Conversational AI — 3 ways it’s changing customer service

The difficulties imposed by the pandemic have led to a serious rethink of the traditional call center — conversational AI is playing a big part in the solution.
19 November 2020
  • How can conversational AI platforms overcome the deficiencies of the traditional contact center next year?

As future-forward as it might sound, conversational artificial intelligence (AI) has been semi-present in our lives for years now.

We’ve even been carrying it around in our pockets. Apple’s Siri, Google Assistant, or Amazon’s Alexa are now commonplace virtual assistants within our mobile devices that also happen to be powerful conversational AI tools capable of capturing voice input, processing the data, and providing navigation re-routing to help us find what we’re asking for.

The ‘AI’ aspect of the tech means it learns and improves the more it’s used, and this is why business technology leaders are so interested in its potential to enhance customer service, in a way that’s both convenient for the customer, and time-saving for the business.

The difficulties imposed by the pandemic have led to a serious rethinking in the traditionally call center-based customer service economy. These crowded comms hubs were shut down by social distancing at a time where customer service and clarity was required most.

In place of these designated sites, a market has emerged for cloud-based or distributed contact centers and unified communications solutions.

And conversational AI has a key role to play here, not just in continuing to support businesses and their customers throughout the remainder of the pandemic, but in providing innovative, longer-term solutions that can enhance the customer-brand interaction in the long run.

Increase customer engagement, decrease costs

Traditional IVR (Interactive Voice Response) systems in contact centers become expensive and outdated to maintain as they are not compatible with newer system migrations, such as a shift to cloud.

Operational costs can go up, while archaic IVR struggles to keep up and wait times become longer, dragging customer satisfaction levels down at the same time.

But a cloud-based, conversational AI platform today is capable of leveraging advances in AI, machine learning, voice recognition, and natural language processing (NLP) to more accurately comprehend the customer’s queries and to respond in a more natural way. The AI and machine learning capabilities will even learn from earlier errors, improving its responses for the next interaction.

In the past, more staff would have to be hired to increase the operational output of the classic call center, driving up costs at the same time. With an intelligent, intuitive AI platform there is lesser need to keep hiring and adding to the expenses.

Instead of hiring more, existing CX staff can now be prioritized to handle higher-level queries, keeping customers engaged, and streamlining operational efficiency.

Conversational service automation

Conversational service automation, or CSA, is a combination of overlapping categories such as data analytics, conversational analytics, IVR systems, voice bots, security, robotic process automation (RPA) and customer feedback history, working together in real-time. This solution optimizes and drives both automated human to machine interactions as well as direct conversations between contact center agents and customers.

Covid-19 has been a catalyst for the uptake of new technologies, and in the context of voice and CSA, this is no exception. A global Uniphore survey found that over 42% of the respondents had recently reached out to a contact center to resolve Covid-19-related issues such as travel, employment, insurance, medical or financial matters, but unfortunately many could not handle the influx of calls.

More than 43% of respondents could not speak with a representative after calling a helpline, according to the survey. Some 40% of respondents also noted that they did not even receive helpful information when finally reaching a healthcare call center.

However, using a combination of conversational AI, data analysis, RPA, and workflow automation, one company that adopted a CSA platform said it had improved the productivity of more than 1,000 of its agents with an 80% reduction in after-call work and a 20% reduction in average handling time – channeling how conversational AI can help manage routine but essential tasks, and supply measurable business value too.

Conversational AI is still improving

Contact center AI is still evolving, thanks to advances that are still iteratively improving including in AI, neural networks, processing power, and speech recognition – itself a multibillion-dollar software market, according to Global Market Insights.

The report indicated that the rapid adoption of advanced technologies such as AI and machine learning are driving the demand for voice-enabled smart devices across the market, contributing to an ever-growing dataset on which to train recognition and voice generation algorithms.

Add to that machine learning and AI progress that has made it possible to not just recognize language patterns but to deduce context and to build relationships, improving the quality of the CX. Additionally, cheap and reliable high-speed networks have made it possible to offload processing to the cloud, enabling rapid “understanding” of peoples’ intents.

The compounded benefits of AI-driven contact center solutions is looking highly promising for next year, and is one of the reasons the international conversational AI market is expected to grow at a compound annual growth rate (CAGR) of nearly 22% between 2020 and 2025.