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4 Ways AI Can Improve Customer Experience Within Contact Centers

Elizabeth Sullivan-Hasson
November 7, 2018

4 Ways AI Can Improve Customer Experience Within Contact Centers

Gone are the days of having quick, polite, and ultimately superficial customer interactions. The people have spoken, and what they want is personalization. It’s no longer enough to make sure your agents are hitting target KPIs such as average handle time or average wait time – although these metrics are still important – and call it a day. The customer service industry has realized that customer experience (CX) is king.

Enhancing CX is one of the best ways to set your business apart from the rest. It’s likely to reap higher ROI too. As of 2017, 81% of companies surveyed in Dimension Data’s Global Customer Experience Benchmarking Report identified customer experience as a key differentiator, and 79% observed the cost-saving effect of investing in CX.

However, customer service companies, like contact centers, still have a ways to go in delivering quality customer experiences.

In their State of the Connected Consumer report in which they surveyed over 7,000 global customers and buyers, Salesforce identified that 66% of consumers and 65% of business buyers will switch companies if they do not have personalized experiences with companies – or vendors.  A recent CX study also discovered that 58% of customers feel as if they are frequently receiving impersonalized, scripted information. And 60% stated that they were frequently asked to repeat themselves. According to the Salesforce report, speaking with agents who knew their history with the company was listed as very important to 68% of customers.

Luckily, the customer service industry – including contact centers – is in the middle of getting a CX makeover. The combination of increasing digitization of customer interaction and advancements in artificial intelligence have spawned multiple innovations in personalized customer care.

Going digital 

Customer service has traditionally taken place over the phone, person to person. But this has been changing rapidly in recent years, and the trend is likely to continue.

The first seismic development was an increase in the number and variety of communication channels available. Customers no longer have to call an agent. Now, they can message them on Facebook – or email, live chat, video chat, fax, text, or tweet at them. Not all contact center software vendors offer the full range of omnichannel capabilities, but most are either developing or have already developed them.

Digital channels have become an increasingly popular means of communication among consumers. More importantly, 75% of customers now expect consistent interaction experience across all communication channels.

What does all this mean for customer service providers?

It means that customers are more engaged, plugged-in, and aware of their own treatment and experience across digital channels than ever. Consumers now expect a certain level of personalization and consistency no matter what platform they are using to interact with agents. Thankfully, artificial intelligence (AI) is rising to meet this challenge in CX hubs like the contact center.

AI in the customer service industry

The second major development in recent years has been the application of AI capabilities, such as machine learning and natural language processing, within the customer service industry. AI use has been forecasted to increase rapidly over the next few years; according to Gartner, 25% of customer service and support businesses will be using virtual customer assistants (VCAs) – or chatbots – by 2020.

There is extremely high potential for machine learning (ML), natural language processing (NLP), and VCA technology to streamline contact center activity and help produce happier customers. Below are 4 key ways that AI technology is making contact centers more efficient and enhancing the quality of customer services.

1. Virtual customer assistants (VCAs)

Also called ‘chatbots’, this technology can help both customers and agents in a couple of ways. VCAs can help resolve more routine customer requests which will free agents up to focus on more complex issues. VCAs can also handle simpler elements of customer requests, within the context of live agent-to-human conversations, to streamline customer interactions.

2. Machine Learning (ML)

Machine Learning technology is used by chatbots but can also be used in other contexts to assist live contact center agents. ML capabilities can help agents personalize customer interactions by prompting live agents with intelligent recommendations based off a customer’s history, such as brand, product preferences, and past issues. These intelligent suggestions can help agents better understand what customers want and need and cut down average handle time (AHT).

3. Customer Interaction Analytics and Natural Language Processing (NLP)

AI led customer analytics can collect, interpret, analyze, and share customer data across interaction channels. Natural language processing capabilities (NLP) can also be used to conduct ‘sentiment’ analysis where the AI software can detect elements of frustration and anger on voice calls and escalate calls to live agents if necessary. Real-time speech analytics (RTSA) help with call quality assurance and finding relevant information to give to call agent mid-conversation. Both of these contribute to making present and future conversations more enjoyable for consumers.

4. Intelligent routing

AI can also upgrade, or potentially even replace, the standard interactive voice response (IVR) systems that direct customers through menu options and route calls. IVRs typically allow customers to listen to a set of menu options and select the number associated with the option that most closely matches the reason they are calling. However, many customers often find that the specific question or issue they have is not included in the standard options. Intelligent routing enhances standard IVR by allowing customers to say out loud what they are calling about. The software then analyzes their statements and directs them to the correct agent or resource. Intelligent routing also has access to past caller history and behavior which determines whether the caller is handed off to a VCA or a live agent.

AI for Contact Center Offerings

Artificial intelligence is quickly becoming indispensable in the contact center, and most vendors are either offering complete AI solutions for contact centers or the ability to integrate with AI tech.

One very recent example of a complete contact center AI solution is Google’s Contact Center AI product. The software was released in July 2018 and adds three core AI functionalities to the contact center software tool belt with its Dialogflow feature: VCAs, AI assistance for human agents, and contact center analytics.

Shortly after the Google released their Contact Center AI product, other well-known vendors in the space such as Genesys, Cisco, Mitel, Twilio, RingCentral, and Five9 announced integrations with the new Google product.

Summing up – CX is King and Robots Will Save the Day

Even though this may sound like a C-grade remake of some bizarre sci-fi flick – these predictions may not be that far off base.

One thing consumers have made clear is that businesses everywhere need to reevaluate how they are interacting with their customers. The CX landscape is evolving, and the shift towards personalization and digitalization is well underway.

The degree of care that a typical customer expects from companies is quickly becoming too much for the individual agent to handle on their own. They are expected to automatically know each individual’s history, preferences, current issues, and make highly informed decisions based on these factors within seconds. Sound overwhelming yet?

This is where AI will continue to shine. There are concerns that VCAs will replace human contact center and call center agents altogether. The reality paints a very different picture. AI promises to enhance agent performance and make human-to-human CX interactions more efficient, productive, and enjoyable.

About the Author

Elizabeth Sullivan-Hasson
Elizabeth is a Research Analyst at TrustRadius where she focuses on tracing the evolution of business software and finding new ways to synthesize and visualize reviewer data. Elizabeth has a BA in Economics and Political Science from the University of Massachusetts Amherst and an MSc in International Development from the London School of Economics. When she’s not in the office, Elizabeth enjoys exploring new cities, hiking, trying out new recipes, and diving into sci-fi novels.

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