The Four Challenges to Quality Assurance in Customer Service

Consumer expectations are continually rising when it comes to customer service. What does this mean for contact centre priorities for the year ahead?

The 2022 ContactBabel UK Contact Centre Decision-Maker’s Guide, sponsored by Enghouse Interactive, has researched current key pain points. And to help customer service teams it includes guidance on how to overcome these problems.

Top of this list is improving quality and performance. However, achieving consistently high levels of quality is not easy. Companies need to deal with an increasing volume of interactions. Moreover, many of these are becoming more complex, across a growing number of channels. How can customer service teams deliver quality responses to every customer query, however consumers make contact?

The Challenges to Quality Assurance (QA)

According to ContactBabel, 97% of companies recognise the importance of performance and quality. However, they face four interconnected challenges to delivering customer service quality assurance:

1. Inability to Cover Multiple Channels

Unsurprisingly, as the historical backbone of customer service, telephony ranked highest for QA effectiveness in recording and analysing calls. 81% of ContactBabel respondents judged their performance as fairly or very effective.

But even of these, only 36% felt they were very effective – meaning nearly two-thirds see substantial room for improvement.

The effectiveness of quality assurance drops even further across other channels. Even though 73% stated that improved QA was key to ensuring agents deliver in today’s omnichannel contact centre. Just 17% of companies said QA was very effective on email, 14% on web chat and 13% on social media.

Given the growth in these channels, contact centres need to put in place solutions that can cover all interactions. Through a single system that can monitor quality across the whole omnichannel experience.

2. Lack of the Right Technology

One of the biggest obstacles for respondents was technology that doesn’t support their quality objectives. 38% said it was a major challenge – and just 31% believed it wasn’t an issue. In some cases, solutions didn’t provide sufficiently high quality data.

Or it was difficult to analyse or share across the business. This holds back the potential of quality assurance data, such as call/interaction recording. These not only enables compliance but also provides key insights into customer sentiment and where the experience and processes can be improved.

In fact, just 10% of contact centres judged their use of QA insight across the organisation as very effective.  Furthermore, 30% said it was very ineffective.

Technology that automates the collection of recordings and can share it with the right people in the right formats to drive improvements is therefore vital.

3. Insufficient Skills to Analyse and Act

Two-thirds (65%) of companies said that a lack of staff skills caused problems when it came to analysing data. This holds back being able to understand what is actually happening within the contact centre, and then making improvements.

QA data is most successful for regulatory compliance (70% judged this very/somewhat effective) and evaluating agent performance (75%). While this is positive, it misses out on the multiple other benefits that this quality information brings.

No wonder that only 33% said that their use of QA data was very effective at improving internal processes and metrics. It is increasingly necessary to use AI to bridge the skills gap and automate data analysis.

4. A Lack of Time

As we’ve said, contact centres have to cope with a growing number of interactions, across multiple channels. The majority of quality assurance is still carried out manually – such as by supervisors listening to call recordings.

Unsurprisingly, contact centre managers listed insufficient available time to analyse data as their biggest issue. 85% said it was a problem in some form, with 35% stating it was a major issue. AI technology is again the answer.

Automating customer service QA and using the results to pinpoint interactions that require human involvement solves the twin problems of lack of time and insufficient skills. It also makes the process more useful and insightful.

Significantly, it enables supervisors and team leaders to focus on the interactions that provide the opportunity to drive improvement. This can be through coaching, process changes, or wider Voice of the Customer programmes.

Delivering a quality response to every interaction is vital if businesses are to keep customers happy and loyal. Every call, email, chat or social media message has to be consistently high-quality or consumers will leave.

Currently customer service quality assurance faces challenges in identifying insights from the huge volumes of customer interactions. Moreover, a lack of time, skills, and technology reduces its effectiveness.

AI can not only overcome these obstacles but ensure that customer insight is used across the business, improving the experience and increasing loyalty.

This blog post has been re-published by kind permission of Enghouse Interactive – View the Original Article

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