Using AI to Boost Agent Performance and Customer Experience
Imagine a call center experience in which your concerns are addressed by the first customer service agent with whom you speak. Where agents know you by name, and make recommendations and take courses of action that are highly relevant and personalized. Thanks to artificial intelligence (AI), that’s possible today.
AI is a major business disruptor, adding value and creating new opportunities for all types of industries, and speech analytics is no exception. The fast-growing speech analytics market is well-positioned to leverage AI technology and enable contact centers to unlock customer insights from the billions of calls made every year. These capabilities could not have come at a better time. It’s estimated that the global contact center industry will hit $481 billion by 2024, according to Global Industry Analysts, Inc. The availability of AI and other digital technologies will play a key role in improving the capabilities and responsiveness needed to support this growth.
AI has had a major impact on the global contact center marketplace, especially in the realm of speech-to-text (STT). Together with today’s cost-effective, lightning-fast GPU processing capabilities, it allows companies to retrieve 100% of the audio from contact center calls without compromising quality and accuracy. This provides the metadata needed to fuel more intelligent speech analytics, enabling companies to better understand their customers. Armed with this knowledge, companies can improve customer experience, reduce customer effort and increase brand loyalty, which will help companies grow their businesses and bottom lines.
The Added Value of AI
Today, AI-driven analytics is bringing cognitive capabilities to speech analytics, enabling companies to identify customer behaviors and buying patterns, and predict potential churn, among other activities. It also enables companies to gain insights into agent performance to improve the customer experience and ensure compliance.
Following are some of the ways that forward-thinking companies are applying AI to their contact centers’ voice interactions:
Enabling immediate analysis of agent performance
Previously, contact centers had to wait days for calls to be transcribed before they could be analyzed by managers for post-call analytics. The advent of real-time STT technology, aided by faster and less expensive GPU processors, now enables contact centers to conduct real-time analytics – during a conversation. In addition to enabling managers to give agents immediate feedback to improve customer experience, the technology can also flag potential compliance issues and provide suggestions to agents in real time, such as reminding them to disclose mandated information. Now, an agent’s screen can be populated with information, such as FAQs, to help him/her provide the right data at the right time to more effectively address customer issues as they are happening and increase first call resolution.
Understanding customer sentiment and emotion
Knowing what a customer is actually saying is critical – but it’s also important to know the customer’s intent behind the words – in effect, how he/she is feeling. Using AI technology, contact centers can now conduct sentiment and emotion analysis to recognize, for example, when someone is getting angry by the tone of voice, and take actions to alleviate the customer’s concern, such as providing an added service or promotional offer.
Predicting customer behaviors and outcomes
Before the availability of AI as an analytical tool, contact centers were limited to using descriptive analysis, which relied upon people to find the root causes of potential issues and to make inferences from the data. Today, contact centers are using predictive analytics to understand what might happen in the future. This form of AI uses statistical models and AI algorithms to not only identify patterns based on historical data, but also to correlate them with current customer interactions to predict the likelihood of specific behaviors and outcomes. While this type of statistical analysis always was able to be accomplished manually, doing so took much more time and was inefficient.
The accuracy of all of these AI-driven insights, however, is critically dependent on having access to all contact center data instead of just statistical sampling. In addition to aggregating historical data, contact centers can integrate data from CRM systems and use individual customer data to help determine potential outcomes. For example, based on an individual’s past behavior as well as aggregated data, predictive analytics can determine if the caller is likely to buy something, and if so, what type of incentive would be most effective. Conversely, by predicting that someone may be likely to cancel services, contact centers can identify the potential issues early on and address them to help prevent churn.
Voice biometrics is another form of AI that is used by contact centers to identify callers by their voice. This is a very useful tool for identifying potential fraud. For example, if known fraudsters call into a contact center, they will be denied access to a customer’s account if their voice doesn’t match the contact center’s database file.
Additionally, voice biometrics can greatly enhance customer service. For example, by identifying a specific customer by his/her voice immediately at the beginning of a call, an agent can retrieve a customer profile and offer VIP service or effectively respond based on previous interactions.
Providing recommended actions
Using prescriptive analytics, contact centers not only can identify the problem, but also suggest the best course of action to address it based on the probability of achieving positive outcomes. For example, the technology can suggest a specific approach to encourage retention based on what has worked well from historical data or recommend an offer based on its popularity with other callers. Prescriptive analytics helps contact center agents make better decisions that can enhance the customer experience.Using AI to provide key and actionable insights into customers is not only a matter of good customer service but also good business, since customer service is responsible for losses of $75 billion annually when handled poorly, according to NewVoiceMedia. Speech analytics, augmented by AI, can open up a world of possibilities to get closer to the customer and improve the contact center experience -- always a sure bet for happier customers along with increased opportunities for business growth.
Voice can expose even the best poker face. When we turn our complete attention to voice, it turns out that we can more accurately predict other people's emotions simply by listening.
14 Aug 2019
Much as developers rushed to put new apps onto Apple's App Store in 2009, we're seeing a bit of a gold rush to develop new skills for voice assistants. But developing for a VUI is an entirely different challenge.
07 Aug 2019
Grand Studio Lead Designer Diana Deibel discusses best practices for culturally inclusive access in voice UI design in this clip from her presentation at SpeechTEK 2019.
12 Jul 2019
Conversational Technologies Principal Deborah Dahl lays out a plan for making more virtual assistants more effective in this clip from her keynote at SpeechTEK 2019.
28 Jun 2019
Advances in conversational AI are removing those barriers to adoption. That, coupled with changing consumer behavior, is causing speech to become the preferred interface for self-service applications.
26 Jun 2019
Wolters Kluwer Health turned to VUI to keep patients engaged, on-track and, ultimately, healthy. Along the way, the team also discovered empathy in technology.
19 Jun 2019