Talkbot Basics

26
Jan
Talkbot Basics  ·  Voice AI Technology
Creating authentic connections to engage customers in the digital age
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

The rise of e-commerce and social media have given businesses new opportunities as well as challenges when it comes to connecting with their customer base. Brands are now able to reach and engage with customers in innovative ways through channels like Facebook, WhatsApp, and even TikTok – and customers expect them to. In the State of the Connected Customer report by Salesforce, 88% of customers surveyed expect companies to accelerate their digital initiatives. And in that same report, 80% of customers say experiences are as important as a company’s products and services. Customer engagement has become more important than ever.

With technology, it’s become increasingly easy to automate interactions and rely on machines rather than humans. This may lead to colder, more sterile communications. On the flip side, more data enables brands to create more personalized experiences, which can build brand loyalty. Businesses need to find a balance between using technology to enhance customer engagement and maintaining the human element that is essential for building authentic connections.

Using artificial intelligence to bring that personal touch

One of the key areas where technology can bridge technology with personal connections is through the use of Artificial Intelligence (AI). AI technology can be used to create personalized, authentic connections with customers through well-designed interactions. With conversational AI, these interactions – whether text-based or voice calls – enable customers to connect with business in real-time. Thanks to natural language processing and machine learning, AI bots can be the first point of contact for common customer queries, reducing the frustration of long waits. And when fed with localized data, AI-powered voice bots like the WIZ Talkbot can adopt local accents and expressions, creating a more human-like engagement.

AI technology can also be used to improve customer service by analyzing customer feedback and providing insights into customer sentiment. This can help businesses identify and resolve issues more efficiently. And when issues are spotted before they gain mass scale, brands can take a more proactive approach to problem resolution by sending updates even before a customer calls or sends a message. All these help build an excellent and memorable customer experience.

Another way AI can be used to improve customer engagement is through personalization. AI-powered algorithms can analyze customer data, such as browsing history and purchase history, to create personalized recommendations and offers. This not only improves the customer experience but also increases the chances of conversion and repeat business. By tailoring the customer experience to the individual, businesses can create a sense of relevance and value for the customer.

Connected omni-channel interactions to delight customers

Recently, Zendesk has coined a new term – immersive customer experience (CX). The concept is anchored on something many marketers already strive for, which is seamless omni-channel communications. When brands give customers a consistent experience even when they switch channels, that’s part of building an immersive experience. It’s about building a customer support environment that’s accessible, engaging, and connected – something that would make customers want to stay.

Part of creating that immersive experience is making interactions less rigid and more natural. Customers are now looking for more conversational brand communications across the entire journey – from marketing to support. This means customer touchpoints also need to be more integrated so that brands provide one consistent message, regardless of the channel or the topic. A solution like WIZ Engage enables brands to design fluid, connected interactions across voice and text, walking alongside the customer on their journey.

The quality of the experience matters; 73 percent of customers say they will leave for a competitor after multiple poor interactions. Even after just one bad experience, more than 50% of customers will consider another brand. The pressure is on companies to provide the kind of service that customers now expect – seamless, proactive, and personalized.

Moving beyond business as usual

In Forrester’s Predictions 2023: Customer Experience report, the research firm suggests that context – more than channels – will drive experience in the coming year. This means brands will have to think harder about how and when their customers engage with them. Businesses will need to know how best to meet customers where they are, regardless of channel.

Customer engagement is a vital aspect for any business. By prioritizing authenticity and personalization through the use of AI technology, businesses can build trust and loyalty with their customers, leading to long-term success.

Wondering how AI Talkbots can boost your customer engagement efforts? Schedule a call with one of our consultants to explore the possibilities
Contact us

19
Jan
Talkbot Basics  ·  Voice AI Technology
AI technology in finance: from concept to implementation
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

In a world of increasing data volume, managing spreadsheets and numbers is becoming more and more complex. Today’s finance organization needs a new set of tools to keep up with this fast-paced development. Enter AI technology. 

In this blog post, we present insights taken from Gartner’s webinar on the AI-Forward Finance Organization

Overload of financial data

Analysts predict that data volume will double to 181 zettabytes by 2025, which is a staggering amount. But what makes going through the world’s trove of digital data more challenging is its complexity, rather than its volume. Gartner foresees that data complexity will increase 4x in three years, twice the increase in volume. At the end of the day, it’s not just how much data we will get – it’s how much harder it will be to make sense of it all.

Many organizations today rely on spreadsheets for its operations, including financial functions. While these tools were powerful in their time, spreadsheets – even macro-enabled ones – are continuously being strained by high data volumes and complex data interconnectivities. It’s not uncommon to find broken links within spreadsheet models, or calculations that fail to present expected results due to technological limitations. The increase in data volume and complexity needs a smarter, more flexible solution.

Artificial Intelligence in finance

The rise of artificial intelligence (AI) in recent years has led to solutions that answer the data accessibility and navigability question. Enterprises are recognizing the power of AI and as of 2022, 59% of organizations surveyed by Gartner Research have started an AI initiative or adopted AI into their processes. And while this is a promising sign, it also speaks of risk to AI laggards. Businesses that fail to embrace AI technology to streamline processes will fall behind even faster in today’s accelerated market.

To embark on an AI journey, the key thing is to embrace change – to think differently and learn new things.

How to begin your AI journey as a finance organization

Start top-down – digital transformation, especially for finance-related activities, gets stronger buy-in when top management initiates the change
Understand that AI deployment is non-linear – adopt an agile working mindset and be willing to have more cyclical deployment patterns
Be open to experimentation – most organizations adopting AI take five iterations to get right things right or succeed

How to pitch a pivot to AI

Many leaders see the value of AI but need to get buy-in from upper management or other stakeholders. Here are some points that can help bring people into the fold and accelerate your company’s AI adoption.

Take the quick adoption route by buying software or subscribing to services. 
Position it as a proof-of-concept exercise where further adoption is dependent on results gained. Taking a packaged solution is one of the most frictionless ways for finance companies to dip their feet into the AI pond. For organizations that require debt collection or payment reminders, WIZ Talkbots are an easy way to introduce AI technology and quickly reap the benefits of AI-powered automation

Optimize human–machine collaboration.
Some of the pushback when it comes to AI stems from the fear of losing jobs. To overcome this, AI advocates should find ways where AI will complement human efforts. Best examples are process automations where humans are still needed for exception handling or complex cases. Remember, Humans are great at strategy and handling exceptions to rules, as well as seeing the big picture and drawing insights. Machines or AI are great at calculating, analyzing, executing processes, sending warnings at critical points, and enforcing rules or guidelines. Find the right balance between human and machine, and communicate how AI can empower humans to do more in less time or with less cost.

30% of businesses with advanced AI adoption report seeing better results than expected
– Faster implementation for new projects
– Greater business impact
– More process efficiencies
Significant AI adoption increased likelihood of financial benefit by 5x

Whether you’re a cutting-edge fintech or a well-established traditional, AI solutions should be in your future-proofing arsenal. And adopting a pre-built solution is one of the easiest ways to get started on your AI journey.

Looking to quickly deploy an AI solution into your debt collection operations? Our consultants would be happy to help
Contact us

13
Jan
Talkbot Basics  ·  Voice AI Technology
How to transform voice data into actionable insights

Many businesses, particularly in the financial services industry, have a treasure trove of data that’s just waiting to be uncovered. “Businesses which have high call centre demand typically store voice data in a system for seven years,” shares WIZ.AI Chairman and co-founder Jianfeng Lu, in an interview with Startup SG. “However, the data is unstructured and not stored in a uniformed way that allows for business, product, and customer improvement.”

Enter artificial intelligence (AI), specifically voice AI. By extracting information from call recordings, voice AI can help companies identify customer trends, detect customer sentiment and provide valuable insights into customer behavior.

Unlocking the power of voice AI

The first step in getting value out of voice data is in transcribing the calls into text. Once the calls are transcribed, the data can then be organized more intelligently. “Voice AI digitalises unstructured voice data by hashtagging and categorising calls,” Jianfieng explains. This newly structured data then becomes valuable to companies that have years of saved recordings.

One of the great advantages of AI is the speed with which machines can process data. Instead of having a human listen to hours of recordings and draw insights manually, companies can use an AI engine to transcribe, categorize, and extract useful data in just minutes. What’s more, AI can capitalize on the power of big data – having large volumes of information to draw meaningful conclusions from. These types of insights would be impossible to find with just a smaller data set, or when doing spot checks and listening to random recordings.

The automated data collection capabilities of AI-powered systems brings data analytics to the fore, allowing teams to leverage data-driven decisions for long-term success.

Continuous improvement from data analytics

With this vast computing power, voice AI can empower businesses to gain a deeper understanding of their target audience. By listening to conversations, voice AI can record how customers interact with a specific product or service. This information lends insight into customer priorities and preferences, which is valuable for future product development. 

Voice data can also provide deeper insights into the customer journey. Artificial intelligence can help businesses uncover customer preferences and behaviors. Machines can drill deeper into multiple customer conversations, picking up cues that may otherwise be overlooked. With voice AI, businesses can make better decisions that are in line with their customers’ needs, enabling them to provide more personalized and effective experiences.

Voice AI can also uncover trends in customer feedback, flagging a rising pattern in product or service issues and prompt a more proactive response. Businesses can use this data to avoid potential customer dissatisfaction by pushing out service messaging even before issues arise. Because voice AI can also detect customer sentiment, companies can use extracted data to optimize their customer experience. Leaders can quickly identify areas of improvement for customer experience management.

Data-driven decisions for personalized experiences

Voice AI analytics is a powerful tool for businesses to gain insights about their customers and make more informed decisions. By utilizing analytics on voice data, businesses can leverage customer data to better understand their customers, anticipate their needs, and personalize the customer experience. This technology can also be used to identify potential opportunities and areas for improvement, providing invaluable insights that can be used to create more relevant products and services.

Voice AI will grow to play an ever-important role in customer service, marketing, and other data-driven activities. Companies are already beginning to leverage voice AI to create more efficient processes, personalize customer experiences, and develop innovative solutions. Voice AI can be used to automate and monitor first-level customer interactions, understand customer sentiment, automatically classify data, and to generate automated reports. But ultimately, the access to data which companies already have is what makes voice AI extremely powerful. “I foresee a future where enterprises will have a rich data pool for each customer,” shares Jianfeng. “This will help businesses better serve their customers’ needs.”

Contact one of our specialists today to take that first step in harnessing the untapped insights in your voice data.
Contact us

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.
Contact us

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.
Contact us

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.
Contact us

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.
Contact us

23
Dec
Talkbot Basics
Driving Viral Growth with Andrew Chen
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

 

Did you ever wish you could bottle the magic that helped companies such as Uber, Tinder and Dropbox transform into the Unicorns they are today?

 
Last Wednesday (15th December 2021), in celebration of Andrew Chen’s book launch in Asia, we were honoured to host him during our webinar on “Driving Viral Growth” as our guest speaker. Over a thousand registrants signed up within the first week and we ever very quickly oversubscribed.
 
Andrew Chen is the legendary Silicon Valley Growth Hacker, and General Partner at Andreessen Horowitz. Through his methodology, he has managed to grow companies the likes of Uber, Tinder and Dropbox to Unicorn status.
 
During the webinar, Andrew had an in-depth discussion on growth methodology with Jennifer Zhang, CEO and Co-founder of WIZ.AI. They discussed the importance of not only a Minimum Viable Product (MVP) but also the necessity of having a Minimum Viable Community (MVC), because even the most brilliant product in the world is useless without users and a strong community.
 
Most importantly, Andrew shared the real-world use cases of how Tinder, Slack and Clubhouse employed the Atomic Network leverage their community of users to grow at scale and create incredible retention rates past even the D +30 mark.
 

On this, Jennifer drew parallels to her experience on how WIZ.AI’s Talkbot solutions are helping our clients scale up customer engagements to help companies overcome the cold start problem while still ensuring cost efficiency. Part of Wiz’s vision is to grow with growing companies and we assist hundreds of companies

Jennifer noted that customers were growing numb to in-app notifications and other text-based engagement methods, providing diminishing returns and that traditional voice engagements provide the best Customer Satisfaction Score CSAT however were costly to set up and maintain.
By combining the cost efficiency of human-like conversational voice AI to provide the voice engagements that customers respond to, together with an omnichannel approach to customer engagement. WIZ.AI were able to help companies from regional banks to fast growing Unicorn start-ups to grow their network at scale.
 
WIZ.AI is committed and will continue to engage with the Growth Hacking ecosystem in SEA and contribute to the regional digital transformation.
 
If you have missed the session, fret not! A short summary of the key takeaways from Andrew was prepared to help you to scale the network effect.
  • A common trait of many successful Silicon Valley companies is that they are very good at connecting people together, to create communities of users that in turn invite more users to join.
  • Viral growth is not just a campaign, it is a journey of engagement and community that invites users to share their experiences with other users.
  • User cases such as Google Docs, Dropbox, Uber, and Tinder demonstrated network effect is equally valuable in both B2B and B2C industries. However, B2B is more about targeted engagements Company by Company and the growing communities within the Company, while B2C usually grows city by city.
  • One of the key approaches to tackling the Cold Start Problem is getting enough users to use the product at the same time. You will need to build not just the MVP (Minimum Viable Product) but the MVC (Minimum Viable Community) for your start-up to take off.
  • When a certain size of network is built, there are couples of metrics to monitor the ongoing viral growth, including yearly & monthly growth rate, retention rate and the number of connections, etc.
  • Growth hackers should be able to not only look at the micro but also the macro view on their strategy.

10
Nov
Talkbot Basics
What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business?
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Inventory Management Talkbot

Artificial Intelligence or AI is the technology behind the fourth industrial revolution that has brought great changes all around the world. It is usually defined as the study of intelligent systems that could execute tasks and activities that would require human level intelligence. Similar to the past three industrial revolutions, AI is leaving an incredible impact on productivity. 

Artificial Intelligence (AI) Revolution

The AI revolution has fundamentally changed the ways people collect and process data as well as transformed business operations across different industries. In general, AI systems are supported by three major aspects which are:  domain knowledge, data generation, and machine learning. Domain knowledge denotes the understanding and expertise of the real life scenario on why and how we need to engineer a task. The data aspect refers to the process of preparing databases required to feed on to the learning algorithms. Lastly, machine learning detects the patterns from the training data, predicts and performs tasks without being manually or explicitly programmed.  

Three key attributes of AI technology.
Intelligent Decision Making 

The simulation of human intelligence by machines can infer a fast solution for the problems that are often faced by humanity. AI is backed by advanced data analytics and machine learning, which means AI can learn and gain new insights as it keeps feeding on new data. With proper input, AI could come up with prompt and accurate decisions. In addition to that, the intelligence attribute of AI promotes productivity and reduces dependency on human support which makes AI highly autonomous and a convenient tool to have. 

Intentionality

Intentionality is often deemed as the technical and ontological attributes of computer programs that derived from the algorithms and knowledge engineering. This attribute can be interpreted as AI’s capability of delivering insights from the real time information and reacting in the way similar to its creators’ and users’. The responses usually strongly reflect the social context that creator and users are in. Additionally, with development of data ingestion, storage capacity, processing speed and analytic techniques, AI gets more capable of responding to the issues with increasing sophistication. This very much differentiates AI with the fundamental function of machines that merely carry out predetermined routines.

Adaptability and prediction

Machine learning facilitates AI to discover the pattern of the data that were previously programmed, which enables AI’s capability of making its own change as circumstances change. The attribute of adaptability profoundly enhances AI’s prediction and decision making. One of the commonly seen examples is Gmail’s Smart Compose feature, which offers the use of  personalised suggestions as users typing a sentence. It illustrates how AI adapts to one’s personal writing pattern and delivers appropriate suggestions.

AI in the Business 

Undoubtedly, the artificial intelligence revolutions had profoundly impacted the way businesses operate. The most common practises are the automation of repetitive tasks that require less human input. However, with the consistent improvement of algorithms, AI technology is no longer only limited to the capability of expanding productivity, but also becomes a necessary tool in engaging customers, providing service excellence, and driving innovation. Here are several industrial scenarios to demonstrate how AI transformed the nature and scope of business activities.

 

Contact Center

The contact center has evolved significantly over the years and has become more sophisticated thanks to the use of AI Automation.  We can see technological advancement of contact centers in the form of Chatbots and Talkbots that enables 24/7 availability and instant response for consumer engagement at scale. Changing the strategies to engage customers with AI based automation vastly boost service capability and reduce service failures that are usually caused by under-performing agents or emotional labour. While human agents require frequent and regular customer service training to maintain the service quality, AI Talkbot learns from every customer interaction and keeps improving to provide excellent service over time. This very much reduces labour cost associated with performance evaluation and contact center training. 

Furthermore, AI systems in contact centers such as Talkbot have the capability to be customized to deliver a more personal experience through goal driven dialogues based on the customer data and business metric. In other words, Talkbots can easily do upselling and cross selling if they are given sufficient information about the customers and the business plan. Even without any customer care training, Talkbots can conduct sentiment analysis from the conversation and unlock the hidden customer data in customer voice calls. This, in turn, provides great insights for future planning. Also, compared to the traditional contact center, AI systems show stronger capabilities in collecting information from each call which are used to generate the report in a more intelligent manner and with better insights. 

 

 E-commerce

Nowadays the e-commerce market is highly saturated and competitive. Top e- commerce companies heavily rely on AI technology to better understand their customers and to give their customers better service in order to remain competitive and profitable. Intelligent product recommendation is one of the typical applications of AI in the ecommerce industry. This is a real-time application of an AI algorithm that attempts to figure out customers’ preference based on their previous purchases, researches, and consumption habits. The collected insights enable e-commerce companies to personalize product recommendations for different online shoppers. To a certain extent, it enhances the shopping experience and potentially boosts sales. However, if the e-commerces overuses intelligent product recommendation and adopts an aggressive marketing strategy, the reverse effect might happen. Beyond the function of personalization, e-commerce businesses also leverage AI technology to support customer service through chatbots and talkbots to assist them with customer care, inventory management via demand forecasting, or product promotion. 

 

 Logistics and Supply Chain

 The use of artificial intelligence and machine learning has fundamentally transformed supply chain management and delivered strong optimization of capabilities associated with accurate management, high productivity, low operating cost and quick delivery.  For example, with the capability of handling big data, the AI technology could be used to automates the workflow of inventory management. Parcels could be packed and sorted in a seamless process at large scale, which would largely reduce processing time and minimize human error. Also, the AI system can forecast market demand from the market and purchase histories, facilitating the prediction of the future sales and providing information to support resource allocation. Moreover, AI algorithms are  also being used to optimize the shipping and delivery route, with some of the most advanced ones even involving the prediction and management of traffic lights.

Overall, in the information and data driven era, the potential of AI is tremendous. Business process automation could reduce stress on internal productivity and decrease reliance on human support while at the same time increase operational cost efficiency. Machine learning enables the company to delve into a more intelligent approach and continually drives the evolution business model. Companies should prepare themselves for the AI revolution wave, so they can leverage on the technology to achieve the optimal operational excellence.


02
Nov
Talkbot Basics
Fintech: Utilizing AI to assist the growth of Peer-to-peer Lending
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

pinjol

Fintech peer-to-peer lending (also known as p2p or p2pl) is all the hype in South East Asia right now.  The region is populated by more than 650 million people who mostly do not have access to proper banking or financial service, making peer-to-peer lending a welcomed financial safety net especially in countries such as; Indonesia, Singapore, Malaysia, Vietnam, and the Philippines . With that in mind, it could be assured that the market growth of peer-to-peer lending will just grow bigger from here. However, those rapid growth will be faced by similarly rapid increase of challenges such as regulation from the governments, increased costs to handle huge amount of customers, increased needs of proper customer engagements, and even small challenges such generating and engaging leads on daily basis which sounds simple but requires a lot of company resources in term of time, costs, and pure efforts. 

Understanding the challenges might be the first step to prepare for the incoming problems in the future. However, companies should also prepare the solution for these future problems. One solution that is available now is the utilization of AI for multiple purposes in the business process. Here are several ways AI could be useful in keeping up with the rapid growth of peer-to-peer lending industry:

1. Big Data Management

Big data has revolutionised value generation for many industries including peer-to-peer lending. With the amount of data that is generated right now, companies without access to AI systems to process data will struggle to keep up with rapid development in the market and the industry. By using AI to process data, companies now could easily collect and map out consumer information such as their habits, likes and dislikes, activities, and personal preferences in a short period of time. This information is a disruptive tool that allows p2p companies to elevate their business processes such as supporting more accurate underwriting decisions, better assessment of prospective borrowers to provide accurate and more fair credit pricing, and also faster and more accurate decision making process based on data. In addition to that, with Machine Learning Technology the AI will improve overtime, which means it would be able to keep up with the rapid growth of the p2p industry.  

2. Business Automation

One of the pain points of business is the resource and effort it takes to finish repetitive and mundane tasks. For p2p companies it is even worse since they have to deal with endless procedures and verification processes. Fortunately, AI automation is more than capable of dealing with all of these repetitive tasks with ease, efficiently and effectively. Even better, they are often resulting in great output since AI systems are capable of consistently operating with best practice . 

With most tasks, procedures, and verification being automated it frees up company’s resources to be focused on strategically valuable tasks. For example, by using WIZ AI Talkbots to automate collection and collection reminders, collectors could redirect their efforts to  focus more on follow-up tasks and customer retention. 

3. Lead Generation & Customers Outreach

AI is not only useful for manual tasks automation. Advanced AI systems could even be used as a tool to reach and engage customers. Through technology such as Conversational Voice AI, p2p companies could automate their call centers to engage customers at scale and with lower costs. By automating lead generation and customer outreach with AI powered systems such as WIZ.AI Talkbots, companies could also redirect the efforts of their call center agents to deal with more important tasks such as closing deals or taking care of high valued customers instead of making calls to collect customers’ information, or answering FAQ which could easily be done automatically. 

Another benefit of integrating Voice AI systems in call centers is the consistency that comes with it. For lead generation activity it means the company could consistently generate a huge amount of high quality leads. As for Customer Outreach, it means that Voice AI Talkbots will help companies to engage more customers and consistently give them better customer service and customer experience, thus ensuring customer loyalty and the future business potential.

4. Risk Mitigation

As mentioned before, with AI taking care of data processing it helps companies to better plan the next strategic moves, including how to deal with potential risks. Not only that, with AI automation companies would have a tool to deal with unexpected situations. 

In the p2p industry, lenders and borrowers always need a reliable way to contact the companies either to find information or file complaints which are usually done through phone calls. In unexpected circumstances, the call center might be flooded with calls and the company will not have any choice but to bolster up the call center capacity. However, training new call center agents takes too much time. Even worse, most of the time after the crisis has passed, companies still have to take care of additional manpower which would be costly for an extended period of time. On the other hand, with AI based automated systems such as WIZ.AI Talkbots, companies could flexibly increase and decrease the number of bots according to the current needs. 

5. Increase Efficiency and Reduce Cost

All in all, in any Industry the expectation of integrating AI to the business process would be to increase efficiency and growth, as well as reducing cost. Luckily, based on all the points that were mentioned before, all of them would lead to business efficiency and cost reduction. By automating most business processes, companies could streamline many operations without sacrificing productivity. Also with AI systems companies would have access to more accurate data that is available real-time, which help companies to make more accurate forecasts, budgeting, and strategic planning to increase the efficiency in every aspect of the business. 

The benefits of integrating AI to business is undeniable. It is only a matter of time before AI automation is integrated in every industry for various business aspects. Peer-to-peer lending companies are privileged enough to be able integrate AI in their business early on. Therefore, as the system gets more advanced, early adopters will be more prepared and have sharper competitive edges in terms of familiarity and understanding of AI technology, also where and how the AI technology will develop and how to utilize the technology for the benefit of their business.


Page 1 of 3