How to Measure ROI of AI Customer Support Solutions?

Set Clear Goals First

Implementing AI across multiple aspects of customer support can bring significant transformation to your organization. However, it could be completely different and disconnected from your current mode of customer support.

Bringing about such a massive change within an organization introduces significant risks and challenges, including, but not limited to, budget constraints. It takes your focus away and forces you to manage multiple issues at the same time.

So, it is more appropriate to take one step at a time for successful implementation, testing, fine-tuning, and deployment. The primary step in implementing AI in customer support is defining clear business objectives. Your objectives could include:

• Reducing the queries directed to live agents and the number of human-handled tickets
• Increasing efficiency by reducing Average Handling Time (AHT)
• Improving Customer Satisfaction Score (CSAT)
• Increasing first-contact resolution (FCR), thereby reducing the need for follow-up
• Boosting agent productivity and reducing churn

Once you have selected a goal, you can set that as a baseline, focus your AI customer support implementation around it, and provide a framework for measuring success.

Track Cost Savings

Return on investment (ROI) is a calculation of how much you invest in AI customer support in relation to how much value you can get out of it. So, customer support cost reduction is a significant aspect of ROI. You must forecast the cost savings you will achieve by implementing AI customer support.

For instance, AI systems handle common, repetitive queries and basic troubleshooting, reducing the need for large human support teams. They save the costs involved in paying wages for additional human staff who would have handled those queries in the absence of AI customer support. You can estimate these cost savings.

Similarly, you can identify other areas of customer support cost reduction by tracking key metrics, including:

• Cost per ticket before and after AI implementation
• Reduction in hiring, training, and onboarding costs
• Infrastructure savings from cloud-based or scalable AI platforms

For instance, if the cost per ticket handled by a live agent is $10 and AI deflects 8,000 tickets monthly, then this would result in a $80,000 monthly savings.

Measure Time Saved

In addition to the direct cost savings, there are also indirect cost savings – for instance, the time spent addressing one query costs in terms of human labor and other resources in traditional customer support. When you implement AI customer support, you can dramatically reduce the time spent on addressing repetitive queries.

While live agents may spend time pulling up customer details, order information, and previous interactions to provide solutions that involve common tasks, such as order cancellation, AI customer support automates all that, reducing the Average Handling Time (AHT). This metric helps assess time saved per interaction. When you combine AHT with volume handled, you can quantify labor hour reductions.

Time saved = (AHT before AI − AHT after AI) × number of tickets

Suppose the Average Handling Time (AHT) before implementing AI customer support was eight minutes, and after AI, it dropped to three minutes. The total time saved handling 10,000 tickets per month would be:

(8 − 3) × 10,000 = 50,000 minutes per month. 50,000/60 = 833.33 hours saved

By multiplying the time saved by the average hourly agent cost, you can calculate dollar savings.

833.33 × $20 (average hourly agent cost) = $16,666.60 of total monthly savings

Monitor Response & Resolution Times

The monetary benefit of customer experience can be hard to measure. A good customer experience can be a key factor in customer retention and repeat purchases. Good customer experience in support stems from faster response and resolution times. Improved resolution speed correlates with higher Net Promoter Scores (NPS) and CSAT.

AI chatbots with NLP can simulate human-like responses and often resolve simpler queries faster, reducing escalations to human support.

Intelligent ticket routing also reduces waiting times by identifying common queries and routing them to chatbots while sending complex queries to live agents. As a result, regardless of whether a query is directed to a human agent or AI support, First-Contact Resolution (FCR) is achieved, improving speed, efficiency, and customer experience.

A few metrics you can track to measure response and resolution times include:

• Average Response Time (ART)
• First-contact resolution rate
• Escalation rate to human agents

Use CSAT and Feedback

Customer satisfaction is another factor to consider while projecting the monetary value of customer support. Customer satisfaction score (CSAT) before and after AI deployment can be measured through:

• Post-chat or post-resolution surveys
• Tracking Net Promoter Score (NPS)
• Sentiment analysis from chat logs
• Open-ended feedback to refine AI responses

Count Volume Handled by AI

Ticket deflection rate is one of the most important AI ROI metrics. It is the percentage of total tickets resolved by AI without human intervention. The more tickets AI resolves on its own (higher ticket deflection), the lower the operational load is, and the higher the ROI would be. This helps in understanding how much of the workload AI absorbs.

Deflection can be categorized into:

• Fully automated responses
• Partially assisted (AI + live agent involvement)
• Transferred to an agent

Calculate ROI Using a Simple Formula

You can consolidate the costs and revenue uplift by AI by tracking all the above-mentioned AI ROI metrics and calculating the ROI of AI customer support with this formula:

ROI = (Total Benefits − Total Costs) / Total Costs

Total Benefits may include revenue uplift by AI, cost savings, including cost per ticket saved, and indirect revenue gains from improved customer satisfaction, retention, and upselling. Total Costs may consist of AI solution purchase/licensing cost, integration and training, and ongoing maintenance.

Suppose the total AI cost is $100,000 per year, and the total benefits are worth $300,000 per year,

ROI = (300,000 – 100,000) / 100,000 = 200% return

AI Customer Support ROI: Measure What Matters

AI customer support solutions transform your business. However, it is crucial to understand their actual value to make a sound, data-driven business decision. Tracking AI customer support ROI is the first step to achieving that.

ROI calculation helps you look beyond a few metrics in isolation, discard vanity metrics that mislead you, and tie AI investments to tangible business outcomes, such as cost savings, time reductions, and customer satisfaction.

By setting clear goals, applying consistent KPIs, and utilizing a proven ROI formula, you can make informed decisions about scaling AI support capabilities.

To learn more about AI customer support ROI and compare it with traditional vs AI ROI, check out this article.