What is AutoQA? Catch up with this Definitive Guide

Automation has progressed quickly from a hot new QA topic to a must-have for ambitious teams. Customer expectations are evolving as fast as technology, and advances in AI and automation help your service team keep up.

In fact, 70% of organizations plan to invest more in support automation in the next 12 months.

 So, let’s talk AutoQA and how you can be doing more with less.

What is AutoQA?

Manually reviewing conversations to find problem areas is like taking a metal detector to the beach for treasure. It’s pretty long and tiring, with no guarantee of a result. With AutoQA, you already have a map to find out where the treasure (or problem areas) are. When you can jump to them directly, you’ll have a lot more energy to strategize for improvement.

AutoQA reduces labor and helps you improve your customer service quality faster than you’ve ever been able to before. 

However, always remember that there is no absolute binary between automated and manual. The human in the loop (your QA specialist) is what brings to light the enhancements of automation and AI.

“Machine learning allows us to analyze every customer message automatically using the latest innovative approaches.

We can focus on quickly reaching a broad and international audience by using multilingual pre-trained large language models. This allows us to, for example, conduct sentiment analysis and named entity recognition. Through this, we added support for two new languages (Polish and Portuguese) in the space of just a few weeks. 

Relying on language models enables AI to achieve things at a speed that seemed impossible just 5 years ago.

In addition to ML models, we focus on AI knowledge interpretability. Not everyone is trained in data interpretation – we know that! It’s our job to help our customers make sense of the vast data. We hide much of the complex AI insights in informative and concise dashboards and graphs.”

Andre Tätter

Machine Learning Researcher, Klaus

Customer service QA automation vs Manual reviews

Let’s talk about division of labor and define the roles of automation, AI, and human (hello you) in customer service QA.

Now, to be clear, manual reviews are still vital in the customer service QA process. The ‘human’ in the feedback loop is irreplaceable – digging into nuanced situations, constructing feedback sessions, in-depth analysis, etc. But the hardest part of the feedback loop is also human behavior. 

For this reason, relying on manual reviews alone dices with risk. 

1. Limited scope
   On average, only 2% of conversations are reviewed manually. 
2. Bias
   Reviewers may have unconscious biases that can affect their evaluations, leading to unfair assessments and decisions.
3. Lack of scalability
   Manual reviews are not easily scalable, especially as the volume of customer interactions grows, which can lead to delays in providing feedback and improving customer service.
4. Human error
   This is inevitable, and leads to inaccurate assessments and bad data.
5. Inconsistency
   Different reviewers may have different standards or interpretations of what constitutes good customer service.
6. Time-consuming
   Manual reviews require a lot of time and effort, more so as your ticket volume swells. This, of course, has a knock on effect on your bottom line.

Full coverage for analyzing trends

Support teams often handle hundreds of conversations daily. 

This ocean of communication is too deep to dive into without getting lost in the details of each and every conversation. It’s time-consuming and not worth the return on time invested. What you’re interested in are the currents. 

Automated ticket reviews increase your reviewing capacity 50x.

100% coverage means that all of your QA bases are covered for every single one of your conversations. Without having to open a single interaction, you can understand the breadth of both overall customer sentiment and team performance – regardless of ticket volume.

The power of the bigger picture

A large proportion of your conversations are very straightforward. Many conversations don’t contain enough nuance to dive into manually. 

For example, one in which there is a quick back-and-forth between agent and customer, solving a common problem, tells you nothing new about performance and processes. Highlighting a conversation like this for review, and rating it on a scorecard with ten categories, is a waste of time. 

But understanding the parameters of this conversation is important in the grander scheme of things – every conversation is worthy when you want to understand conversation quality from a statistical standpoint. Like, how much of the time are agents succumbing to bad grammar? Or how many conversations aren’t closed properly? 

AutoQA can label and score these conversations with the details that matter

One third of support teams say customer data & analytics is a priority in 2023. Automated reviews are crucial in helping you accurately identify trends and understand the bigger picture of performance – without you having to lift a reviewing finger. 

At Klaus, we understand that businesses may have specific requirements and nuances that they want to measure within each category. While our solution may not capture every single detail of what a company wants to measure in each category, we’ve designed it to be low-risk and non-penalizing.

We focus on accurately capturing what is present – what we know how to catch. We do not penalize agents or reduce their scores for something our system may not have accurately captured.

Mervi Sepp Rei, PhD

Head of Machine Learning and Data, Klaus

Finding the signal among the noise

Getting every single item into the system is step one. 

There are still golden tickets of caramel goodness for quality improvement purposes that require human eyes. So it’s important not to let them stay mired in the status quo. 

It is crucial that QA specialists are at hand to scrutinize certain conversations for several reasons:

• They help identify specific areas of improvement and facilitate targeted training or support to help agents improve performance.
• They can better analyze customer sentiment and address issues that are negatively impacting customer satisfaction – whether those problems lie in customer service or need to be communicated to other departments.
• QA specialists can also identify potential risks or compliance issues in customer interactions. 
• They also deliver constructive feedback using AutoQA as evidence of performance.

You don’t need context to decide about a grammar mistake, or whether someone used a proper greeting. That’s one reason why it is a boring task, depleting human reviewers’ patience and attention span – while machines can learn it easily (and without complaints). 

You definitely want to see context when, for example, emotions run high and the conversation has strayed from the original problem. A machine can tell you that this conversation went longer than average. But that could mean the agent did not control the conversation, or, on the contrary, that they managed to calm down an upset customer and bring them back onto a path to resolution. 

Let the machine spot the conversations that need human review, and then let a human reviewer give feedback that makes sense.

Valentina Thörner

Remote Leadership Expert, Klaus

Discovery is easier with AI

A third of all teams surveyed are using AI to assist in the selection of conversations for review.

Klaus’ Spotlight is a unique conversation discovery feature that automatically samples the conversations critical for review. These are selected through a multilevel statistical analysis of your own communications metadata. In other words, based on criteria AI has customized for your support team. 

By reviewing conversations highlighted by Spotlight, you can ensure your QA efforts are focused on conversations that are critical to review and contain the most influential learning moments.

The scalability of combining automated and manual reviews

While manual reviews can dive deep into nuance, they are also time-consuming and not easily scalable. 

On the other hand, automated reviews can analyze a large volume of interactions quickly, but may not be able to capture the intricacies and subtleties of human communication. 

We have officially entered the era in which humans work best when alongside machines (and, really, vice versa is true also). There are 5 key principles that, when followed, aid optimal human-machine collaboration: 

1. Reimagining business processes
2. Embracing experimentation and employee involvement
3. Actively directing an AI strategy
4. Responsibly collecting data
5. Redesigning work to incorporate AI and cultivate employee skills. 

AutoQA taps into each and every one of these principles, when used by a QA specialist with the eye to pluck out critical conversations and knowledge to put the ensuing analytical data into action.By combining both methods, companies can leverage the benefits of both approaches. Automated reviews can quickly identify issues, while manual reviews can provide more detailed analysis of interactions and identify areas for improvement. This approach provides an efficient and scalable way to ensure high-quality customer service across a large volume of interactions.

And ultimately, an easier, smarter way to keep customers happy as you scale. 

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