Data analytics

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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02
Mar
Talkbot Basics  ·  Voice AI Technology
Build richer customer profiles by unlocking hidden preferences with AI
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

The surge of interest in artificial intelligence (AI) brought about by the launch of GPT-3 – the engine behind OpenAI’s ChatGPT bot – has gotten more businesses exploring possibilites in AI technology. One of the more obvious benefits of AI technology is its ability to analyze large amounts of data quickly and accurately. Voice AI pushes this boundary even further by converting unstructured voice data into structured datasets.

Voice AI can capture a wide range of data points, including the customer’s tone of voice, sentiment, and the specific words or phrases they use. This data can provide businesses with insights into customer preferences, needs, and behaviors, depending on how deep and well you dig into the information. One key ingredient in unlocking and enhancing customer interactions is what the AI training world calls intents.

Voicing out customer intents

Intents play a crucial role in AI technology, particularly in voice AI systems. Intents are essentially the goals or purposes of a user’s spoken or written input. They can be signalled by specific words or picked up contextually, and are used by AI engines to determine what action should be taken in response. In other words, intents help AI systems understand what a user wants or needs, allowing them to respond appropriately.

For example, if a user asks a voice assistant to play a song, the intent of their input is to request music playback. The voice assistant’s AI technology recognizes this intent and responds by playing the song that was mentioned. Similarly, if a user asks a bot for recommendations on a particular category of product, the intent of their input is to seek advice on product choices. The bot’s AI technology recognizes this intent and, taking cues from previous interactions, responds with personalized product recommendations.

Intents are typically defined using natural language understanding (NLU) models, which are trained on large datasets of human language to identify and classify intents accurately. NLU models use a combination of techniques, such as machine learning algorithms and semantic analysis, to interpret the meaning behind a user’s input and identify the intent accurately.

Getting intents right enables businesses to provide more accurate and personalized interactions between users and machines. By understanding users’ intentions, AI systems can provide more effective responses, improving the overall user experience and increasing user engagement.

Discovering what customers like

Voice AI can discover hidden customer preferences by picking nuances and taking note of them into customer profiles. These extra data points can help paint a more complete picture of customers, enabling businesses to better personalize customer journeys. Here are some ways voice AI engines can pick up hidden cues from call data.

  1. Tone of voice analysis – Just as human agents can sense a customer’s emotional state through their tone of voice, voice AI engines can also be trained to pick up these signals through changes in pitch or speed of talking. When a bot detects that a customer is agitated, it can switch to a more soothing or calming tone to reassure customers. If the customer shows a persistent pattern of getting agitated, this can signal a preference to speak with a human agent. Talkbots can use mellow speaking tones during initial interactions, then hand off the interaction to a human agent who has high empathy and emotional intelligence.
  2. Sentiment analysis – Voice AI technology can analyze the sentiment of customer interactions, categorizing them as positive and negative experiences based on how the customers respond. This information can provide businesses insights into areas where they need to improve, as well as how, when, and in which channels customers prefer to be spoken to.
  3. Natural language processing – By leveraging natural language processing, AI can attribute meaning to customer statements, even when they use colloquial language or non-standard grammar. As bots engage with customers, the AI engine begins to develop its understanding of certain expressions and can classify specific words or phrases as having positive or negative connotations. As the patterns are strengthened through various interactions with different customers, the bot can then refine its future interactions.
 

Building better customer profiles with data

All these tools enable AI assistants to collect data from every interaction. While humans may struggle to record, analyze, and interpret vast amounts of data from call recordings, voice AI engines can transcribe, process and analyze customer interactions in a matter of minutes. This analytic capacity enables businesses to identify patterns and trends in customer behavior that may not be picked up in a timely manner, or even altogether missed.

The ability of voice AI engines to collect data on customer interactions is proving to be more and more useful in building up robust customer profiles. helping build more robust customer profiles. This, in turn, helps create more personalized customer experiences and tailored marketing, which ideally improve customer satisfaction.

We are just starting to build our capabilities in AI technology. As large language models continue to become more sophisticated and AI neural engines more accurate, intent recognition will continue to improve. Ultimately, this will lead to even more seamless and personalized interactions between users and machines, creating experiences that customers will not just enjoy, but also remember.

Capture even more information from customer engagements with WIZ.AI Talkbots. To learn how our data analytics can help you understand your customers better, schedule a call with one of our consultants.
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13
Jan
Talkbot Basics  ·  Voice AI Technology
How to transform voice data into actionable insights

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

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

Unlocking the power of voice AI

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

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

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

Continuous improvement from data analytics

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

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

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

Data-driven decisions for personalized experiences

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

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

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