by Larry Fleischman, on Jun 19, 2024 2:09:45 PM
Key takeaways from the blog
- QA Automation helps move beyond sampling to achieve 100% QA Analysis of the target data sets.
- It uses technologies, such as Text Analytics, Sentiment Analytics, Topic Modelling, etc., to parse through the data.
- QA Automation also helps assess agent performance and their effectiveness at solving customer issues.
In an omnichannel customer interaction environment, Customer Experience Management (CXM) becomes a challenge. However, a strong Quality Assurance (QA) function that oversees customer interactions can improve customer experience by manifolds. Businesses with global operations handle heavy workloads in customer-facing environments. QA in such scenarios mostly gets addressed on a sampling basis. Here, automating the QA function enables 100% QA analysis and brings in rigor in CXM.
What is Quality Assurance (QA)?
Quality Assurance or QA is a systematic approach to assess whether a process or a service follows the set quality standards. QA is a mandate in businesses that strive for customer service excellence. It is primarily aimed at improving business credibility, process efficiency, customer trust, and customer satisfaction.
Why is QA important in CXM?
Customer stickiness is crucial to the business. Hence, it becomes important to identify and alleviate issues related to agent and customer interaction or environment, in time. Integrating QA as part of the organization’s CXM function ensures that customers are heard, and their issues are addressed in time. Addressing business and customer-related issues early on ensures that customers continue to invest in the business for a continued smooth business partnership. Integrating QA in CXM is paramount for building customer trust, stickiness, and loyalty. A strong QA function ensures that a satisfied customer brings in 10 to 20 other customers thus exponentially increasing the collective customer lifetime value.
How does Text Analytics help businesses in their QA practice?
Businesses collect mammoth quantities of customer communication data in omnichannel environments. Manually evaluating such unstructured data to take corrective and preventive actions is humanly impossible. In such a case, sampling is the only method of evaluation, and it covers at the most 5 to 10% customer-interaction data. Text Analytics enables the QA executives to cover 100% of the data for QA evaluation without any gap. It extracts patterns or sentiments that are otherwise not easily identified by the human eye. They can be labelled as pain-points, accolades, and any other data labels as per business specification for appropriate QA addressal. Text Analytics also helps assess agent performance from customer communication. It helps to understand if the agents are following the right rules and regulations and their effectiveness at solving customer issues.
How do businesses leverage Sentiment Analysis with audio files?
Businesses can automate their QA function by using Sentiment Analysis across all their customer communication formats, including voice call recording audio files. Sentiment Analysis first uses automatic speech recognition tools for converting audio to text. It then leverages AI/ML models to identify the sentiment and categorize the text according to different pre-defined labels of customer engagement ranging across a pre-defined scale. It helps to gain deeper insights into the customer voice to gauge their levels of satisfaction and measure the changes in their engagement over a period of time.
How do Text Analytics and Record Management complement each other in QA automation?
Record Management and Text Analytics are a powerful combination for QA automation. Organizations organize and categorize customer communication related information using different record management tools, databases, and ECM tools. It helps retrieve data quickly for analysis.
Text Analytics atop Record Management solutions uncovers hidden sentiment in the customer communication generated in an omnichannel environment. Sentiment Analysis and Topic Modelling help to optimize the outcome and gain deeper insights to improve customer service and take CXM to a new level.
Why is QA automation and Text Analytics important in Personalized CXM?
Text Analytics is at the crux of QA automation that uses huge quantities of customer communication data. It helps to derive keen insights out of the customer-specific data gathered across different channels and surveys. It helps to tap into the customer-sentiment and address it aptly through targeted communication. Acknowledging satisfied customers and supporting the neutral and negative ones goes a long way in building strong customer relations through Personalized CXM.
In Summary
QA Automation enables businesses to perform QA Analysis of 100% of the customer communication work-load in an omnichannel environment. It brings rigor to the CXM function and helps achieve customer service excellence. It helps move beyond sampling to achieve the primary QA goals of improving business credibility, process efficiency, customer trust, and customer satisfaction. Technologies, such as Text Analytics, are at the core of QA Automation. It helps achieve Personalized CXM by supporting targeted communication.
