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.
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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.

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.

3. 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.

4. 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

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

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.