E-Commerce Retention Strategies for 2022: A Mini Guide

The market value of the fast exploding e-commerce industry is expected to grow to USD 16,215 billion by 2027 (Meticulous Research) globally. While the e-commerce companies battle it out for a larger slice of the pie, they also need to maintain a laser-sharp focus on retaining their existing customers. In the new digital economy, they need to constantly innovate with agile business models to promote their products and services in a personalized manner to avoid obsolescence.

Many think that retaining e-commerce customers is straightforward. Unfortunately, providing a great product or a fast shipping experience is not enough for retaining customers. With increasing competition and demand for a seamless customer experience, e-commerce customer retention is becoming increasingly difficult.

But why focus on retention?

Retaining customers not only helps in reducing acquisition costs but also in increasing the customer lifetime value, directly impacting the bottom line. Research shows that a 5% boost in customer retention increases profits by 25% to 95%. While acquiring new customers is also important, building a long-term relationship with an existing customer is 16 times more cost-effective than acquiring one. 

Hence, for consistent growth, it’s critical for e-commerce companies to focus on retention and employ customer-centric strategies that help in bringing back customers to the website/application. Below are a few proven customer-driven tactics that can help e-commerce organizations increase customer retention and improve loyalty –

Personalized customer engagement across different stages

Over 91% of consumers are more likely to shop from brands who recognize, remember, and provide them with relevant offers and recommendations (Forbes). Engagement across the customer funnel is important for both converting users into customers and reactivating existing customers. 

Here are few effective forms of customer engagement –

Effective customer onboarding

While often ignored, an effective onboarding program is the first step you can take towards creating customer loyalty. It helps customers learn about your products/services and the mission of your company. This in turn allows them to choose the right products and gain maximum value from your offerings. 

Promoting Offers/New Launches 

If you want customers to return back and buy from you after their initial purchase, it’s very important to constantly be in touch with them and build a relationship. There are multiple strategies e-commerce companies can use to achieve this, including – 

  • Promoting new product launches and other important announcements through AI voice bots.
  • Triggering promotional outbound calls through AI voice bots for reactivating existing customers. 
  • Notifying customers about price drops and product availability alerts by leveraging AI voice bots. 

Let’s understand this better using an example. Assume a user is looking to purchase a specific product that is currently out of stock. To ensure he/she visits again when the product comes back in stock, e-commerce companies can automatically trigger calls informing the customer about the product availability. Assuming there are hundreds of such customers every day, it’s an ingenious way to bring back potential buyers and increase sales.

Alongside calls, e-commerce companies can also notify users using push notifications, text messages and emails.

Offer excellent & proactive customer support

Customer service directly impacts customer loyalty. Hence, whether companies are active on only one or multiple channels, it’s important to ensure that they provide a delightful and seamless customer experience consistently across all channels. Since over 70% of users prefer to shop using their phone, the most popular channels are call, chat and SMS.

While chat and SMS don’t require a lot of resources to handle, calls are resource-intensive and require hiring and training of call centre agents. However, with limited bandwidth, e-commerce companies struggle in answering customer calls quickly and in providing them first call resolution. The situation worsens during events like festivals, a sale or a big product launch. 

To overcome this challenge, more and more e-commerce companies are adopting Voice AI built using advanced Artificial Intelligence (AI) algorithms that can not only understand the context and intent but also hold natural human-like conversations. By leveraging the technology, e-commerce companies can handle the majority of the outbound calls especially the ones that are mundane without the need for a human agent. AI voice bots can answer questions like a human and resolve common queries (for example, order status enquiry). When required the AI voice bots can also easily transfer calls to agents with context. 

This allows e-commerce companies to handle mundane calls and surges (during festival season or a sale) while freeing up agent bandwidth. Since the resolution is instant, AI voice bots drastically reduce call waiting times and increase first call resolution. 

Collecting feedback and acting on it

While often undervalued, collecting customer feedback is a very effective way of increasing customer retention. This is because it’s impossible to retain a customer without knowing what they expect from a product or service. 

Hence, irrespective of the company size or the number of customers, e-commerce companies should proactively collect customer feedback and analyze it to improve product experience and service.

Most companies are leveraging emails, text messages, agent calls and push notifications to collect feedback. While these methods are effective to an extent, the number of responses received using these methods are very poor. We’ve all been guilty of ignoring emails and text messages sent to us for feedback. To fix this, e-commerce companies can leverage Voice AI.

AI Voice bots that are powered using Voice AI can intelligently trigger calls at certain milestones (eg. when the product is delivered) to collect feedback from customers in a personalized manner. It can even reschedule calls in case the customer doesn’t pick or when they request for a callback. Collecting feedback at the right time not only makes the customer feel valued but also yields a higher conversion rate compared to other channels.

Irrespective of the channel you’re utilizing, remember to ask customers for additional context with the rating. In order to improve CX, it’s important to understand in depth their challenges and reasons for both poor and good experiences. 

Wrapping it up 

While a few retention strategies might take additional resources and effort to implement, they’ll go a long way in increasing customer lifetime value and ultimately revenue. For faster implementation and in-depth analytics, companies need to leverage the latest technologies (like Voice AI, ML) and tools.

Lastly, to get the maximum out of the above strategies, e-commerce companies need to focus their efforts on the right set of users (that create maximum value for them in the long run).  

About Skit

Skit is an Augmented Voice Intelligence Platform, helping businesses modernize their contact centers and customer experience by automating and improving voice communications at scale. By enabling preemptive, intelligent problem solving and seamless live interactions, we have automated over 15 million calls for global enterprises across industries. We help our customers streamline their contact center operations, reduce costs, and also enhance customer experience and engagement.

Connect with us if you’re interested in learning more about the platform and how it can streamline your contact center strategy.

Voice AI for Banking: Streamline Outbound Calling

The recent pandemic has reshaped consumer banking behaviours in many ways and has skyrocketed digital transformation in the banking sector. With social distancing becoming the new normal, most consumers prefer utilizing digital banking services over visiting the branch, even for important tasks. This in turn has spurred the evolution of agile business models backed by technologies like Artificial Intelligence (AI), Big Data, Blockchain etc. These technologies are also critical for cost reduction, an increasing priority for banks due to weak investment returns and market uncertainty.

As COVID-19 accelerates digital adoption across banks, CX will act as a major differentiator to help leapfrog competition by engaging customers with tailored and intelligent value propositions based on deep customer insights. In order to do so, banks need to transform their technology capabilities across the complex landscape of their technical assets, to deliver unique and highly personalized experiences at the right time, at scale. 

With the spike in the usage of digital banking, banks have also seen an influx of inbound calls. More customers are picking up their phones to get queries resolved. A similar trend is being seen in the number of outbound calls made by the banks for repayment reminders, Know-Your-Customer(KYC), and account registration.

Streamlining Outbound Calling 

Technologies such as Voice AI are empowering banks to automate inbound contact centres. This has enabled them to reduce average call waiting times, improve customer satisfaction scores and free agent bandwidth. While streamlining inbound calls is extremely critical for CX, equal attention needs to be given to streamlining outbound calls. 

Banks make thousands of calls each day to customers for various reasons. These calls can be for welcoming new customers, reminding them about a due payment, lead qualification, and more. By engaging with customers at the right time, banks strengthen their existing relationship with the customer which directly helps them in creating trust and building loyalty.

However, since all the calls are made by agents manually, banks are unable to meet the required goals. They’re in dire need to optimize the process and make it more efficient. To provide customers with a consistent experience they need to leverage new-age technologies like Voice AI.

Voice bots that are powered using Voice AI can converse with customers in a natural and multi-turn conversational style. The experience is very human-like. Voice bots can trigger outbound calls to engage with customers 24*7 in a scalable manner. You can completely customize the calls according to different parameters like frequency, during specific events, and more.

Lead Qualification

Banks receive millions of leads every month through various sources including the website, social media, partnerships and advertisements. Usually, agents call each lead up to understand the customer’s requirements better and gauge their interest level. However, a major problem is that a huge chunk of these leads are junk and agents end up spending their important time speaking to the wrong users rather than prioritizing the interested ones. This has a major impact on the number of conversions. 

However, voice bots can greatly help solve this problem for banks. Since the problem is with the qualification process, it can be completely handled by the voice bot without any human intervention. By seamlessly integrating with the CRM, the voice bot can fetch the customer’s phone number and trigger an outbound call. During the call, the voice bot asks the user different questions required to qualify them for a product. In case the customer has any questions, voice bots can also resolve them. If interested, the voice bot can directly transfer the call to the agent or schedule a convenient time for a callback. In case the call is missed, voice bots can also make periodic follow-up calls. 

According to the data collected by the voice bot, agents can prioritize their calls. This way they end up reaching the interested users first, significantly increasing the chances of conversion.

Let’s understand with an example. Assume, a user applies for a credit card online. They enter a few basic details like name, monthly salary, age, contact details, and more. Once the details are submitted, it is transferred to the voice bot. The bot fetches the contact details and triggers an outbound call. It asks the user multiple questions including the credit limit they were looking for, whether they have an existing bank account with them and more. All this information is automatically updated on the CRM. Agents can then go through all these users and filter out the interesting ones suitable for calling. 

Customer Activation

Converting a potential lead into a customer is not enough for banks. To generate revenue out of them, they need to ensure that they’re using their different products and services. For this, they need to focus on customer activation. They need to employ different strategies to help customers move faster in their life cycle. But onboarding thousands of customers every day requires a lot of resources and time. For banks to provide their users with a personalized onboarding experience and engage with them at regular intervals affordably, they’ll need to leverage the power of technology and automation.

When a retail customer opens a savings account, s/he doesn’t only get access to the account but other services such as net banking, debit card, phone banking and more. However, most customers don’t end up using these services. This is why banks need to onboard them and send periodic reminders to nudge them to use the product. Few banks do have dedicated in-house or outsourced teams who handle this. However, the process is not scalable and is extremely difficult to follow for all the customers. 

So, how can banks solve this? 

To onboard customers and engage with them across the customer journey, banks can leverage voice bots. Firstly, the bot can call each customer and onboard them by taking them through each service, answering FAQs, and resolving questions in case any. By educating them it removes the initial friction the customer might have in trying a particular service. Further, a voice bot can call the customer after a certain period to understand their experience and suggest different services. This helps banks in delivering personalized engagement across the customer lifecycle consistently. 

To further improve customer activation, banks can – 

Map the customer journey – Banks can map out all the important stages to ensure they engage with customers at the right time. For example, for a credit card user, different stages can be –

  • Credit Card Activation
  • First transaction
  • Reward Redemption

Customer Segmentation – To deliver a personalised customer and effective communication, banks need to segment their customers. Without this banks can end up spamming users with notifications each day making for a very poor experience.

Improving Propensity 

Most banks use product propensity to increase customer’s lifetime value and reduce attrition. For example, if there’s a customer X who’s been using the bank’s credit card services for multiple years, the bank can upsell a home loan to them at a special interest rate. Hence, by leveraging rich customer insights and segmentation, banks can with minimal effort upsell and cross-sell related products. This acts as an important lever for growth by directly contributing to the total revenue.

However, we cannot ignore the fact that even with data analytics and machine learning models, the number of customers who actually end up buying a product or showing interest is substantially lower. This is a huge problem for agents who usually are the ones who end up calling these customers. They end up wasting a lot of their important time. This is also one of the reasons why banks haven’t set up dedicated teams. 

One effective way to solve this is by doing a pre-qualification through a voice bot. Voice bots can call the customer and share the offer details. The bot can collect the interest level of the customer, get additional details required to process the offer and also answer common questions. By doing this pre-qualification, agents end up only speaking to customers who’re interested in the offer.

Not only does it save agent bandwidth but also increases agent productivity and reduces operational costs.

Friendly Payment Reminders 

Banks continually invest in resources and implement strategies to improve their payment collection rate. This is because even a marginal drop has a negative impact on their business and increases collection costs.

While often underutilized, the simplest way to ensure customers pay in a timely manner is by triggering reminders a few days before the repayment (be it credit cards or loans). This can be through different channels including calls, text messages and emails. By doing this customers can make repayments on time and avoid unwanted hassle and late payment charges. 

Banks can further increase the effectiveness of their reminders by using a voice bot. Unlike playing a recorded message, voice bots can allow banks to send personalized reminders, collect information and even help them to make payments in real-time. For example, if a user wants to make a repayment, voice bots can send a payment link on Whatsapp or text message. The voice bot can also help customers enable automatic payments or change the payment type. 

By enhancing the repayment experience, banks can significantly improve the collection rate and reduce collection costs. 

What’s Next? 

Banks have taken many rapid decisions to meet the changing customer needs. Be it ramping up security, digital banking capabilities or launching products that fit customer’s needs. This is the reason why they were so quick to adapt to the changes made by the pandemic. However, they need to continually innovate and launch new initiatives that focus on customer’s needs and their banking experience.

About Skit

Skit is an Augmented Voice Intelligence Platform, helping businesses modernize their contact centers and customer experience by automating and improving voice communications at scale. By enabling preemptive, intelligent problem solving and seamless live interactions, we have automated over 15 million calls for global enterprises across industries. We help our customers streamline their contact center operations, reduce costs, and also enhance customer experience and engagement.

Connect with us if you’re interested in learning more about the platform and how it can modernize and transform your contact center.

Behind the Scenes: Leveraging SLU to Improve Customer Service

In this age of information, the most important asset that enterprises rely on is data. With rapid improvements in data analysis and visualization techniques, it has become the norm for enterprises to leverage the power of data for streamlining and improving business processes.

However, what we don’t often realize is that contact centers can prove to be one of the most important sources of data for enterprises. The thousands of hours of call recordings are a storehouse of information for consumer attitudes, complaints, and feedback that enterprises can use to gain valuable insights. 

But how to go about it? The answer lies in the burgeoning field of speech analytics. 

Gartner says “Audio mining/speech analytics embrace keyword, phonetic or transcription technologies to extract insights from prerecorded voice streams. This insight can then be used to classify calls, trigger alerts/workflows, and drive operational and employee performance across the enterprise.”

Intelligent solutions like Skit’s Digital Voice Agent can not only handle customer service calls but perform advanced speech analytics in the very near future.

Using advanced Spoken Language Understanding (SLU) algorithms, the recorded speech in contact centers can be analyzed to extract crucial insights that can help enterprises streamline their performance.  

Read on to know more about the three ways in which speech analytics with SLU can help your enterprise.

Provide personalized services

With a continuous focus on innovation, Skit.ai has added the revolutionary “idiolect” layer to existing cutting-edge capabilities. In the world of linguistics, “idiolect” simply means the unique speech style of a group of people that differentiates them from other groups.

The state-of-the-art technology in the idiolect layer will enable VASR to perform advanced speech recognition and analytics to uncover more information about the speaker such as gender, age, language, and accent- and build a unique speaker profile.

Moreover, the application of certain SLU algorithms can help can further insight into the customer’s attitude and state of mind:

  • Sentiment Analysis: These algorithms can detect whether the customer’s attitude is positive, negative, or neutral during the call.
  • Emotion Detection: Such algorithms can help determine the emotions of a customer and their state of mind during the call.

With the combined help of unique customer profiles and SLU-enabled analysis of customer’s speech, it becomes easier to deliver personalized services to the customer- depending on their characteristics and current state of mind. 

Research by Epsilon has indicated that 80% of consumers are more likely to make a purchase from a brand that provides personalized experiences.

With hyper-personalized customer service experiences, you can keep your customers satisfied and reduce customer retention costs in the future.

Gather consumer insights

With multiple agents handling multiple customers in a day, it is not possible for agents to always correctly determine what consumers want or expect. Moreover, customers themselves might often be confused as to what they expect from a brand and what improvements they want in the service or product they receive. As Steve Jobs had once famously quoted:

It’s not the customer’s job to know what they want !”

However, it is crucial for any enterprise to determine the needs of their consumers to provide better services. With COVID changing consumer behavior and expectations, analyzing consumer insights can prove crucial to the path ahead.

“Businesses need to understand how this new world affects all of their touchpoints with the customer if they are to actively reinvent their own future and not be at the mercy of external events.” (PwC)

The advancements in research in Spoken Language Understanding have made it possible to use different techniques to derive important information from analyzing customer service calls. Some algorithms that can be used to derive such insights are:

  • Topic Modeling: This is a technique in SLU with which customer calls can be analyzed to create a list of natural topics that frequently occur in service calls and can help companies realize what services/products frequently need troubleshooting and have scope for improvement.
  • Text Summarization: The duration of calls might often be extremely long. With summarization algorithms, it can become easier to create summaries of calls that can be easily read through/analyzed for consumer insights.
  • Aspect Mining: It refers to a class of SLU algorithms that discovers different aspects or features in data, and along with sentiment analysis, can be used to determine the different sentiments associated with those features. For example, in a customer call, the customer may express a positive sentiment when it comes to pricing but a negative opinion on customer service quality. 

With easy access to consumer insights with SLU, enterprises can easily leverage them to make crucial decisions on how to improve business processes and products in a way that makes their customers happier.

Improve automated quality assurance

By harnessing the power of SLU, it not only becomes possible for Voice AI platforms to provide quality service but also to ensure that call quality is maintained at all times in contact centers- be it a service agent or a virtual agent.

Traditional QA teams depend on the right data to correctly analyze service quality and with contact centers handling an immense amount of calls, the process is bound to become time-consuming and even inefficient.

The use of SLU and speech analytics algorithms can provide structured insights by analyzing calls, which makes it easier for QA teams to act on those insights to streamline contact center processes for increased KPI metrics.

As brands continue to explore innovative ways of connecting with customers, they need to plug in AI technologies into their business processes to glean consumer insights that can be the driver to elevating customer experiences

Indeed, the future is undoubtedly bright for Voice AI platforms that can truly harness the power of Spoken Language Understanding. Even as we talk about these improvements, researchers are working to improve SLU and develop newer techniques that can have an even greater impact on Voice AI systems.

Behind the Scenes: Leveraging SLU to Enhance Customer Experiences

Voice-first platforms are here to stay and without doubt, they will play an important role in accelerating the adoption of technology across personal and commercial spheres. Users are growing increasingly comfortable with voice-first platforms as they are much more hassle free when compared to traditional written modes of communication, and this is reflected in consumer behaviour across industries.  

Data from OC&C Strategy Consultants shows that voice-shopping is expected to jump to $40 billion by 2022 from $2 billion in 2018, suggesting that voice-first platforms might be the next disruptive force in the retail industry.

Voice-activated virtual assistants like Siri or Cortana have become an integral part of our daily lives and enterprises have started implementing Voice AI platforms for enhancing business processes. 

There have been several recent breakthroughs in the field of Spoken Language Understanding (SLU) and this has enabled the rise of SLU-enabled Voice AI platforms that are capable of holding seamless human-like conversations.  



One of the industry sectors that illustrates a supremely successful use case for intelligent virtual assistants is the field of customer service.

“…businesses across industries are also aware of this on-going shift in the technology and customer behavior. In fact, many have already begun their voice journey and are transforming the way how customers interact with their brand.” (Trantor Inc)

With an increasing base of digital consumers worldwide, contact centers have been reeling under the pressure of ensuring good customer service while efficiently handling the immense call load that contact centers face. This has led to the adoption of Voice AI platforms for contact center automation- and advances in SLU have allowed such voice assistants to turn into quality customer service agents.

Here is how SLU-enabled voice AI platforms deliver superior customer experiences:

Increased Ease of Usage 

Since the beginning of human history, voice has been the primary mode of communication for people and has been around for much longer than written communication systems. It is no surprise that humans tend to be “voice-activated” naturally and find it much easier to interact with technology through voice commands.

Now, with rapid progress in AI-enabled speech-to-text and text-to-speech services, seamless voice-driven customer experience is a reality. As the ecosystem around voice enabled technology matures, customers are starting to rely more on voice. (PwC)

SLU has helped the development of such voice-first platforms, allowing your customers to easily connect with virtual assistant platforms in your contact center. 

Such platforms do not require your customers to trudge through the interminable IVR options, enabling them to easily express their concerns or queries in simple spoken language statements, leaving your customers happy about their experience with your brand.

Understanding the Customer

Spoken Language Understanding has taken voice AI to a new level with the ability to simulate a near-human understanding of speech by such platforms. SLU-enabled platforms don’t merely react to a fixed set of commands but rather use various techniques and algorithms to arrive at the true purpose of the customer in making the call.

  • Intent Recognition– No matter how customers frame their queries and statements, intent recognition algorithms are able to decipher the customer’s intent at one go using keywords or action words, without requiring multiple clarification from customers.
  • Named Entity Recognition– These algorithms extract important information from the customer’s speech to recognize important names, places or times that the customer talks about. 

All these SLU techniques have enabled voicebots to easily achieve human-like “understanding” capability that allows them to easily converse with the customer, eliminating the machine-like qualities from a conversation. 

Innovation in voice technology is reshaping consumer behaviour and brands need to pursue creative approaches to accelerate the adoption of Voice AI to align with customer expectations and maintain a competitive edge

Quick Query Resolution

In a world where each second matters, time is of the essence – for you and your customers. If they spend precious time on hold with your contact center while agents are busy, it can only be expected that customers will get frustrated with their experience and shift their loyalties to other competitors.

SLU enables voice AI platforms, which act as virtual agents, to easily access the required information from databases and respond quickly to customer queries. Intelligent solutions like Skit’s Digital Voice Agent have been shown to result in a 50% reduction in average handling time in contact centers.

Zendesk Research Survey discovered that 69% of respondents associated good customer service experience with a quick resolution of their issue. 

This will result in improved customer satisfaction and increased customer retention rates, translating into increased revenues and goodwill for your enterprise.

Consistent Service Experiences

Every time customers engage with your brand, they develop an opinion about the brand. To ensure that the impression your brand gives to consumers is excellent, consistency is key. No matter when and where your customers approach the contact center, their service experience needs to be consistent.

According to Forbes, 71% customers desire a consistent experience across any channel, but only 29% receive it.  About 76% receive conflicting answers to the same questions from different agents which leads to loss of customer confidence. 

Advances in SLU have enabled voice AI platforms to maintain a uniform dialog flow across the board in all customer interactions. From a standard welcome greeting to the last goodbye, everything progresses in a pre-planned flow which gives customers a sense of stability and familiarity. Every time they get in touch with your contact center, they know exactly what to do and how to do it. 

Customers will come to trust your brand as the reliable option and will increasingly engage with your enterprise and not your less-consistent competitors.

There are, thus, several ways in which SLU has enabled voice bots to deliver superior customer experiences that keep your customers pleased and induce loyalty in them. 

Keeping customers happy not only helps enterprises increase customer retention, but also helps reduce customer acquisition costs by increased word of mouth marketing and recommendations from loyal customers.

Why CX is the Next Disruptor in Financial Services

During the pandemic, financial services companies like brokers/AMCs saw a huge surge in the number of retail investors. According to Statista, Zerodha, India’s largest stockbroker, has added over two million users in 2021, more than twice compared to last year. 

According to Jonathan Craig (Senior EVP and Head of Investor Services, Charles Schwab), “A big part of this growth is Generation Investor — the large number of people who are bound together not by their birth years but by when they got started in their investing journey — who is now on a path to ownership and reaching their financial goals”. 

However, the pandemic is not the only reason for this growth; a simplified trading platform and low brokerage fees from financial services companies such as Robinhood and Zerodha have a lot to do with the increased surge in the number of retail investors, especially millennials & gen-z. 

While the increased growth has significantly boosted key metrics including revenue, it has also brought up multiple challenges like investor engagement and improving customer lifetime value. 

Technology is no longer the moat 

Platforms like Zerodha and Robinhood disrupted the market with their flat-fee pricing model. This helped them grow rapidly and stand out from their traditional counterparts. They also leveraged new-age technologies and made their platforms fast and easy to use making it very easy for first-time investors to get started. However, with traditional companies changing their pricing models and improving their user interfaces, the seemingly strong moat is evaporating.

With so many alternatives in the market, switching platforms has become incredibly easy. It takes 30 minutes to open a new account and the same time to switch to another platform. This line sums up the current market situation and the competition. 

Challenges that financial services companies are facing

Financial services companies customers’ can be broadly classified into two buckets – first-time investors and experienced investors. Since most of the new users are first-time investors, the biggest challenges are around them. For starters, companies face a hard time seeing regular engagement on the platform, mainly driven by poor financial literacy. This leads to a high number of dormant accounts, as a norm, in the industry.

So, how can companies tackle these challenges? 

The only way for financial services companies like brokers and AMCs to promote loyalty and increase customer retention is by providing a delightful experience across different touchpoints, every time. The hard truth is that CX will be the dark horse driving the growth along with technology. 

CX across different stages in the customer journey 

The pandemic did help attract users to the capital markets, however, engaging the user with the first investment and eventually retaining them is where the main challenge lies. Hence, it’s critical for securities companies to reimagine the customer journey from start to finish. 

For example, for the first investment, it’s important for companies to –

  1. Educate investors about the different products and services through interactive videos and webinars. 
  2. Provide them with suggestions according to their financial goals. 
  3. Nudge them intelligently over different channels to ensure they make their first investment.

In addition, they need to proactively support them and resolve their queries quickly. They’ll also have to monitor their behaviour and usage and tweak their communication strategy accordingly. 

Alternatively, for an active trader, their approach needs to be different. This is because their priorities are different. For example, in case of downtime, they can proactively inform customers about it rather than waiting for users to reach their support to raise concerns and ask additional questions. Similarly, they can keep them informed about important educational initiatives, newer products, and more. 

Voice AI and its role in CX 

The highest number of touchpoints when an investor usually interacts with a company is the inbound/outbound support centre, it should be an absolute priority for the winners to enhance this experience.

To enhance customer experience and improve customer engagement, several securities companies are adopting Voice AI solutions. AI voice bots that are powered using Voice AI are built using sophisticated and advanced Artificial Intelligence (AI) algorithms and have the ability to understand the context, intent, and hold human-like conversations.  

Let’s look at different ways securities companies can leverage it – 

Streamlining inbound support

No one likes waiting on the IVR.

But why do we even have the IVR in the first place? Can we get rid of it?

Yes, we can!

Compared to other industries, support queries raised by investors are more time-sensitive and must be resolved as quickly as possible. Customers cannot afford to wait minutes to get a response. However, with the increasing number of support queries (due to the surge in new customers) and limited bandwidth, securities companies are facing a hard time resolving them within the promised time. 

To fix this, companies can leverage AI voice bots. They can answer mundane support queries (like a/c status, upcoming SIP due date, current NAV, common questions around selling and buying stocks, etc) quickly while freeing up important agent bandwidth. 

This allows more time for agents to focus on solving complex queries thus increasing customer satisfaction. A win-win for both.

Engage investors

AI voice bots can proactively nudge customers and notify customers about exclusive offers at the right time in a personalized manner to ensure that they transact regularly on the platform.

83% of customers are willing to share their data to enable a personalized experience (Accenture report). 

Hence, since the new wave of users is first-generation investors (those who’re starting their financial journey), it’s very important to not only educate and constantly engage with them but also invest in crafting an exceptional customer journey, to retain their minds and wallet share. 

What’s next?

While there’s a huge growth potential for the industry, increasing competition coupled with low customer retention rates are a few of the many challenges companies will have to tackle for sustainable growth. The only way they can solve this is by competing on the CX front by completely reimagining their customer strategy and providing an enhanced customer experience across different customer touch points.

About Skit

Skit is an Augmented Voice Intelligence Platform, helping businesses modernize their contact centers and customer experience by automating and improving voice communications at scale. By enabling preemptive, intelligent problem solving and seamless live interactions, we have automated over 15 million calls for global enterprises across industries. We help our customers streamline their contact center operations, reduce costs, and also enhance customer experience and engagement.

Connect with us if you’re interested in learning more about the platform and how it can modernize and transform your contact center.

7 Reasons Why Debt Collection Companies Are Deploying Voice AI

In the new normal, key players in the debt collection industry, from creditors to every downstream collection agency, face significant challenges to improve collections. This is happening mainly for two reasons. First, there are rapidly evolving regulatory and compliance frameworks to which collection agencies must adhere. Second, the mitigation of cost has become an extremely uphill task.

However, there is an additional issue at play: The most common solutions prevalent in today’s market, such as Robocaller and outbound IVR voice blaster, are incapable of conversations, feedback, and insights. Instead, an AI-enabled Voice Agent is capable of meaningful and human-like conversations with customers.



Unlike the most common solution prevalent today, i.e. Robocaller or outbound IVR voice blaster (incapable of conversations, feedback, or insights), an Intelligent Voice Agent is an AI-enabled machine capable of meaningful human-like conversations.

Learn more about the differences between Robocaller and AI-powered Digital Voice Agent.

Why is an Intelligent Voice Agent Ideal for Collections?

Intelligent Voice Agent, which is the blend of conversational voice AI and human intelligence, holds me

The rapid rise in call volumes, defaults, demand for remote resolution of disputes and diminishing CX have resulted in collection agencies scrambling to catch up.

The need for better outbound collections efforts—along with managing increasing volumes of inbound inquiries from customers—is putting pressure to scale contact center teams, an undesirable and herculean task.

Call center turnover (30 – 45%) has always been a challenge and has generally been twice as high as the industry average (13.5 – 18.5%), while collection agencies perform worse, with some reporting as high as 100% employee turnover. The concatenation of these factors—higher call volumes, regulations, and agent turnover—has made companies lookout for technology solutions such as Voice AI-enabled contact center automation.

Read More if you are interested to know how Intelligent Digital Voice Agents work in detail.

Research provides plenty of information to support the cause of automating collection calls. Apart from research provides plenty of information to support the cause of automating collection calls. Apart from improved recovery, 1 in 4 US consumers prefers interacting with an Intelligent Voice Assistant when handling awkward financial situations, according to a 2018 consumer sentiment survey by The Harris Poll.

Solving Collection Challenges with an Intelligent Voice Agent

The rapid rise in call volumes, defaults, demand for remote resolution of disputes and diminishing CX have resulted in collection agencies scrambling to catch up.

The need for better outbound collections efforts—along with managing increasing volumes of inbound inquiries from customers—is putting pressure to scale contact center teams, an undesirable and herculean task.

Call center turnover (30 – 45%) has always been a challenge and has generally been twice as high as the industry average (13.5 – 18.5%), while collection agencies perform worse, with some reporting as high as 100% employee turnover. The concatenation of these factors—higher call volumes, regulations, and agent turnover—has made companies lookout for technology solutions such as Voice AI-enabled contact center automation.

Let’s compare the challenges collections agencies are facing to how a conversational AI-enabled Intelligent Voice Agent meets every challenge.

7 Reasons Why Augmented Voice Intelligence Is Transforming Debt Collections

Augmented Voice Intelligence, which is the blend of Conversational AI and human intelligence, creates meaningful conversations with customers to support them throughout their entire collection journey while staying true to compliances and regulations. Let’s delve deeper into the 7 core reasons:

1. Automation And Human Bandwidth Prioritization

The beauty of deploying an Augmented Voice Intelligence is that it can call all the customers and it then filters out the complex cases that need human agent intervention. In the present system, agents call the entire list of contacts, be it a simple case or a complex one, not creating desired value in the process.

With a virtual voice agent, all the contacts in the portfolio are called at the right time of the day and within a couple of hours. The entire portfolio is then segmented based on the disposition collected for each debtor. The dispositions captured can be: propensity to pay, refusal to pay, wrong-party contacts, disputed debt, call-back later, validation requests, etc.

For willing debtors, the virtual voice agent can not only collect the payment during the call but can also negotiate and offer alternate payment options. It also reminds them of the next due date. 

Additionally, the Digital Voice Agent calls back all the debtors who could not be reached in the first attempt without the need for human agent intervention. This takes a huge burden off them.

For the dispositions in which human intervention is required, the Voice Agent can segment the portfolio so that relevant human agents can be assigned the downstream tasks based on the importance of the disposition for the portfolio and the company.

This automation and prioritization of bandwidth unlock massive value for the collection companies.

2. Improved Portfolio Volume and Customer Coverage

If, let’s say, 66% of the debtors are handled by digital voice agents end-to-end, now collection agencies can take up 3X more portfolios or cover 3X more customers with the same set of human agents. This illustrates how the same support team can manage higher levels of business with even better results. 

Collection agencies can take up more portfolios or take bigger ones, as they now have better customer coverage.

3. Lower Cost and Faster Collection Speed

Contact center automation with Conversational Voice AI assistant ensures that service quality and speed remain consistent, which otherwise will be volatile as new human agents with less experience join the team. Also, continuous hiring and training is a great operational hassle.

The Digital Voice Agents can make hundreds of concurrent calls at scale, economically, and in just an hour. Not only that, voice agents, being a machine, are very punctual and reach out to debtors that request a callback or make reattempts right on time when the probability of connecting to contact is highest. All this is done within the prescribed compliance framework. 

4. Superior Recovery and Collection Efforts 

Better collection and recovery require persistent efforts. When nudged at the right time, a debtor who is willing but unable to pay now might pay a few months down the line. Thus, what matters is how persistently collection agencies can reach out to a certain segment of debtors, ideally disposed to pay.

Understandably, a significant section of debtors will not pick up calls in the first attempt or might request a call-back at a certain time in the future. It is near impossible for human agents to follow up on every single contact, but the intelligent voice agent can do it with perfection. 

It’s a piece of cake for a Digital Voice Agent to schedule follow-up calls, honoring the regulatory guidelines, spread over weeks/months, and ensure better recovery rates. With timely and adequate calls going out to customers, and 24/7 support, the right voice-tech solution checks all the boxes to improve collections and recovery. 

5. Minimize Errors, Ensure Compliance and Security 

A significant amount of agent training and monitoring can be avoided with the deployment of Voice AI agents. High employee turnover, clubbed with significant training costs makes the entire exercise of meeting compliance, extremely costly. While the possibility of potential errors as regulatory regime complications is on the rise, it cannot still be eliminated. 

Conversational Voice AI Agents operate with negligible errors and can be easily updated, thus improving compliance significantly. Also, a Voice Collection Agent can be well trained in regulatory frameworks and will therefore ensure strict adherence to consumer data security and protection (encryption and redaction) by sticking to industry best practices. 

6. Enhanced Customer Experience

A Voice AI agent can ensure a smooth, courteous, and positive debtor experience, leading to a positive attitude towards the collections process and ultimately a positive brand image. 

7. Seamless Support Scaling for Any Call Volume

Business volatility and fluctuations put an economic strain on collection agencies that need to maintain a qualified team of human agents which has to grow and shrink with demand volatility. Scaling becomes further challenging as employee turnover is the highest among industries.

With the deployment of Augmented Voice Intelligence, there is no need for maintaining a large contact center team to deal with large call volumes, as voice automation helps in handling a majority of calls. Thus the problem of team management becomes minimized.

Intelligent Voice Assistants: The Future of Agile Customer Service

At times of disruption, it’s essential to leverage technology to craft a sustainable competitive edge by addressing core business challenges.

Growing evidence hints at the power of Augmented Voice Intelligence to enable cost optimization, and handle a broader customer base while minimizing significantly the operational challenges relating to regulatory compliances, and team management. 

With a tad steep learning curve, it’s best to be an early bird. The evidence abounds, with the right tech solution partner, there is a great amount of value creation possible.

Move early, move fast, grow faster!

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Voice AI To Resonate With and Retain Customers

Customers dislike long wait hours for query resolution and chatbots aren’t suitable for emergency requests. To ensure better services, Voice AI-led solutions work best.

In 2020, the University of Texas at Austin conducted an interesting experiment wherein 200 participants were invited to reconnect with an old friend through either a phone call or email. Despite admitting that a phone call would be more effective, some participants chose email to feel less awkward. And expectedly, those who connected through a phone call were able to form a stronger bond with their friend. It is the overall interaction experience that counts, be it in personal or professional settings.

Whether you were able to communicate, whether the other party understood your feelings, whether any misunderstandings were cleared and whether in the future both parties will be able to reestablish a connection. In the customer experience journey too, brands have chosen to connect with users over multiple points. There is text messaging, email, social media support, chatbots and the customer care centres/call centres.

Depending on the type of query, each customer is redirected to the specific touch-point. For instance, a customer seeking a bank account statement can simply get it through their net banking application while another customer looking for term insurance policies can get information through a chatbot. But for queries that require detailed insights, say reporting or KYC-related changes, customers are redirected to voice-based customer service executives.

Voice is powerful and unique to human beings. Speech goes back to human beginnings, which is almost a million years ago. The Linguistics Society of America estimates that writing was invented around 3200 B.C. It is the voice that gave rise to text, words and other forms of written communication. Because interacting through voice comes naturally to humans, it is self-taught. It is also easier to communicate thoughts through voice than any other medium simply because it is also upto seven times faster than typing. This means that one can have a longer conversation using voice.

Wait times are long and Interactive Voice Response (IVR) may not be helpful for emergency requests. Imagine your credit card getting stolen. You call up the bank’s customer care, but it takes you two minutes just to get to the appropriate node.

Another five minutes in reaching a customer care representative. And the ordeal still isn’t over because the customer care executive puts you on hold to verify details. Total time elapsed: 12 minutes. By the time the query is resolved, your card has probably been swiped at half a dozen places. An immediate solution is critical to protecting the brand reputation of companies. Ignoring customer grievances can often cost a company its clients. A study by Qualtrics XM Institute in the US found that 53% of consumers have cut spending after a single bad experience with a company.

Customers also complained that they missed a responsive mechanism in grievance resolutions. The answer is obvious. Customers prefer voice-based real interactions because this resolves queries quicker. And the practical solution is Voice AI. Built on the strong backbone of AI and Spoken Language
Understanding (SLU), Voice AI uses human-like mechanisms to receive requests, interpret and provide solutions.

NLP is the technology that the system uses to learn, understand and provide content in human languages. Unlike other solutions in this space, Voice AI is evolutionary. It can adapt to different commands and languages as it learns ‘on the job’ like us humans. Since NLP is at its core, Voice AI first hears the customer speak, converts it to text, filters out the noise, and then processes it with its neural networks. Following this, the system finds out the context of the conversation using AI. Based on this, a response is created and then communicated to the user by a
human-like voice. For instance, an individual who has a chequebook reissue request will have a different state of mind than someone who lost his debit card. Voice AI systems will differentiate between these two grievances and offer immediate support.

How customer expectations evolved

Whenever a customer contacts a company, they expect an instant response. An insurance customer only looking to renew their car policy is inundated with information about new products offerings. The same goes for bank/NBFC customers, where needless personal loans are pushed by the systems during such calls.

The pandemic added to such woes of customers. A Harvard Business Review study showed that there was an over 10% spike in ‘difficult’ callsigns in just two weeks between March 11-26. Customers wanted urgent resolution for travel issues, insurance claims, and payment extensions. Here, having a Voice AI solution can not just improve productivity and efficiency, but also improve customer trust during crisis periods.

Market Potential

The Voice AI market is at a nascent stage across the globe. It forms a part of the conversational AI segment that includes voice assistants and chatbots. With customers more accustomed to conversing with voice-devices at home, the same has translated to preferences in a business setting as well.

Gartner estimated conversational AI platforms would have $2.5 billion revenue in 2020, with a 75% year-on-year (YoY) growth. This is built on the premise that speaking is the most natural form of communication that is only set to deepen further.

Data from research platform Allied Market Research showed that the conversational AI space could potentially touch $32.62 billion by 2030, registering 20% YoY growth between 2021-30. For transaction-heavy sectors like healthcare, Voice AI could help solve existing bottlenecks.

In insurance, for example, a Voice AI could guide a customer to immediately file motor claim requests. Gauging the customer’s reactions, a human-like AI system could help calm their nerves and send help accordingly. Additional requests like towing services and highway pickups could also be provided. Since Voice AI is a system that adapts by interacting with customers constantly, the sooner it is deployed, the better will be the user experience.

Global Power

As part of an endeavour to reach all customer touchpoints, brands have globally deployed text-based solutions. But the varying internet penetration and variation in literacy levels could prove to be bottlenecks in customer experiences.

Using text for communication would not be effective in this case, so voice AI for customer services works best here. When it comes to sectoral requirements too, voice could help reduce the turnaround time for financial requests. Chat is able to process a lot of these queries too, but eventually customers prefer the medium of talking for final resolution. The high wait-times at contact centres of all financial institutions is proof.

Picture this. An insurance customer on an international trip meets with an accident and has to undergo an emergency procedure. But the hospital states that the authorities will need to verify the policy terms or speak to an insurance company official before conducting the surgery.

Here, waiting for an IVR response would simply delay the process, while chatbots may have to reroute the query to seek confirmation. On the other hand, a Voice AI would be able to disclose the policy details after authenticating the customer’s KYC details.

Identifying customers based on Know-Your-Customer and personal contact details is the next phase of growth for Voice AI systems. Once a customer’s voice is recorded for a couple of transactions, this would be used for all future conversations to ascertain and authenticate that it is indeed the registered user who is contacting the company. This would be useful for leisure services as well. For instance, seeking a special child seat at a restaurant on arrival often leads to chaos. Sending in written requests seldom works. Here, having a Voice AI that can decipher the messages and relay them back to the restaurant for timely service can be effective.

Future of Businesses

There was a phase where automated messaging was touted as the most preferred form of communication. This changed when voice assistants started seeping into the system. Globally, urban consumers have gotten used to voice assistants at home through connected devices and smart speakers. In fact, the number of Indians using voice queries daily on Google is nearly twice the global average. Since voice is a popular choice for customers’ personal use, this automatically translates into similar trends for businesses, too. A Deloitte study said that by 2030 there will be a proliferation of voice-led technology across the globe and that 30% of sales will happen via voice by 2030. Through voice-led interactions, sales will not only be more intelligent, but companies will also be able to refer to these calls to investigate user complaints.

The premise is clear. Voice is intuitive, easy to use, and has a quicker turnaround time. For customer-facing companies, it is a technology that can no longer be ignored. What’s better? Employees stay happy too. Goodbye to calls from irate customers, abusive user messages, and long working hours during busy seasons. Voice AI could become their complementary solution and improve their quality of work as well.

In a world where emotional intelligence and personalised interventions hold more value than automated responses, Voice AI will spearhead the change. The ones who adapt quicker and deploy voice will be the real winners in the long run.



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Voice vs. Text: A Fundamental Difference in Approach

While chatbot vendors are now trying to offer an embedded solution that contains text and voice, these models cannot be clubbed into one platform.

By 2022, close to 200 million jobs would be lost globally due to the Covid crisis. A lot of those unemployed will need to make changes in their monthly payments like home loan tenure, convert their credit bills into EMIs, and remove value-added services. Such customers connecting with a company during an emotionally volatile state may not just be looking for a solution, but could also be seeking a sympathetic ear.

In such a situation, a vulnerable customer will prefer speaking to someone to differ payments rather than type a series of requests for each liability.

For instance, converting a credit bill into an EMI is one command that is executed after typing out a few details. Then comes home loan tenure increase, which requires another set of instructions. Here, it is faster and smoother for the information to be captured via voice.

Let’s take another example. A customer who lost a parent to Covid can file a death claim online. But speaking to a service executive who could empathetically listen to their concerns could soothe nerves during distress. Not only can the voice-led channel help minimise claim delays by specifying the exact documents needed, but the customer can also understand the formalities over a single call. We have read how the current customer service models are missing out on the primacy of voice. There is a perception in the market that having a single solution for text and voice will help bridge the gap. But simply building a voice solution over existing text solutions may hamper the user experience.

In customer service, voice is designed to understand the nuance and gravity of a request. This is true especially for emergency situations where customers may not have the time nor the mind space to sit and type requests like finding a network hospital or an unauthorised transaction through a bank account Trivializing voice and offering it as a ‘good-to-have’ solution by chat providers is counter-intuitive because voice is a specialized solution that encompasses the layers chat requires, plus catches peculiar behaviours like tone and pauses in speech. The demand for Voice AI has grown exponentially in the
past few years.

According to a report by Statista, the number of digital voice assistants is likely to reach 8.4 billion units by 2024. So, it makes sense that companies want to adapt to this growing trend.

Voice is convenient, especially because humans speak and perceive things differently over speech than text. For instance, an indecisive food-delivery customer who keeps changing his/her order may find it easier to finalise an order over voice rather than typing and selecting products. Having a voice conversation also enables them to make a faster decision on what food to order.

While the thrust is on ‘omnichannel’ presence by brands, deploying voice effectively could help resolve a lot of customer complaints across product and service categories. Being present across customer touchpoints is good, but resolving queries constructively and consistently on a single voice-led platform is better.

How is voice different from text?

Chabots follow a flow wherein the text input is fed into the spoken language understanding engine. This engine understands the input/query and decides on the next course of action. Based on the context of the conversation, the response is prepared in a text format but relayed back to the user.

Voice AI, on the other hand, has two engines specifically available to understand speech. One is a speech-to-text engine, and the other is an automatic speech recognition engine.

The last part of this process is the dialogue manager, which acts as the orchestrator of the entire conversation. This is the block that manages the flow of data among the above three blocks and the flow of the conversation. And all these processes happen within milliseconds over the cloud, so it is device agnostic.

The end goals of voice and text are also fundamentally different. Text is intended to resolve basic customer requests and redirect complicated questions to customer service personnel. For instance, a customer looking to book a restaurant table is able to ask multiple questions in one go through voice. These could be the waiting times at certain points of the day, the chef’s menu, and specific details about the dishes (ingredients, spicy, vegan alternatives, etc.). An added layer of benefit are newer concepts like paralinguistics being used in the Voice AI ecosystem. This involves communication other than spoken words, including tone, pitch, pauses, and gestures. For sales teams of customer-facing brands, this offers a tremendous opportunity to gauge a customer’s interest in the product and gauge their buy intent.

Once Voice AI determines who is more inclined to buy a product/service, additional time can be spent to explain to convince the customer. This essentially means that cross-selling products will be far easier and effective if these Voice AI solutions are deployed. Some sectors that could take advantage of this concept are hospitality chains, restaurants, and financial institutions selling retail products like credit cards and quick personal loans.

It is often noticed that customers need to be nudged to reveal information, a process that can be done effortlessly over voice. Say, a newly launched shoe brand wants deeper feedback on the products. Using the customer database, a caller could be contacted using Voice AI to seek a detailed response on the pros and cons of the shoes. A customer may like the product quality but may have found its pricing to be steep while another customer may be looking for newer colour options.

Customers seldom fill long review forms that are sent post-purchase, hence bringing voice into this equation helps in better assessment. Based on the collective feedback, companies will also be able to tweak their product offerings accordingly, leading to improvement in sales. Customers, too, feel satisfied that their opinions have been taken into consideration.

A clubbed solution isn’t effective

Voice is an ideal turf for AI to learn, evolve, and constantly upskill by taking due note of user sentiments and emotions. And the best part? The user doesn’t need to be able to write a language fluently. Voice AI provides the unmatched ability to interact through casual conversations.

Critical user feedback, including anger, can’t be spotted immediately on text. This is essential for companies involved in product development, where continuous feedback generation is the key to success. As stated earlier, chatbots rely on key terms such as bad, poor, or terrible to deduce that the experience is unsatisfactory. Voice, on the other hand, listens attentively to different users to understand their sentiments.

Vendors offering ‘text+voice’ combo products do not understand the performance requirements of Voice AI systems. Low latency or quick processing of data to offer the right answers is crucial. Right now, there seems to be a rush among brands to implement AI for customer service. But the key here is to operationalize a solution that is accurate and solves a given problem. The thing to remember is that context and slang change with geographies. They are different in different markets. This means each Voice AI system needs to be modified to suit the audiences in that location. This is where the expertise of market providers, such as Skit, comes in handy.

The emerging dynamics of voice

Voice works best for context-led conversations where tone and inflection can convey a response without using actual words. And as the technology develops, its use-cases have also been evolving. In areas like sales and product testing, Voice AI could be used to pitch the product better and sound more persuasive.

Customers are also more likely to interact for a longer duration with a Voice AI system that understands his/her specific needs. These conversations are also useful for training the internal systems and for conducting quality checks at a later stage. For example, a fintech company developing a buy-now-pay-later (BNPL) product could use an advanced Voice AI system to capture the purchasing patterns of a customer. Since it is responsive, the customer can also cross-question Voice AI on the relevance of the terms and conditions of the BNPL feature and default penalties.

And if the Voice AI notices that target customers are enquiring repeatedly about penalties, this can be relayed back to the brand so that its messaging can be tweaked to include the terms upfront.

Here, deploying voice to recognize and identify customer details will help prevent such risks. This is because the AI can identify regular pauses and also spot any nervous tones indicating the presence of fraudsters on the call. Psychological concepts like entrainment could be complementary to the existing services where customer interactions can be improved. For instance, an angry customer could be pacified through Voice AI speaking in a calmer voice tone. Similarly, if a customer will be understood even if he/she switches to a different language midway into the call.

Voice solutions are getting richer. While a lot of vendor solutions already exist in the market, specialized products are far and few in between. A Voice AI product that is constantly tested for different use-cases across sectors is what will be suitable for commercial use. In markets like the US where financial frauds lead to brands losing millions of dollars in revenue and also reputation loss, Voice AI could come handy in adding a layer of voice-led authentication.

Here, deploying voice to recognize and identify customer details will help prevent such risks. This is because the AI can identify regular pauses and also spot any nervous tones indicating the presence of fraudsters on the call.

Psychological concepts like entrainment could be complementary to the existing services where customer interactions can be improved. For instance, an angry customer could be pacified through Voice AI speaking in a calmer voice tone. Similarly, if a customer will be understood even if he/she switches to a different language midway into the call.

Voice solutions are getting richer. While a lot of vendor solutions already exist in the market, specialized products are far and few in between. A Voice AI product that is constantly tested for different use-cases across sectors is what will be suitable for commercial use.



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