiple functions that require text analysis and classification, from spam detection, to automated translations, text parsing, sentiment analysis and conversational AI.

NLP and efficient Conversational AI design

Having seen that natural languages are not “designed” in the same way as formal languages, they tend to have many ambiguities. The same word, phrase or entire sentence can have multiple meanings and can be expressed in multiple ways. 

The best conversational AI platforms such as Inbenta’s have natural language processing technology as its core. Theoretical linguistic frameworks like the meaning-text theory (MTT) — used for the constructing of models of natural language — allow computers, and thus your search technology to process natural language by understanding the meaning behind the words.

By using MTT, Inbenta has created a semantic search engine that allows users to efficiently search for complex information, even if what is typed is incomplete, ambiguous, unstructured questions in their native language. With this, there are fewer obstacles to overcome to ensure that customer interactions are easy to understand and deliver the right outcomes.

How can Conversational AI be implemented?

Conversational AI uses Natural Language Processing and AI algorithms to engage in contextual dialogue by processing and contextualizing the written or spoken word in order to figure out the best way to handle and respond to user input. This process usually happens in three steps, as follows.

Artificial Intelligence project implementation

Step #1: Understand what the user’s intent is

First, the application receives information input from the user, which can be either written text or spoken phrases. The AI then uses Natural Language Understanding (NLU) in order to understand the meaning of a question regardless of grammatical mistakes, spelling mistakes, jargon or slang. This capability is very different from recognizing a keyword or phrase and answering with a canned response that was scripted for that specific keyword.

Step #2: Select the correct answer

Based on its understanding of the user’s intent, the AI then must determine the appropriate answer in its knowledge base.

Step #3: Deliver the answer

Finally, the AI uses Natural Language Generation (NLG), the other part of NLP, to generate the appropriate response in a format that is easily understood by the user. Depending on which channel is used, the answer can be delivered by text or through voice, using speech synthesis or text to speech.

Additionally, deciding the conversational AI design is an important process. The interactions in the conversational AI platform must be aligned with the company’s business model, goals and customer personas.

How a Conversational AI solution is implemented and how customers can access or interact with a brand can vary as there isn’t one single approach. Here we will look at some of the ways Conversational AI can deliver solutions to customers.

Chatbots

Chatbots, aka “conversational agents” or “virtual assistants”, are increasingly becoming key players in many company’s digital transformation strategies. Businesses from all sectors including e-commerce, banking, insurance, industry, and telecommunications are adding chatbots to their future plans as they seek to automate processes and handle growing requests for customer service while saving costs. A study by Juniper has highlighted that chatbots are projected to drive cost savings in banking and healthcare of over $8 billion per year by 2022. 

There are different types of chatbots, such as button-based, keywords based or conversational bots. Basic chatbots might be limited to answering standard questions, but intelligent chatbots allow humans to interact contextually at any time of the day with technology using various inputs from text, voice, gesture and touch. 

A chatbot project’s success is not based solely on the interface. It depends above all on the ability to combine your expertise and the provider’s feedback with a natural language solution and an adequate knowledge base. That way, when implemented correctly, chatbots can deliver noteworthy results that can transform your customer service. 

Customers want and expect immediate access to information to help them solve problems or make an end-to-end transaction. When these expectations are not met, customer satisfaction rates, and therefore brand loyalty, can dwindle. 

This is why it is so valuable to have an AI chatbot that understands what a customer is trying to tell it, and quickly provides the relevant information. By using AI chatbots that understand context, customers feel heard and understood, while human agents also have increased productivity as they can focus on solving more complex issues or on improving their skills. 

Despite some trends that inaccurately tend to vilify chatbots, the myth that chatbots are not intelligent enough has been debunked. In fact, there is no longer an issue of whether a chatbot should be used or not, but rather which conversational AI platform best suits your business, your clients and your customers

Advanced conversational AI bots like the Inbenta AI chatbot can help businesses supercharge their customer interactions while automatically engaging in complex conversations with minimal training. 

Unlike keyword-based chatbots, the Inbenta Chatbot uses Symbolic AI to power its Natural Language Processing technology, enabling it to understand human languages in all their variations. It can detect the meaning of words without requiring the lengthy data training usually associated with machine learning algorithms. This results in an intelligent chatbot that can be swiftly deployed in a matter of days and that delivers high response rates and can increase case resolutions from 30 to 50%.

Businesses need to choose chatbot platforms that are easy to build, deploy and maintain, while delivering personalized, seamless, omnichannel capabilities. Additionally, chatbots can do more than interpret a user’s request, detect sentiments and recall past requests and conversations to deliver tailored responses, by providing actionable insights with collected data to further increase personalization and to automatically suggest improvements or upselling opportunities to further drive revenues.

With users expecting companies to include self-service applications, many companies are looking to optimize their FAQs and search pages to guide prospects towards making purchases or resolve their problems and maintain brand loyalty.

Conventional FAQs have been little more than a sequence of answers to typical problems that can be accessed on a static web page. Customers have usually had to figure out how to navigate to the specific question they are looking for and to be meticulous with the phrases and keywords they use.

Businesses need to improve their FAQs and deliver information to visitors on their terms, without frustrating them by having them search through the webpage. Chatbots and automated communication tools that process natural language leverage existing information in an FAQ with NLP to cross-reference the meaning of a query with the data already stored in the company knowledge base.

The result is an interactive experience that goes beyond the binary features of a typical FAQ and that resembles asking a live human agent for help finding a specific point, even if the keywords that are typed are not exact.

The answers provided are also different from conventional FAQs in that they are not long, general, and imprecise. The use of advanced chatbots can deliver personalized responses and offer links to other related content and topics to ensure that the customer is fully satisfied with the query being made. This increases self-service rates, boosts customer experience, and reduces inbound customer support tickets.

Importantly, these new platforms allow you to take advantage of advanced NLP technologies to optimize your FAQs into a proficient chatbot experience can be delivered in weeks instead of months.

Knowledge Management

With customers wanting to access information seamlessly and easily, dynamic and intelligent knowledge management systems can save valuable time and reduce bounce rates and customer frustration when they are looking for specific information or products. 

A knowledge base is a database containing all the information the user can be asking for. In particular, it gathers the questions/answers and media that are offered as answered to the end-users.

Knowledge management systems help users find, manage and create knowledge bases by organizing frequently asked questions, product details and more, and making it easy to access. With this, customers can benefit from self-service and staff can receive better support by accessing updated, accurate and homogenous information. Additionally, knowledge content can be indexed, which actually helps google ranking because of its long-tail SEO functionality. 

By using a Symbolic AI, a.k.a. meaning-based search engine, knowledge management systems like Inbenta’s can interpret human language in order to swiftly answer user queries and boost customer satisfaction. 

By improving customer experience with Knowledge Management systems, businesses can reduce costs and better understand consumer habits and preferences. Users learn to self-serve and find solutions as they navigate through product pages or conversion funnels, and proficient Knowledge Management centers like Inbenta Knowledge act as a deflection tool that drastically reduces the number of incoming support queries. 

Importantly, it is easy to monitor the performance of these knowledge management systems at any time in the back-office via dashboards that provide real-time views. These insights and usage reports can be leveraged to optimize existing knowledge bases by identifying potential gaps in content and discovering areas of improvement. 

Managing content in Inbenta Knowledge is simple too. Its powerful and easy-to-use Workspace lets you add, edit and organize content using a tool that integrates with a large catalog of applications such as the AI-powered semantic search engine, and other integrations to ensure that the help site seamlessly blends with the rest of the website.  

By combining knowledge across multiple systems, Knowledge Management systems help people access information regardless of where it resides.  

Inbenta Knowledge is also easy to monitor in the back-office through a dashboard that can detect potential gaps in content and discover areas of improvement. These can be easily edited in a Workspace that includes integrations like Inbenta’s AI-powered semantic search engine, help-site manager and an SEO optimizer to make it easier to organize.

Overall, there are numerous benefits to Knowledge Management systems. They can help people within an organization share, access and update important company information, while also helping boost creativity and decision-making processes and minimizing risks.

Voicebots and IVRs

As user demands for optimal customer service are growing, consumers expect immediate replies, avoiding waiting times on the phone and autonomy, preferring self-service ahead of phone conversations. However, this does not mean that they avoid using their phones or defer from using voice applications while looking for answers. 

The increasing use of voice-activated devices further highlights how consumers are becoming used to making requests using their voice and without having to type their questions. 

The adoption of voicebots is increasingly popular among younger generations. 51% of consumers aged 14-17 have said that they have already interacted with some sort of voice or speed recognition device. Coincidently, these younger generations are also raising the bar when it comes to the standards and expectations towards customer service. The more digitally savvy they are, the likelier they are to prefer new ways to communicate with brands and avoid manual typing. 

In a similar way to chatbots, voicebots and IVRs can receive, analyze, interpret and respond to customer inquiries spoken to them in natural language, with the objective of answering a customer request in real-time or maintain a verbal interaction with the aim of clarifying the customer’s question and pass the inquiry to a human agent if necessary. With this, users experience a swifter customer experience through conversation, streamlining the customer journey and alleviating the number of contacts of a customer support team.  

Voice can deliver substantial benefits to a business’ customer services, many of these like chatbots. For example, voicebots can answer to standards regardless of how many people are contacting a call center. The fact that they are scalable means that there is never a need for waiting lines because of a lack of staff, and voicebots can carry end-to-end transactions and support, contributing to the automation of business operations and increasing efficiency while reducing time. 

There are differences between voicebots and chatbots, however. Firstly, text-based channels are generally easier to implement, and it is easier for bots to understand what a customer wants and parse through data to find a solution. Voicebots specifically require added speech recognition capabilities to understand and discern the intent of customer requests in order to reply accurately. While doing so, voicebots still need to access customer information like chatbots do to build a customer profile and deliver personalized responses. 

Voicebots achieve this by synthesizing voice requests, including interjections like “Okay” and “Umm”, and converting this information into text for further processing and then coming up with a reply in a matter of seconds.

Proactive Chatbots

We have already explored the importance of chatbots when it comes to delivering customer experience. Most chatbots successfully fulfil the role of assisting users when they need more information and contact the chatbot for information. 

These chatbots are reactive, because they are automated chat instances that wait for the customer or visitor to reach out before communicating with them. 

This means they won’t send any messages unless the user asks a question. 

Proactive chatbots are another approach to using chatbots and conversational AI. Instead of waiting for a user to engage with them, proactive chatbots initiate conversations to encourage the user to carry out a certain action based on a series of behaviors. 

These behaviors can come in different forms, for example:

Pages Visited: depending on the pages a user visits, chatbots can recommend certain content or suggest different actions that lead to conversions. 

Time on page: Chatbot interactions can be triggered depending on how long a user has been browsing on a specific page. 

Scrolling: Chatbots can be activated when a user reaches a certain point of their scroll down, in a less invasive way to a pop-up, to recommend related or similar content, offer discounts or facilitate processes. 

Shopping cart: Proactive chatbots can also use other metrics, such as the number of items in a shopping basket, or the total value that order would have in case it is converted. This means that a chatbot can offer discounts only to customers considering a purchase over a certain amount of money.

Proactive chatbots are assets because they can provide substantial benefits to businesses. A study by Microsoft showed that 70% of customers tend to have a better image of brands that offer proactive notifications. Along with strengthening a brand’s image, proactive chatbots excel in anticipating customer needs, and using data and behavioral insights to assist users at the right time. Almost 90% of successful businesses are sure that anticipating their customer needs and assisting them along their journey is essential to foster business growth. 

By engaging proactively with customers, there is less risk of shoppers abandoning their purchase, and can substantially improve customer satisfaction rates and brand loyalty. 

Of course, care and thought must be given to know when and how to proactively engage with a customer to not seem pushy, and to use the proactive chatbot at specific points when visitors may be leaving to ensure that their customer journey continues. 

Used wisely, with efficient copy and a chatbot that is visually appealing and dynamic, proactive chatbots can be a game-changer on any brand’s website.

Mailbots

Used more as a tool to assist human agents, mailbots can support customer service teams by automatically replying to contact center’s incoming emails by filtering, redirecting or redistributing incoming emails to the right person by parsing through the content. Once the main issue of concern has been detected, pre-built templates serving different objectives such as status updates, product recommendations or informative replies can be sent, saving time usually spent drafting an email or newsletter. 

How to apply conversational AI to a self-service strategy?

Customers are increasingly turning to self-service to avoid waiting lines and to find solutions to their requests on their own. A Zendesk study shows that 81% of customers try to resolve problems on their own before reaching out to support channels. Businesses are aware of this and are looking to fine-tune their customer self-service solutions to enable web users to access information or perform simple tasks autonomously, without needing assistance from a customer service representative. 
Customers may want to use self-service for numerous tasks, such as tracking a package, requesting a quote, or paying a bill online without having to talk to a human agent at the company to carry out these actions.

Conversational AI and self-service strategy

The benefits of automated self-service strategies

While there are still queries that cannot be handled by self-service due to their complexity, self-service solutions are very efficient at solving tier-1 repetitive queries. These types of requests represent approximately 80% of the questions received by customer service agents, and take up a large amount of time, so automating these results are an effective way of saving time and resources and allowing human agents to focus on the more complex tasks. 

When customer service departments are overburdened with numerous online requests, as was witnessed during the first months of the Covid-19 pandemic, the implementation of one or more self-service solutions becomes imperative. Additionally, self-service also caters to new customer demands for greater autonomy and faster service delivery. 

The stakes are high if companies fail to meet these demands, as word-of-mouth or bad customer reviews can have a damaging impact on business and drive away potential prospects, to the extent that 76% of clients have stopped doing business with an organization due to its poor client service, and 39% have immediately abandoned their purchase to switch to another supplier when this occurs. 

Businesses therefore must look for the best forms of ensuring self-service to their clients. These can be chatbots, dynamic FAQs, semantic search engines, customer knowledge bases and more. The solutions they choose to implement must be tied to their needs and be able to cater to customer demands for 24/7, seamless omnichannel services. 

By leveraging the features of Natural Language Processing technology, these solutions can understand the true intentions behind customer’s questions and instantly retrieve the right answer from a knowledge base. Whether the solution is found in a knowledge base or through a chatbot, these self-service solutions will provide the desired autonomy customers want, answer their queries, and reduce the number of incoming contacts to your customer service

Automating customer services will also help reduce queues in contact centers and allow human agents to concentrate on more complex queries or dedicate more time to winning back dissatisfied customers. 

Customers are quick to voice their discontent when their needs are not met, so it is important to have effective dissatisfaction management tools. Human agents can make the most of automated self-service tools to spend time identifying signs of frustration, often with the help of auxiliary dissatisfaction detection tools such as those provided by Conversational AI platforms such as Inbenta’s. These tools can proactively trigger a case escalation to an agent, guaranteeing a direct treatment to a frustrated customer. 

Internal customer service teams can also benefit from self-service as they can use intelligent FAQs, knowledge bases and conversational chatbots to assist them in finding the answers to customer requests.  Human agents can have access to predefined responses or to an entire dissatisfaction management procedure.

These solutions can help both customers and advisors at the same time, helping to seamlessly harmonize the customer service process and ensure that responses are consistent, accurate and updated. 

Conversational AI Industry use cases

Conversational AI is an essential feature of nearly every business’ digital transformation strategy across multiple industry verticals. However, each case must be tailored to each business’s unique objectives and areas of improvement. This is where it is important to value successful conversational AI examples to choose the best one for each enterprise’s targets.

Conversational Artificial Intelligence uses cases and examples

Conversational AI in banking and finance

Banks and financial services have accelerated the use of digital technologies to find new ways to meet customer demands. Those banks that are efficiently deploying Conversational AI with seamless, personalized and contextual capabilities are gaining a competitive edge in their sector. 

Banks can increase the quality of their customer care without sacrificing time tending to redundant user queries. Conversational AI platforms like Inbenta allow agents to focus on critical issues and divert repetitive tasks to chatbots and semantic search tools. 

By automating bank-specific requests, customers can check their accounts, report issues, apply for loans, process mortgage payments or carry out transactions without the need for human assistance. 

Customers need to be fully in control of their finances. Whether they are planning ahead or spending money now, customers want to stay aware of the transactions they make, the money they save and what features they have access to. 

Inbenta’s conversational AI platform gives banking customers control of all the relevant information they need with industry-leading self-service tools. They can access their accounts and carry out transactions or make customer requests without having to queue or wait, at any time of the day and in multiple languages. 

The benefits affect both customers and employees, as they can access accurate and updated information without having to rely on human assistance or without the risk of human error. 

The perks of Conversational AI analytics and data is that future interactions can be personalized as previous interactions are stored to ensure that every interaction with a brand is like talking to an actual employee. This way, customer satisfaction remains high, support costs go down and revenues grow. 

Use Case: How Inbenta’s Chatbot helped Group BCPE streamline HR operations

As we have seen, it isn’t just customers who benefit from conversational AI. HR staff are one of the main beneficiaries of chatbots and automated services. These services are especially useful as they can help employees swiftly find information from different sources whenever they need it. This can be anything from internal communication updates, FAQs, DPR and Compliance, internal policies, Health and Welfare information or Benefits.

Chatbots can inform employees on important issues such as their benefits while relieving the HR department from responding to repetitive queries. 

An example can be found at Groupe BPCE. The French banking group found that it was important to inform members about withholding tax and its impact as part of the tax withholding support system. 

Groupe BPCE decided to set up a chatbot to raise awareness of the subject and reply to questions from employees from all of the Group’s companies. They chose to deploy Bot’PAS, an internal chatbot that can answer basic questions on tax retention along with their specific tax-related issues. 

The PAS chatbot comes from a collaboration between Inbenta and Ayming, a leading player in business performance consulting, under the guidance of the BPCE Group’s HRIS Department. 

This chatbot is the result of Inbenta’s BotFeeder program, an outsourced knowledge base design service, with a ready-to-use knowledge base written by business experts

Inbenta designed a chatbot based on its automatic language processing technology, with more than 1000 new syntactic and lexical relations, to guarantee the correct answers. With Ayming taking charge of the editorial aspect of the project and using its business expertise to design an optimized knowledge base with more than 300 pieces of content, the collaboration allowed for ultra-fast deployment with the minimum use of resources. 

The chatbot was effective in delivering information on tax retention and in saving time and resources to the HR team. When asked about the effects of the implementation of the bot, Christian Verhague, Director of HR Operations within the BPCE group stated, “The Inbenta HR chatbot has made our HR department more open to human interactions by enabling the resolution of recurring cases that can be automated thanks to the HR chatbot.”

Conversational AI in Insurance

Insurance can be a complicated issue. Too often, a simple question can turn into a complex dialogue. So, streamlining processes can seem to be difficult for simple chatbots. Proficient conversational AI capabilities, however, stand out for being able to understand context and swiftly deliver intelligent and personalized responses. 

Insurance chatbots can remove any points of friction that can make carrying out insurance claims, updating policies or onboarding a little bit easier. Advanced conversational AI platforms make it easy to integrate into back-end systems so that even the most complex and tedious of claim forms can be automatically completed in a matter of minutes at any time of the day. 

As expected, this relieves pressure on contact centers and helps human agents who need access to accurate information. Insurance firms are also using conversational AI, albeit chatbots or knowledge bases to assist in internal processes.

Use cases: Partenamut and AG2R La Mondiale – assisting policymakers and employees with Conversational AI

Insurance employees need to be updated on all their company’s information. HR teams may not have the time to reply to all employee demands, and many businesses have optimized their Intranet to provide this information, but time is still wasted searching through FAQs to find help. 

Partenamut, is a mutual fund mainly active in Belgium with more than one million customers. Partenamut sought to improve their Intranet by asking Inbenta to set up a chatbot for employees in more than 70 contact points. 

With this, the solution helped answer questions automatically and 24/7, improving employee self-service capabilities and autonomy.

The chatbot, named Charly, deals with all topics relating to human resources, such as paid leave, compensations and training. In other words, it handles nearly 1000 questions per month, with a correct answer rate of 80% and 100% self-service capabilities.  If Charly cannot answer a question, it seamlessly escalates the issue to the HR department. Its efficiency has reduced requests for HR assistance by 20%

As it is integrated on Sharepoint, Charly comes with an AIML social layer that lets it manage non-executive requests in addition to its basic functions. It also comes with a feature that allows the viewing of the top 3 content.

A spokesperson for Partenamut highlighted, “In addition to relieving our HR support, the employee chatbot allowed us to identify the seasonal patterns of questions and then better manage our internal communications”.

This is relevant because it showcases how to use data and analytics to provide better assistance to users. Data can be used to deliver personalized messages to employees based on past interactions, or actionable insights. These solutions can be carried out across all sections and processes of an HR department, integrating with other departments if necessary. This way, for example, HR requests that need assistance can be resolved by connecting with IT departments and seamlessly integrating with ticketing systems to provide IT support for internal employees with all the required data to deliver the help that is needed. 

But a key focal point for insurance companies is their clients. And here, Inbenta has also delivered solutions tailored for them. AG2R La Mondiale is the leading insurance group specializing in personal protection in France. AG2R chose Inbenta to increase the rate of its keyword search for selfcare using semantic technology. The deployed solution focused on developing customer autonomy, reducing the volume of low value-added calls. The solution also directed requests to the most suitable processing channels and offered the possibility of exploiting the knowledge base on other channels. The semantic search engine has been a success, managing nearly 15,000 requests per month.

Conversational AI in Travel

Covid-19 may have caused a downturn in travel, but tourism isn’t going to die down, and customers are eagerly awaiting the chance to return to their favorite locations. However, it has been a busy time for customer service teams handling flight cancellations, rearranging travel arrangements or answering to customers who want to know more about Covid-19 policies and claims when planning their trips. 

Conversational AI helps travel agencies deliver fast, streamlined responses that are accurate and updated while also guiding customers through booking processes, leaving valuable time for human agents to handle more specific requests from customers. 

Chatbots and conversational AI solutions in travel can allow travel agents to save and effort answering routine queries. Best of all, they can improve the customer experience by delivering personalized, 24/7 assistance to help users book their flights, access their booking plans, flight status and boarding passes and even proactively suggest the best deals and times to users based on their preferences. 

Use case: GOL Airlines- improving flight booking services with Conversational AI

In such a competitive landscape, airlines have had to step up their game to improve their customer experience and strengthen brand loyalty. GOL Airlines is a Brazilian airline company that has been operating since 2021. Today they are one of the fastest-growing airlines in the world, operating around 900 flights every day. 

GOL has never shied from using technology to improve its customer experience. They were pioneers in launching the first mobile check-in service, providing mobile geolocation services to their passengers and designing a website that featured resources to assist people with visual and motor impairments. 

GOL’s website has heavy traffic, with around 2.5 million travelers using their website every month. However, the airline initially used conventional channels (human agents, email and telephone) to deal with requests for actions from assistance with checking-in, purchasing tickets or finding out about travel or luggage restrictions. 

GOL Airlines needed to relieve pressure on their call center to improve their customer experience and satisfaction and reduce waiting times in their contact center by automating simple queries. They turned to Inbenta to achieve these goals. 

When analyzing the situation, Inbenta recognized that the treatment of support requests on the various channels was putting significant pressure on staff and resources.

To address these concerns, Inbenta created a customer service chatbot called Gal on its website. Gal uses Inbenta’s Symbolic AI platform to offer GOL customers support 24/7. Today, GAL handles approximately a third of the whole enquiries received by GOL and has an impressive retention rate of 85%. Customer satisfaction has increased, and Gal keeps on learning and improving every day, freeing time for agents to focus on more complex queries.

GOL’s ability to foresee the need to use conversational AI allowed them to adapt to some of the new obstacles from the Covid-19 pandemic. The airline thought outside the box to use WhatsApp as a channel for customers to access their human agents. Inbenta also implemented Gal on WhatsApp, along with other functionalities such as online check-in, booking management and seat selection, to automate the channel and relieve pressure on the call center.

Today approximately 35% of customers finalize their check-in process through WhatsApp. Among those customers, 90% say that the service is very good or excellent. 

When asked about the collaboration with Inbenta, Elisa Moreira, Customer Service Manager at GOL, stated “thanks to Inbenta’s solution, we are able to update the information that we deliver to our customers in a quick and easy way, making our customer’s life easier. The solution is very user-friendly, and our team can go in there and update content in a matter of minutes, without having to rely on Inbenta for help.”

Conversational AI in healthcare

Covid-19 has accelerated the need to find ways to deliver customer healthcare to mass numbers of users. With so many patients having requests from home during lockdowns, the growing omnichannel and personalized demands from healthcare consumers raised the bar for the sophisticated versions of chatbots and automated systems needed. 

As the Covid-19 has challenged company digital transformation strategies, we have seen conversational AI play a key role in helping health systems tackle overstretched call centers and front-end staff who are either overwhelmed by the large numbers of calls or downsized as employees cannot access their facilities. 

The fact that sophisticated conversational AI systems can deliver personalized interactions by leveraging the large amounts of data they store, means that healthcare bots can address complex and highly personalized answers about a wide array of issues, such as benefit eligibility, coverage and costs, or symptom checks and guidelines to name a few.  

Conversational AI platforms have been used as triage and symptom checkers for patients navigating over-burdened health systems, as well as to provide information such as the locations of testing sites or to inform staff, patients and pharmaceutical providers of updated guidelines. 

Conversational AI can also be used in healthcare to deliver actionable, personalized interaction to facilitate healthcare decision making. Data analytics from interactions can provide insights to improve workflows and communication while facilitating patients on their healthcare journeys. 

Use case: Delivering Conversational AI solutions to a Fortune 500 pharmaceutical giant. 

A Fortune 500 pharmaceutical giant, was looking for a solution to help them with their growing monthly chat volume. Their live agents were unable to keep up with this increase and performance was slipping. The company decided to leverage a robust technology that will bring relief to their teams and integrate with their existing solutions. 

In their search for a proficient chatbot, the company knew that they needed a smart chatbot with advanced NLP technology and that would easily and seamlessly integrate with existing systems.

They sought to relieve their staff by giving them more time to handle complex queries while streamlining simpler requests, in order to improve performance and boost customer satisfaction.

The company found its solution in Inbenta’s chatbots, making the most of the seamless integration capabilities and Customer Relationship Management system Inbenta can provide, allowing their chatbot to go live in just a few months.

Since the implementation, customer service agents have had more time to work on complex requests, making them happier and improving productivity and customer service. 

Additionally, as Inbenta’s solution is easily adaptable, scalable and seamless, this pharmaceutical group can extend the solution as their digital transformation process grows and they seek to expand the chatbot in other languages.

The chatbot has brought measurable benefits to the company. Inbenta’s chatbot met the set targets of handling at least 40% of all monthly incoming transactions, providing valuable extra time for live agents, and increasing customer satisfaction by providing accurate and helpful services to patients and clients.

The senior leader of customer service was very happy with the service. “Before we worked with Inbenta, when somebody went to our website and selected to chat, we saw an incredible drop-off rate because it took a long while for the live agents to get to customers. With Inbenta’s auto-routing chatbot, this drop-off rate has gone down significantly. And now customers are getting questions answered faster.”

Conversational AI in E-commerce

In the past four years, global E-commerce revenue has more than doubled. Behind this year’s $2.8 trillion of online spending are customers searching for products that meet their needs. While online shopping may sound effortless, there is a lot of work that goes into trying to deliver an optimal customer journey. 

The Covid-19 pandemic has further transformed how consumers purchase their items. Consumers are increasingly buying online and are getting used to the comfort of having their purchased goods delivered to their homes. One month into the pandemic, e-commerce revenue had already grown by 68% and conversion rates had risen 8.8%. These are numbers usually expected on Cyber Monday. With retailers closing their stores, e-commerce reached an all-time high of 16.4% of total global sales. 

Even though consumers are looking online, purchase processes should be more like the conversational experience consumers have in a store with a salesperson, where you can get their insights and recommendations to make better purchase decisions. 

Businesses know how important intelligent automation is and have accelerated the deployment of these services to boost productivity, increase customer satisfaction and save resources. At the same, automated services provide an opportunity to improve and personalize shopping experiences. 

E-commerce businesses have also had to downsize their staff due to the pandemic. Marketers have turned to digital means and real-time customer data to trigger campaign assets based on their customer actions and preferences. They then use this data to engage shoppers with targeted content throughout their customer journey.

E-commerce will keep growing at a vertiginous rate. But successful e-commerce businesses must stay competitive by using cutting-edge technologies that meet the growing demands from modern customers, who want to buy their products at any time of the day, from any place and in their preferred language. 

Conversational AI in e-commerce ensures that customer journeys are engaging. By incorporating omnichannel capabilities to meet customer demands, the deployment of conversational AI is influencing how companies seek to deliver an optimal customer experience. 

Today’s consumers demand speed and efficiency, with easy-to-use, intuitive digital experiences across channels and devices. 

From chatbots that deliver personalized suggestions, help solve customer queries and carry out end-to-end transactions, to automated e-commerce site search. The latter is important because the built-in or integrated search engine can find products that users are looking for by directly matching the search keywords with products available in the store. This looks like an easy task, but its importance must not be undervalued. Automated e-commerce search can be an invaluable business tool that can drive sales and conversion and deliver a positive user experience. 

While not every user carries searches on a site, searches account for 40% of total revenue. However, e-commerce brands must fully leverage the capabilities of conversational AI and automated e-commerce search, such as swiftly connecting customers to their services, delivering personalized searches that are tailored to each user, reducing bounce rates while increasing conversions and making the most of actionable data insight to proactively provide advanced cross-selling capabilities.

This can be done with features like autocomplete, related searches and analytics, alongside machine learning, proactive chat and conversational AI. Product catalog searches such as Inbenta’s empowers customers by detecting the product traits used in their search queries, which are then reflected in highly accurate search results. 

Inbenta can deliver numerous Conversational AI capabilities for e-commerce. The Inbenta chatbots understand customers in their natural, colloquial language. Using semantic technologies, customer queries are matched to existing FAQs with up to 95% accuracy, without relying on keywords or exact phrase matches. 

The Inbenta chatbots can improve search-to-cart ratios by answering relevant user questions throughout the buyer journey, allowing users to make better decisions without interrupting the shopping experience. 

If the chatbot cannot help, or live agent assistance is requested by the customer, the conversational platform automatically escalates to the next available agent. The agent is also given key insights from previous interactions so that the hand-off is seamless. 

Groupon is an American global e-commerce marketplace that connects subscribers with local merchants by offering activities, travel, goods and services in 15 countries. Using a semantic Enterprise Search box on their customer support page, customers can now easily access Groupon’s knowledge base to find answers to their queries. The dynamic FAQs under the search box allows customers to automatically find relevant answers based on their inputs and to quickly scan the results that relate to their query.

Using Inbenta’s Enterprise Search, Groupon offers over 1 million answers to its customers, which translates to lower email wait times, faster customer service and increases customer satisfaction.

Conversational AI in Administration and Education

Education and administration are increasingly becoming mobile, and institutions are seeking ways to enhance learner experiences by using technology. Covid-19 has accelerated the need for these institutions to turn to digital means to help students, from virtual classrooms, online exams and forums to name a few. 

The academic community must be able to access the information and resources they are looking for, and institutions have turned to AI technologies that can respond naturally and 24/7 whenever a student wants to know more about admissions, scholarships, fees, exam dates, grades, courses, extra-curricular activities and more. 

Conversational AI chatbots in education can help students retrieve information on their assignment deadline or modules, and deliver personalized assistance. 

Students are also changing their habits, and the use of library halls have dwindled during the pandemic. Learners have turned to search bars to find their information and conversational AI in education and administration can also contribute to changing the panorama.

AI chatbots can interact with students at any time of day, through multiple channels and in many languages. Chatbots can also access student data and past interaction to know the level they are in with regards to the lectures and keep them updated, while recommending relevant learning content, making learning easier.

Advanced chatbots can also act as virtual teaching assistants, answering questions that are stored in a knowledge base. Students can also access information. 

The fact that chatbots can integrate with multiple channels is particularly useful as students use multiple channels and devices. Chatbots can integrate with social media platforms, increasing student engagement and acting as a medium for student-teacher communication, delivering insights and feedback to teachers to improve their teaching efforts. 

How to frame a conversational AI project

Having seen all the ways that Conversational AI platforms are helping businesses become more competitive, improve customer engagement and boost brand loyalty, the next step is to determine how to frame a conversational AI project. Here we can help you.

AI conversational interfaces

Best practices when framing a conversational AI project

Designing an advanced AI chatbot is a tricky exercise that cannot be improvised. To avoid common mistakes witnessed by other companies, it is best to follow a set of practices. This will ensure that you create a bot that is helpful, engaging and meets customer expectations. Here are the top 8 chatbot best practices when it comes to designing proficient conversational experiences. 

#1 Set a goal for your chatbot

You can create a bot for almost anything these days, which is why it Is important to set a clear goal and outline for your own bot or virtual agent from the beginning to prevent you from getting carried away.

Defining what can be automated is a good place to start, but you must remember to always keep your user’s needs in mind. Regardless of whether the tasks carried out by the bot are simple or more complex, it is essential that the chatbot is user-centric and focused on solving their problems in order to be successful.

#2 Give your chatbot a personality

Another point you should consider when creating a conversational chatbot is to ensure that it doesn’t sound like a robot. Part of the customer experience is based around comfort and establishing a relationship between a customer and a brand. This means giving the chatbot a personality and a tone of voice that is aligned with your brand’s value. Care must be put however to make sure that there isn’t a lack of personality, that can result in a dull and uninteresting chatbot, or too much personality that can be annoying and ruin the customer experience. 

How do you find the right balance? Ask yourself the following questions:

  • How would your target audience speak? A chatbot often mirrors the personality of its audience by writing in the style they speak.
  • What’s the name of your chatbot? It can be straightforward such as your brand’s name followed by ‘bot’ or ‘chatbot’, or a play on words for example.
  • Does it have a gender and a visual representation? Inbenta gives you the option to choose from a vast gallery of avatars so that you can find the one that will become the perfect representation of your brand. 

#3 Introduce your chatbot and set expectations

We know that it is important to establish a set goal for your chatbot. Once that is clear, you need to be able to communicate this to your users. The best place to introduce your bit and list its capabilities is in a welcome message. 

Be upfront about the functionalities of the bot, as well as its limitations. This way you will manage user expectations and prevent any frustration and potential disappointment. 

#4 Break up the information into small chunks

A well-designed bot can present users with informative and interesting content. However, the information must be broken up into digestible chunks of useful and engaging material. It is better to send multiple short messages rather than a long one, as huge blocks of text are difficult to read and can overwhelm users. Shorter messages mimic the flow of human messaging and provide a better user experience. 

When developing a chatbot with Inbenta, you also have the option to use a side-bubble where you can develop more in-depth content, which means you can break up the content and it can be expanded upon the user’s request. 

#5 Test, monitor, tune. 

A testing phase before releasing your chatbot is a key stage, but once you have successfully gone live it is equally important to keep on monitoring results to know how to fine-tune your bot. 

Inbenta’s Workspace provides data and analytics to help you analyze your bot performance, perform a gap analysis by detecting all the questions that did not get an answer or were not viewed by the user 

Using this dashboard to monitor your bot will let you optimize it by adding extra content or improving matching between user requests and content in the knowledge to guarantee high quality results. 

#6 Request user feedback

Users must have the option to rate the answers they have been given as it allows them to express their satisfaction with the service, but it is equally as important for the company to receive this feedback. It can be as simple as adding a thumbs up or down button or offering users the option to provide written feedback when a negative mark is given so that users can provide detailed information of why they were dissatisfied with the bot’s performance. 

Businesses must pay close attention to ratings and feedback as they can provide opportunities to detect gaps in a knowledge base or ways to use a bot or ask questions that hadn’t been thought of before. 

#7 Detect frustration and handoff to a human

Every bot has its limitations regardless of how well designed it is. These limitations will sometimes cause frustrations, which is why it’s necessary to have a technology that can detect your user’s emotions by analyzing their tone and language. 

Inbenta’s NLP technology and intent detection detects a user’s sentiment through the interaction and escalates the conversation to human agents if the issues cannot be resolved by a bot. 

#8 Choose your provider and technology wisely

The most important practice when developing a chatbot is to choose wisely when it comes to selecting the technology and provider that your bot will use.

We know that there are different types of chatbots, such as button-based, keywords based and conversational bots with NLP technology and symbolic AI. The latter provides the best performance and obtains the best results out of your AI-powered chatbot.

When choosing a conversational AI platform, look out for providers with a repertoire of successful use cases, and experience in delivering high-quality conversational AI solutions with the strongest combination of technology. 

How to launch a conversational AI project – Chatbots

Whether you want to launch a conversational AI project such as chatbots or site search specific considerations must be kept in mind. 

Before starting to work on any conversational AI project, it is necessary to ask yourself three key questions: Why, How and What? 

These three questions should underlie any project launch. It allows you to determine the nature of the project, its final objective and its fulfilment. Here are three steps to allow you to properly frame your chatbot project. 

  1. Define the stakes and set clear objectives for your AI Chatbot Project

Once your company has decided that it wants to set up an AI chatbot, you need to know what objectives you have. There may be several internal needs that need to be determined beforehand. For example: 

  • Improve user experience by adding a new service tool that is available 24/7 and that makes it easy for users to navigate and search for information on the desktop site. 
  • Better manage contact flows by reducing the numbers of calls with low added value and the flow of incoming emails from the desktop site. 
  • Increase conversion rates and customer knowledge.

Regardless of the objectives, these need to be measurable both qualitatively and quantitatively. Therefore, you need to think carefully about the measurable metrics and KPIs to see how to improve the solution and see if it is a success or not. 

  1. Structure your AI chatbot team and assign them missions

Project teams need to be created from both the client and the provider’s end to manage the chatbot project. Each side must assign a Project Manager or Product Owner, Editorial Managers (Botmasters and Chatbot Authors) and a Developer. If your chatbot project belongs to a global self-service experiment you may need to involve additional roles such as experts focused on customer journey, analytics, legal issues and business. 

Once the roles are established, you need to define how these parties are going to collaborate. Organizational processes must be set for example to

  • Set up common management tools
  • Set a common messaging tool
  • Create a messaging address for the entire project team
  • Set up daily meetings and organize weekly meetings between the project team
  • Report regularly to the steering committee. 

Each team member must have a specific mission. This can be focused on the preparation, implementation or follow and monitoring stages of the process. The main stages when setting up a chatbot project are as follows: 

  • Drafting a requirements specification
  • Defining customer journeys
  • Drafting an editorial charter
  • Writing chatbot contents and organizing them as a dialogue hierarchy
  • Drafting a graphic charter
  • Defining KPIs
  • Elaborating user test-scenarios and analyzing their results
  • Technical integration of the chatbot on the test environment
  • Internal and external communication on the project

Each stage of the project requires an established methodology that needs to be followed by all team members. 

  1. Experiment with the “Test and Learn” mode

To deploy a quality chatbot quickly, you need to be as agile as possible. The best way to do so is to work in iteration. Firstly, deploy an initial version then test and adjust before deploying a second version and repeating the process until you reach a product that meets your requirements and objectives. 

Even though your chatbot may lack some things, you need to make adjustments only if you consider that it is truly necessary. If relevant changes are required, then it indicates that your initial objectives were not adequately defined. The difficulty when using agile methods is that there will be moments where you will want to test everything, but it is imperative to not lose sight of your initial goal. 

How to launch a conversational AI project – Site search

We have seen some of the steps required to build a conversational chatbot, but what if your conversational AI project focuses on an advanced site search? There are three ways to implement site search. 

Build it in-house

This is the most expensive and time-consuming option. Building your on-site search engine in-house has the advantage of giving you full control over its technology and functionality, but requires you to personally maintain it, which can become a massive (and costly) burden over time.

When it comes to CMS search, you get what you pay for. While it’s a cost-effective option, the search is often very simple and not very functional.

Purchase a dedicated search solution from a 3rd-party vendor

Choosing to work with a 3rd-party vendor provides you with an “out-of-the-box” experience. Simple implementation, ample features, and quality support make this the most comprehensive option. Purchasing an on-site search solution such as Inbenta’s semantic Search engine is a clever choice that will ensure you get a tool that’s optimized to your needs and that doesn’t leave your visitors frustrated.

When choosing a site search, the more advanced it is, the better the customer journey. If a site search doesn’t deliver results, it can rapidly lead to customer frustration and increase the bounce rate on websites and result in lost revenues. Here we list some of the key functionalities to look for in a site search.

Semantic search using NLP technology

Unlike lexical search, which only looks for literal matches for queries and will only return results when a keyword is matched, semantic search understands the overall meaning of a query and the intent behind the words. 

Therefore, when choosing a site search, it is essential to ensure that the solution has the capability to understand human language. Inbenta’s Search module is powered by Symbolic AI and Natural Language Processing technology, which enables it to understand the meaning of users’ questions regardless of slang, jargon, and spelling.

Auto-complete

Autocomplete is a mechanism that provides suggestions in a menu below the search while users are typing their queries. These predictions can be tailored to your site’s specific content, or their search history, or common keywords and tend to be a limited number of keywords to not overwhelm users with excessive suggestions. 

The objective of auto-complete is to guide the user and help them construct their search query as users sometimes are not very good at formulating search queries and are easily frustrated if they don’t find their results on the first try. 

Faceted search is a feature that allows users to find their search results thanks to filtering with facets. Facets are checkboxes, dropdown menus or fields usually presented on top or on the side of a search result to allow users to refine their search queries. 

This can be seen, for example, in retail shirts, where users can narrow down the items they are looking for by choosing the color, size and price range. By eliminating the need for users to scroll through endless results, users save time and experience a better user experience, increasing the possibility of having more conversions. 

Federated search indexes information for numerous sources such as documents, internal knowledge bases, FAQs and external websites, unifying the information under one main search engine. 

When the user types a query, the federated search engine simultaneously browses multiple disparate databases, returning content from all sources in a unique interface. This functionality is particularly useful in complex organizations with thousands of sources of information in the cloud and on-premise. It encourages users to go beyond what they were originally searching for and enables organizations to collect valuable data about popular products. 

When launching a site search, here are some best practices to consider:

Place the search where users expect it to be

Users shouldn’t have to look for your search function. Placing the search bar in the top-right or top-center guarantees visibility of the search functionality in a place where users expect it to be. 

The search box must be accessible on every page, including 404 pages to ensure that users can conduct searches on all pages, and not just only the homepage. 

Use a magnifying-glass icon

The magnifying glass icon is a widespread symbol of search that is easily recognized by users, so it is recommended to place it in the interface.

Separate the text field and a search button

Most search bars consist of two elements: the search box, where users write their queries, and a search button, which users click to initiate a search. These elements are most often placed horizontally adjacent on a single line.

Size your search bar appropriately

The size of your search bar depends on its importance on your site and the expected length of a typical query. If the field is too short and only a portion of the text is visible, there will be bad usability as customers can’t review or edit their query. 

A rule of thumb is to have a 27-character text input, as it would accommodate 90% of queries.

Integrating your Conversational AI platform with other solutions

Businesses often make the mistake of trying to bite off more than they can chew when deploying technological solutions. This includes trying to do something that has been proven to work for years and already exists and wanting to change it. With the growing need to use omnichannel capabilities, some businesses try to deploy solutions and build-in their own features without playing on their strong skills.

Adaptability should be a key element of a successful product, and that means allowing partners or other features to be built on top of your solution. The best AI chatbots have the capacity to integrate to third party software, such as CRM, HR platforms, or inventory management tools.

This ability allows chatbots to retrieve information to answer a specific query with a personalized answer as it can find the information in an inventory or database it is integrated into. This way, for example, if you are a retail company site and a user wants to know if there is a size 10 red dress available, the chatbot can connect to the inventory database and know the categories of all the products and stock levels. It will then inform the user of the availability of the dress, all in a seamless, swift conversation. 

As these integrations can be implemented across multiple channels including social media, users can experience a quality customer experience that will increase their customer satisfaction rate. 

How to maintain a project

Going live is only one of the steps of a successful conversational AI project. Maintaining the project is just as important to ensure its performance increases over time until it reaches the level required and then keeps on operating successfully. 

Depending on the provider that has been chosen, you will get maintenance fees or not. Either way, human resources should be deployed to ensure that conversational bots are optimized and maintained on a regular basis. 

As a consequence, the cost of monthly maintenance can be calculated as follows: 

Monthly maintenance cost = provider’s monthly support fee + (time spent internally * employee(s) monthly salary)

We know that self-service, automated solutions such as chatbots and knowledge management systems can help businesses save on customer service costs, but it is important to establish measurements of KPIs and ROIs to assess the success of the implemented technology. 

Maintaining a successful conversational AI project required more than good planification. It is important to possess the right tools to ensure that you are getting the most of your chatbot or conversational AI solution, just as much as it is vital to keep up with an ever-growing market that keeps evolving and transforming itself digitally alongside a tech-savvy customer base. 

Future-proofing your project is key, and this is where it is essential to leverage the amount of data and analytics conversational AI platforms accumulate to optimize your projects. 

KPI dashboards with qualitative analytics and identify trends and convert data into actionable outcomes, by tracking conversations, feedback, user habits and sentiments. This lets you know more about your user and your solution.

Conclusion of our Ultimate Guide

Businesses know that there is a growing need to automate their services and save time and resources. However, they must rely on solutions that can optimize these resources while providing faster, better support to boost customer engagement and brand loyalty. 

Conversational AI has become a key element in nearly every company’s digital transformation strategy and this has been further enhanced since the Covid-19 pandemic. Recognizing the need to implement conversational AI is a given, but choosing the ideal solution can still be a challenge. 

With the need to find quality packages with proven use-cases promptly, Inbenta has stood out as a provider that can guarantee guidance and a quality solution that can perfectly fit each company’s needs.

Whether it’s a chatbot, a knowledge base or advanced site-search, Inbenta delivers numerous solutions that can adapt to each business’ needs and transform their revenues and customer experience. It is not only customers who can benefit from Inbenta’s conversational AI solutions, but employees and HR teams too. 
Businesses and customers can be better informed on products and services and can access information 24/7, in multiple languages and through multiple channels, while also receiving personalized prompts and actionable insights.

With 15 years of experience and over 250 customers globally, Inbenta has built a solid reputation and can help you determine how you interact with your users. 
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