15 CSAT Statistics and Customer Satisfaction Benchmarks by Industry

Customer satisfaction remains under pressure. Forrester reported that U.S. customer experience quality declined for a third consecutive year in 2024, with 39% of brands experiencing a significant decrease. At the same time, current industry benchmarks show that satisfaction varies meaningfully by sector, channel, and measurement method.

Organizations should interpret transactional CSAT alongside broader measures such as the American Customer Satisfaction Index, Net Promoter Score, Customer Effort Score, first-contact resolution, and qualitative feedback. AI agents can support this work by resolving repetitive requests, extending service availability, and helping human teams focus on complex cases that require judgment, empathy, and relationship-building.

Key Takeaways

• Customer experience remains challenging: 39% of brands experienced significant CX declines in Forrester's 2024 U.S. index.
• Human access remains essential: 87% of customers say companies using generative AI for service must provide an option to reach a human agent.
• AI can extend team capacity: A large workplace study found that AI-assisted support agents resolved 14% more issues per hour on average.
• Industry benchmarks vary: Current ACSI scores among the industries covered range from 73 for internet service providers to 82 for full-service restaurants.
• Support roles are expanding: 85% of service leaders report expanding human agent responsibilities as AI handles more routine work.
• Maven AGI supports satisfaction at scale: Rho maintained 95% CSAT while monthly support contacts increased 12% with Maven AGI's AI Copilot.

Understanding CSAT and Customer Satisfaction Benchmarks

1. 39% of brands experienced significant CX declines in 2024

Forrester's 2024 U.S. Customer Experience Index found that customer perceptions of CX quality declined for a third consecutive year. The research also reported that 39% of brands experienced a significant decline, compared with 17% in 2023.

This finding is broader than transactional CSAT, but it reinforces the need to measure customer sentiment consistently. CSAT captures satisfaction with a specific interaction, product, or experience, while broader CX indexes evaluate customer perceptions across multiple dimensions and touchpoints.

2. Customer-obsessed organizations report 51% better retention

Forrester reports that customer-obsessed organizations achieve 51% better customer retention than organizations that are not customer-obsessed. The same research associates customer obsession with 41% faster revenue growth and 49% faster profit growth.

These results support a measurement approach that connects CSAT with business outcomes. Satisfaction data becomes more useful when teams can relate it to retention, repeat purchases, customer effort, and the recurring issues that affect the overall customer experience.

Measuring Customer Satisfaction and Survey Quality

3. 87% of customers say GenAI service must include human access

A 2026 Gartner survey found that 87% of customers consider it essential for companies using generative AI in customer service to provide an option to reach a human agent. The same survey found that 50% of customers say interactions are easier when companies use generative AI.

The two findings should be considered together. Customers can value faster AI-supported service while still expecting access to human judgment for sensitive, complex, or exceptional situations. Effective CSAT programs should therefore measure both the quality of automated resolution and the quality of escalation.

Useful CSAT survey questions include:

• Overall satisfaction: "How satisfied were you with your experience?"
• Resolution quality: "Was your issue fully resolved?"
• Customer effort: "How easy was it to get the help you needed?"
• Escalation quality: "Did the transition to a human agent feel seamless?"
• Open feedback: "What could have improved this experience?"

4. AI-assisted support agents resolved 14% more issues per hour

An NBER study involving 5,179 customer support agents found that access to a generative AI assistant increased productivity by 14% on average, measured by issues resolved per hour. The gains were larger for less experienced and lower-skilled workers, while the effects were limited for the most experienced agents.

This research supports positioning AI as a capacity and knowledge tool rather than a substitute for human expertise. Teams should evaluate AI with a balanced set of measures that includes CSAT, resolution quality, first-contact resolution, handle time, escalation rate, and agent feedback.

The AI Agent Designer helps teams monitor resolution rates, sentiment, predicted NPS, deflection, and behavior trends. This gives CX and operations leaders a clearer view of where automation performs well and where human review or configuration changes are needed.

Current Customer Satisfaction Benchmarks by Industry

The American Customer Satisfaction Index reports industry benchmarks on a 0-to-100 scale. An ACSI score is not the same as a transactional CSAT percentage, so organizations should avoid comparing the two as though they use identical methodologies. ACSI benchmarks are most useful for understanding broad sector performance and year-over-year movement.

5. Full-service restaurants scored 82 in 2026

Full-service restaurants received an ACSI score of 82 in 2026, unchanged from 2025. The industry's experience benchmarks included scores of 91 for order accuracy and 88 for food quality, restaurant cleanliness, beverage quality, and waitstaff courtesy.

For restaurant and hospitality operators, satisfaction depends on consistent execution across digital ordering, in-person service, fulfillment, and post-purchase support. Comparing channel-level feedback can reveal where the overall score hides specific points of friction.

6. Banks scored 80 in 2026

Banks maintained an ACSI score of 80 in 2026. Regional and community banks scored 83, while national banks scored 79. Bank call center satisfaction increased from 81 in 2025 to 82 in 2026.

Customer satisfaction in financial services depends on speed, accuracy, security, and clear communication. AI agents can support routine account questions, transaction status requests, fraud reporting workflows, and policy-based service while escalating sensitive cases to qualified employees.

7. Online retailers scored 79 in 2026

Online retailers received an ACSI score of 79 in 2026, unchanged from 2025. Mobile app quality scored 88, checkout and payment ease scored 86, and the helpfulness of customer support scored 78.

The gap between strong transaction scores and lower support scores shows why retailers should measure the full customer journey. Delivery issues, returns, replacements, and order changes can have an outsized effect on satisfaction after checkout.

AI agents can support policy-based order changes, status updates, refund requests, and replacements across retail and e-commerce workflows while routing exceptions to human agents with relevant customer and order context.

8. Lodging scored 77 in 2026

The lodging industry received an ACSI score of 77 in 2026, up 1% from 2025. Reservation ease and mobile app quality both scored 86, while call center satisfaction scored 80.

For travel and hospitality organizations, satisfaction can change quickly when customers face booking modifications, cancellations, loyalty questions, or service disruptions. Effective support should combine real-time information, clear policy application, and contextual escalation when exceptions require human judgment.

9. Airlines reached 76 in 2026, up 3%

Airlines received an ACSI score of 76 in 2026, a 3% increase from 2025. Call center satisfaction rose to 81, while timeliness of arrival and baggage handling scored 82 and 81, respectively.

Airline CSAT programs should separate routine interactions from disruption-related experiences. A customer may be satisfied with booking but dissatisfied with rebooking, baggage recovery, or refund communication, making issue-level segmentation essential.

10. Internet service providers reached 73 in 2026

Internet service providers received an ACSI score of 73 in 2026, up 1% from 2025. Non-fiber providers scored 71, while call center satisfaction for that segment reached 70.

Telecommunications support teams manage high volumes of repetitive troubleshooting, billing, outage, and account questions. AI can extend support capacity across nights and weekends, while human agents remain central to complex diagnostics, service recovery, and emotionally charged conversations.

11. Hospitals scored 77 in 2025, up 3%

Hospitals received an ACSI score of 77 in 2025, a 3% increase from 2024. Inpatient care scored 81, outpatient care scored 83, and emergency room care scored 72.

Healthcare organizations should treat patient satisfaction data with appropriate governance and privacy controls. AI can assist with administrative and support workflows, but sensitive health discussions, clinical judgment, and complex patient needs require qualified human involvement.

AI and Customer Support Statistics

12. 91% of service leaders faced pressure to implement AI in 2026

Gartner reported that 91% of customer service leaders faced executive pressure to implement AI in 2026. The research emphasized that leaders were being asked to improve customer satisfaction, not only operational efficiency.

Successful AI programs therefore need clear customer outcomes. Teams should define which requests are appropriate for autonomous resolution, which workflows require approval, and which situations should move directly to a human agent. The AI Agent Platform connects knowledge, reasoning, actions, channels, and governance so organizations can manage customer interactions across chat, voice, email, and existing enterprise systems.

13. 85% of service leaders are expanding human agent responsibilities

A separate Gartner survey found that 85% of service leaders are expanding human agent responsibilities as AI reduces routine contact volume and shifts work toward higher-value tasks.

This trend reinforces the strategic role of support teams. When repetitive work is handled consistently, support professionals can spend more time identifying product issues, detecting churn and sentiment trends, improving knowledge, managing exceptions, and bringing customer insights to product and leadership teams. Maven AGI is designed around this human partnership. When judgment or empathy is required, cases can be escalated with conversation history, relevant customer context, actions already attempted, and the information agents need to continue without starting over.

14. Papaya answered 90% of inquiries autonomously and reached 70% FCR

Papaya reported that Agent Maven answered 90% of inquiries autonomously through chat, while reaching a 70% first-contact resolution rate and reducing cost per ticket by 50%.

The distinction between answers and resolutions matters. Answer rate shows how often AI responded, while first-contact resolution measures whether customers received a complete outcome without additional exchanges. Monitoring both metrics helps organizations avoid overstating automation performance. Maven AGI's data insights help teams examine conversation trends, sentiment, performance, and areas where knowledge or workflows need improvement.

15. Rho maintained 95% CSAT while monthly contacts increased 12%

Rho maintained 95% CSAT while supporting a 12% increase in monthly contacts after implementing Maven AGI's Zendesk-native AI Copilot. The deployment helped human agents reduce time spent on routine activities and add capacity for high-complexity investigations.

This result demonstrates how AI can support satisfaction at scale without removing human agents from the service model. The strongest use cases keep repetitive work off agents' plates while preserving human expertise for complex, sensitive, and high-impact customer needs.

What These Benchmarks Mean for Support Leaders

CSAT should be interpreted within the context of the organization's survey design, customer segment, channel mix, issue types, and historical performance. A single universal cutoff does not apply consistently across industries.

A practical measurement program should:

• Use a consistent survey scale and definition of a positive response.
• Segment CSAT by channel, issue category, customer type, and resolution path.
• Compare automated and human-assisted interactions without assuming one model should handle every case.
• Connect CSAT with first-contact resolution, customer effort, repeat contact, and qualitative feedback.
• Review low-scoring conversations to identify knowledge gaps, policy friction, or escalation failures.
• Track whether human agents receive sufficient context to continue escalated cases efficiently.

Maven AGI supports this approach through agent channels, performance analytics, enterprise knowledge, and configurable workflows. Maven Voice can handle real-time conversations, interruptions, approved actions, and contextual human handoffs for customer calls.

Implementation Priorities for CSAT Improvement

For organizations below their industry benchmark:

• Identify the recurring interaction types associated with low satisfaction.
• Validate whether policies, knowledge, wait times, or incomplete resolutions are driving the result.
• Strengthen customer support workflows for the highest-volume issues.
• Establish a consistent baseline before setting improvement targets.

For organizations near their industry benchmark:

• Segment results to uncover underperforming channels or customer groups.
• Automate repetitive requests where policies and outcomes are clearly defined.
• Improve knowledge quality through the knowledge graph.
• Give agents clearer escalation summaries, attempted actions, and recommended next steps.

For organizations targeting leading performance:

• Use conversation insights to identify recurring product and process friction.
• Extend service availability across nights, weekends, holidays, and demand spikes.
• Personalize interactions using approved customer and account context.
• Give support teams a larger role in CX, knowledge, product, and retention strategy.

Gartner predicts that agentic AI could autonomously resolve 80% of common customer service issues by 2029. Reaching that level responsibly will require strong governance, accurate measurement, clearly defined permissions, and reliable human escalation.

Frequently Asked Questions

What is a good CSAT score for my industry?

A good CSAT score depends on the survey scale, response criteria, sample, channel, customer segment, and issue type. Compare transactional CSAT with organizations using a similar methodology, then evaluate progress against your own historical baseline. ACSI industry scores provide useful broad context, but they should not be treated as directly equivalent to a CSAT percentage.

How is CSAT calculated?

CSAT is commonly calculated by dividing the number of positive responses by the total number of valid responses and multiplying by 100. On a five-point scale, many organizations count ratings of four and five as positive. The organization should document its methodology and keep it consistent over time.

How often should customer satisfaction be measured?

Transactional CSAT surveys are typically sent shortly after an interaction or resolution while the experience is still recent. Teams can review operational trends weekly or monthly, depending on response volume, and should avoid drawing conclusions from segments with insufficient data.

What is the difference between CSAT, NPS, and CES?

CSAT measures satisfaction with a specific interaction or experience. Net Promoter Score measures likelihood to recommend, while Customer Effort Score measures how easy it was to complete a task or resolve an issue. Using the three measures together can provide a more complete view of immediate satisfaction, loyalty, and friction.

Can AI improve satisfaction without depersonalizing service?

AI can improve speed, consistency, and availability when it handles appropriate repetitive workflows and provides accurate outcomes. It should also preserve access to human agents for cases requiring judgment, empathy, exception management, or relationship-building. Contextual escalation is critical because customers should not have to repeat information or restart the interaction.

How can negative CSAT feedback become actionable?

Categorize negative feedback by root cause, such as wait time, incomplete resolution, knowledge gaps, policy friction, tone, or escalation quality. Prioritize issues by frequency and customer impact, then compare scores before and after process, knowledge, or workflow changes. Conversation-level analysis can reveal patterns that aggregate scores alone may miss.