CRM Quality Assurance: 4 Steps to Cleaner Data Using Artificial Intelligence
[Estimated read time: 4 minutes]
Did you know that traditional contact center quality assurance (QA) processes only check 1-5% of customer case data? That means 95-99% of data is never reviewed. Manually checking all of your CRM data would require a large team of highly skilled people that would have to review every single case that comes into your system. QA testers review cases one by one and often fix the same mistakes over and over. In this system, it can be tough to spot overall trends and pinpoint actionable insights to improve the quality of your data capture.
Key areas where technology can improve the quality of your data
Nothing is better than entering data correctly the first time. It’s best to use a guided approach that provides a rules-based intelligence that enhances agent performance, rather than scripting each customer interaction. Utilizing tools like dynamic input, associated resources, automated workflows, response management, and suggested fulfillment, you can increase the efficiency of your agents and the accuracy of your data capture before you even run anything through the QA system. Adding intelligent technologies to assist your agents is a great way to improve the quality of your initial data capture.
Optimizing quality assurance
Rather than using a traditional QA process that only checks a small percentage of your customer case data, you can utilize new technologies to optimize the process. By using an automated QA system powered by artificial intelligence, you can ensure that 100% of your CRM data is covered in your quality checks. The four steps to this process are the main focus of this article.
Step 1: Building the model
The first step towards an automated QA system is building the model using machine learning. The system looks at actual historical cases in your CRM that your top agents have recorded. It will read the chat transcripts, the emails, and the manual notes your agents have input into the case. How did they code the case? What was the reason code or product? Then an AI model is built and used to predict what your best agents would do for each case.
Step 2: Performing bulk analysis
Every day or every week, all of the customer cases that were captured by your CRM during that period are run through the AI model, covering 100% of your customer case data. The results can then be analyzed by leadership and QA agents. You’re able to compare how each agent coded cases: what products they assigned, what the reason codes were, what their notes were vs. how the model predicted your top agents would’ve coded the same case.
Step 3: Taking action
After comparing, you’re able to take action. If an agent coded it incorrectly, then you can go straight into the CRM and correct that data. You can have a big picture view at the performance of your agents. Is there a particular reason code or specific product information that is causing the majority of your agent errors? It could be that there is confusion about that code or product. Identify the trends to pinpoint where you’re able to make improvements.
Step 4: Providing feedback
The final step towards cleaning up your CRM data is to provide feedback using the trends you’ve identified. Is a particular agent having more trouble than the rest? Perhaps he/she needs additional training. If the agent was correct, make updates to the model for continuous improvement. As you provide feedback to both agents and into the model, the process will continue to adapt and get better.
Better coding = better results
Part of steps three and four are using agent training and tools to help agents better understand their mistakes. More importantly, you can utilize the exact same AI-model and integrate it into your CRM system in real time. As your agent is engaging with a customer, they can send their notes from the case to the model. The QA system will say, “Based on the notes you’ve filled out, I’d code this case as ‘X’ product for ‘X’ reasons.” The final result is getting better results from more accurate coding by leveraging the same model you’ve been using in the bulk analysis in a transactional mode. The benefit of the model is that it puts the performance of your best agents on every customer case.
About Astute Verbatim
Our comprehensive data audit system means no more spot checking. Astute Verbatim automatically audits 100% of CRM case data to catch mistakes, improve the accuracy of customer data, and identify larger trends that may be causing issues in your contact center. Using artificial narrow intelligence and deep learning, Astute Verbatim trains itself to recognize errors by reviewing historical data from your best agents.