Analyzing and monitoring AI support performance

How to track, analyze and optimize the performance of your AI tools.

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Develop an AI strategy for customer support

In the fourth tutorial of this course, we’ll dive into the crucial task of continuously monitoring and analyzing how AI is performing within your customer support framework. Monitoring the performance of your AI workflows will make sure they’re meeting the intended objectives, and can also uncover where you can further refine and enhance them.

We’ll cover some of the key information, then we’ll end this tutorial with an activity to help you effectively measure and monitor your own AI workflows.

Points we’ll cover:

  • How to conduct a detailed analysis of AI on your support operations
  • What an improvement plan should include
  • Things to consider when creating employee training plans
  • Exercise: create an AI performance monitoring and improvement plan
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Defining key metrics and benchmarks

So you can accurately measure AI performance, you should establish metrics that are directly tied to your strategic AI objectives. These metrics should focus on operational improvements and customer satisfaction enhancements.

They may include:

  • Average Handle Time (AHT): Measures the total time from initial contact to the resolution of a customer issue.
  • First Contact Resolution (FCR): Tracks the percentage of queries resolved on the first interaction, without follow-up.
  • Customer Satisfaction (CSAT): Gauges the percentage of customers who are satisfied with their support experience.
  • Net Promoter Score (NPS): Assesses how likely customers are to recommend your service.
  • Resolution Rate: The rate at which problems are solved within a given timeframe, which can indicate the efficiency of AI tools.

Detailed analysis of AI's impact on support metrics

Beyond tracking basic metrics like those mentioned above, you can also go deeper to reveal the nuanced impacts of AI on your support operations.

Conducting a detailed analysis may include activities such as:

  • Data Segmentation: Analyze performance data by different demographics, channels, or issue types to identify specific areas where AI excels or needs improvement. For example, you might find that AI performs exceptionally well for simple billing inquiries but struggles with more complex technical support issues. Or, you may discover that AI is highly effective on chat and email channels but less so on social media.
  • Control Group Experiments: Conduct controlled experiments by comparing AI-assisted interactions with those handled by human agents to isolate the effects of AI on various metrics.
  • Sentiment Analysis: Employ advanced sentiment analysis tools to better understand the emotions and satisfaction levels of customers interacting with AI systems.

Creating an improvement plan

Based on your analysis, you should create a comprehensive improvement plan to optimize and enhance your AI performance over time.

Your plan may focus on areas like:

  • Key Areas for Improvement: Focus on critical metrics that have the most significant impact on customer experience and operational efficiency - for example, reducing false positives in your AI chatbot's responses or increasing the accuracy of your AI-powered ticket routing
  • Root Cause Analysis: Employ sophisticated analytical methods to uncover the underlying reasons for performance gaps, such as data quality issues or inadequate AI training.
  • Strategic Implementation: Apply targeted strategies, such as refining AI algorithms, updating training datasets, or enhancing user interfaces, to address identified issues.

Developing training plans for employees

As AI becomes an increasingly integral part of your support operations, it’s on you (or someone!) to make sure employees have the knowledge and skills they need to work effectively with AI tools and processes.

A few ways you can ensure you team has the right expertise:

  • Advanced Skills Development: Beyond basic training, provide advanced courses on AI management and troubleshooting.
  • Cross-Functional Training: Encourage understanding across departments to foster a holistic approach to using AI in customer support.
  • Feedback Loop: Establish a feedback system where agents can report AI performance issues and suggest improvements, fostering continuous development.

✍️ Exercise: Create your AI performance monitoring and improvement plan

Apply the concepts you’ve learned in this tutorial. Create a comprehensive plan for monitoring and optimizing the performance of AI in your customer support operations.


  1. Identify 3-5 key metrics that align with your AI objectives and customer support goals (e.g., Average Handle Time, First Contact Resolution, Customer Satisfaction).
  2. Set specific target benchmarks for each metric based on your current performance and industry standards.
  3. Produce a report that breaks down your AI performance data into smaller, more specific subgroups - to gain a more nuanced understanding of how AI is performing across different contexts within your customer support operations. You might segment your data by:
    1. Customer segments (how is AI performing for different customer demographics, new customers vs long-term customers, and for customers with different purchase histories or product preferences?).
    2. Support channels (how is AI performing across chat, email, social media, phone - is it meeting your response time expectations, communication style etc?).
    3. Issue types (evaluate AI performance for each issue type, e.g., billing questions, technical support, product recommendations. Identify areas where AI excels or struggles).
  4. Create an improvement plan.
    1. Based on steps 1-3 above, outline 2-3 key areas for improvement in your AI performance e.g. reducing false positives, increasing ticket routing accuracy.
    2. For each area of improvement, write down the root cause, e.g., data quality, AI training gaps.
    3. From there, develop specific, actionable strategies to address each identified issue, e.g. refine AI algorithms, update training data, enhance user interfaces.
  5. Design a comprehensive training program to upskill your support team in working effectively with AI tools and processes.
    1. Include both basic and advanced training modules, covering topics such as AI fundamentals, troubleshooting, and performance monitoring. Include links to customer support tutorials on Ben’s Bites, too!
    2. Describe how you will foster cross-functional collaboration and knowledge-sharing between your support team and other departments (e.g., IT, data science).
    3. Develop a feedback mechanism for agents to report AI performance issues and suggest improvements.

By defining clear metrics, developing a detailed analysis approach, creating targeted improvement strategies, and prioritizing employee training, you'll be well-equipped to ensure the ongoing success and effectiveness of AI in your support operations.

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