Out-of-the-Box vs. Customized GenAI Bots: Part 2

Part 2: Metrics and continuous improvement

In part 1 of this series, we explored the limitations of old-school chatbots and why AI-backed language model agents are the next evolution of customer interaction. We also covered the significance of the underlying datasets used to train these language models and how custom language models are the most reliable way to successfully engage with customers–especially in a contact center environment.

Intelligent agents aren’t just a tool for reducing call and email volume; they’re a form of self-service technology that your customers can either embrace or be deterred by. Naturally, we aim for customers to appreciate the self-service chatbot, but it’s important to acknowledge that there are always bumps along the way. 

Building an intelligent agent is a continuous process, not a one-time effort. Ongoing improvement is essential. The underlying technology will evolve, and so will your customers’ expectations. Regular updates help you stay competitive and keep your customers satisfied. After all, who doesn’t want better and faster service?

First measure, then improve

To improve anything, two key steps are essential: 1.) Understand your current state, and 2.) define what “better” looks like. This means you have to assess where you stand before determining where you want to go and how to measure your success along the way.

Many contact centers track metrics such as: 

• First Contact Resolution (FCR): This measures whether a customer’s issue is resolved or question answered in a single interaction, without the need for follow-ups. High FCR indicates efficiency and effectiveness.
• Average Handling Time (AHT): This measures the average time it takes to complete an interaction, including talk time, hold time, and post-call tasks. Lower AHT can indicate smoother operations.
• Customer Satisfaction Scores (CSAT): This metric gauges how satisfied customers are with their service experience, usually collected via post-interaction surveys. High scores reflect a positive customer experience.

If you're already measuring these types of metrics, you likely have a sense of your performance and the areas that need improvement.  However, even after making improvements, it’s common to see diminishing returns. This is where custom intelligent agents and language models can make a significant difference, driving more meaningful progress. 

Developing AI-powered metrics

AI-powered solutions with custom language models help you maintain consistency while optimizing current efforts and uncovering new growth opportunities. For those who are particularly enthusiastic, AI can help you collect, combine, and provide actionable insights based on your metrics. Then, the AI can provide recommendations!

Think of AI as a high-performance race car and consider the following key areas for your contact center: 

• Speech recognition accuracy: Think of this as the precision of your vehicle’s steering. If speech recognition isn’t accurate, your AI system won’t properly understand customer inputs, making it harder to guide them to a solution. 

• Word Error Rate (WER): A lower WER means that the system accurately interprets and transcribes spoken words. A low WER ensures customers won’t repeat themselves or face frustrating miscommunications.

• Intent recognition: This is like your car’s GPS: how well does the AI understand what the customer wants? Accurate intent recognition ensures the right solutions or agents are engaged, preventing frustrating detours.

• Misrouted calls: Here we are counting how many wrong turns you’ve taken. If calls are constantly being transferred, re-routed, or lost, it’s like driving around in circles until the customer hits the eject button.

• Self-service completion rate: This is like measuring your car’s fuel efficiency. A high rate means your AI is resolving a lot of issues on its own without needing help from a live agent. You’re getting the maximum mileage out of your tank of gas!

By monitoring these metrics, you can fine-tune your system for optimal performance. Over time, as your AI-driven intelligent agents mature, the success of your contact center will involve new metrics and reprioritizing the ones you’ve always counted on. 

Measuring and testing the success of a custom agent

Once you have baseline metrics in place, your custom AI solution can learn and improve through user interactions. This training process involves analyzing usage data, identifying patterns, and refining the language model to better meet customer needs.

Continuous monitoring is key, as the system must adapt to changes in your business — whether that’s new products, processes, or data integrations. Just like updating your website, your intelligent agent requires regular updates and maintenance to stay relevant and effective. You wouldn’t launch a new product without making sure your model is ready to support it.

Effective improvement also means robust testing. Changes can introduce new issues, so in addition to monitoring analytics, you should run testing suites to ensure functionality and identify potential system failures. Multiple testing environments—such as functional tests and health checks—can catch problems before your customers experience them.

Ensuring the long-term viability of your custom agent

Your custom agent may start as an experiment, but to become a long-term asset, you’ll need a comprehensive plan for its ongoing success. This plan should include:

• Preventative Maintenance: Regularly checking and maintaining the agent to address potential issues before they arise, such as updating software, debugging, and ensuring smooth operation. Think of it like taking your race car in for routine maintenance to avoid breakdowns.

• Monitoring and Availability Management: Continuously track the chatbot’s performance in real-time, ensuring it’s available when customers need it and resolving issues swiftly to avoid downtime.

• Innovation: In the chatbot world, innovation refers to continuously improving and updating with new features, better AI capabilities, or more efficient ways to handle conversations. It's all about staying ahead of the curve and ensuring that the intelligent agent adapts to evolving customer needs and technology advancements.

• Upgrades: Updating the system with new functionalities, software versions, or more advanced AI models enhances its performance. Just like upgrading car parts for better performance, regular upgrades ensure your agent remains cutting-edge.

Custom chatbots are here to stay

Custom chatbots are not just technological novelties; they’re becoming integral to customer service. Their role in enhancing customer interactions and solving problems is more important than ever. By incorporating AI into your customer service operations, you’re not just improving efficiency — you’re also enhancing the customer experience.

However, the journey doesn’t end at launch. Continuous evolution is crucial. As customer expectations change and technology progresses, your custom agent must adapt. Regularly updating your AI model, understanding shifts in customer behavior, and fine-tuning its responses to align with your brand voice will ensure it stays relevant and effective.

A custom chatbot is much more than a tool. It’s a dynamic, evolving member of your customer service team. With strategic planning and continuous management, it can become one of your most valuable assets, driving customer satisfaction and business growth. Keep measuring, improving, and innovating, and your intelligent agent will become an indispensable part of your service arsenal.

In the final part of this series, we’ll dive into how a custom language model gives you greater control over your data, customer interactions, and decision-making processes.