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Google has been supporting enterprises with a whole host of productivity-enhancing software for years now. From Gmail to Google Docs to Hangouts and your Google Drive, it’d be difficult for many of us to live without their tools. But did you know that Google is also heavily invested in technology for niche markets? 

And, did you know that Google has its own conversational AI platform that is specifically designed to support call centres? Simply named Contact Center AI, it’s built to drive efficiency and next-level customer experience. 

Before you write this off as something too advanced for your operation, take yourself back 16 years ago to 2004 when Gmail was first released. Not everyone jumped on the Gmail train then, but look at how ubiquitous the email service is now. 

The same is true when it comes to AI for contact centres. Take a look below at how this cutting-edge technology can be put to use today in order to set your business apart from its competition. 

Case Studies: How to Use AI for Contact Centres

While Google’s application of AI for contact centres is one example, there are a few others in the marketplace that are worth showcasing. Conversational AI is the baseline technology that is deployed differently by each engine, like Google’s Dialogflow or IBM’s Watson. The conversational AI platform enables the chatbot uses to first train on a data set and then to converse with the end-user. In the case of contact centres, the conversational AI could be trained on past call transcriptions, and as it’s in use for longer periods of time, it automatically gets better, maximizing your upfront investment in the technology. Here are two case studies specifically on how to use AI for contact centres.

1. Streamline Call Quality Assurance with Conversational AI

National Debt Relief (NDR) decided to invest in Observe.AI’s conversational AI platform to streamline its call centre quality assurance (Audiotex Update, 2021). 

The AI Solution:

Observe.AI used conversational AI combined with automatic speech recognition and Natural Language Processing (NLP) to analyze 100% of their contact centre calls. Traditionally, only a fraction of calls could be analyzed from recordings by people, because the amount of data was simply too large. But now, there is full transparency into the contact centre’s calls.

The Result:

By streamlining their processes and having the conversational AI software record, transcribe and decipher the calls, NDR has been able to improve its frontline coaching for agents. The massive amount of data collected ensures that training is focused where it’s most needed, and agents are now armed with the training they need to provide excellent customer service. 

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2. Bring Automation to Customer Service

One start-up has developed a conversational AI platform specifically for contact centres in order to use automation to empower the agents already in place. PolyAI was created in the UK with a small group of engineers from Cambridge’s Dialog Systems Group (NewsRX LLC, 2019). 

The AI Solution:

PolyAI deploys enterprise-ready voice assistants, based on proprietary machine learning and Natural Language Process (NLP) technology. The conversational chatbots can scale seamlessly and can even detect and converse in many world languages. Similar to the use case above, PolyAI listens to the calls received and learns to provide improved responses. When there are too many calls for the human agents to tackle, the automated AI chatbots can handle calls themselves.

The Result:

PolyAI recognizes that excellent customer service in a call centre, and the resulting CSAT scores, can only be achieved with human customer service agents. So the AI chatbots are there to assist, solving routine issues when customers call in and leaving the more complex issues to the experienced agents. The Co-founder and CEO of PolyAI, Nikola Mrksic emphasizes that “AI agents are not a replacement for the human touch, which is essential for great customer experience. However, automation is key for changing the economics of a contact centre” (NewsRX LLC, 2019).  

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Planning for the Future using AI in Your Contact Centre

The case studies show just how useful a chatbot running on a conversational AI engine can be for increasing your CSAT scores and improving your overall operational efficiency. That’s reason alone to look at how a chatbot could help you put in place money-saving systems now. 

As some businesses take a more cautious approach to a post-COVID world, spending on certain customer service and marketing tools will be limited. But if the technology helps to reduce operational costs, offsetting the investment, and creating for a long-term financial benefit, then companies have a solid business case supporting AI for contact centres. 

On top of that, a conversational AI chatbot designed exactly for your particular business type and contact centre use case will deliver the largest ROI for your investment. Spending on technology can be seen as risky in some industries and during certain unstable time periods, but a chatbot is actually a low-risk way for you to start figuring out how to put this technology to use so you stay ahead of the curve.

You are in control of the chatbot’s roll-out, and using an agile approach, The ChatC Group advises that you design and release the software in stages. This also saves you time and money, because you focus on the minimum viable product, validate the use case you’re working with on a small group of users, and then commit to developing the chatbot for a wider release to your contact centre customers and agents. 

When you work with a chatbot consultant, rather than contracting out the project to one of the players described in this article, you also ensure you have someone negotiating on your behalf. Our team of seasoned experts knows which conversational AI platform you need and how to get the best results for your budget. If you’re curious to learn more about us, book a call! We love to chat about how to put AI to work for your contact centre.