A Guide to comply with the EU AI Act
Published by
Rolf Gordon Adamson
on
Enhancing your AI’s training data through Quality Control: A Guide for Contact Centers on an approach to comply with the EU AI Act
In the fast-evolving world of customer service, the quality of interactions between agents and customers is paramount. As companies increasingly rely on AI-driven solutions to enhance the customer experience, the need to ensure the accuracy and quality of data used in training these AI systems becomes critical. Here, we explore how statistical quality control methods can be employed to measure and assess customer interactions, ensuring that only high-quality data informs AI training, ultimately leading to better customer service outcomes.
The Importance of Quality Data in AI Training
AI platforms, particularly those used in customer service, rely heavily on vast amounts of interaction data to learn and make decisions. However, the adage "garbage in, garbage out" holds true; if the data used to train these systems is flawed, the resulting AI will be ineffective, or worse, detrimental to the customer experience. Therefore, rigorous quality assessment of customer interactions is essential.
The AI will replicate what we already do – so accurate measurements are essential for efficient AI performance. If your data is flawed, those flaws will be reflected in the AI's responses. Determining what inquiries are correctly resolved and sorting the data accordingly is crucial, as the AI's accuracy will only be as good as the data it is trained on.
Most present metrics in the contact center simply do not account for this measurement. The terms, Right First Time or First Time Fix, are not relevant when addressing training data. This realization is often surprising to many customer service organizations and internal data teams who find themselves unsure of how to obtain this data.
Not using actual data to train your AI’s, but solely relying on knowledge base articles, FAQ’s etc. is similar to training self-driving cars only on the present traffic laws. It doesn’t account for real-life situations. In reality, drivers focus on what's happening around them, not just traffic laws, just as we want our employees to respond to customers based on the interaction's context. They focus on what is going on in traffic, just as we want our employees to match a customer in an interaction. This real-life adaptability is something we also need to instill in our AI systems to ensure effective customer interactions.
But how is that done? And why isn’t it already a metric that is used in all contact centers.
Statistical Quality Control in Customer Interactions, doesn’t sound as exciting as it actually is, as it is the foundation of quality control in contact centers we really should celebrate it more than we do today.
Statistical quality control (SQC), or Statistical Process Control (SPC), encompasses various methods to monitor and control processes, ensuring consistent quality. In the context of customer interactions, SQC can help identify errors and rectify deviations from desired performance levels. It actually helps us improve by pointing out to us what isn’t working very well.
We are a positive group of people within the CX industry, always eager to enhance staff performance. It's great to focus on this – please keep it up. However, it's simply not a method of either measuring process control or quality, so let's not substitute one for the other. We need to do both. Why? Because improving staff performance within existing work processes won't help identify processes that fail.
There are multiple methods available, with SPC/SQC being particularly efficient. Here are the key steps to implementing a quality process in contact centers that measures process capability, identifies errors and provides you with measurements that help you quality asses the crucial training data that will feed into your AI.
- Define your Quality Standards
The first step in implementing SQC is to define what constitutes a "right" and "wrong" answer. This involves setting clear, measurable criteria for successful interactions.
These criteria should include:
- Accuracy of information provided
- Resolution of customer issues
- Adherence to company policies
It's as simple as that. Some might wonder, “But what about all the rest?” The answer is that it's beneficial to measure those aspects too, alongside what's most important to your customer.
Ultimately, what customers really want is a correct answer to their inquiry. While other aspects are appreciated, but a correct answer is a demand and crucial for maintaining customer loyalty.
- Collect and Categorize Data
Once standards are defined, collect data from various customer interactions. This data can be gathered through call recordings, chat transcripts, and customer feedback. Categorize this data based on the interaction outcomes (e.g., resolved, unresolved, escalated).
It's important to resist the urge to create overly complex metrics. The key question is simple – did we resolve the issue or not? and not “to what extent did we resolve the issue?”. The AI required clean data, and this process is focused on exactly that. Keep that in mind!
- Sample Data for Analysis
Given the high volume of interactions, it's impractical to analyze every single one. Instead, use statistical sampling techniques to select a representative subset of interactions for detailed analysis. This approach ensures that the analysis is manageable while still providing reliable insights.
It's crucial to remember that without this step, training an AI to supplement manual processes becomes impossible. Many believe an AI can handle it because manual analysis is too costly, but the key question remains: how can you train your AI to identify errors if you're unable to point them out to it?
- Apply Statistical Methods
Several statistical methods can be used to assess the quality of customer interactions, but keeping it simple is the most efficient method.
Pareto Analysis involves identifying the most common issues affecting customer interactions. By focusing on the "vital few" problems, contact centers can prioritize improvements that will have the most significant impact and will help your Data scientist compile that all important good data for the AI.
- Continuous Improvement
Quality assessment is not a one-time activity but an ongoing process. Use the insights gained from statistical analysis to implement changes and improvements. Regularly review and update quality standards based on evolving customer expectations and feedback.
Ensuring Data Quality for AI Training
The insights from SQC not only enhance customer interactions but also ensure that the data used to train AI systems is of high quality. Here’s how contact centers can ensure their training data meets the necessary standards:
- Filter Out Poor-Quality Interactions: Use the results of SQC to exclude interactions that do not meet quality standards from the training dataset. This ensures that only accurate and helpful interactions are used to train AI systems.
- Annotate Training Data: Clearly annotate training data with outcomes and quality indicators. This helps AI systems understand the context and quality of interactions, leading to more effective learning.
- Regularly Update Training Data: Customer expectations and service processes evolve over time. Regularly updating the training data with recent, high-quality interactions ensures that the AI remains relevant and effective.
Compliance with the EU AI Act
The European Union's AI Act emphasizes the need for high-quality, unbiased data in AI training. By implementing SQC methods, contact centers can ensure compliance with these regulations. This not only mitigates legal risks but also fosters customer loyalty by demonstrating a commitment to quality and transparency.
Conclusion
Implementing statistical quality control in contact centers is essential for ensuring high-quality customer interactions and effective AI training. By defining clear quality standards, analyzing interaction data using statistical methods, and continuously improving processes, companies can enhance the customer experience and comply with regulatory requirements. As AI becomes increasingly integral to customer service, the importance of quality data cannot be overstated. By prioritizing quality assessment, companies can build more reliable and effective AI systems, ultimately leading to better customer satisfaction and loyalty.
In the competitive landscape of customer service, investing in rigorous quality control measures is not just a regulatory necessity but a strategic advantage. Contact centers that excel in this area will be well-positioned to deliver exceptional customer experiences and harness the full potential of AI technology.
Your Partner in Compliance and Quality
At Nexcom, we are dedicated to supporting our clients on their journey towards compliance and excellence in AI-driven customer service. Contact us today to learn more about how our AI solutions can help your organization thrive in the era of regulatory compliance and digital innovation.