Quality Automation for Quantitative Process and Performance Improvement

It’s no surprise when we say quality assurance (QA) is a crucial aspect of any contact center. The end goal is always to provide consistent service that exceeds customer expectations in an efficient manner. It can make or break your Brand.  

 According to a report from Microsoft’s Global State of Customer Service, 90% of respondents indicated that customer service is important to their choice of and loyalty to a brand. What this means for companies is that customers' expectations and their value for quality of customer service is high. Poor customer service can lead to lost business, with 58% of consumers breaking the relationships if their expectations fall short, and their expectations only grow each year. 

With such a high stake on customer service and customer satisfaction, you’ll need to improve the Quality of each interaction with customers. It involves strategies, monitoring, and commitment from QA/team leads and support agents.  

While there are tools that can crunch the numbers in for all your calls and analyze your basic KPIs: Average Handle Time, response time, dead air; analyzing qualitative data still involves a lot of manual work. Quality involves more than just metrics, it encompasses aspects such as tone, attitude, ability to solve problems, their courtesy and the customer experience and level of satisfaction.  

With the advent of AI, some are transitioning to automating their quality audit process, allowing for large amounts of quantitative data to be put at the fingertips of leadership improving performance and providing data to support product improvement.

Automating your Quality process can be a cost-effective way for businesses to deliver results that are just as accurate.  

The process involves: 

1. Choosing the Right Tool
2. Implementation of the Tool
3. Analysis of Data
4. Sharing the Findings

1. Choosing the Right Tools 

Automating Quality can be done through a large number of tools that are proliferating in the market. You will want to care for a Natural language Recognition in conjunction with Natural Language Process. At ACI, we leverage CallMiner and Balto for depending upon the program requirements. Additionally, visualizing the data is important so we utilize a PowerBI instance to provide access to internal and external stakeholders! When combined with AI, Cognitive Services features in Power BI can streamline the quality process with functions such as Speech Analytics, Sentiment Analysis, Key Phrase Extraction, and Language Detection. 

 2. Implementation of the Tools

As you begin to implement the tools, the initial hurdle to overcome is ingestion. In some instances, it may require utilizing Robotic Process Automation to ingest individual .wav files, in others you can ingest live from the telephony feed. Once installed, it is a simple integration into the analytic tool! 

3. Analysis of Data

Now that your data has been integrated, you can analyze the data powered by AI analytics and Natural Language Processing (NLP). Let’s look at a few examples of use cases:

• Sentiment Analysis: capture word frequency, or pre-defined keyword that customer or agent say, then report on sentiment or mood of customer. For example, “My service is not working” or “I’m very frustrated” “This is my third time calling”. It is well known that customers' sentiment changes at key times such as a new product launch or product recall. Custom keyword data sets can be fed into speech analytics software (“I don’t like this new ___” or “This is so much better than your old ___”.
  
  
• Agent response Analysis: from the opening of the interaction or the closing, you can analyze the response to ensure standard and compliance. Capturing phrases, keywords and statements such as “I can certainly help”, “I don’t know”, “I’m sorry to hear” 
  
  
• Building in additional phrases is important, though becoming less burdensome than it has been historically. One of the main challenges that occurred with quality automation was due to errors in transcription. However, this challenge can be overcome by using new AI technology - ASR, or Automatic Speech Recognition. ASR is a technology that uses Machine Learning and AI to replace faulty transcription, fix errors by filling in the gaps, and providing analogous transcription.

 We can’t highlight this enough, investing in speech analytics professionals is critical to see returns from your investment! A fool with a tool is still a fool!

4. Sharing your Findings 
Now that your quality program is automated, it is important to update your monitoring process to reflect the new tooling. Focused monitors, playbacks, and dashboards should all be established for your team members along with manager inspection processes to enable continuous development. 

You will also want to install a regular cadence of publication of findings to the greater organization. From customer journey to revenue optimization, you have a treasure trove of data. Resist the temptation to focus on a contact center centric viewpoint, instead, focus on the organization when communicating externally.  

By implementing quality audit automation, companies can streamline these management activities and gain a deeper understanding of their overall performance. Automation tools can provide real-time insights, enabling companies to identify areas for improvement and implement targeted strategies to enhance their process efficiency and customer satisfaction, all while lowering the overall expense. Get in touch with us today to talk about how Advantage Communications can help your company with our Quality Automation process.