Digital transformation

22
Mar
Talkbot Basics  ·  Voice AI Technology
Transform your operations with voice AI technology
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

Events in recent years have accelerated digital transformation across the globe. More than ever, businesses see the importance of leveraging digital technologies to stay competitive in today’s rapidly evolving landscape. These same businesses are embracing new digital tools to improve operational efficiency, enhance customer experiences, and drive growth.

One of the key technologies driving digital transformation is artificial intelligence (AI). With this technology, tasks that traditionally require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data, can now be – to some degree – outsourced to machines. From automating repetitive tasks to delivering personalized customer experiences, AI is transforming the way businesses operate and engage with their customers. In particular, voice AI has emerged as a powerful tool for improving customer experiences and optimizing business processes.

Here are some of the benefits of voice AI in three specific business areas – sales and marketing, customer service and support, and personalization and user experience.

Voice AI for sales and marketing

Adopting voice AI solutions can have significant impact in sales and marketing. Conversational AI can be used to power voice bots that can engage with customers through voice calls. In the case of WIZ Talkbots, these conversations can even be initiated in the user’s local language.

Voice AI-powered bots are especially useful in handling large volumes of outbound lead generation calls. Through automation, Talkbots can handle up to 10 million calls in an hour, exponentially growing a brand’s reach through telemarketing.

Businesses can also improve customer satisfaction and loyalty through voice AI adoption, thanks to the rich data which AI engines can extract from recorded calls. Analytics from voice data can help brands identify customer preferences and tailor product recommendations and marketing messages accordingly. This can, in turn, help businesses increase their conversion rates and generate more revenue.

Voice AI for customer service and support

AI-powered Talkbots can also be used to answer and triage inbound calls, providing customers with instant assistance and support. With voice AI, businesses can provide their customers with 24/7 support without the need for human intervention. Voice AI can handle a range of customer service tasks, from answering simple queries to helping customers troubleshoot technical issues. This can significantly reduce the response time for customer queries and complaints, leading to improved customer satisfaction. Furthermore, voice AI can handle a high volume of queries simultaneously, reducing the need for human agents to handle simple and repetitive queries.

Voice AI in customer service can also provide more personalized support. By analyzing customer data and behavior, voice AI can identify individual customer needs and preferences, and provide customized solutions. This level of personalization can help to build a strong relationship between the customer and the business, leading to improved customer loyalty and retention.

By providing round-the-clock support, personalized assistance, and quick response times, voice AI can help businesses to meet the evolving needs and expectations of their customers.

Voice AI for personalisation and user experience

The previous use cases have shown how voice AI technology can drive hyper-personalization. But there are other instances where personalization can come into play. Voice AI has the ability to adapt to different contexts and situations, providing more appropriate responses based on the user’s location, time of day, and other contextual factors. This helps build a more seamless and intuitive experience and a stronger connection between users and brands, leading to improved customer loyalty and retention.

In general, leveraging analytics from voice data can help businesses to create more engaging and intuitive interactions, leading to improved customer satisfaction and loyalty.

Voice AI for general operations

Voice AI is not limited to the above scenarios, and can be used to optimize a lot of internal business processes. For example, it can be used to automate repetitive tasks such as call transcription, tagging, and data entry, freeing up employees to focus on more complex tasks. In almost all use cases, voice AI can help businesses to increase their revenue, improve customer satisfaction, and optimize their internal processes. By leveraging the power of voice AI to deliver personalized experiences and engaging interactions, businesses can stay ahead of the competition and meet the evolving needs and expectations of their customers.

Voice AI can be a key step in the digital transformation of your call operations. If you need more guidance, speak with one of our business executives today.
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22
Dec
Talkbot Basics  ·  Voice AI Technology
Spot and tag credit risks with voice AI technology
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

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

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

 

How AI technology is transforming risk management

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

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

 

Detecting intent with natural language understanding

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

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

 

Using data analytics to inform strategy

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

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

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

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

To find out other ways AI can improve your processes and build smarter customer engagements, speak to one of our specialists today.
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