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Talkbot Basics

07
Jul
Talkbot Basics
AI in Customer Service: Conversational A.I Talkbots
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Imagine a future where you can have phone conversations with robots. And no, we’re not talking about monotonous sounding robots but hyper realistic ones that bear an uncanny resemblance to humans. Once in the realm of science fiction, this scenario has become reality today. Conversational talkbots are artificial intelligence (A.I.) machines powered by natural language processing (NLU), automatic speech recognition (ASR) and several other mechanisms that makes the hyper realism possible. These technologies translate to improved cost efficiencies through lower labour costs and increased sales volume. With that said, what are the benefits of incorporating talkbots into your call centre?

A disruptive technology in the world of business

It is in every business’ interest to reduce their costs in order to maximise their earnings. In a time of economic disruption, it is essential for businesses to ensure that their resources are optimised. When it comes to call centres, time consuming tasks include those that are repetitive in nature, such as calling customers to check in on their interest in a product or to confirm an appointment, for instance. When a call centre agent that is known to have a knack for securing sales is tasked to only confirm appointments, we would consider this to be a misallocation and underutilisation of talent. This is where Conversational A.I. Talkbots can come in handy.

Riding on the worldwide push for task  automation, conversational A.I talkbots can augment your existing workforce by automating repetitive calls such as sending appointment reminders or other relatively straightforward tasks. Improvements in Natural Language Understanding (NLU), Natural Language Processing (NLP) and Automatic Speech Recognition (ASR)  has equipped talkbots with the ability to identity the caller’s intent as well as nuances in their speech. By automating such repetitive calls, businesses are then able to divert their best agents to handle more complex call tasks, thereby ensuring cost efficiency when running a business.

As Wiz.A.I creates highly customisable talkbots for your business needs, these talkbots will mirror your best service agents and their best practices to ensure customer service excellence. When it comes to human agents, any changes to their script or sales tactics requires training, and this in turn consumes precious time with an indefinite outcome as to whether the new skill is effectively learnt and applied. With a talkbot, any updates to the information disseminated to your callers can be implemented almost instantaneously, bypassing steep learning curves. Not only is implementing a new sales tactic now a seamless process, feedback is also available almost immediately through data insights and analytics that track the reception of customers.

Driving Sales with A.I

Closing a sale is also a time consuming and time sensitive process. When a company is able to call a customer when he/she is in the midst of deliberating, there is a higher likelihood of successful conversion. Like the proverbial striking of the iron while it is hot, getting the time right is already half the sales battle won. It is however, tricky to know when that window of opportunity will open. This is where customer analytics becomes crucial. Using the Conversational A.I Talkbot, companies would be able to conduct some form of customer segmentation after identifying their intentions. After retrieving this valuable information, businesses can then devise a better strategy to tackle each customer persona. For example, the talkbot would be able to identity who are the customers who have expressed great interest, before shifting their attention to persuading them.

 

Using A.I. for customer service calls can also allow for greater customer outreach and loyalty. Being able to reach out to numerous people at one time while checking in on their interests not only allows for companies to have a higher chance of sealing the deal, it also makes for a memorable customer and brand experience. Small gestures like congratulating customers or letting them in on an exclusive deal will also build brand loyalty, leading to higher returns in the long run.

Furthermore, machine learning which is an integral part of these talkbots allows these intelligent systems to become progressively better at picking up the intentions of the callers. The more data and exposure to different conversations, the better it is and the faster the progress. The rapid rate of technology development in A.I. also allows for system upgrades and hence, customer service excellence.

Most customer service calls are often outsourced to countries where labour costs are more affordable. As such, these agents who are not working directly under the company may not be able to understand the image that the company is trying to present to the general public. Engineering a talkbot and scripting to accurately reflect your company image to deliver the right information to the audience makes a world of difference. Building a talkbot for your company allows your business to regain control of your brand image, while maintaining its consistency through A.I. delivered service standards.

Every call centre agent has their fair share of nasty calls, making it increasingly difficult to maintain a positive and professional tone during a long day at work. Talkbots may hence be in a better position to handle such tricky situations as its tone of speech is maintained. When necessary, the call can be also be transferred to relevant departments. Such arrangements prevent unwelcome scenarios such as when an exasperated customer meets a tired call centre agent who is misunderstood to be insincere.

Companies may also sometimes experience a surge in call volumes where increasing the number of call centre agents in such short notice would be impossible. Once again, talkbots are well equipped to rise to the challenge, handling sudden fluctuations with ease.

All in all, adopting A.I for higher levels of automation is becoming a business strategy proven to have significant returns. This is a golden opportunity to supercharge your call centre services and elevate your business to the next level.


07
Jul
Talkbot Basics
How COVID-19 has changed the call centre
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Covid-19 not only pushed the world to intensify digital transformation efforts, it also made companies aware of the necessity of automating some business processes to meet their immediate needs in times of crises. Numerous call centres screeched to an abrupt halt during the lockdown as many companies were unprepared for remote work. More often than not, call centre agents are outsourced and not within the premises of the company’s office. Moving the phones and its accompanying infrastructure was a logistical nightmare especially in such short notice. While Covid-19 forced the majority of the world to adopt remote work arrangements, it drove companies to realise the potential of Artificial Intelligence (A.I.) and how automation could revolutionise call centres.

A.I and the changing landscape

The call centre is typically in charge of handling outbound and inbound marketing calls or other relevant customer enquiries. A large team of call agents is often sufficient in handling large call volumes, but when lockdowns are imposed and everyone is forced to stay at home, the workforce immediately gets thinned out, and the usually manageable call volume becomes a formidable task. Enter, Conversational A.I. talkbots. These intelligent talkbots are powered by A.I. and are able to engage in human-like conversations with caller. This is possible because the talkbot is driven by systems like Natural Language Understanding (NLU), Automatic Speech Recognition (ASR) and Machine Learning; to name a few. Equipped with the capability to engage a customer, high call volumes are easily handled and when confronted with complex enquiries, the talkbot is smart enough to transfer the call to a human agent who can promptly provide a solution.

As mentioned previously, most companies outsource their call centre agents to other countries where labour costs are lower. As a result, these call centre agents may not have a deep understanding of the company they are working for. By contrast, the talkbot is curated, scripted, and customised according to the company’s needs. The process of building a conversational talkbot is centred on the company’s messaging, the image they would like to portray to the public, or a sales tactic they would like to employ on customer calls. All in all, this allows the company to have better control over their content, messaging, and image – elements which are easily lost when telemarketing calls are outsourced to external labour.

Businesses that were forced to look into automating some processes would also have realised how A.I. is able to reduce the cost of production significantly. Furthermore, the quality of the service is not only maintained but arguably, has become better than before. How does this happen?

While constructing the talkbot, Wiz.A.I looks at the best practices of your top-performing call agents. We then incorporate these practices into the structure of the talkbot’s conversation to ensure that inbound and outbound marketing calls are not only intuitive, but also engaging and convincing. The talkbot is engineered to be strategic in the information that is provided to the caller to maximise the efficiency and effectiveness of the conversation; two qualities which have become increasingly important in a society where time is precious and attention spans are shorter than ever.

Take the following scenario: A call centre agent is tasked to call 200 customers by the end of the day to confirm their appointments. It is not surprising that by the time the agent calls the 50th customer of the day, his enthusiasm would have dampened and his tone, noticeably fatigued and exasperated, especially if he/she has endured difficult calls. Talkbots by contrast, are able to maintain a consistent tone throughout all 200 calls, which in turn translates to higher quality calls and consistent service standards. Frustrated customers are more likely to be put at ease when the call agent is patient and sincere.

With one talkbot to handle a large volume of customers at once, your call centre agents can be put on standby to handle more complex tasks instead of repetitive ones. It’s better to let your talkbot do the mundane task of recording the caller’s intentions like whether they are able to make it for the appointment or need to reschedule, while the valuable skills and emotional capacity of your call centre agents can be fully utilised in more productive areas.

This new work environment sounds too good to be true?

A.I. technology is no longer in the realm of science fiction or just another pipe dream, it is being applied to several aspects of daily life. Take smart home devices, for example. With A.I., our personal lives are much easier and it is a given that companies would try to leverage technological advancements to improve their businesses.

Wondering if callers might realise they are speaking to talkbots? Wiz’s A.I. technology has refined the art of human-like conversations to the extent that 90% of our callers thought they were speaking to a human! This is possible because of the text-to-speech technology which generates talkbot voices that sound as human as possible. Additionally, talkbots also recreate the various nuances of human conversations – pausing when interrupted, clarifying questions when confused, and adapting the conversation flow according to new questions. This all makes for a hyper-realistic customer service experience. Additionally, machine learning allow the talkbot to be continually refined in its accuracy in understanding nuances in the human language; a tricky task that is second nature to humans but not so to computers. Trained on large amounts of data, the talkbot only gets better with time. Instead of lengthy learning curves, any updates to the information disseminated or the incorporation of a new sales tactic in outbound marketing can be incorporated into the calls instantaneously, saving the company precious time and money.

In spite of the economic challenges of the past year, there is a golden opportunity to ride the wave of A.I. adoption. Rather than a dystopian narrative of a “robot takeover”, A.I. technology constitutes a handy tool that compliments your workforce, positioning your business for elevated growth, and revolutionising the call centre industry as we know it.


07
Jul
Talkbot Basics
Age of Automation: Which calls should I automate?
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

The  pace of workplace automation is expected accelerate in the next three years. The automation of key segments of the production process is critical to boosting efficiency while maintaining competitiveness in the economy. Cutting edge technology such as artificial intelligence (A.I.) is also becoming increasingly popular among companies seeking IT solutions to meet their business needs. The surge in the adoption of A.I. solutions is spurred by the twin pressures of Covid-19 lockdowns, and the constant need for efficiency. When offices closed and remote work became the default arrangement, automation became a solution to labour shortages, causing a substantial spike in the demand for A.I. related technologies. For instance, A.I. can be used to automate both outbound and inbound calls for call centres or companies engaging in telemarketing. Wiz. A.I. specialises in creating hyper-realistic talkbots that are powered by natural language processing (NLP) and machine learning— tools that can be useful for meeting sudden surges in call volumes and increasing your customer outreach.

Wiz’s talkbots are engineered to be able to understand the nuances in the human language (and yes, this includes complex ASEAN languages) while engaging customers in meaningful conversations. Far from being science fiction, our talkbot technology has refined the art of humanised conversations to the extent that over 90 percent of users could not recognise that they were speaking to A.I. over the call. Instead of a monotone, robotic voice that one would commonly associate with voice-layered chatbots like Alexa and Siri, customers speaking to Wiz’s Talkbots are greeted with friendly, human-like voices. This experience is only possible because of the text to speech (TTS) speaker model that is meticulously developed and consistently refined by Wiz’s dialogue engineers to mirror the human voice.

Which types of calls can be handled by talkbots? 

  1. Repetitive tasks

Take the healthcare industry, for example. When it comes to managing appointments, it is not uncommon for people to forget their appointments or cancel them prior to a consultation. To ensure that appointments are managed in an orderly manner, staff in charge of administrative matters will have to spend hours calling patients who have made a booking, lest they miss their appointments entirely. Tasks such as appointment confirmation and reminder calls are rule-based and can be automated with talkbots. By carefully constructing a conversation flow that is user centric and intuitive, talkbots can be utilised for this task. Crucially, the time spent confirming appointments is significantly reduced as the talkbot can manage multiple callers at once. Additionally, the intention of the caller (whether they can make it) is automatically recorded in the system, significantly reducing administrative complexity.

But what if someone does not pick up on the first call? Talkbots are able to identity missed calls and automatically schedule a later time to redial.  With human error minimised, you can rest assured that every caller on the list will be contacted. In the event that the caller has a unique and complicated request that requires the assistance of a human staff member, the call can also be immediately transferred to relevant departments.

  1. Telemarketing

According to Deloitte, voice-based communications are still preferred over emails or chats when it comes to complex conversations. Telemarketing or ‘cold calls’ can also be considered a repetitive task and is arguably straightforward. A user can have one of the following three intentions: (1) interested, (2) uninterested, or (3) on the fence. If it is the second or third option, the talkbot can also be designed to be persuasive by mirroring some of the best practices of top performing call centre agents. At the very least, the talkbot will leave some form of information that the user can easily recall, such as the name of your website. This piece of information can be disseminated either verbally at the end of the call or via a text message. If your potential customer decides to change their mind, they would at least know where to find more information.

Cold calling is both a tedious and time consuming process when done manually. Hence, it is also important to conduct audience segmentation to ensure that calls are targeted at the right people to maximise conversion rates. To this end, call logging or transcribing is necessary but onerous. With conversational talkbots and its speech-to-text capabilities, call logging is automated and the conversations can also be easily analysed. Not only does this generate valuable insights, it can also be used as a guide on how to further improve your script. Furthermore, as talkbots are essentially computers, learning curves are no longer an issue; any updates or changes are instantaneous and no time is lost while attempting to secure a customer. The talkbot is able to provide timely calls and ‘strike while the iron is hot’— and that is the most ideal way of securing a deal.

  1. Calls that require large amounts of emotional labour

Emotional labour is defined as the arduous task of having to perk yourself up every single time you pick up a call from another customer, regardless of whether you have been picking up calls for the past 3 hours or if your previous call was an extremely unpleasant one. This takes an emotional toll on call centre agents which in turn, inevitably affects their performance in the long run. As employees either make or break the business, especially in customer-facing roles, it is in the company’s interests to protect their emotional needs. Adopting talkbots to handle difficult calls allow employees to  monitor calls from a distance and intervene at the right time. Additionally, because the talkbot is able to keep its tone consistent at all times, customers may be more satisfied with the call as it is highly unlikely that they will be speaking to a tired call agent who may come off as insincere – a disastrous setup if customers are already frustrated on their end.

Talkbots are the future

All in all, A.I.-driven automation might be your best bet at increasing cost efficiency and optimising your work processes. Seize the opportunity to leverage A.I. technologies to elevate your business for success.

Schedule a demo now

14
Apr
Talkbot Basics
A Guide to Conversational Voice Artificial Intelligence: The Terms explained
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Despite its recent rise to the limelight, Conversational Voice AI has only just started to gain recognition, with many still unfamiliar with the terms that are used. Here is a quick guide on what the acronyms are and an explanation of its functions.

What is Conversational Voice Artificial Intelligence?

Conversational Voice Artificial Intelligence comprises of what we termed as voice activated machines, with notable examples including Apple’s Siri, Google’s Home Assistant, Alexa by Amazon and including WIZ.AI’s Talkbot. Under its broad umbrella, it also includes other intelligent assistants such as the chatbots that appear at the side of your screen when you visit a website.

In Conversational Voice Artificial Intelligence, humans would use not only use their voice to provide these machines with commands or to ask questions; it is also possible for the AI to have hyper-realistic conversations with its users. The AI’s unique capability of understanding nuances in the user’s responses and context of the conversation are made possible with machine learning, text to speech engines, natural language processing and natural language understanding, hereby creating a lifelike experience for whoever it interacts with. The above terms would be explained in the following sections.

An Explanation on Natural Language Processing (NLP)

Natural Language Processing focuses on the interaction between computers and human language and allows the machine to comprehend the content of the language, be it speech or written text. Natural Language Processing also gives the computer the ability to understand the context of the conversation as well as the nuances in the user’s response, a process also known as intent recognition. Used not only in speech recognition but also in machine translation and predictive typing, Natural Language Processing is a foundational building block of artificial intelligence that gives the computer the capacity to understand the human language, process it and generate useful information for humans in an efficient manner.

What about Natural Language Understanding (NLU)?

This is where it gets a little more complicated (but not to fear! We’ll explain it). Natural Language Understanding is a subtopic of Natural Language Processing and it utilizes syntax (or arrangement of the words) and grammatical rules in the language to understand the user’s responses and its context. It involves processes like sentiment analysis where lines are interpreted to decipher their emotions (whether positive, negative or neutral). Commonly used on survey responses or customer reviews, NLU processes data with speed and efficiency, while rendering value-added insights which fit the context and emotions in the situation it is used. Lastly, NLU is also capable of categorizing natural language into topics to ensure that the user is transferred to the right agent for each nuanced customer service need.

Text to Speech (TTS)

Text to speech involves the use of a human voice to produce a realistic recitation of any written text into spoken words. An example of how it is used in a customer service A.I would be when the customer’s phone number (which is specific to the caller and different for everyone) has to be read in the call for a personalized experience. As it is impossible to hire a voice actor to record every single combination of numbers to form an identification number, text to speech speeds up the process with its ability to immediately convert a written text into a verbal recording. An immense amount of work is required to make a robotic voice sound realistic given the unique intonations and emotions that are often embedded in our day-to-day speech.

Speech to Text (STT)

If we follow the train of thought from the above section, logically, the Speech to Text feature is demonstratedwhen the caller’s voice is transformed into text. Also known as Automatic Speech Recognition (ASR), it basically means to “log” or “transcribe” the call. With the contents of the call automatically transcribed into text, it is much easier for the company to analyse and conduct audience segmentation, which is essential for creating targeted marketing strategies to boost business results. As transcribing calls can be a tedious process that requires a good listening skills and lighting-speed typing for any human agent, it is not surprising that this process is automated for higher efficiency and cost-savings. 

Dialogue Management

In the processes of creating a computer which can communicate with customers, it is important to build thestructure of how the conversational flow is like to ensure that the call experience is as intuitive and realistic as possible. This involves analyzing real life phone calls, putting yourself into the shoes of the customer to understand their needs and thought process. Dialogue management can involve two main processes: First, Dialogue Modeling which involves tracking the state of the dialogue, and Dialogue Control, where dialogue managers determines how the flow of the conversation with the A.I would be like.

Interactive Voice Response (IVR)

More often than not, the chirpy jingle of the customer service hotline is followed with a an instructional speech that says something like, “For inquiries related to  ­­­___, press one” and then you would proceed to input the right number into your keypad. This input then transfers you to the agent that specializes in handling your calls. The process of keying in a number into your keypad signals to the IVR; which is a basic feature used to manage your call and divert it accordingly to the appropriate handling agent.

Overall, the aforementioned components work together to create an intelligent robot that is not only able to increase your cost efficiencies but also drive your sales as it is able to encompass all the best practices of your agents. Coupled with machine and deep learning technologies, the innovation Conversational AItechnology improves every time with each customer interaction and call. With every customer conversationtranscribed and documented for easy analysis, companies are able to derive useful customer insights with no effort at all, which goes a long way in creating more personalized customer experiences and hence ensuringbrand loyalty.

In Conclusion

Though Conversational Voice AI is definitely an innovative technology which is constantly evolving, there is still a need for a human touch in the world of customer engagement. The best solution would be a combination of the two, Conversational Voice AI to help handle the rule-based, self-serve option, together with a Human Agent who can take care of the high value customer engagements.

Register for a quick demo to see how your business can benefit from WIZ.AI Talkbot automations.

 

 

 

 

 

 


26
Feb
Talkbot Basics
Chatbot VS Talkbot
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

What is a chatbot?

Have you ever encountered a pop-up along the sidebar of the website you are browsing, asking if you need help? That would be a chatbot. Driven by emerging technology in Artificial Intelligence, chatbots use Machine Learning (ML) and Natural Language Processing (NLP) to understand and answer a range of questions that are commonly asked. Given that chatbots can answer queries at any time— even in the dead of the night – they have become a popular form of technology adopted by many businesses looking to make the most of their website traffic.

However, chatbots are generally limited in their capabilities. While they are fed with a answer script that mirrors the way call centre service agents respond, chatbots usually only provide very general answers to a selected range of queries which may or may not be satisfying to the customers. More often than not, complex questions and requests would still require escalation to a customer service agent before they can be resolved.

Examples of common FAQ questions handled by a chatbot may include the operating hours of the company, or directions to their office. While the chatbot is able to fulfill some fundamental needs of a business through its on-demand service or prompt clarification of basic queries, the answers are mostly structured with little variance to the questions asked.

What then is a Talkbot?

A Talkbot, on the other hand, is more than a simple bot service. Involving more customisation to build, Talkbots are highly sophisticated customer service tools which are able to understand the user’s natural spoken language. A combination of technologies which include Natural Language Understanding (NLU), Natural Language Processing (NLP), Automatic Speech Recognition (ASR) and Machine Learning (ML) allows the Talkbot to understand questions in context and even detect and handle multi-intentions in a single sentence.

With its more nuanced understanding of the user’s queries, the conversation becomes more life-like, allowing a multitude of questions that can be posed and answered. Beyond simply fulfilling a passive role in the dialogue, the Talkbot is capable of initiating conversations and thereby prompt deeper engagement in any assigned topics—A key to building customer loyalty for your business.

A more superior solution to chatbots, Talkbots are able to proactively call on customers and engage in a realistic conversation. In other words, it is no different from a customer service agent. This is the magic of conversational A.I., which has the potential to supercharge your customer service standards.

What should I use? Chatbot or Talkbot?

It depends largely on the needs of your company. As mentioned earlier, chatbots are easily deployed and capable of answering simple queries like the email address of your office or even a list of services you provide in a short and concise manner, at any given time of the day. To a certain extent, they can also provide valuable insights by tracking what the common questions from your customers are. However, chatbots take a more passive role in the dialogue, are capable only of answering questions asked in a direct manner, and are often not able to understand and operate on contexts. When dealing with a chatbot, the customer would also have to be the more proactive party who initiates the conversation.

In contrast, Talkbots utilising conversational A.I technologies are able to proactively engage with customers in multi-round dialogues and can even handle vague responses in various contexts. Its capabilities can be deployed in a wide range of applications, across both inbound and outbound scenarios, such as FAQ hotline, appointment reminder calls, debt collection, customer surveys, cold calls, making them viable solutions for companies facing labor shortages during peak business periods or labor attrition problems. 

As opposed to using a human agent, Talkbots provide specific and useful customer insights to businesses as they are capable of tracking the call conversation and sieving out critical intentions. The Automatic Speech Recognition function in Talkbots allows all calls to be automatically logged, transcribed, labeled, and filed away for future action.

With Talkbots being more data-driven as compared to chatbots, audience segmentation can commence with ease, allowing for more targeted and strategic marketing efforts. In the long run, the Talkbot system can be continually improved with machine learning and can be tweaked to meet your customer’s ever changing needs.

It is not surprising that customers who surf the net often are able to easily identify who they are speaking to, whether it is a bot or a human agent. Customers may still feel distant from the companies they approach, even when there is a chatbot, because they know that this process of answering their queries are often automated. However, 95% of users are unable to distinguish between a Talkbot and a human agent. Talkbots listens, understands and speaks several different ASEAN languages with localised accents, it even incorporates human-like actions such as pauses, clarification statements and chasing statements. Furthermore, the text to speech (TTS) function of the Talkbot allows for the A.I’s voice to be programmed to read out certain client details. So instead of a cookie-cutter, generalised answer, Talkbots are able to embed the client’s information into their responses and this would facilitate a more personalized and realistic experience. For instance, Wiz. A.I’s bots are able to embed your date of purchase of an item into parts of the conversation in order to better relay information that might be useful for you.

The key defining feature that differentiates the Talkbot from the chatbot is the Talkbot’s ability to build a stronger relationship between the customer and your business. Picture this: When someone gets a call from a clinic to remind them of an appointment or if patrons of a website hosting service are called to be reminded of a free service that can boost their website traffic, the business will often come off as sincere and conscientious and this is enough to build a strong brand loyalty. With the Talkbot’s ability to proactively engage the clients and provide them with a customer service experience that highly resembles that of a human agent, Talkbots can automate repetitive processes and contribute to the optimization of your workforce.

Both the chatbot and the Talkbot are all part of our efforts to automate and optimise work efficiency. Before choosing which is better for your business, it is the best to think about your immediate and long term goals and how these two intelligent machines can meet your business needs.

 


29
Jan
Talkbot Basics
What is a Talkbot?
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Voice Artificial Intelligence that listens, understands and even sounds like us

What exactly is a Talkbot and more importantly how can it improve your business? But before we talk (pun intended) about Talkbots, we need to first understand that it is not just a quantum leap in futuristic technology but the culmination of decades of research and development into Artificial Intelligence (AI).

In 1950, Alan Turing—the father of computer science—first proposed a test which became known as the Turing test. Turing proposed that a computer is said to possess artificial intelligence if it can exhibit human-like behaviour, indistinguishable from that of a human. The goal of the test was to showcase a computer’s ability to exhibit intelligent behaviour and if human evaluators were able to be “fooled” into thinking they were chatting with a human when it was in fact a computer programme, that programme would have been deemed to have passed the Turing test.

In 2013, that test was finally passed by a programme called Eugene Goostman, during a demonstration at University of Reading. However, that test was done using text and today, these computer programmes became known as chatbots or virtual assistants.

The next natural step for artificial intelligence development was to create a virtual assistant which could interact with a human through natural spoken language. This has led to popular applications of conversational A.I. like Apple’s Siri and Amazon’s Alexa which assist millions of lives every day. Despite such progress, these consumer applications still remain one dimensional and have limited robotic functionalities due to the lack of development in certain specialised verticals—e.g. you can ask Siri where the best pizza restaurant is but she cannot order or customize the pizza on your behalf nor can she sell your firm’s unique product offering to masses of prospective customers.

Enter the Talkbot

This is where Talkbots come in. Talkbots are highly specialised conversational voice AI assistants that can do the work on your organisation’s behalf and automate the many complex tasks needed to be achieved over a phone conversation.

A good Talkbot is not only one that speaks like a human but also understands like one. At Wiz.AI, we have a team of highly specialised data scientists who have spent years refining our solution for our customers.               

Wiz.Ai’s conversational AI Talkbots provide a hyper-realistic voice AI experience that engages customers through phone calls—so realistic that 95% of users do not recognise that they were talking to a voice AI in a real-life scenario.

Even though the human language is dotted with nuances, ambiguities and is often reliant on context and cultural cues, Wiz.AI’s natural language processing engine coupled with its customisable automatic speech recognition (ASR) technology enables the intents to be recognised, thereby facilitating conversations that are virtually indistinguishable from human-to-human ones.                         

What contributes to its hyper-realism is the Talkbot’s ability to identify and handle interruptions during calls and to listen continuously. When the caller has expressed confusion by keeping silent or is asking for a repeat of the statement, the Talkbot is able to pose clarification statements or chasing statements, thus maintaining the conversational nature of the Talkbot

We progressively improve our system’s ASR accuracy and natural language understanding (NLU) through regular NLU model training and the expanding of our speech corpus based on context-specific speech data. We continue to build and refine each successful AI model to give the most seamless call experiences to customers.

How can Wiz.AI optimize my business operations?

The Talkbots we build are part of an emerging technology that ensures rule-based tasks or repetitive jobs, such as calling clients to remind them about their debt payment and appointment dates, can be fully automated. This way, valuable labour can be channelled to other aspects where the expertise of the call centre agents can be fully utilised. Additionally, time-crunched customers are able to get direct responses to their problems, efficiently and effectively. As opposed to a reactive customer engagement, the Talkbots enable clients to shift their call conversations to ones that are more proactive.

When call centres experience unforeseen times of high-volume surge in calls, there may be a shortage of labour to respond to every call. Conversational AI Talkbots are able to handle the increased call volume in unexpected situations like these. Furthermore, in the context of Covid-19, Talkbots could also be a solution to labour attrition.

To add, labour efficiency comes in being able to consolidate all the best practices of your call centre agents into one entity—the Talkbot. There would not be any steep learning curve or time spent training an entire team, not to mention the follow-up action of having to monitor the calls to ensure that the directions have been adhered to. Instead, the Talkbot ensures that the service standard in the call centre is kept consistently high and is continuously improved over time with our machine learning models. Additionally, scaling up the call centre to cater to a multilingual audience is no longer a Herculean task for the HR department all thanks to the high customisability of a Talkbot. Wiz.AI currently supports English, Mandarin and Bahasa Indonesia—even creoles such as Singlish. This allows businesses to better manage their customer base in the forms of local languages.

Given that everything is becoming increasingly outsourced, especially call centre agents, Talkbots also allow for companies to once again take control of their branding and marketing. It is now easier than ever to ensure that your Talkbot embodies your company persona and more crucially, concretize changes and keep the service standards consistently high.

While our inboxes continue to be flooded with promotional emails, voice communication nevertheless remains a medium that is heavily relied on to express our thoughts and to catalyse actions. Conversational A.I Talkbots are equipped with the ability to deliver timely messages and facilitate direct interaction with your customers, and this in turn builds customer loyalty and hence higher returns on marketing investments.

Data-driven innovation

To ensure higher returns on investment, businesses would definitely need to know how and what to improve on. Talkbots provide empirical insights into what the calls were like so that changes can be intentional and targeted. For instance, our Talkbot allows you to identify the common points where the call drops off which then prompts you to improve on the scripting to ensure higher levels of customer engagement. In other words, data driven upgrades can be made to ensure that your Talkbots meet your business goals over time.

Now that personalised or targeted marketing is all the rage and have proven to effectively drive sales, conversational AI Talkbots can be used in a similar approach. Once we have established a customer persona, talkbots can be engineered to tackle your customer’s needs and in return, the information obtained from the call log can also be used to further improve the AI system in terms of making the script more strategic in order to ensure that not only high service standards are achieved but also conversion rates are raised.

I already have a call centre system up and running, can I merge it with a Talkbot?

We can integrate our Talkbots with your pre-existing call centre systems as well as relationship management software such as WhatsApp or emails to maintain healthy customer relations.

Wrapping up

The cutting-edge technology that is a Talkbot is the key to optimizing work at call centres and lowering costs. This emerging technology is bound to revolutionize customer management and take your business to the next level.


17
Nov
Talkbot Basics  ·  Voice AI Technology
Understanding Asia – Natural Language Processing in AI
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

NATURAL PROCESSING LANGUAGE

 Natural processing language is an aspect of artificial intelligence and computer science that handle the interface between human languages and computers. It involves the computational modelling of different characteristics of language and the deployment of variety of systems. These systems include spoken language systems incorporate natural language with speech. NLP works with linguistic computational features, it employs computer in comprehending, handling speech and text of natural language to achieve useful feat. There are several fields NLP can be applied to; speech recognition, expert systems, artificial intelligence, cross language information retrieval (CLIR), text processing, language translation, speech recognition, and user interfaces. This innovative technology is saddled with getting computers to communicate and process human languages, and perform closer to human level of language thoughtfulness. Computers are yet to reach same instinctive comprehension of natural language like humans do. There is clear difference in the method in which human communicates with one another and the way they do with computers. During program development phase, the structure and syntax are carefully selected to suit the intended task, unlike conversing with other people whereby a lot of freedoms are considered. Ranging from sentence length, sarcasm and jokes, to several ways of expressing same thing.

Recent advancement in innovative technologies has enabled computers to perform range of things with human or natural language. Deep learning supports the implementation of programs to perform task like text summary, language translation, and semantics understanding. The rise in the implementation and application of artificial intelligence to our daily activities has made it ubiquitous. It is imperative for human to be able to communicate more with computers in the language we are familiar and comfortable with, speaking to computers in their natural language. Natural Language Processing (NLP) is seen as the canopy term that binds other natural language technologies which include Natural Language Understanding (NLU), Natural Language Generation (NLG), and Natural Language Interaction (NLI).

COMPLEXITIES OF UNDERSTANDING DIFFERENT LANGUAGES USING NATUAL LANGUAGE PROCESSING

Recently, significant feat has been recorded in enabling computers to comprehend human language using Natural Language Processing (NLP). Nevertheless, the multifaceted multiplicity and dimensionality features of data sets, make the execution a problem in some cases. Concerning implementation of NLP in Asia, with main focus on south East Asia, voice and text-based data and their practical applications will vary. In other to capture the whole process, NLP needs to include several diverse procedures for interpreting Asia local language. It could involve machine learning, statistical, algorithmic, or rules-based approaches. Ambiguity is an aspect of cognitive sciences without a definite resolution, range of language ambiguity differs greatly based on the speaker. Technically, any language sentence with plenty grammar can generate another meaning, for human to find it challenging in dealing with conversation vagueness sometimes, then it is inevitable for natural language understanding systems.

  1. TYPES OF AMBIGUITY

Outlining ambiguity can sometimes seems vague. There are different forms of ambiguity regarding natural language processing (NLP), and artificial intelligence (AI) systems.

  1. Lexical Ambiguity: This is a single word ambiguity. A word can be ambiguous with respect to its syntactic category. Lexical ambiguity can be decided by Lexical type clarification like parts-of-speech labeling. It also stores word and complementary knowledge.
  2. Syntax: This is a part of grammar that define how words are assembled and linked with one another to make a sentence. Syntax involves the transformation of a linear order of tokens (a key to each word or punctuation mark in natural language) into a classified syntax tree. The main issue with syntax level are: sentence assembling, speech tagging, and identifying syntactic categories.
  • Semantics: This type of ambiguity is characteristically associated with sentence interpretation. It includes task like interpreting one natural language to another, synonyms searching, creating question-answering systems, and clarification of word sense.
  1. Morphology Ambiguity: This ambiguity came into being due to advance processing carried out on the root words to make use of them in a specific sentence. It involves processing of word forms.
  2. Discourse: Discourse level processing needs a pooled knowledge and the interpretation is carried out using this context. Anaphoric ambiguity comes under discourse level. One of the exhausting task in Natural Language Processing (NLP), some of the problem are belief, sentiment, and user intention processing. It also process connected text.
  3. Pragmatic Ambiguity: This is refer to the situation whereby whereby the circumstance of a phrase gives it multiple meaning. It involves user modelling, and intention processing.
  • Referential Ambiguity: When a phrase or a word in a particular sentence could refer to two or more properties or things, it is referential ambiguity. It is always clear from the circumstance which meaning is intended but not always.
  • Phonology: It is described as words that sound the same way but have different meaning. This type of ambiguity forces the NLP model to interpret the context of the sentence and place it in the right context. It can be referred to processing of sound.
  •  

STAGES IN NATURAL LANGUAGE PROCESSING (NLP)

Basic steps necessary to be followed to build Natural Language Processing (NLP) model are as follows:

Stage 1: Segmentation of Sentence

The first stage required to build NLP model is breaking of prearranged paragraph into single sentences. This is done to process the meaning line by line.

Stage 2: Word Tokenization

After sentence segmentation, it is followed by word extraction from each sentence one after the other. The tokenization algorithm can be programmed to identify a word whenever a ‘space’ is observed. All these would be achieved following Asian natural language.

  • Stage 3: Prediction of Parts of Speech

It involves classifying words into their respective part of speech as duly represented in Asian language. Parts of speech classification will help the machine learning model to comprehend its role in sentence. Machine learning might not actually know the meaning of each word in sentence setting the way human being do. A lot of data has to be fed into the model along with precise label of each word’s meaning and part of speech.

  1. Stage 4: Text Lemmatization

The machine learning model learns to identify the most basic form of words in a sentence. By differentiating between closely related words.

Stage 5: Pinpointing Stop Words

This stage is saddled with identifying the importance of each word in a sentence. There are a lot of filter words in that appear frequently in English language, and it is definite that Asia Language will also have some commonly used filter words that introduces a lot of noise into a sentence. It is necessary for machine learning to identify them and flag them as stop words i.e. words that can be filtered out before undertaking statistical investigation.

Stage 6: Dependency Parsing

It is the stage where grammatical laws of Asian language would be employed to identify how words are related to one another

Stage 7: Entity Analysis

This is achieved by going through the entire sentence in Asian Language and identify all the important words in the text. And the words in the sentence will be categorized as been programmed to work.

Stage 8: Pronouns Parsing

This is the last stage in building NLP model and it is one of the hardest stage. This stage will employ machine learning to keep track of pronouns with respect to the sentence context. It is very easy for human to comprehend the meaning right from the context of the sentence unlike computers. Therefore, a Machine Learning model is required to be fed with a lot of data alongside correct tags for the model to be able to identify the pronouns effect in a sentence.


17
Nov
Talkbot Basics  ·  Voice AI Technology
The Rise Of Conversational AI In Customer Service
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

THE RISE OF ARTIFICIAL INTELLIGENCE IN ASIA AND THE WORLD

First coined by American computer scientist John McCarthy in 1956, artificial intelligence (AI) refers to the cognizant abilities of machines which have been programmed to autonomously perform, think and learn like a human. Today, from unassuming home appliances such as robotic vacuums, to more awe-inspiring projects such as autonomous cars and self-learning weather forecast technologies, AI has permeated into various aspects of industry and society. As its applications become increasingly adapted into various aspects of our lives, its ability to bring about the digital transformation of the world would pose various disruptive implications for the economy, environment and our day-to-day lifestyles.

Arguably, the main attraction or headliner of AI, as it has captured the attention of the commercial world, is its potential to drastically increase productive and cost efficiencies. It’s transformative effects have been echoed by various industry experts, who have projected that such technologies would boost corporate profitability in 16 industries across 12 economies by an average of 38% by the year 2035[1]. By eliminating repetitive, low level tasks which were traditionally performed by humans, one of AI’s most noticeable disruption would definitely be on the labor market, as companies look into using AI to optimise internal operational process and alter the way service and products were originally offered to their customers. The nature of long-established roles are also likely to evolve in the meantime.

Global investment in AI is growing rapidly, with an estimated investment of $26 to $39B investment in AI. At present, the two major global hubs of AI development are the United States and China. Funding for artificial intelligence companies in the United States has increased exponentially in recent years, growing from around 300 million U.S. dollars in 2011 to around 16.5 billion in 2019[2]. For China, PricewaterhouseCoopers predicts that $7 trillion of China’s $38 trillion GDP by 2030 would be attributed to AI through new business creation in fields such as autonomous driving and precision medicine, as well as existing business upgrades in terms of improved efficiencies and reduced costs[3].

While the bulk of AI investments are presently made by tech giants Alibaba, Amazon, Google, Baidu and Facebook in a race towards AI as a long-term strategy for business sustainability and competitiveness, AI as a concept is still poorly understood and intimidating to the ASEAN region. However, despite the region’s relatively slow advances in AI technologies, its oncoming impact is undeniable. Previously published MGI research estimated that currently demonstrated technologies have the potential to automate roughly half of the work activities performed in ASEAN’s four biggest economies: Indonesia (52 percent of all activities), Malaysia (51 percent), the Philippines (48 percent) and Thailand (55 percent), with these tasks currently generating more than $900 billion in wages[4].

Out of the ASEAN member states, Singapore, as the region’s technology capital, has made the greatest AI advances thus far, and is a natural first choice for AI tech startups to establish their presence in Asia. WIZ.AI, having its research institute based in Nanjing, China, established its first overseas headquarters in Singapore in 2019, from whence it aims to continue developing its pioneering and proprietary conversational AI talkbot technologies and push it out to ASEAN. 

 

CONVENTIONAL CHATBOTS VERSUS AI CONVERSATIONAL TALKBOTS

2.1 THE RISE OF CHATBOTS IN CUSTOMER SERVICE

Chatbots are computer programs built on the concept of artificial and data analytics, and is commonly installed on websites or social media platforms. With a chatbot application,  companies are able to automatically respond to customer messages round the clock, through a virtual assistant which recognises entered keywords and is able to provide instantaneous, standardised text replies and guidance to the customer.

 

This rise of chatbots is projected to be significant; as reported by Global Market Insights, the chatbot market will be worth $1.34 billion by 2024, with 42.52% of that alone from the customer service sector[5]. The rise of chatbots is tightly linked to new technological advancements and evolving customers’ expectation of brand interactions. With the prevalence of social media and mobile messaging applications, the average consumer now expects a company’s to resolve issues and respond to requests with speed.  Based on a 2017 customer survey conducted by Microsoft, 54% of those polled expressed higher expectations for customer service today compared to the previous year, with the percentage increasing to 66% for younger respondents at the 18 – 34 age group[6]. Falling back on traditional forms of communication such as emails or text messages are no longer acceptable; in the same survey, 68% of the respondents have a more positive view of brands which take the initiative in providing proactive customer service notifications.

 

In addition to it being used as a medium to provide basic customer service, chatbots are also suitable for use in marketing & sales of products, which further spurs demand and market growth in this sector. These platforms aid companies to expand their reach by connecting with a larger audience, aiding in decision making by addressing customer queries on the spot and subsequently pushing suitable product recommendations.

 

2.2      WIZ’S AI CONVERSATIONAL TALKBOT – ONE STEP UP FROM CHATBOTS

In the 2017 Global State of Customer Service Report by Microsoft, email and telephone are still the primary communication channels for many customers, while live chat, self-service, social media, and chatbots are relatively lower in terms of raw volume[7].

 

It would seem then that maintaining a hotline available for urgent customer queries 24/7 is key to keeping customers happy and loyal. Despite the increasing number of self-service options made possible by technology, customers still express a preference for live-agent support, with 30% of global respondents frustrated when they are not able to do so. The value of being reachable to customers is further demonstrated when 30% of people polled are of the opinion that being able to speaking with a knowledgeable and friendly agent is a significant factor that makes or break a customer service experience.

 

Working from the same belief that voice conversations are still the most natural way of interaction for humans, and that artificial intelligence is key in the next wave of hyper-personalized customer engagement, WIZ.AI combines the best of both worlds, integrating its intuitive AI conversation capabilities into the simplicity and familiarity of a phone call. Pioneering a new market category, its AI Conversational Talkbots takes centre stage as a cutting-edge, turnkey customer service solution.

 

Capable of understanding human speech in a variety of accents, expressing empathy and engaging in unlimited multi-round conversations with the customer, WIZ’s AI Conversational Talkbots provide a more interactive, human-like and fulfilling customer service experience that is superior to text-based chatbots. Compared to human agents, its Talkbots are also more consistent and reliable. Offered at the fraction of the costs of maintaining a human call centre, WIZ’s AI Conversational Talkbot allows any corporations with heavy customer communication needs to maintain its personal touch with its customers in a cost-effective manner, with endless scaling possibilities for both new inbound and outbound call campaigns. Its backend CRM system captures and sorts through customer intentions in real-time, collecting and digitalising valuable customer intention data.

 

WIZ.AI’s proprietary AI talkbots have been adapted for mass commercial applications in the banking, telecommunications, health care and e-commerce industries. Besides Singapore, WIZ.AI also operates offices in China, Jakarta and the Philippines with a team of scientists, developers, linguists, and dialogue designers.

 

  1. AI – BRINGING CONVERSATIONAL AI INTO THE MAINSTREAM

3.1      COMPONENTS OF WIZ’S AI CONVERSATIONAL TALKBOTS

WIZ.AI’s proprietary AI talkbots can be broadly broken down into three main elements:

  • Natural Language Processing & Understanding: Recognises and processes speech patterns and nuances through the responses of the speaker, allowing the talkbot to understand the intent of the speaker.
  • Automatic Speech Recognition: Recognises and understands local accents and lingo
  • Text to Speech: AI voice tailored to sound human and speak with local accents

 

3.1.1   NATURAL PROCESSING LANGUAGE

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence, employing the computer in comprehending and handling speech and text of natural language. There are several fields NLP can be applied to; speech recognition, expert systems, artificial intelligence, cross language information retrieval (CLIR), text processing, language translation, speech recognition, and user interfaces.

In other to capture the whole process, NLP needs to include several diverse procedures for interpreting Asia’s local languages. It could involve machine learning, statistical, algorithmic, or rules-based approaches. The steps to to build a Natural Language Processing (NLP) model are as follows:

  1. Stage 1: Segmentation of Sentence

The first stage required to build an NLP model is the breaking of prearranged paragraph into single sentences. This is done to process the meaning line by line.

  1. Stage 2: Word Tokenization

After sentence segmentation, it is followed by word extraction from each sentence one after the other. The tokenization algorithm can be programmed to identify a word whenever a ‘space’ is observed. All these would be achieved following Asian natural language.

  • Stage 3: Prediction of Parts of Speech

It involves classifying words into their respective part of speech as duly represented in Asian language. Parts of speech classification will help the machine learning model to comprehend its role in the sentence. Machine learning might not actually know the meaning of each word in sentence setting the way human being do. A lot of data has to be fed into the model along with precise label of each word’s meaning and part of speech.

  1. Stage 4: Text Lemmatization

The machine learning model learns to identify the most basic form of words in a sentence by differentiating between closely related words.

  1. Stage 5: Pinpointing Stop Words

This stage is saddled with identifying the importance of each word in a sentence. Similar to the English language, Asia Languages also contains many commonly used filter words that introduce noise to a sentence. It is necessary for machine learning to identify them and flag them as stop words i.e. words that can be filtered out before undertaking statistical investigation.

  1. Stage 6: Dependency Parsing

It is the stage where grammatical laws of Asian language would be employed to identify how words are related to one another.

  • Stage 7: Entity Analysis

This is achieved by going through the entire sentence in Asian Language and identifying all the important words in the text.

  • Stage 8: Pronouns Parsing

The last and most challenging stage of building a NLP model, this step employs machine learning to keep track of pronouns with respect to the sentence context, allowing the bot to comprehend the meaning right from the context of the sentence. To achieve this, the Machine Learning model has to be fed with a lot of data alongside correct tags for the model to be able to identify the pronouns effect in a sentence.

3.1.2 AUTOMATIC SPEECH RECOGNITION (ASR)

This is where a chatbot is differentiated from a AI conversational talkbot – the latter has the added challenge of first having to understand the different accents and local lingo in order to recognise the customer’s intention. ASR training involves collecting speech samples from a variety of language backgrounds, and through machine learning over time, the talkbot gets more adept at deciphering the local accents and lingo.

3.1.3   TEXT-TO-SPEECH (TTS)

Text-to-Speech (TTS) is the channel whereby the talkbot responds and converse with the customer in real-time. WIZ.AI’s proprietary TTS generation system has been designed to be life-like, encouraging customers to share more, and allowing companies to gain deeper insights into customer intentions and needs.

With customisable voices, the customer call experience is further enhanced with a quality TTS which matches the brand persona. The use of a single distinguishing voice sets your company apart from competitors and also ensures brand consistency across all different customer touchpoints.

  1. LOOKING FORWARD

A disruptive technology, AI is transforming the face of customer service and forcing companies to relook into their customer touchpoints and service strategy in order to retain customer loyalty, business sustainability and competitiveness in the long run. Conversational AI technologies are likely to lead this evolution, and as the talkbot product gets better understood and commercialised, we expect more companies in Asia to adopt this product, changing the landscape of ASEAN customer service and economies permanently. 

[1] Accenture, How AI boosts industry profits and innovation, June 21, 2017

[2] Shanhong Liu, Artificial Intelligence funding United States 2011-2019, June 6, 2020 https://www.statista.com/statistics/672712/ai-funding-united-states/ 

[3] PwC Global, Sizing the Price – PwC’s Global Artificial Intelligence Study – Exploiting the AI Revolution, June 2017. https://www.pwc.com/gx/en/issues/data-andanalytics/publications/artificial-intelligence-study.html

[4] Mckinsey Global Institute, Artificial Intelligence and Southeast Asia’s Future, 2017 https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/ai%20and%20se%20asia%20future/artificial-intelligence-and-southeast-asias-future.ashx

[5] Global Market Insights,  Global Chatbot Market worth over $1.34bn by 2024, August 26, 2019 https://www.gminsights.com/pressrelease/chatbot-market

[6] Microsoft, State of Global Customer Service Report, 2017 https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-Report-DynService-2017-global-state-customer-service.pdf

[7] Microsoft, State of Global Customer Service Report, 2017 https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-Report-DynService-2017-global-state-customer-service.pdf


17
Nov
Talkbot Basics  ·  Voice AI Technology
The Changing Landscape Of Customer Service
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

To date, customer service is widely known as being the top-most business priority with a sure link between customer satisfaction, as well as retention, and profitability. Recently, the Aberdeen group statistically reported that organizations that achieved a height of over 90% customer satisfaction rate reached an annual service growth of 6.1%, and overall revenue growth of 3.7%, and also 89% customer retention level. With approximately 78% of the UK GDP gotten from the service sector, customer service is becoming hugely seen as a strategic plan and, as stated by customer service institute, Organizations/businesses that do not add it in their boardroom meetings and discussions won’t last for a very long term.

Now, for businesses and organizations across all industries and niches, It has been proven that artificial intelligence (AI) is seen as a perfect solution for efficiency improvements, the efficacy of customer experience, and a lot more. Starting from startups to large multinational corporations, AI otherwise known as artificial intelligence has the power to transform specific aspects of businesses. Most times, we have heard about how AI can help a business to become hugely efficient with its resources. Other than this, AI also has tremendous value for customer experience, and should be introduced by brands/companies if being the best is the upmost priority of that brand/company.

Why Introduce AI into Customer Experience?

What do you understand by the statement ‘customer experience’? Most times, people mislabel it as a ‘customer service’ but this is just one aspect of the idea. Customer service is a specific area of customer experience (sometimes referred to as CX); I will be throwing more light on how to actually boost customer service with AI marketing soon. However, customer experience (CX) actually covers the entire customer journey. Right from the point of contact with the brand to the moment they get to see your product, this is regarded as a customer journey. When an organization gets customer feedback, it usually covers every stage rather than the customer service section only.

How AI Supports Marketing & Sales in Understanding the Customer Journey

The marketing and sales tools strengthened by Artificial Intelligence (AI) isn’t something to be scared of, rather it’s something we should embrace. The reason because Unlike usual human customer service, the AI technology can boost a better understanding of a company and its customers, as well as the journey of individual buyer customers before making a purchase.

Research on Google proves that 53% of visitors will leave if a mobile page takes more than three seconds to load. When customers interact with your marketing, they won’t have the patience to wait around till they get a response from you. A quick response is vital when talking about customer experience.

If you are unable to respond immediately when B2B buyers make inquiries about your product or service, there’s every tendency that you will miss that opportunity. So what do you do in other not to miss out on closing opportunities? Well, it’s all about bringing AI to the frontline.

For instance, if you work for a university or an educational institution, and someone requests information directly from your website through a lead gen suite, or at a recruiting event, your AI assistant can help you interact conversationally with the use of the email or a chatbot on your website. Then log pieces of information gotten into its system for proper analysis, Thereby serving as an all in one team; a sales team, a marketing team, and an assistant team.

The AI assistant can also answer questions, handle objections, as well as respond to requests. Immediately the connection is made, you can guide customers through the tunnel with the help of automation to intelligently nurture your lead with personalized follow-ups.

It directs conversation as it engages, until the lead becomes qualified (based on parameters) then hands the lead to the right person on your team.

The artificial intelligence (AI) is seen as a set of algorithms that informs a machine on what to do and what to learn. These algorithms assist marketing sales professionals by reducing time on important repetitive tasks such as analyzing data, locating opportunities in content, monitoring social posts and so many more.

With this, it means a better improvement in marketing and sales jobs. And it means a more personalized and great customer experiences.

Below, I have explained how artificial intelligence or machine learning can be utilized during the four stages of the customer journey — attract, engage, win, and support and delight. Stage 1: Using Machine Learning to Attract the Right Audience

In the recent digital world, one of the key ways to succeed is to get found. This may sound simple, but in the real sense, it isn’t as easy as it sounds. It is mostly easy for a brand to get lost in the noise of the online world today.  Check out these 2019 statistics:

  • There are 1.94 billion websites on the internet.
  • 388 billion People using the internet.
  • There 3.484 billion people using social media.
  • And 90 percent of brands are on social media

This proves that a brand needs to distribute, as well as promote the right content at the right time, in other to be outstanding in the eyes of its target audience.

How can AI be of help in supporting this? No. 1 — Buyer personas. Artificial Intelligence can help collect data about your target audience, and in turn, allows you to create precise buyer personas. The more you know about your ideal consumer, the easier it is for you to sell your products and service.

Having a concrete buyer persona, marketing gets a vivid understanding of your interests, prospects, spending motives, buying habits and obstacles, as well as frequent questions.

With this, you can create a personalized social media campaigns and contents for your platform or website that appears in search engines and gets the attention of the audience your company seeks,

Another reason for the existence of AI technology is to support social media monitoring. Because social media moves so swiftly, so it’s quite easy to Miss Key opportunities. Making use of AI monitoring can help a company to identify thought influencers, stay firmly on top of brand mentions, see all customers’ feedback and sentiments, and also identify phrases or topics that are trending.

Stage 2: Engaging Your Buyers with Optimal Efficiency

Haven successfully generates traffic to your website and social media channels, now, how do you engage with these potential buyers? AI technology can assist you in efficiently creating more personalized experiences.

See these statistics from the 2018 salesforce’s state of Marketing: 52% of customers would probably want to change the brand if the company doesn’t give personalized communication. The method of sending out generic mass emails to the purchased list is no more valid in today’s world.

Platforms that are AI-enabled can analyze behavioral patterns from a list of inputs that will probably take your team years to capture, organize, and understand manually.

Having this data, your market team can start to segment your audience and also create personalized content based on different factors such as demographics, interests, level of engagement, and behaviors of your customers.

The artificial intelligence system can also assist your team to see when, and how frequently they should distribute content via social channels, emails, and websites for maximum impact and stop wasting time on content that that has no value to customers.

Stage 3: No More Cold-Calling; Let AI Lead Sales to Top Prospects

With the aid of machine learning, team sales can now receive notifications when a lead moves from cold-to warm-to hot — all before sales have even reached out. Good-bye, cold-calling for both sales and the customer!

Using AI, a system a can receive the behaviors of potential customers and score them based on things like the social media post they saw, the website pages they visited, the emails they opened, how long they were on a website, the number of times they came back to the site, and lot more. The machine helps to predict when a lead will become hot so your sales team will be informed on when and when not to reach out.

With the aid AI-powered data, sales can work at a higher rate much more efficiently.

Stage 4: Delight Customers so they keep visiting

You can improve customers’ loyalty by offering them what they want, and when they need it. Artificial intelligence technology can collect data to assist your team in creating personalized customer experiences even after purchase. This could be in incentives form, related products for upsells, or content that supports the product or service purchased by customers.

Good examples of AI technology in place in the delight and retention stage of the buyer’s journey are Amazon, Netflix, and Spotify. Amazon provides “other products you might be interested in”, while Netflix and Spotify both select and recommend movies or music based on your interests and previous interactions.

With the aid of AI technology, the above-mentioned companies have been able to keep their customers satisfied and loyal.

A lot of companies are also putting AI-powered chatbots in place to assist customer service. Instead of calling a company for product support, you can easily visit the website, type in what you are searching for and a chatbot will instantly provide you the answer.

There are a lot of things a brand may not do well, from a rather complicated on boarding when customers are not offered easy-to-understand information about product usage as well as its capabilities to poor communication, an example is lack of feedback or delayed answered to queries or pondering questions. Another point: Long-serving clients may feel they are less appreciated because they don’t get as many bonuses as new ones.

Bad experiences may alienate even loyal customers. Source: PwC

Generally, it is the all-round customer experience that defines brand perception and influences the way customers recognize the value of the money of the product and service they make use of.

It is ideal for a business or brand to know that even loyal customers will not tolerate a brand if they’ve had one or numerous issues. For example, according to pricehousewaterhouseCoopers (PWC), 59% of the US respondent surveyed by them noted that they will say goodbye to a brand after several bad experiences, while 17% of them say they will say goodbye after just one bad experience.

In other to eliminate the idea of prospect customers having a bad experience with your brand/company, there are things AI assistance can help you with, to make sure customers do not have to wait for a long time before they are attended to.

Collections/Payments

You can make use of a VoiceBot when you are having several pending payments. You can reach out to your customers with the aid of your AI assistant and inform them about bill dues that are already overdue or upcoming payments. Schedule as many calls as you need and monitor your payments; when necessary, have an account executive step in.

Surveys and Customer Reviews

You can also make use of your VoiceBot for surveys as well as customer review calls; this will help you to know the customer’s thought on your products or service, as well as the overall rating of your product or service.

Custom Branded Messages

With the use of Ai assistance, you can spread the word! You can reach your prospects with custom messages using different VoiceBots. You can send messages to maybe recommend similar products, send thank you messages or promotion messages.

Customer Support

A VoiceBot can help provide basic customer service and support to your clients. The Bot can swiftly take care of your all customer’s inquiries and provide way simple solutions.

Promote a new product

Are you launching a new version of your product and looking for a means to get the word out? Well, you can easily build a simple Workflow/sequence that includes an email/call combo, and the VoiceBot can take over by helping you carry out a simple and straightforward call that will allow you to warm your leads and start qualifying them.

Event invitation/promotion

Drive more engagement and attendance to your events with the aid of an AI assistant. You can give more information to your leads about the event, venue and also track RSVPs with a simple call, the Bot will give you reports of all live call.

The reality is that artificial intelligence has greatly affected every stage of the customers’ journey; right from prospects finding your company, to potential customers engaging with your product or service down to gaining loyal customers.

If you wish to stay on top as a brand or business, now is the ideal time to leverage AI within your sales and marketing teams

 


17
Nov
Talkbot Basics  ·  Voice AI Technology
Employing AI To Attract, Engage And Delight Your Customers
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

In the world of business, there are two key priorities; to maintain a healthy equilibrium between time and money, and to ensure customer satisfaction. AI has already transformed modern living, with presence everywhere we turn – from home to phone, from car to self-service checkout. In the context of business, there is a particular strain of AI technology that serves to both enhance the customer experience while ensuring a harmonious balance of time and money. That is in the form of AI and AI voice-driven chatbots.

 

Chatbots respond to humans like humans through adaptive machine learning technology. AI voice-driven chatbots have come a long way since humble beginnings in 1966, with the affectionately named ELIZA who had the technological prowess to answer a whole three questions. AI technology has developed and evolved, and chatbots have become as familiar to us as air in 2020. One of the greatest functions of AI voice chatbots is in the realm of customer service.

 

So, why should a company employ AI technology to attract, engage and delight its customers? In this article, we will examine the benefits of implementing such technology to accomplish both customer satisfaction and business KPIs.

AI Voice Enhances The Customer Experience

Through harnessing machine learning, AI voice technology is advancing at an incredible velocity. AI voice technology is readily available. It has the ability to troubleshoot, multitask and provide a warm customer experience at the drop of a hat, making it an obvious solution to round-the-clock customer service. Chatbots are accessible at every touchpoint of the customer journey, and the tailored response is shaped by the comprehensive customer profile produced by machine learning.

 

AI voice systems are a fantastic facilitator to traditional call centres. From the first point of customer contact, AI will identify the needs and respond by either directing them to the appropriate person/department, or leading through a series of questions to answer autonomously. AI voice has the capacity to take the load off humans at busy periods (and with COVID testing all manners of customer service, this couldn’t be a more current requirement) while taking up no physical space. The streamlined nature of this modern process is a massive benefit to effective customer service for being straightforward and adaptive. 

Machine Learning Chatbots Cut Processing Time

Each manufacturing brand producing AI technology boasts an edge to process certain business requirements (ours, for example, is the ability to understand different Asian languages and their local dialects). The ever-evolving capabilities of machine learning offer AI technology the means to identify, process, and respond to greater business demands while also reacting to customer requests simultaneously.

 

Primarily, AI has the ability to aggregate data from multiple sources to generate a tailored response in real-time. This is critical both for condensing the time to process data and demands, but also for improving the net promoter score – the system where a brand or company is measured against customer loyalty. If a customer is satisfied with the processing time of the service they receive when contacting a company, they are likely to remain loyal.  

 

AI technology also works to the benefit of the customer’s processing time. Through machine learning, AI tailors an experience of a product or service to the interests of the user; for example, ‘suggestions for you’ on Netflix, or a personalised playlist on Spotify. Such measures take away the time to ‘think’ (or process) what to watch or what to listen to. Therefore, AI provides time-efficiency for the customer while ensuring the brand is providing autonomous top-quality service.

Implementing AI To Deliver Personalised Customer Service

AI has the ability to aggregate data to deliver a comprehensive personalised service that takes all available information about a customer into account – from language to local weather, from buying behaviours to social media interactions. AI formulates a customer profile while synchronously producing bespoke content specific to the user needs (for example, tailored suggestions on Amazon in response to your search for an umbrella as the weather turns sour in September). Such a response enhances customer satisfaction for recognition of immediate needs at the exact moment it is required.

 

In the context of AI voice chatbots, the level of customer satisfaction is enhanced by the bots’ ability to build an intuitive profile on the customer when a conversation is initiated (using data such as account information, past purchases, and geographical location). Chatbots are then able to manipulate the service so that it is personalised and aligned to the customer profile. Chatbots are able to build a conversation centred specifically on the customer’s requirements in a way that is totally relevant and organic according to the immediate needs. 

Using AI To Attract, Engage And Delight Your Customers

Customer satisfaction and customer loyalty are crucial elements when it comes to a successful business – and this can be difficult to accomplish with limited resources. However, machine learning AI technology makes the process of understanding and responding to customers with a level of care and quality both accessible and cost-effective. The tailored experience for the customer not only increases their level of satisfaction but also enhances productivity. Less time is spent directing them to the right area of business, their questions are answered almost entirely autonomously. Service is slick and consistent, personalised and specific, and enough to attract, engage and delight each and every customer.


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