Voice AI Technology

05
Jan
Talkbot Basics  ·  Voice AI Technology
CX trends and how to make the most of them
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

The customer experience space is changing rapidly. As new technologies emerge, customer behaviours shift – but sometimes not in the way businesses expect. Here are the top CX trends identified by Gartner research, and how brands can leverage these changes.

Proactive service prompts more engagement, not less

Outbound customer engagement is on the rise, and rightly so. In a world that competes for audience attention, proactive service helps keep brands top of mind. Gartner predicts that proactive customer engagement interactions will surpass reactive customer engagement interactions by as early as 2025.

Interestingly, customers continued to engage with a brand after proactive interactions. The nature of the additional interactions are also interesting to note – instead of opting to use self-service channels, customers gravitated towards assisted digital or voice channels after the initial outreach.

In general, proactive service improved customer satisfaction (CSAT) ratings. Brands can ride on this trend and build greater customer loyalty by designing smart proactive service.

How to capitalize on this trend

Now is a good time to revisit customer journey maps and update them based on gathered data. Try creating new prompts that push a customer further down the funnel or along the journey. Prompts to finish incomplete applications – say, for loans or credit cards – is a simple example. WIZ Talkbots have been deployed for this type of use case, helping customers complete application forms through assisted channels. The proactive service can start as a follow-up call regarding the open application, then continue with tips or guidance on how to complete more complex fields.

Customers fall back on familiar channels, even when resolution is slower

Humans are creatures of habit; customers tend to rely on previously used channels to resolve issues or raise concerns. Even when new, more efficient channels are presented, customers go back to the old methods. This is because customers underestimate the time it takes to resolve their case. Many times, this results in abandoned self-service operations.

32% of customers actively engage in a customer service phone call for more than 10 minutes

75% of customers who use the phone report the interaction taking longer than expected.

How to capitalize on this trend

To avoid abandoned calls or self-service tasks, brands can use AI to guide customers through self-service. This starts from the first point of interaction, which can be the company landing page. AI-powered interactions can then move customers down the funnel based on the steps taken, be it answers to FAQs or guided scripts for common transactions. Brands can make the most of previously collected interaction data to design scripts that address the most common queries. Over time, as more data is collected, businesses can refine scripts and update customer journeys.

Customers don’t mind switching channels to resolve issues if the experience is seamless


Omni-channel engagement is becoming more and more important in customer experience. All customer touchpoints now need to be interconnected and integrated so that handoffs become seamless and easy for customers. Success in omni-channel engagement hinges on customers expending the least amount of effort when moving from one channel to another. Then there is the issue of consistency – customers expect that each channel will have access to their data and history from the previous channel. Maintaining consistent experiences across channels will become the test of great customer engagement.

How to capitalize on this trend

Brands must ensure that data from one channel is completely transferrable to another, and that customer interactions are carefully interlinked. Customer histories and profiles should be kept updated, no matter which touchpoint was last used. Language and tone must also be kept consistent across all channels, to make customers feel that they’re only talking to one person or entity. Having a centralized omni-channel platform will help keep things sychronized and seamless.

“Value enhancing” service experiences drive retention and growth

Great service experiences only help to avoid attrition; good service has become the minimum expectation. To improve customer loyalty, service experiences should help customers get more value out of their purchase or subscription. A product or service is the reason why customers patronize a brand in the first place. Value enhancing services could come in the form of tips or guides, or even short testimonials on how other users are maximizing the product or service.
When customers experience a value-enhancing service experience, they have:

82% probability of being retained 
86% probability of spending more money 
97% probability of sharing positive word of mouth

How to capitalize on this trend

Understand how customers use your product or service, and help them get more out of it. This could be another area where proactive service comes into play. Quick prompts on how things have been so far can lead to a series of how-to articles or videos that guide users into a richer product or service experience. Value enhancing service can also bring about opportunities to upsell, once the desire for greater value has been established.

How does AI come into the picture?

These CX trends are closely linked to the possibilities presented by artificial intelligence and AI-powered automation in customer service. Forward-looking enterprises harness the power of AI technology to stay ahead of the curve and deliver delightful customer experiences – services that are seamless, personalized, and value-enhancing
To find out how AI can help power your customer experiences, contact one of our specialists today.
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29
Dec
Talkbot Basics  ·  Voice AI Technology
How AI can boost results in debt collection
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

For many companies that need to conduct payment collections, resolving accounts that are past due is one of the trickiest parts of the business. Many of these customers are hard to contact, and once contacted, getting commitment to settle dues is another hurdle. Fortunately, conversational voice AI can help.

Harnessing the power of AI technology for debt collection

Artificial intelligence can be used in the debt collection process in a number of ways. First, AI can be used to automate manual processes like transcribing calls and categorizing customers based on their willingness to pay. These two tasks alone provide companies greater control over their debt collection processes because of the wealth of data that gets collected. Automatic transcription of calls transforms unstructured voice data into structured data. With it, enterprises can now do more detailed data analytics of their customer interactions. Those tasked with debt collection can gain a better understanding of risk behavior and improve collection tactics, moving from a reactive position into a more proactive one.

Loan provider gets more payment commitments with voice AI calls

This example features a finance company that provides loans and similar products to consumers. One of its biggest challenges is reaching accounts that are past due and making sure that commitment to payments are made. This often requires persistent outreach and multiple calls, which take a lot of time and effort for human agents. By switching to an AI-powered agent, the company was able to ramp up its call intensity proportionally to the account’s days past due.

call intensity 1-day, 2-day, and 3-day past due customers

With artificial intelligence, the company was able to call customers with overdue payments more consistently and with greater frequency. This resulted in obtaining “promise to pay” commitments from more customers – up to 60% for accounts that are three days past due.

Traditional bank gets clearer picture of consumer credit portfolio

In this second example, a large regional bank decided to deploy voice Talkbots for their credit collection operations. With each call, the AI engine records outcomes and tags customers accordingly.

The first layer of tags involve the call status – whether the customer was contacted or if the call failed to connect. Once the call connects and the Talkbot is able to speak with the customer, the AI engine then identifies whether or not the customer is willing to pay. If agreement is obtained, the AI agent then secures a payment date within a three-day window and records the response.

Having collections information structured in this manner enables the bank to see just how many of their delinquent accounts were willing to pay, and how soon. With this information, the bank is able to forecast its cashflows more accurately and update its collections strategy more proactively.

Fintech company gets record results in three-day collections campaign

This final example involves a non-traditional financial services provider that undertook a three-day call campaign for uncollected payments. Utilizing voice AI Talkbots, the fintech company was able to reach almost half of its customer database. Over 300,000 calls were made across a three-day period, obtaining payment commitments from two-thirds of customers that had overdue accounts. 

Of those that had committed to pay, over half promised to settle their dues within the day. Almost a fifth committed to pay the next day, bringing committed payments to 71% within a 48-hour window.

Getting ahead of back payments with artificial intelligence

AI powered much of the success of the above examples – artificial intelligence is what enabled companies to undertake the huge volume of calls to customers. And thanks to well-designed scripts, these companies were able to obtain payment commitments that previously eluded them. Results can be seen even in a short three-day campaign.
Are you looking to improve the outcomes of your debt collection activities? See how our voice AI Talkbots can help by booking a demo with us today.
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22
Dec
Talkbot Basics  ·  Voice AI Technology
Spot and tag credit risks with voice AI technology
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

For many financial institutions, consumer credit risk is the biggest risk on their balance sheets. This is underscored by the reality that credit risk is the biggest cause of bank failures, according to Van Greuning and Bratanovik of the World Bank.

To effectively manage credit risk – specifically consumer credit risk – companies in banking and finance need to accurately assess an individual’s ability to pay back a loan on time and in full. This means they need rich and accurate data to make the right judgements. But even with advances in technology and digital transformation, financial institutions still face challenges with data for credit risk management.

 

How AI technology is transforming risk management

Thankfully, artificial intelligence is changing how companies manage consumer credit risk. Voice AI technology is particularly helpful in unlocking data that has already been collected but just sits in storage. One prime example is customer call recordings, which are stored as voice data. While these recordings may be useful as reference, much of them sit as unstructured data that take time to sort and analyze. Recordings are usually not used or reviewed except for quality checks and investigations into specific incidents.

With AI technology, rich customer information can be extracted from this previously untapped resource – data that may be useful for credit risk assessments. Artificial intelligence enables call recordings to be quickly transcribed and converted into structured data, which can then be used for analytics. Within all that new data would be signals or keywords related to risk management.

 

Detecting intent with natural language understanding

Transcribing and structuring voice data is only the first step in making use of conversational AI capabilities. One of voice AI’s more powerful capabilities is intention detection – figuring out what a customer plans or wants to do, based on verbal cues. Questions like “How much do I owe again?” or “What’s the minimum amount due?” can signal an intention to settle the account soon. When the AI neural engine detects these words, it can automatically tag the caller as “willing to pay” and proceed to obtain a commitment date on payment.

Intention detection is useful for less common circumstances, too. Keywords like “family emergency” or “job loss” can signal personal distress, and the AI engine can then tag the call as “special case”. The call can be routed to a live human agent, who can then talk the customer through workable options to refinance their debt. This helps banks and financial institutions take a more humane approach towards their customers who might be going through a rough patch. It’s a win-win situation: customers feel cared for, and the finance company gets an early opportunity to rebalance its risk portfolio.

 

Using data analytics to inform strategy

Another key benefit of having calls tagged automatically by AI is the ability to notice patterns as they arise. For instance, if a significant number of calls have been tagged with “don’t know how to pay”, the bank can deploy an information campaign on payment modes. This reduces the risk of non-payment for future customers who may struggle with how to pay their bills online or offline.

What’s more, AI labels can be customized based on business patterns. Companies can choose any number of tags to attach to their voice data, using the most relevant information that can shape their decision-making. Tags can vary from committed payment dates – which help companies follow-up on payments – to payment modes, which inform the bank which channels are more effective for collections.

With more data in their hands, banks can also take a wider view of their risk portfolio and recalibrate their strategy in a more proactive manner. Tags such as “invalid number” or “uncontactable” can signal a bad debt, and keep companies from wasting time on chasing down a non-payer.

The use of smart tags and automatic labeling have already helped a number of fintech companies and traditional banks optimize their debt collections. And these are only some of the ways artificial intelligence can help streamline financial operations.

To find out other ways AI can improve your processes and build smarter customer engagements, speak to one of our specialists today.
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14
Dec
Customer Stories  ·  Voice AI Technology
JAGADIRI achieves 5.3x higher cost efficiency with WIZ Talkbots
illustrate better return-on-investment

JAGADIRI works to protect the aspirations of its many Indonesian customers under the umbrella of PT Central Asia Financial (CAF). JAGADIRI provides life insurance, accident, and health insurance through digital marketing, telemarketing, and direct marketing channels.

“The thing with insurance is that it’s not like banking,” began Ibu Fitriah Betan, Head of Customer Experience at JAGADIRI. “As an insurance provider, JAGADIRI needs to make premium reminder calls to existing customers and continue providing service even up to 90 days past due.” Because the actual need for insurance happens in urgent situations, and the company does all it can to keep customers protected. On their part, customers need to pay their premiums promptly, in order to ensure that their policy remains active and can cater to unexpected incidents.

Creating a consistent customer experience with conversational voice AI

The need to ensure continuous coverage is why JAGADIRI puts a lot of effort into premium payment reminders. But the challenge is timing. Contacting customers for premium reminders can be tricky, as customers may be busy with their activities during the day. Another factor lies in the personal nature of the interaction – the mood of the agent or the way he or she communicates varies from one call to another. JAGADIRI wanted a more consistent way of engaging with its customer base, and ensure that these customers remain with the brand. 

In June 2022, JAGADIRI decided to transition its premium reminder calls to the WIZ.AI Talkbot. The implementation was done in phases; by September 2022, all premium reminder calls were automated. The results spoke for themselves. “Before Talkbot, we get less than 10 percent of connected calls for premium reminders. Now we get, on average, around 50 percent,” explains Ibu Fitriah. Talkbots are also 5.3x more cost-efficient than using human agents; the cost ratio for Talkbots is 6 percent, compared to 32 percent for agents. 

Using WIZ.AI’s solutions brought very tangible results in our premium reminder deployment. Before Talkbot, our agents get less than 10 percent of connected calls. Now we get, on average, around 50 percent.

– Ibu Fitriah Betan, Head of Customer Experience at JAGADIRI Tweet

An evolving partnership based on two-way communication

Beyond the results, JAGADIRI loves that WIZ.AI regularly checks on how the Talkbot is working, and provides recommendations on how to optimize the AI solution. From sales to project management to customer success and even customer experience, the WIZ.AI team was ready to help JAGADIRI make the most of its Talkbot. “WIZ.AI CX Designers help us refine our strategy by improving the scripts based on the data we get,” shares Ibu Fitriah. “There’s excellent two-way communication between us and the WIZ team. WIZ continually suggests how to update the Talkbots and implementing the suggestions improves our results.”

With the success of automated premium reminders, JAGADIRI is now looking to deploy the WIZ.AI Talkbot for more use cases. As of this writing, the team is developing debt collection scripts for due and overdue accounts. JAGADIRI is also planning to deploy Talkbots for Welcome Calls to new customers. “Talkbot has made contacting our customers much easier for the CX team,” concludes Ibu Fitriah.

The best thing about our experience is that WIZ.AI CX Designers are always helping us refine our strategy. There’s excellent two-way communication between us and the WIZ team. WIZ continually suggests how to improve the performance of the Talkbots and implementing the suggestions improves our results.

– Ibu Fitriah Betan, Head of Customer Experience at JAGADIRI Tweet
To find out how you can leverage voice AI for your customer engagements, speak to one of our specialists today.
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30
Nov
Talkbot Basics  ·  Voice AI Technology
Deal with surges in customer queries using voice AI
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

The year-end holiday season is upon us, and for many businesses, this is typically when things get really busy. Industries like retail and hospitality often see a surge in sales and inquiries – and as a by-product, an increased need for customer support.
 
If the increased volume were not enough, the holiday season is typically when everyone gets put under pressure. Shoppers are pressed to make their purchases in time for parties and events. Callers have to navigate competing demands for their attention. And call center agents tend to bear the brunt of customers’ short fuses and higher demands.
 
This is true for non-seasonal surges, too. Unforeseen changes in external regulation or policy can affect businesses in a critical way. New travel restrictions are a prime example; back in 2020, companies in the travel industry got inundated with calls and emails practically overnight. When scenarios like this happen, agents are typically pushed into high-stakes, high-stress situations. This comes on top of the mounting pressure to answer a growing number of calls.
 
In some ways, managing surges can be like a game of Russian roulette – you’re not quite sure when you’ll get shot. Fortunately, these risks are easy enough to understand and manage, with the right tools.
 

Inelastic resources can result in insufficient live support

For most call centers or customer support departments, resources and headcounts are fairly fixed. This lack of elasticity means sudden surges in support requests are hard to meet. Resources – specifically call center agents – can get really stretched. Say an agent typically handles 200 calls a day; that number can easily double to 400 calls on a peak season. Unless extra resources were brought in to handle the surge in volume, there will be potentially 200 unaddressed calls, or at the very least, poorly addressed calls. Businesses never want to be in a position where they have 200 unhappy callers – customer loyalty will take a hit, and eventually, so will sales.
 
Hiring additional agents would be the natural solution to this resource crunch. And while this might be workable, training and onboarding agents take time. Companies often spend at least two weeks to a month before deploying a new agent into the live environment. If you’re looking to simply fill a temporary gap, training new people may not make sense, especially if the cycle needs to be repeated after six months.
 
And that’s if talent is readily available. Recruitment is a time consuming process in and of itself. Finding the right people to serve customers is a tricky business; candidates must possess not just the skills, but the right attitude and in some cases, a love for the brand they represent. What’s more, contractual hires for short-term engagements can be even harder to find in a tight labor market. Sometimes this talent problem is solved through outsourcing – but quality can become an issue when it comes to service delivery.
 

Using AI customer service for busy seasons

Addressing elasticity and scaling issues without compromising on quality can be done by deploying artificial intelligence (AI) agents for customer service. Unlike human agents, machine-powered bots can take on more queries as and when they come. A human agent can probably take 20 calls in an hour; in contrast, an AI-powered bot can handle one million calls in the same timeframe. This makes AI call centers more flexible than a traditional call center manned by only humans.
 
Training bots also take less time than training humans. Once a proper script is in place, a Talkbot can be deployed and take customer calls within days. Machine learning capabilities enable the bot to adapt to situations intuitively, collecting more data points and refining its process as it goes along. For queries it can’t address, the bot redirects calls to a human agent that’s identified as the best person to address the issue.
 
Because they run on scripts and programmed dialogues, Talkbots can easily be redeployed to address rapidly developing situations. A call center or customer service team can choose to quickly shift AI bot resources to another function – from products to payments, for instance – in as short as a day’s time. This makes bots a lot more flexible than traditional hires.
 
Finally, AI call center bots can be decommissioned as quickly as they are deployed. This means a company doesn’t need to keep paying for a service it no longer needs. Services can be scaled down once the peak has passed, and more bots can be brought back on board once the business sees another surge coming.
 

Using data from artificial intelligence to stay on top of change

One often overlooked advantage of using AI bots is the wealth of data businesses get from customer interactions. This is particularly true for voice data, which comes as unstructured, messy, and hard-to-extract fragments of information. Unstructured data makes analytics a lot harder and time-consuming. With AI, voice data can be quickly transcribed, tagged, categorized, and subsequently analyzed for insights.
 
As an example, suppose a retailer has put one of its popular toys on a special weekend sale. Several parents buy the product, and a week later, a number of them call the store asking if they can have the item changed. In most cases, the agent will ask for the reason for a return or exchange, make notes, and file the information for reference. The data may not be looked at until after the surge has passed.
 
With AI customer service, the call will be transcribed in almost real-time, the data structured and analyzed, and patterns reported on dashboards almost immediately. The retailer may learn that 80% of buyers changed their minds because buyers realized that their child is no longer interested in the toy. This alerts the retailer to changing buyer preferences and may lead them to reassess their product inventory.
 
Moving it even further, if the AI agent is integrated with the business’ CRM system, the bot can make intelligent suggestions to the shopper based on purchase history. The machine can then intelligently offer products based on known preferences. If the database shows that the caller is a loyal customer, the bot can initiate a promo that gives an extra 10% off on purchases made that day. The result is a delighted customer, and a new closed sale.
 

Providing great customer care at reasonable costs

For many businesses, a customer satisfaction (CSAT) score of above 95% is the holy grail of customer experience. But to provide that level of service often means investing a lot into live support. This tricky balance can be thrown off when seasonal surges happen. After all, nobody can precisely determine how many additional agents they’ll need. Hire too many and you end up spending more than you should, hire too little and you get irritable customers waiting for long periods of time.
 
The good news is that AI call center bots can help manage that balancing act of delighting customers while managing costs. With AI-powered agents, customers will experience shorter wait times and human agents get more manageable workloads. Because a huge chunk of easily addressable queries are managed by machines, humans have more bandwidth to handle the most important tickets – giving the right attention to the right issues. Businesses end up with happier customers and fewer unresolved queries.
 
Artificial intelligence can also empower companies to be more proactive rather than reactive to sudden market shifts. Companies can prepare for upcoming surges by training bots once they see changes on the horizon. Furthermore, data collected by AI bots can be quickly analyzed and used to inform strategy to help mitigate new risks.
 

Customer service that’s personalized and deeply human

Today’s technology makes it possible to deploy bots that speak and text like the humans they serve, providing a personal touch that customers crave. That’s something that outsourcing your customer care operations will find difficult to achieve, especially if services are offshored. In customer service, shared context matters a lot in creating enjoyable experiences.
 
By using AI to automate low-value tasks, businesses are in a better position to deliver high-value services that delight, creating experiences that breed customer loyalty.
Wondering if AI customer service is the right solution for your business? Speak with a representative today.
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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.


03
Apr
Talkbot Basics  ·  Voice AI Technology
How To Design A Talkbot
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

With thanks to major digital advancements, customer service has evolved and there are now more ways than ever to communicate with your audience and customers. AI voice talkbots have become a particular favorite as a means for offering customers immediate, straightforward, yet economical service when it comes to over-the-phone contact. With limited waiting times and an inanimate (but seriously efficient) middle-man, talkbots fulfill both the needs of the customer and the company. This form of AI is advancing at an extraordinary level, quickly developing to approach more diverse and complex tasks with ease and competence, from using the customer’s name to confirming and modifying appointments or setting up product returns.

The optimized user experience is incredibly cost-efficient. A team of bots takes the pressure off the human team, who are then more able to provide customers with the specific assistance they require.

However, producing the right talkbot to fit your business needs can be a little more complicated. In this article, we provide a checklist for whittling the perfect talkbot for your company or brand. So, let’s discuss how to design a talkbot.

Why do you want a talkbot?

As AI technology has become widely acknowledged as part of the customer journey, users are less hesitant to succumb to the talkbot experience. Anything to make the communication quick and painless for the customer is a major attribute. Coincidentally, contact center advisors are relieved of an influx of questions they may not be able to instinctively answer, unlike voice AI.  As well as offering an enhanced customer experience, talkbots are also consistent, practical, and agile to customer needs. Through infinite technology and data that make up their fabric, they are experts on every call. Meanwhile, the voice AI component drives the ability to recognize voice cadences, inflections, and feelings, and deliver a refined and dependable service – ensuring the customer always leaves happy.     

 

But what other business goals could a talkbot help you to accomplish? This will be determined by the business needs, aims, and competitive edge. Say you have a call center with 50+ human advisors; talbots reduce the atmosphere of stress and optimize call duration by acknowledging precisely what the caller wants, and taking the appropriate action. So, where will a talkbot cultivate value in the customer journey in the context of your brand? Whether you need a team of bots in the selling department – readily available with detailed product information, or more in the area of post-purchase assistance, it’s important to outline the goal of the talkbot. You may then shape their expertise around the needs of the company.  

 

Will your talkbot be manufactured in-house, or will you be employing a talkbot builder? The creator’s degree of competence will reflect in the features you’re able to access. The stylistic choices you make from the beginning – from its ability to pick up on human utterances to using the customer’s name – will contribute to the concluding product. And the final talkbot design will be a part of how well you accomplish business goals.

Who will the users be, and what are their needs?

You likely have a target consumer. At this point, you should consider the most effective approach for the voice AI talkbot to build a rapport and communicate with them at a personalized level. Concurrently, it should be blended with simplicity; callers want to quickly solve their queries and move on.

 

Think about your customers such as their age, gender, profession, culture, language (a SWOT analysis would be beneficial here). Consequently, ask yourself where are your customers? Are you going to build your talkbot to encompass a localized or global offering? Therefore, should you apply an NLU (Natural Language Understanding) component to produce a geographical-adaptive service that acknowledges local dialects? In which case, you may require localized language trainers. Perhaps, instead, it will engage with a set conversational path, therefore, maintaining a more general disposition.

 

Ultimately, it boils down to how comprehensive the talkbot needs to be in identifying and reacting to customer needs; will it answer queries singlehandedly, or will it build a picture and pass this on to a human advisor?   

How far will the talkbot be part of the customer journey?

Your devoted talkbot will act as the first impression of your brand’s level of call center assistance, but how long will its provision of service last as part of the journey? Should it simply gather information and send the customer to the next appropriate advisor, or should it take them on a richer, more intuitive path to accomplish their needs? This must all be considered in the design process. The exciting thing is, voice AI has the capability to do both!

 

The algorithmic driving force of a talkbot allows it the capacity to engage, inform, and to teach (to a certain degree), depending on the topics you decide to map. In the design stage, you should cover how elaborate the talkbot delivery of service will be. Your talkbot may simply retrieve the necessary information, then guide the customer to the right advisor. Alternatively, it may be an intermediary of your brand and will focus on a plethora of likely user questions and queries. Another beauty of talkbot technology is in the ability to recognize the caller’s needs in realtime – it can switch and change the delivery of service, advice, and assistance as it evolves throughout the call.

Design the user flows

Talkbot contact should be as streamlined as your website UX. If you decide to implement an NLU component, an AI voice can identify user semantics and accent. When it comes to AI voice, the offering can be as limited or limitless as you see fit. You can design a more simple or elaborate journey, depending on the implementation of certain technological components. Just keep in mind, your talkbot helps to carve the customer journey from the first point of contact on a call.

 

Talkbots refine the customer journey, so communication should develop in a direction that is concise and straightforward. Keep the conversation simple and options limited, but create a flow that ensures each query obtains a solution.

How will the talkbot fit in with your company?

The choices that you make throughout the talkbot design process will influence the result. As the talkbot will become a primary member of the service team, you should identify how it will fit in; it needs a persona!

 

The design of the talkbot should enable it to match the personality of your business while personalizing the customer experience. It should not only speak the same language as your consumers but also the language of your business, with its service approach and tone reflecting your vibe. Is that more formal, or congenial? Serious or lighthearted? Warm, or to-the-point? Consider the ways your talkbot will communicate and enhance the customer experience into one that is unique to your brand.

 

It’s then a matter of cultivating a personality for the bot. What kind of human utterances will it have? Which gender and accent should it have? How will it apply a conversational flow and emotional connection within the context of its transfer of service? One goal of employing a talkbot is to create a deeper connection with your customers who put their trust and invest their time and money in your brand or business – in turn, you want to award them the best call experience possible.

Using talkbots to humanize the service experience

Ultimately, the final design of the talkbot should balance efficiency with affability, conciseness with attentiveness, and practicality with personality. The all-round customer experience that is shaped by the talkbot will be integral to the impression of your company. This is both in the way it communicates directly with customers, and how it functions ‘under-the-hood’. In conclusion, the time spent on the design process of your talkbot is an investment into the customer experience you aim to give, and this can be either terrible or fantastic.


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