Finding the Right AI Mix to Address Contact Centre Challenges

By
Jaime Scott
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Finding the Right AI Mix to Address Contact Centre Challenges

Call centres need to leverage interaction intelligence to monitor every interaction with customers on every channel. These insights need to be delivered at a level of efficiency that enables leaders in both the contact centre and wider business to put them to use. This is where AI can be invaluable. 

In an increasingly competitive and digitised world, boosting the quality of interactions with customers in the call centre is a critical element of retaining customers. Unfortunately, this can be difficult to achieve, given the increases in both volume of interactions and number of channels for each interaction. 

When you combine that with the increasing pressure to reduce costs and protect profit margins, the high number of calls, chats, tickets, and emails can be overwhelming to manage and scale.  

For years, contact centre software providers have promised to automate evaluations and improve efficiency in the quality management process, but many solutions still require costly and complex implementation processes. Generative AI applications like ChatGPT have understandably generated a great deal of excitement about the prospect of enabling advancements such as quality assurance coverage in the call centre, but the majority of contact centres are still yet to successfully launch any form of automation, analytics or AI. 

So, how can we, as an industry, support contact centres in embracing the newest technology to maximise efficiency, while still maintaining the necessary amount of human touch required for an exceptional customer experience? 

Closing the insight gap 

As any CX professional can confirm, high-performing, engaged teams are a critical asset in supporting and enhancing the value of a brand. That’s why, to drive business and operational improvement at scale, it’s critical to prioritise Quality Assurance (QA) in the contact centre.  

To be clear, generative AI is not a suitable replacement for human oversight. QA teams are still valuable and essential, and generative AI apps can help them to become more effective and productive by automating various tasks and helping with the evaluation process for training and engagement purposes. A survey recently conducted by evaluagent with ContactBabel indicates that there has been a significant change in attitude towards the role of AI in the contact centre over the past six years, with a decline in the proportion of businesses believing that AI will replace contact centre employees (dropping from 60% in 2017 to 26% at the end of 2022.) 

But to effectively improve the customer experience, brands need to gain a better understanding of what’s going on with every customer interaction, across every channel. Until now, traditional QA processes have typically monitored around 2%-10% of interactions. This makes it extremely challenging to identify ways to improve CX, as well as other issues (e.g. failures in compliance) or to measure the impact of any improvement actions being taken.  

To truly understand the issues and behaviours that are positively and negatively impacting the customer experience, call centres need to leverage interaction intelligence to monitor every interaction with customers on every channel. These insights need to be delivered in an actionable way, at a speed and level of efficiency that enables leaders in both the contact centre and wider business to put them to use.   

This is where AI can be invaluable. By automatically transcribing and summarising call centre conversations, assigning insight topics for evaluation, and providing necessary context AI can drive several critical benefits in the contact centre: 

  •       The ability to immediately evaluate any conversation faster  
  •       More time to focus on coaching and training, to augment the value of QA evaluators 
  •       Streamlined coaching, with AI-suggested coaching tips, reviewed by evaluators 
  •       Automated identification of key out-of-the-box characteristics such as customer frustration, repeat contact, complaint escalation and other customised options. 

Also Read: Use Your Contact Centre Data Like It’s 2024 (Because It Is)

Striking the Right Balance 

Management typically wants to find new ways to leverage AI to boost efficiency, but for Heads of QA, it’s also critical to identify specific areas that need to change in order to improve the customer experience.  

Success needs to be measured not only in terms of how the solution elevates the CX with real-time insights, but also in terms of how these features translate into measurable business benefits and improvements, such as faster resolution time. Improved agent training results in higher CSAT scores. Robust reporting and AI-driven workflows result in tighter alignment and improved efficiency across the organisation.  

The overall impact on the brand also needs to be considered. In an increasingly competitive and digitised world, it’s getting tougher all the time for brands to stand out. For many of our clients, offering a better customer experience is the only way to truly differentiate.  

Here are 5 tips for contact centre managers who want to implement AI effectively in the call centre: 

  1. Define clear objectives.  What do you want to achieve with your AI implementation? Be clear on what metrics define success for your business and which specific AI tools will help you achieve them. 
  2. Start with a pilot. Now is not the time to be all-or-nothing. A pilot helps you get a small group started and identify any snagging problems, without affecting your usual processes. Having a ‘control’ group makes proving value easier too. 
  3. Train agents and evaluators. Once you’ve decided to move ahead, use your findings from the pilot to train agents and evaluators in the whats, wheres and whens of AI. Get them comfortable with how it works and have processes already documented and in place. 
  4. Monitor and optimise. Issues and surprises will no doubt crop up along the way. Keep an eye on efficiency and in particular, accuracy. Are your team getting regularly stuck somewhere? Is AI truly helping to improve CX? 
  5. Strike the balance.  The human-AI balance is crucial to success. It needs to be emphasised from the very beginning that AI is not here to replace teams, but support them. AI can do much of the manual legwork, but don’t let your humans rest on their laurels – oversight is vital to keeping QA focused, and prevents it becoming a tick-box exercise.