Artificial intelligence

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.
Contact us

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.
Contact us

06
Dec
Featured Articles  ·  Main Display  ·  Main Posts
transcosmos launches CX services powered by voicebots in Indonesia

Voicebot solution co-developed with WIZ.AI for both inbound and outbound contact center services

WIZ.AI Talkbot Architecture – deployment of conversational AI voicebot into contact center architecture

 

transcosmos inc. (Representative Director, Co-presidents: Koichi Iwami, Masaaki Muta) recently developed a voicebot solution in partnership with WIZ.AI (Headquarters: Singapore; CEO/Co-founder: Jennifer Zhang), a leading innovator in voice AI technology. transcosmos has begun using the WIZ.AI Talkbot solution at contact centers for the Indonesian market, providing more engaging customer experience (CX) services.

Using WIZ.AI’s localized conversational talkbots, transcosmos now offers automated customer services in three main languages in Indonesia, namely Indonesian, Javanese and Sudanese. This solution is helping transcosmos clients effortlessly connect to their customers at scale, with the right message at the right time, over telephony, chat, messaging and email, to create real connections with their customers. WIZ.AI Talkbots are currently being used for CX surveys and other outbound services, as well as for inbound operations such as providing an initial response to customers. With strong process automation capabilities, the Talkbot solution will help trasncosmos and its clients scale CX operations across the region.

“Our Talkbots have been developed and optimised for the ASEAN market, we are excited about our partnership with transcosmos and are looking forward to helping more clients in the Indonesian market, enabling enterprises to automate highly human-like customer experience at scale, while delivering enhanced business results.” shares Jennifer Zhang, CEO and co-founder of WIZ.AI. “Bahasa Indonesia is one of the many Southeast Asian languages supported by WIZ.AI, which help our global customers automate millions of customer interactions on a daily basis. Because the Talkbots Listen, Understand and Speak like a local, they are highly humanized, up to 95% of users don’t recognize that they were talking to a machine.”

In June 2013, transcosmos co-established PT. transcosmos Indonesia (Headquarters: Jakarta, Indonesia; CEO President Director: Seisuke Kobayashi) with PT Cyberindo Aditama (Headquarters: Jakarta, Indonesia), an IT company under the major Indonesian conglomerate, Salim Group. Today, the company offers various services including contact center services via calls, emails, and chats; Trust & Safety for monitoring and policing user-generated online content; internet ads and social media operations; and app development for digital marketing services from three centers in Jakarta and two in Semarang, and with a total of 2,500 members.

Within the ASEAN region, with its operating locations in Vietnam, the Philippines, Thailand, Malaysia and Indonesia, transcosmos offers a variety of localized services including contact centers, digital marketing and e-commerce one-stop services. With the aim of assisting many more clients in boosting their CX, transcosmos will continue to deliver extensive services that help both local companies and global companies planning to expand into local markets, expand sales and optimize costs. transcosmos will strengthen its partnership with WIZ.AI and jointly deliver services that meet the needs of clients in ASEAN member countries where the company operates.

Talkbot features – speech-to-text, text-to-speech, outbound call managment

 

About WIZ.AI

WIZ.AI, a global leading conversational voice AI technology innovator, is revolutionising traditional B2C communication. Its solutions enable businesses to deliver hyper-personalised, omnichannel customer engagement at scale. WIZ.AI has over 200 clients, many of which are Fortune 500 companies from various industries, including Banking, Insurance, Fintech, Telecommunications, E-commerce, Healthcare and the Government.

About WIZ.AI Talkbot

WIZ.AI’s intelligent Voice solution, the Talkbot, leverages highly customizable, adaptable, and humanised AI to deliver more than a hundred million automated customer interactions every hour. This enables its clients across various industries and functions to deliver quality, attentive and engaging customer services. WIZ.AI Talkbots are designed to intuitively understand customer intents, helping to reduce the wait times by quickly connecting customers to the relevant department and business unit.

95 percent of all Talkbot users are unable to tell that they are engaging with a machine. WIZ/AI’s Talkbot has powerful self-learning extended dialogue management, speech pattern recognition, and text-to-speech voice customisation that allows it to communicate in over 9 countries’ local languages and accents. Some of the Talkbot’s language capabilities include English, Bahasa Indonesia, Malay, Mandarin, Thai, Tagalog, and Vietnamese, as well as Singlish and other informal forms like Taglish (an informal variant of Filipino English).

The Talkbot platform also empowers and optimises data driven customer engagement through its analytics capabilities. By transforming previously unstructured voice data into. rich, structured data, clients using WIZ.AI’s analytics platform can map out personalised customer journeys and identify common pain points. Insights generated by WIZ.AI’s Talkbot can be leveraged to get the right messages at the right time via the right channels, boosting customer acquisition and the sale of value-added services.

Related Services

* transcosmos is a trademark or registered trademark of transcosmos inc. in Japan and other countries.
* Other company names and product or service names used here are trademarks or registered trademarks of respective companies.

About transcosmos inc.

transcosmos launched its operations in 1966. Since then, we have combined superior “people” with up-to-date “technology” to enhance the competitive strength of our clients by providing them with superior and valuable services. transcosmos currently offers services that support clients’ business processes focusing on both sales expansion and cost optimization through our 172 bases across 28 countries/regions with a focus on Asia, while continuously pursuing Operational Excellence. Furthermore, following the expansion of e-commerce market on the global scale, transcosmos provides a comprehensive One-Stop Global E-Commerce Services to deliver our clients’ excellent products and services to consumers in 46 countries/regions around the globe. transcosmos aims to be the “Global Digital Transformation Partner” of our clients, supporting the clients’ transformation by leveraging digital technology, responding to the ever-changing business environment.

Originally published on https://www.trans-cosmos.co.jp/english/company/news/221206.html 


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.

22
Nov
Featured Articles  ·  Main Posts
Tokio Marine Partners WIZ.AI to Automate Customer Service Via “Conversational AI Talkbot”

JAKARTA, Indonesia, Nov. 18, 2021 /PRNewswire/ — In its recent regional digitalisation transformation initiative, PT Asuransi Tokio Marine Indonesia (“TMI”) partnered with WIZ.AI, the ASEAN Voice AI leader, to launch its conversational voice AI Talkbot.

As one of the largest general insurers in Indonesia, TMI has a strong commitment to put customer satisfaction as main priority, guided by its core mission “To be a Good Company”. Combining the efficiency of self-serve solutions and the warmth of human engagements, the Talkbots have improved TMI’s Customer Satisfaction Score across the board.

Mr. Sancoyo Setiabudi, President Director of TMI, commented: “The digital age arrives with a set of big communication challenges for conventional communication strategies. It is important for us to find innovative, efficient and interactive ways to handle mass communication with our customers and partners. I believe this implementation will help us to provide more services to existing customers and proactively approach potential customers.”

The Talkbot is indistinguishable from a human call centre agent, with over 95% of callers not being able to tell the difference. It incorporates AI techniques, such as pauses, intent recognition and varying pitch and tone to interact with the customer in a natural human-like manner. It is also backed by their proprietary Natural Language Processing and Natural Language Understanding technologies, which enable the Talkbot to understand and speak several different ASEAN languages.

WIZ.AI Talkbots work closely with TMI’s human agents and only calls that have been identified to require more of a human touch will be channelled to a human agent. This cultivates a more agile contact centre, which boost TMI’s service capability and labour efficiency.

“I believe customer service should be accessible, human-like, warm and most importantly hyper-personalized. By working closely with TMI, we have pushed the boundaries of human-AI collaboration to provide a better customer experience,” said Jennifer Zhang, CEO and Founder of WIZ.AI.

About Tokio Marine Group

Tokio Marine was established in the year 1879 as the first insurance company in Japan and has grown over the decades, now offering an extensive selection of General and Life insurance products and solutions in 46 countries and regions worldwide.

About WIZ.AI

WIZ.AI is a fast-growing start-up specializing in providing humanistic AI conversational solutions to transform customer service. The company is headquartered in Singapore and with market presence in Indonesia, Philippines and China.

Source: Tokio Marine Asia


11
Nov
Pengenalan Talkbot
Pentingnya Revolusi Kecerdasan Buatan Bagi Bisnis
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Inventory Management Talkbot

Kecerdasan Buatan atau secara global dikenal dengan nama Artificial Intelligence (AI) adalah teknologi utama dibalik revolusi industri 4.0 yang telah membawa perubahan besar. AI biasanya didefinisikan sebagai studi tentang sistem pintar yang dapat melakukan aktivitas atau menyelesaikan tugas yang membutuhkan tingkat kepintaran layaknya manusia. Sama seperti revolusi industri sebelumnya, AI juga mempunyai dampak yang sangat signifikan pada produktivitas industry. 

Revolusi AI telah mengubah cara pengumpulan dan pemrosesan data secara fundamental, selain juga mentransformasi aspek bisnis pada berbagai industri. Secara umum, sistem AI didukung oleh 3 hal penting yakni; Domain Knowledge, Penghasilan Data, dan Pembelajaran Mesin (Machine Learning). Domain Knowledge mengacu pada pemahaman dan keahlian untuk mengetahui mengapa dan bagaimana kita harus melakukan suatu pekerjaan. Aspek data mengarah kepada prose persiapan database yang dibutuhkan yang akan diintegrasikan dengan algoritma pembelajaran. Machine learning dibutuhkan untuk mendeteksi pola dalam data, kemudian memprediksi tugas yang harus dilakukan dan melakukan tugas tersebut tanpa dilakukan pemrograman manual atau dengan kata lain dilakukan secara otomatis. 

Tiga Aspek Utama Teknologi AI
Kemampuan Pengambilan Keputusan Yang Cerdas

Simulasi kecerdasan manusia yang dilakukan oleh mesin dapat memberikan solusi cepat bagi kendala-kendala yang dihadapi manusia. AI dilengkapi dengan teknologi machine learning dan sistem analisis data yang canggih, yang artinya AI dapat belajar dan mendapatkan pengetahuan mendalam selama sistem diberikan data baru. Dengan input yang tepat, AI dapat membuat keputusan yang akurat dalam waktu singkat. Selain itu, aspek kepintaran AI meningkatkan produktivitas sistem dan mengurangi ketergantungan kepada bantuan manusia, yang membuat AI menjadi alat yang sangat berguna untuk dimiliki. 

 

Intensionalitas

Intensionalitas seringkali dianggap sebagai aspek teknikal dan ontologis dari program komputer yang dihasilkan dari algoritma dan pengetahuan teknis. Aspek ini dapat diinterpretasikan sebagai kemampuan AI untuk menghasilkan pengetahuan mendalam dari informasi yang didapatkan secara real time dan memberikan respons yang sama dengan respons kreator dan pengguna sistem AI tersebut ketika berinteraksi dengan informasi yang dimaksud. Respons yang diberikan biasanya merefleksikan konteks sosial yang dimiliki oleh kreator dan pengguna sistem. Selain itu, dengan perkembangan pengisian data, kapasitas penyimpanan, kecepatan pemrosesan, dan teknik analisis, AI menjadi lebih mahir merespon isu-isu dengan kecanggihan yang terus meningkat. Hal inilah yang membedakan AI dengan fungsi fundamental AI yang hanya berguna untuk tugas-tugas rutin yang sudah ditetapkan sebelumnya. 

 

Adaptabilitas dan Prediksi 

Teknologi Machine Learning memfasilitasi AI untuk menemukan pola dalam data yang sebelumnya telah diprogram, yang memperbolehkan AI untuk membuat perubahan secara otomatis sesuai dengan situasi dan kondisi. Aspek adaptabilitas secara mendalam memperkuat kemampuan AI untuk melakukan prediksi dan mengambil keputusan.  Salah satu contoh yang sering ditemukan adalah pada fitur penulisan pintar pada Gmail, yang memberikan saran kata atau kalimat yang dipersonalisasi saat pengguna menulis sebuah kalimat. Hal ini menggambarkan bagaimana AI beradaptasi dengan dengan pola penulisan seseorang dan memberikan saran yang sesuai. 

 

Penggunaan AI Dalam Bisnis

Tidak bisa dipungkiri lagi, revolusi kecerdasan buatan telah memberikan dampak besar dalam operasional bisnis. Praktik paling umum yang sering ditemui adalah otomasi dari pekerjaan repetitif yang membutuhkan sedikit input dari manusia. Tapi, dengan peningkatan algoritma yang konsisten, Teknologi AI tidak lagi hanya terbatas untuk meningkatkan produktivitas,  tapi juga menjadi alat untuk berinteraksi dengan pelanggan, memberikan pelayanan terbaik, hingga menjadi katalis inovasi-inovasi baru. Berikut beberapa contoh skenario yang mendemonstrasikan bagaimana AI mentransformasi aktivitas bisnis. 

 

Contact Centers

Contact Center telah mengalami perubahan signifikan seiring berjalannya waktu dan telah menjadi lebih canggih berkat otomasi berbasis AI. Kita dapat melihat kemajuan teknologi contact center dalam Chatbot dan Talkbot yang memperbolehkan perusahaan untuk siap selama 24 jam dan memberikan respon cepat dalam interaksi pelanggan yang dapat dilakukan dalam skala luas. Perubahan strategi interaksi pelanggan berbasis AI dapat dengan signifikan meningkatkan kapasitas layanan dan mengurangi kegagalan layanan yang biasanya terjadi karena kelalaian agen atau emotional labour. Agen contact center membutuhkan pelatihan pelayanan pelanggan secara terus-menerus untuk menjaga kualitas layanan, tapi Talkbot berbasis AI dapat belajar dari setiap interaksi pelanggan dan terus memperbaiki sistemnya untuk memberikan pelayanan terbaik seiring berjalannya waktu. Hal ini juga mengurangi biaya operasional yang berhubungan dengan evaluasi pekerjaan dan pelatihan contact center. 

Lebih dari itu, sistem AI di contact center seperti Talkbot memiliki kelebihan yaitu dapat disesuaikan untuk memberikan pengalaman pelanggan yang lebih personal melalui dialog dengan tujuan spesifik yang berdasarkan data pelanggan dan target bisnis. Dengan kata lain, Talkbot dapat dengan mudah melakukan up selling maupun cross selling ketika diberikan informasi pelanggan yang cukup, dan perencanaan bisnis yang matang. Talkbot juga mampu melakukan analisis sentimen dari percakapan untuk mengetahui informasi pelanggan yang lebih dalam melalui panggilan telepon, dan hal ini dapat dicapai Talkbot tanpa melakukan pelatihan pelayanan pelanggan. Selain itu, dibandingkan dengan contact center tradisional, sistem AI menunjukan kemampuan yang lebih superior dalam hal pengumpulan informasi yang setelah itu digunakan untuk membuat laporan dengan cara yang lebih pintar dan dengan informasi mendalam yang lebih baik. 

 

 E-commerce

Di masa sekarang pasar e-commerce dipenuhi oleh berbagai pemain dan sangat kompetitif. Perusahaan E-commerce terbaik harus bergantung pada teknologi AI untuk lebih baik memahami pelanggan mereka dan memberikan pelayanan terbaik agar mereka dapat tetap kompetitif dan dapat tetap meraup keuntungan. Fitur rekomendasi produk merupakan salah satu aplikasi penggunaan AI yang umum ditemukan pada industri e-commerce. Fitur ini merupakan aplikasi algoritma AI yang digunakan untuk memetakan preferensi pelanggan berdasarkan transaksi yang dilakukan, pencarian, dan kebiasaan konsumsi. Informasi yang dikumpulkan memperbolehkan perusahaan e-commerce untuk melakukan personalisasi rekomendasi produk untuk setiap pelanggan. Di satu sisi hal ini dapat memperkuat pengalaman belanja dan bahkan meningkatkan penjualan. Tapi, jika digunakan terlalu sering bersamaan dengan strategi marketing yang agresif efek sebaliknya mungkin akan terjadi. Selain fungsi rekomendasi produk, bisnis e-commerce juga sebaiknya menggunakan teknologi AI untuk kegiatan pelayanan pelanggan yang dapat dilakukan melalui chatbot atau talkbot untuk berinteraksi dengan pelanggan, melakukan manajemen stok lewat perkiraan permintaan, atau promosi produk. 

 

Logistics and supply chain

Penggunaan kecerdasan buatan atau AI dan machine learning telah secara fundamental mengubah manajemen supply chain dan menghadirkan optimasi yang berkaitan dengan manajemen yang akurat, produktivitas tinggi, biaya operasional yang rendah, dan pengiriman cepat. Sebagai contoh, dengan kemampuan untuk mengolah big data, teknologi AI dapat digunakan untuk otomasi alur kerja manajemen stok. Barang dapat dibungkus dan disortir dengan rapi dalam jumlah banyak, yang dapat secara signifikan mengurangi waktu pemrosesan dan meminimalisasi kesalahan manusia atau human error. Selain itu, Sistem AI juga dapat memperkirakan permintaan pasar berdasarkan sejarah pasar dan pembelian, yang informasinya dapat digunakan untuk memprediksi penjualan di masa mendatang, dan dapat membantu alokasi sumber daya. Hebatnya lagi, algoritma AI sekarang juga digunakan untuk mengoptimasi rute pengiriman barang, dimana beberapa sistem yang paling terdepan bahkan mampu memperhitungkan kondisi lampu lalu-lintas di rute yang akan dilewati. 

Secara keseluruhan, di era informasi dan data ini, potensi penggunaan AI dalam bisnis menjadi salah satu hal yang sangat penting. Otomasi aspek bisnis dapat mengurangi beban produktivitas dan ketergantungan pada tenaga manusia. Di saat bersamaan otomasi sistem bisnis juga mampu meningkatkan efisiensi biaya operasional. Adanya teknologi Machine Learning juga memperbolehkan perusahaan untuk mulai menggunakan pendekatan yang lebih cerdas dan secara berkelanjutan membawa perubahan bagi aktivitas bisnis. Bisnis harus mempersiapkan diri menghadapi gelombang revolusi AI, sehingga mereka dapat mencapai aktivitas operasional bisnis yang optimum. 


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.


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