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Infographic: 5 Benefits of Using AI To Measure Customer Effort

By Mary Cleary

5 Benefits of Using AI to Measure Customer Effort

Customer effort is a mix of customers’ subjective feelings and what they objectively experience during interactions with your business. Measuring and reducing customer effort is a top priority of many CX organizations and with good reason.

According to Gartner, reducing customer effort can increase customer retention — 96% of customers who exert high effort are likely to churn vs. only 9% who enjoyed effortless experiences. Additionally, a low-effort interaction costs 37% less than a high-effort interaction. Furthermore, low-effort experiences reduce costs by decreasing up to 40% of repeat calls, 50% of escalations, and 54% of channel switching.

The Customer Effort Score (“CES”) survey is the traditional method for understanding customer effort, but it mostly reflects how customers feel and focuses less on what customers objectively experience. In addition to surveys, CX leaders are looking to advanced AI to help them deepen their understanding of customer effort — AI can help identify and track specific themes that drive customer effort, help assess the business impact, and alert you to when and where effort is spiking so that you can stay ahead of customer expectations.

The AI-driven organic customer effort score captures both subjective and objective effort signals — it measures several variables from customers’ language to operational data and interaction-specific events like whether customers asked for status updates or whether an escalation occurred — regardless of whether customers answered a survey. Read on for 5 benefits of the AI-driven customer effort score:

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