You may wonder: “We’re already running SMS campaigns, why switch?”

Well, 70% of users are more likely to communicate with brands through interactive messaging.

Clearly, customers crave something richer than bland text. That’s where rich communication services (RCS) come in. RCS chat features combine the trust of SMS with the interactive messaging your customers expect.

Let’s look at how RCS message features go beyond “rich messaging.”

Why RCS?

A plain text message can inform or ask, but it rarely engages. And today’s customers expect visual stimulation. No surprise, then, that 73% of businesses considered this rising need and boosted their budgets for visual communication platforms in the past year.

RCS messaging turns those standard texts into a fully branded experience with product images, quick-reply buttons, appointment confirmations, and even order tracking.

Sam Jacobs, the Head of Marketing at Ammo, on switching from SMS to RCS, says, “Our audience is visually driven, and with RCS, we send them images of our new collections and restocks. The 160-character limit on SMS limited how well we could tell a brand story and share features of our products.”

Apart from the 160-character limit, SMS falls short as:

• It supports only basic text and lacks interactivity or branding options.
• There’s no way to know if a message was read, and to track customer engagement beyond delivery.
• No sender verification means a higher risk of fraud and reduced customer trust.

SMS and RCS: How do they compare?

Before we dive into the RCS chat features, here’s a side-by-side breakdown of RCS vs. SMS to help you see how these messaging technologies differ and why RCS might be the smarter pick for your business.

Now that we know how these compare, let’s dive into RCS chat features and their benefits.

Key RCS features that boost customer engagement

Customers want rich media, two-way communication, and interactive branded texts. But how do these features benefit your business?

Rich media support that keeps your brand top of mind 

With rich media support, you can engage customers with images, videos, carousels, and GIFs directly in the message, making campaigns visually compelling and memorable. 

So a retailer can showcase new arrivals with product images and videos, rather than just a plain text link.

How does this benefit your business?

• Grabs attention instantly
• Boosts engagement and click-throughs
• Improves product discovery
• Strengthens brand recall
• Reduces friction in the buying journey
• Ideal for mobile-first shoppers

If your business has visual appeal, frequent promotions, or products/services that benefit from being “seen,” this feature helps.

Typing indicators and read receipts to optimize campaigns 

Unlike traditional SMS, you’ll know if your offers have been read, so you can fine-tune your send times and messaging strategies for maximum impact.

For example, if you see most customers read your message in the evening, you can schedule future offers to hit inboxes at that peak time for better results.

How does this benefit your business?

• Follow up at the right moment
• Get clear visibility into customer engagement
• Reduce response time with proactive support
• Refine campaign timing based on user behavior

Any business with a strong need for customer service, fast issue resolution, or timely decision-making benefits from this feature.

Suggested replies and quick actions for a smooth buying journey  

You can make it easy for customers to respond or take action with a single tap, boosting conversion rates and reducing friction.

For example, a bank can send a payment reminder with actionable buttons like “Pay Now” or “Contact Support,” while a sports facility can let users confirm or reschedule turf bookings — all through Plivo’s RCS messaging.

How does this benefit your business?

• Speed up customer responses
• Guide customers toward desired actions
• Reduce support workload
• Gather better data

Verified sender and branding to foster consumer trust

With verified business profiles, logos, and colors directly in the chat, users are more likely to trust your business. Plus, when they see your logo and a verified badge, it reduces fraud risk and increases open rates.

How does this benefit your business?

• Build immediate trust with customers
• Increase open and engagement rates
• Protect brand reputation against spoofing
• Enhance message consistency across campaigns
• Reinforce brand identity in every interaction

Any business that wants to minimize fraud and prevent spoofing benefits from this feature. 

Two-way interactivity to decrease wait times

RCS message features enable real-time, dynamic conversations where customers can ask questions, get support, or make purchases without leaving the chat.

For example, an airline can handle booking changes or customer queries instantly instead of relying on slow SMS replies or call centers. Passengers can even browse flight options, seat upgrades, or travel add-ons without leaving the chat.

How does this benefit your business? 

• Increase customer engagement through personalized messaging
• Resolve queries faster with back-and-forth messaging
• Reduce support wait times with instant communication
• Improve satisfaction by making customers feel heard

Delivery and engagement analytics

How can you be sure customers are even seeing, let alone clicking, your messages?

RCS messaging features include access to detailed analytics on delivery, reads, clicks, and engagement to maximize delivery and empower data-driven marketing. The data helps you see which promotions drive clicks and optimize future campaigns. (something SMS simply can’t provide!)

How does this benefit your business?

• Gain visibility into message delivery and read status
• Optimize send times and content based on real engagement data
• Reduce guesswork in campaign performance
• Improve ROI by doubling down on what works
• Track user behavior to personalize future campaigns

This feature helps marketing teams across industries analyze key success metrics and optimize strategies based on data.

End-to-end encryption (where supported)

This feature assures customers' privacy and security, especially for sensitive transactions or personal information. Industries like healthcare providers or banks can communicate securely, unlike with unencrypted SMS.

How does this benefit your business? 

• Protect sensitive customer data in transit
• Build trust by ensuring message privacy
• Comply with industry regulations and standards
• Reduce liability from data breaches

Any business handling sensitive information, like patient data protected under the Health Insurance Portability and Accountability Act (HIPAA), financial transactions, or compliance-regulated customer details, can benefit greatly from this feature.

Interactive messaging functions to increase open rates

A travel agency that lets customers swipe through a carousel of dreamy destinations right inside their chat is far more likely to secure a booking than one that sends a plain text list.

With interactive messaging functions, you can use carousels, rich cards, and buttons to create immersive, actionable experiences within chat.

For example, Elizabeth Lawrence, Managing Editor at American Military News, shares how using RCS for state‑specific polling initiatives boosted customer engagement and helped them build more precise, targeted strategies.

“We sent a message that had the poll link, a short issue summary, and two reply buttons: 'I Support' or 'I Don’t.” Once clicked, the respondent got follow-up content based on their answer, like tailored videos, quotes, and even links to community events.”

On talking about how it helped them segment their audiences better, she said:

“Instead of shouting at everyone with the same message, we let them direct the conversation with one tap. That gave our analytics team cleaner segments and gave users content they wanted.”

How does this benefit your business?

• Improve engagement by delivering location-relevant content
• Increase conversion rates through personalized messaging
• Gather more accurate customer insights and preferences
• Reduce campaign waste by avoiding irrelevant outreach
• Support A/B testing based on region or segment behavior

Engage smarter with Plivo’s RCS features

With rising expectations for faster support, more personalization, and app-like messaging experiences, you can't rely on basic SMS alone. Plivo’s RCS messaging capabilities make a significant difference here. It brings the power of rich, interactive communication to your existing systems to drive engagement, automate conversations, and build trust at scale.

Plivo easily plugs into your existing communication stack via intuitive APIs, so you can start using RCS without overhauling your infrastructure. You can also pair it with AI Agents to automate FAQs, drive personalized recommendations, and resolve queries 24/7.

Whether you’re a retailer promoting a product drop, a bank confirming transactions securely, or a healthcare provider following up on appointments, Plivo’s RCS solution ensures your message is timely delivered, seen, clicked, and remembered.

Get started for free to see it in action!

SMS has helped businesses with a wide reach for decades, but its limitations are clear: no branding, basic text only, and zero interactivity.

With rich communication services (RCS) business messaging, you’ll never have to choose between rich features or universal reach. It offers both and transforms standard texting into an interactive experience without requiring customers to download a third-party app.

Today, over a billion users have access to RCS across major platforms. So it’s high time we moved past the “should you use RCS” to “how quickly you’ll capitalize this unified messaging ecosystem” ones.

Here’s everything you need to know about RCS business messages, their types, capabilities, and use cases to streamline customer engagement.

What is RCS business messaging?

RCS messaging is a protocol that upgrades text messaging beyond the basic limitations of SMS. It adds features like high-resolution images, typing indicators, read receipts, and interactive buttons — all delivered directly to a mobile phone's native messaging app.

Simply put, RCS is SMS with superpowers.

While SMS limits you to 160 characters of plain text, RCS lets you send videos, offers suggested replies, and includes branded messages to help recipients take action right from the message.

Here’s an in-depth SMS vs. RCS discussion to catch up on their differences and benefits.

Barclays used RCS for business to reduce branch visits by guiding customers to their mobile app.

Their messages asked if customers knew they could make payments themselves, included a "See how" button that played a tutorial video, and provided a direct download link. This self-service approach improved customer experience while reducing operational costs.

Capabilities that RCS provides your business with

Here are the key features that make RCS better than traditional SMS.

Verified sender profiles

Trust drives purchasing decisions, and 87% of customers will pay more for products from brands they trust.

Every RCS business message comes from a verified sender profile that displays your business name with a verification badge. This tells recipients they're receiving legitimate communications, not spam or phishing attempts.

Branded messages

Your logo, brand colors, and business name appear at the top of every RCS communication. 

Studies show that 86% of consumers made two or more purchases in the previous year directly from branded text messages. When customers see your logo and signature colors in their messaging app, they're already primed to engage.

Modern interactive features

RCS supports a range of interactive elements that static SMS can't match.

Rich cards combine media, text (title and description), and calls to action (CTAs) in a single message. These cards can link directly to products, allowing customers to go from message to purchase with one tap.

Further, carousels let customers swipe through multiple products or offers without leaving the messaging app.

Cross-platform support

Apple's iOS 18 update, which introduced RCS support, has significantly reduced the communication gap between Android and iPhone users.

With both Android and iOS supporting RCS, you can now reach virtually all smartphone users with rich, interactive messages. There’s no need for separate strategies for different devices.

Advanced analytics

RCS provides detailed engagement metrics beyond simple delivery reports.

You can access read receipts, typing indicators, and click-through rates (CTRs) for every interactive element. This data shows which cards, buttons, or documents drive the most campaign engagement.

You can monitor read reports, customer actions, and identify trends to adjust your campaign strategies.

RCS vs. SMS vs. MMS: What's the difference?

You've used all three at some point: SMS for basic text, MMS for image transfer, and maybe RCS without even realizing it.

But what makes them fundamentally different? Let’s see.

Types of RCS for business messaging

Moving beyond the technical differences, let's look at how businesses apply RCS messaging in practice. Unlike SMS, RCS gives you three distinct message formats to match your communication needs.

Basic messages

Basic messages work much like SMS but with verification. They contain text-only content within 160 characters and include URL previews that recipients can tap to visit your website.

What sets these messages apart from SMS is that they come from verified business profiles with a brand logo displayed. This verification immediately creates trust, even though the message itself is simple.

Basic messages work best for:

• Authentication codes and one-time passwords
• Appointment reminders
• Order confirmations
• Service updates

The biggest advantage is that you can often use your existing SMS API and pricing structure. Many providers automatically "upgrade" SMS to Basic RCS messages on compatible devices, making this a zero-effort enhancement.

Single Rich messages

Single Rich messages break free from text-only limitations by adding visual and interactive elements. Here’s what you can include with these messages:

• High-resolution images and videos
• Interactive buttons for direct actions
• Rich cards displaying product information
• Carousels showcasing multiple items
• Location data with map previews

For example, take a travel company sending a booking confirmation as a Single Rich message. They add the hotel image, display the check-in date, and use action buttons to "Add to Calendar" or "View Reservation Details."

The result is high engagement with minimal backend efforts.

Conversational messages

Conversational messages enable real-time, two-way discussions between brands and customers.

They come in two forms. Brand-initiated (A2P) starts when a customer responds to your Basic or Single Rich message within 24 hours. On the other hand, user-initiated (P2A) begins when a customer messages you first or responds after the 24-hour window.

With these messages, your business can:

• Handle customer service inquiries
• Provide personalized recommendations
• Guide customers through complex processes
• Qualify leads before transferring to sales

The messages require more backend work but deliver the full RCS experience.

Say a travel company could help a customer select vacation packages, add excursions, and complete the booking, all without leaving the message thread.

RCS business messaging use cases

Now that you know the different types of RCS, it’s time to see them in action. Here are some ways businesses use RCS messaging and drive results.

Marketing campaigns

With RCS marketing, it’s easy to create immediate connections through visual content that customers can act on directly. You send product showcases with embedded purchase buttons, surveys with tappable answers, and promotions with instant booking options.

For example, EaseMyTrip used RCS for a post-COVID travel survey with quick-tap answer options and a thank-you coupon.

The campaign achieved a 4x higher CTR than email and 10x more survey completions, resulting in a 2.7% increase in conversion rate.

Holiday promotions

Holiday shopping periods demand special attention. RCS makes your seasonal promotions stand out with visual product showcases, limited-time offer countdowns, and immediate purchase paths.

Nespresso used RCS for their holiday campaign to showcase their lesser-known accessory collections as gift options.

Their messages featured product carousels with direct purchase links. Making the experience interactive and circular (allowing users to replay or explore more) facilitated a 25% increase in purchase intentions.

Payments and transactions

RCS streamlines financial transactions with secure, interactive payment flows. Banks can send payment requests with verification buttons, fraud alerts with immediate action options, and bill reminders with one-tap payment capabilities.

Take Axis Bank, which implemented RCS for loan, credit card, and fixed deposit management.

Their interactive banking chatbot activated over 2,000 users, enabled 45% cross-sell opportunities, and processed 6.5% of service requests directly through RCS messaging.

Customer engagement and loyalty

Loyalty programs often fail because they're forgettable. Instead of sending a static "You have 250 points" message, RCS creates interactive experiences.

Here, customers swipe through reward options, play mini-games to earn bonus points, or receive personalized recommendations based on their preferences.

ENGIE tackled its inactive loyalty program with an RCS Advent Calendar. Each day revealed new surprises, educational content about their "Mon Programme pour Agir," and opportunities to earn and immediately redeem points, transforming a forgotten program into a daily habit.

Travel check-in and boarding passes

RCS streamlines travel experiences by combining reservation management, check-in, and digital boarding passes in a single conversation.

Airlines can walk passengers through seat selection, meal choices, and safety information with visual guides and simple taps, then deliver a scannable boarding pass directly in the messaging thread.

Google's RCS integration with Google Wallet lets travelers save their boarding pass with one tap. Once saved, the pass updates automatically with gate changes or delays, ensuring travelers always have accurate information without searching through emails or downloading airline apps.

RCS for business messaging is the future

As we've seen across these use cases, RCS is not an emerging technology anymore; it’s become a communication standard. But where is this technology headed?

According to Juniper Research, RCS business messaging will grow from just 3% of operator messaging revenue in 2024 to 18% by 2029. Total revenue will jump from $1.8 billion to $8.7 billion — a 370% increase in just five years.

What's driving this growth? Trust.

As SMS becomes increasingly compromised by scams and phishing attempts, businesses are migrating to RCS, where brand verification comes standard. 

Market penetration is another factor. With Android holding over 70% of the global mobile market share and Apple's adoption, RCS now reaches every smartphone user. This universal reach without requiring app downloads gives RCS a significant advantage over platforms like WhatsApp or Facebook Messenger.

The integration of AI with RCS will further accelerate adoption.

Smart chatbots that understand customer intent can handle routine inquiries and provide a personalized experience. Unlike traditional chatbots, RCS bots can share rich media, walk customers through complex processes with visual guidance, and seamlessly transfer to human agents when needed.

For your business, this transition represents an opportunity to get ahead. Companies that adopt RCS now will build familiarity with the platform while developing communication strategies that take full advantage of its capabilities.

Simplify your customer engagement with Plivo RCS API

With RCS positioned to transform business messaging, you need the right tools to capitalize on this opportunity.

Plivo's RCS API helps you bridge the gap between current SMS capabilities and the personalized, advanced experiences customers now expect. The platform's pay-on-delivery model means you only pay for messages that reach customers.

You also get valuable engagement metrics, including read receipts and button click data that traditional SMS can't provide. Plus, there are intelligent chatbot flows to help with instant issue resolution.

With RCS expanding its user base and showing engagement rates 35x higher than email, Plivo's API gives you a direct path to this growing audience. If your customers are among the 70% more likely to engage with brands through RCS, Plivo gives you the tools to meet them there.

Get started for free and build better customer conversations today.

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Many businesses invest heavily in customer experience(CX) strategies, believing that the right tools and technologies will immediately improve their bottom line. 

They make changes, launch campaigns, and yet...

Customer satisfaction scores do not bulge, retention rates stay the same, and the promised growth in revenue feels elusive. 

This often leads to the question: Is CX really the key to success?

Here's the thing: Successful CX strategies depend on measuring what truly matters, not just implementing random initiatives. When you track and optimize the metrics that capture genuine customer behavior patterns and pain points, your customer experience strategy transforms from a costly obligation into your most powerful revenue engine.

In this article, we'll explore the critical customer experience stats and CX trends for 2025 that provide actionable insights to not only meet customer expectations but exceed them in ways that impact your bottom line.

Why CX statistics matter for businesses

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• Improving customer engagement and satisfaction: CX statistics help businesses understand customer behavior and preferences. This allows them to tailor interactions for better engagement and satisfaction.
• Using data to support budget allocation: With clear CX data, businesses can demonstrate the value of customer experience improvements. This justifies the allocation of resources and budget for CX initiatives.
• Identifying key areas for improvement in CX strategies: CX statistics highlight pain points and gaps in the customer journey. This enables businesses to focus on areas that need optimization to enhance the overall experience.

36 eye-opening customer experience statistics 

To stay ahead, businesses must keep a close eye on emerging trends and some important customer experience data that will define the future of CX. 

1. Customer satisfaction rates and benchmarks

As we look ahead to 2025, businesses must stay on top of evolving customer experience (CX) trends and performance metrics. According to Gartner, conversational generative AI (GenAI) is a top priority in 2025 to enhance customer interactions and drive enterprise growth through upselling and cross-selling.

Customer satisfaction is typically assessed using several key metrics:

Key statistics on CX satisfaction and expectations

• 94% of customers rate video support as a positive experience.
• In a global survey of over 75,000 consumers across 33 markets, the average customer service satisfaction rating for banks was 3.97 out of 5 in 2025, with customer service ranked as the fourth most important aspect of banking.
• 90% of CX leaders report that customer expectations are higher than ever before.

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As customers use multiple channels, businesses need to focus on offering smooth experiences across all touchpoints.

2. Response times and customer expectations

In 2025, customer expectations regarding response times have become more stringent. 

According to IBM, 60% of service professionals report that customer expectations have increased since before the pandemic, making timely responses critical to customer retention and business growth. 

IBM also highlights that quick resolution and reducing handling time to 53 seconds per interaction for one of their clients significantly improves customer satisfaction.

This is primarily influenced by technological advancements in digital communication channels. 

Here's an overview of the current landscape:

• Customer service teams respond to requests in an average of 12 hours and 10 minutes.
• In the US, 46% of consumers are willing to wait up to three days for a response from a brand they contacted.
• Email response time is a critical KPI; responding within 5 to 10 minutes can reduce customer dissatisfaction by 400% 

Meeting customer expectations requires strategic investments in staffing, technology, and process optimization to ensure timely and effective customer service.

3. Understanding the shifting channels of customer interaction

The digital transformation of customer interactions has fundamentally reshaped how businesses engage with consumers. Channel preferences are evolving rapidly, and omnichannel strategies have become non-negotiable for competitive success.

The dominance of traditional and digital channels

A survey conducted in 2024 in the United States examined consumer preferences for channels when interacting with AI-powered customer service. 

For both consumers aware and unaware of AI, phone support emerged as the top choice, with 72% of the aware group and 65% of the unaware group preferring it. 

The second choice for the aware group was online support, with around 60% opting for it, while for the unaware group, SMS or text support was the second choice, with about 55% preferring it.

This persistence highlights the enduring value of human-like interaction, even in AI-enhanced systems.

However, text-based channels are gaining ground; 86% of businesses utilized SMS marketing in 2023, marking a 56% increase from 2022.

Generational differences make it harder to create effective channel strategies. Gen Z consumers prioritize social media and mobile-first platforms, with 78% of U.S. TikTok users making purchases based on influencer recommendations. 

On the other hand, 57% of shoppers aged 18 to 24 discover new products through recommendations, highlighting the importance of personalized suggestions for younger demographics. This contrast underscores the need to tailor strategies to different generations.

4. The role of AI and automation in CX

AI-driven solutions are becoming an integral part of customer support. The adoption of AI technologies simplifies customer operations and also reduces costs. 

Let’s take a look at some key statistics showcasing the impact of AI in customer support and service.

• Global AI adoption in customer support:  80% of companies are either using or planning to adopt AI-powered chatbots for customer service by 2025. 
• AI in healthcare: NIB Health Insurance saved $22 million through AI-driven digital assistants, reducing customer service costs by 60%. 
• AI's Impact on customer support efficiency: AI-enabled customer service teams have saved 45% of the time spent on calls, resolving customer issues 44% faster, leading to reduced operational costs.
• Increased AI utilization across functions: A McKinsey survey reveals that the use of AI has risen from 72% in early 2024 to 78%, with significant adoption in marketing, sales, and service operations

5. The impact of personalization and hyper-personalization in CX

Customers expect tailored experiences that address their needs, and businesses that excel at personalization are reaping the rewards. 

Generative AI is helping brands to scale this personalization, and as AI continues to evolve, hyper-personalization is now possible, where every interaction feels unique. 

The global revenue of the customer experience personalization and optimization software and services industry is projected to reach 11.6 billion U.S. dollars by 2026, marking an increase of over 65 percent from current levels.

AI-driven platforms, like Plivo, are enabling businesses to scale hyper-personalization. By integrating communication channels, they tailor experiences to meet each customer's unique preferences. 

Here are some key statistics that highlight its influence on customer behavior:

• 84% of customers prefer companies that offer a personalized experience 
• 79% consider a personalized experience important when combining "Very Important" and "Important" ratings
• AI-powered personalization can drive up to a 15% increase in revenue
• 61% of consumers would spend more for tailored experiences, yet only a quarter (25%) of experiences are considered highly personalized.

Unifying customer data for intelligent experiences

The key to providing personalized customer experiences is integrating and unifying data across multiple platforms. Businesses that connect customer touchpoints can create a more intelligent and cohesive experience.

• 70% of marketing leaders struggle with disconnected customer data and are unable to reach ideal segments
• Unified data enables both AI and human agents to offer more accurate and personalized service.

CX ROI and business outcomes

Investing in customer experience improves customer satisfaction and also delivers clear business outcomes, from higher revenue growth to greater profitability.

According to Forrester, here are some key stats showing CX's impact on organizational performance:

• 41% faster revenue growth for organizations that excel in CX.
• 49% faster profit growth and 51% better customer retention for customer-obsessed businesses.

The impact of poor customer experiences on churn

Failing to meet customer expectations can have severe consequences like lost sales and damaged reputations. Poor experiences are a leading cause of churn and can cost companies billions.

According to IBM, voluntary customer churn-where customers leave due to reasons such as bad customer experience-accounts for about 3% of the average churn rate, compared to 1% for involuntary churn. 

This highlights that poor customer experience is a significant driver of customer loss.

How industries are adapting to CX shifts

Here’s a look at how various sectors are adapting to meet the rising customer expectations.

Retail

Retail has undergone a dramatic transformation with the rise of e-commerce and the demand for seamless omnichannel experiences. Retailers are now focusing on integrating their online and in-store services to provide a smooth shopping experience for consumers.

For example, luxury auto manufacturers and retailers improved their industry-average CX score in 2023, with brands like Infiniti, Lincoln, and Tractor Supply joining the top 5% of brands in the CX Index.

Banking

In banking, the shift toward digital-first solutions is changing how customers engage with financial institutions. From mobile banking apps to AI-driven financial advice, banks are leveraging technology to meet the needs of tech-savvy customers.

• 78% of financial institutions have implemented generative AI to enhance customer service, simplify interactions, and boost efficiency.
• Banks are increasingly adopting hybrid cloud technologies to securely store and manage customer data, with over 60% of CEOs in the banking sector prioritizing cybersecurity as part of their CX strategy.

Telecom

The telecom industry is embracing automation and AI to improve customer service and manage the increasing demand for better connectivity. Telecom companies are now offering more digital services to enhance customer engagement and satisfaction.

• In 2024, the Communication & Media sector, which includes telecom companies, had the lowest customer satisfaction (CSAT) scores compared to other industries. However, in 2025, their CSAT score showed improvement, reaching 22.
• 62% of telecommunications providers enhance customer experience through generative AI, with expectations to rise to 90% by 2027. 

Healthcare

Healthcare has become increasingly digital and is integrating new technologies to enhance patient care and improve operational efficiency.

A 2021 Accenture study revealed that two-thirds of Americans have had a negative healthcare experience, highlighting the need for significant improvements in service delivery.

Simplifying CX with Plivo’s AI-driven solutions

It is, undoubtedly, not an easy task to provide personalized, efficient customer support at scale is a significant challenge for many businesses today. Traditional customer service models struggle to meet the growing demand for real-time engagement and great customer experiences across multiple touchpoints. 

This is where Plivo CX steps in.

As an all-in-one omnichannel customer engagement platform, Plivo CX offers AI-driven solutions that simplify customer interactions and enhance overall customer engagement.

We deliver a suite of intelligent tools designed to optimize every aspect of customer service. This ensures timely and personalized experiences without draining your money.

Here’s how Plivo CX helps you to enhance CX and customer service:

• Flexible customer segmentation: Use the Audiences feature to categorize customers based on their behavior, preferences, and past interactions. This ensures that each message is relevant and personalized for the right audience.

Timely engagement: Automate messaging and scheduling to engage customers at the optimal time across different time zones.

• Sales conversion agent: Utilize AI to create personalized offers, guiding potential customers through the sales funnel with targeted messages.
• Customer retention agent: Proactively reach out to inactive customers with tailored messages. This helps businesses re-engage and retain valuable customers while reducing churn.
• Copywriter AI: Leverage Copywriter AI to craft compelling, personalized SMS with the help of AI-driven suggestions and pre-built templates. This ensures each message resonates with your audience.

• Pre-sales inquiry agent: Plivo’s AI-powered agents quickly and accurately handle inquiries. This provides prospective customers with the information they need to make informed purchasing decisions.
• E-commerce platform integrations: Seamlessly integrate with popular e-commerce platforms like Shopify, BigCommerce, WooCommerce, and Magento. You can send notifications directly from your system.

With Plivo’s robust carrier network spanning over 220 countries, businesses can slash SMS expenses by up to 70% while experiencing a threefold increase in ROI. 

Businesses using Plivo CX have experienced impressive returns with $71 generated per dollar spent on SMS marketing campaigns. 

Book a free demo today and succeed in delivering exceptional customer service.

Contact centers are stretched thin. Agents spend nearly 70% of their week on tasks that don’t drive revenue: searching for information, entering data, or answering repetitive queries.

While they jump between systems, customers are kept waiting. Supervisors catch these inefficiencies too late, and reactive tools only add to the delay.

Real‑time agent assist (RTAA) turns this around. It delivers the right information at the exact moment agents need it, helping them move faster and stay focused.

The results speak for themselves. Early adopters report a 15% increase in agent productivity.

Want to see how RTAA can transform your support ops? Let’s get into it.

What is real‑time agent assist?

Real‑time agent assist is a tool that gives support agents the information they need during a live call. It relies on natural language processing (NLP) and machine learning (ML) to understand what’s being said.

RTAA pulls relevant data from knowledge bases and customer relationship management (CRM) systems to show the best suggestions in real time. This cuts down on guesswork and lets agents handle issues more smoothly.

How does real-time agent assist work?

When an agent-customer call starts, the tool quietly works alongside them.

First, the system uses automatic speech recognition (ASR). The technology listens to the conversation and turns spoken words into written text.

Next, both the text and voice inputs are sent to spoken language understanding (SLU), which analyzes the words to understand their meaning and the emotions behind them. Then, before anything appears on the agent's screen, all this processed information passes through the recommendation system.

It analyzes everything and decides what insights are most useful for the agent at that exact moment. Some information becomes real-time guidance, while other details might be shown with specific advice.

Finally, agents see these processed insights instantly on their screens.

These insights can be used through various features. Let’s examine these one by one.

Core capabilities of real-time agent assist

While the agent talks, this tool listens, understands, and acts — all at once. These are the core capabilities that make it happen.

Real-time guidance

Real-time guidance means agents get on-screen tips while they chat with customers. These suggestions help them fix issues and offer the right products. 

If the customer sounds frustrated, it even suggests what to say or when to escalate. Each tip shows up at just the right moment, so agents know what to do next without guessing.

The result is faster replies, fewer mistakes, and happier customers.

Plivo’s Unified Agent Desktop makes this possible by pulling everything into one place.

Let’s say a customer sends a WhatsApp message asking about a missing order.

The message pops up on the agent’s screen along with the customer’s full history. Past orders, complaints, call logs, and previous chats are right there.

That’s because Plivo integrates with your e-commerce, CRM, and ticketing platforms — so all the context an agent needs is centralized.

If a customer asks about an order, the system spots the issue and instantly suggests the right tracking link.

The agent clicks, sends it over, and the problem’s solved in seconds.

Dynamic knowledge integration

When agents don’t have quick access to the right information, calls drag on, customers get frustrated, and issues go unresolved.

The challenge isn’t that answers don’t exist, it’s that they’re buried across tools, tabs, and team documents.

Dynamic knowledge integration solves this by pulling the right info from the right system at the right time without making agents search for it. Plivo brings it to life inside its Unified Agent Desktop with integrated knowledge bases.

Guided script

Live calls can go off track fast, especially when the issue is complex or the customer is upset.

Guided scripts help agents stay sharp by showing a live, step-by-step checklist during the call. It highlights what to ask, what to confirm, and what’s still pending. If the agent misses a key question, the system immediately flags it, ensuring nothing important gets overlooked.

During an insurance claim, for example, it might remind the agent to ask for accident details, vehicle info, and witness names.

This results in more structured calls, fewer mistakes, and customers feel like they’re in good hands.

Workflow automation

After every call, there’s more work to do — forms to fill, follow-ups to schedule, tickets to update. It eats into time and pulls agents away from the next conversation.

Workflow automation removes that extra load. It handles repetitive steps in the background, so agents can stay focused on helping customers.

Plivo makes that happen with AI Agents. They can log conversation details, trigger workflows, and update internal tools using existing business logic.

Tasks like refund approvals, appointment confirmations, loyalty updates, and survey requests are handled in the background. The agent finishes the call, and everything else moves forward on its own.

No repetition, no delay.

Why traditional support tools fall short

Most support tools weren’t built for fast-moving conversations or rising customer expectations. Here’s why they fall short:

Agents waste time chasing information

Customer data is often spread across disconnected tools like spreadsheets, emails, CRM entries, and chat logs.

A simple billing issue might mean checking payment history in one place, looking up complaints in another, and searching inboxes for policy files. By the time the agent pulls everything together, the customer is already frustrated, and the call has gone longer than it should.

Supervisors can’t see what’s happening

Most tools only surface problems after the call ends, when it's already too late to fix anything.

Supervisors can’t see when agents are struggling or when conversations start to go off track. They miss patterns like a surge in refund requests or customers repeatedly asking about the same policy.

Without real-time visibility, there’s no way to coach during the moment or step in before things escalate. Feedback feels disconnected, and agents miss the chance to improve where it matters most.

For example, a customer keeps pushing back on refund terms, and the agent hesitates. With live monitoring, a supervisor could instantly suggest a better explanation or offer backup, turning a tense moment into a smooth resolution.

Reactive alerts that come too late

62% of customers expect companies to anticipate their needs, yet most support tools only react after an issue is raised. A missed upsell, a delay in apologizing, or a dropped follow-up often surfaces after the call ends, when it’s too late to fix.

Agents need prompts while the conversation is still happening.

For instance, during a billing call, a customer might ask if their current plan includes international coverage, signaling potential interest in an upgrade. Without a timely prompt, the agent moves on, missing a clear opportunity to upsell.

Without this real-time guidance, teams stay stuck in reactive mode as missed chances pile up, and customer trust takes a hit.

Benefits of real-time agent assist

With real-time support built into every step, agents don’t have to slow down or second-guess. Here’s how it plays out on live calls:

Live call and screen monitoring

Real-time tools follow both the conversation and what’s happening on the agent’s screen. If a customer asks a tricky question, the system can suggest a reply or surface the right info instantly.

The agent responds faster, and the call keeps moving.

Support that works anywhere

Agents get the same real-time guidance and assistance no matter where they’re based: at home, in the office, or working a hybrid schedule.

The system stays consistent across every setup, so there's no drop in support quality, no missing context, and no disruption to the customer experience.

Faster fixes, lower costs

When agents get instant guidance, they don’t need to put customers on hold or transfer the call. Problems get solved faster, customers don’t have to wait, and agents save time.

In the long run, this can bring down call costs without reducing service quality.

Less agent churn

Live calls are tough, especially when agents feel stuck. Real-time support takes some of that pressure off by offering help in the moment. It builds confidence, shortens training time, and makes the day-to-day feel less overwhelming.

How to implement real-time agent assist

Rolling out real-time agent assist successfully takes the right setup, the right tool, and a plan to make it stick.

Here’s how to do it step by step:

Step #1: Assess readiness

Before you roll out real-time agent assist, take stock of what you already have. Start with your tech. Are your current systems ready to connect with an RTAA tool? If they’re outdated or too siloed, you may run into issues early on.

Next, look at how your agents work. Where do they lose time or get stuck? Knowing this helps you spot where RTAA can step in and where you might need to adjust workflows.

And don’t forget your customers. What are the recurring problems they face? If you know what frustrates them, you can shape RTAA to address those gaps from the start.

Step #2: Choose the right solution

Real-time agent assist only works if it fits your operations, not the other way around. Many solutions promise "AI-powered support" but fall apart in practice with too many logins, clunky integrations, or dashboards that agents avoid.

To find a solution that works, look for these essentials:

• Centralized experience: Agents shouldn’t have to juggle five tabs to help one customer. Look for an interface where voice, chat, tickets, customer history, and AI prompts all show up in one place.
• Plug-and-play integrations: The tool must work with your existing CRM, ticketing, order systems, and knowledge base without breaking workflows or needing months of custom dev work.
• Scalable performance: As teams grow and call volumes spike, the system should remain responsive. Look for solutions with proven uptime, low latency, and robust application programming interface (API) coverage.
• No learning curve for agents: If it takes a training manual to use it, agents won’t. The tool should feel intuitive, like a natural extension of their workflow.

That’s exactly what you get with Plivo’s Unified Agent Desktop. It brings every customer interaction (voice, messaging, tickets, and insights) into a single screen powered by AI. The tool works behind the scenes to prompt, suggest, and guide in real time.

Step #3: Train and empower agents

The success of any real-time assist tool depends on agent adoption.

To build trust, start with a clear explanation of how the system works. Show agents what it listens for, how it generates prompts, and what kind of help it provides during live conversations.

The goal is to position it as a support system, not a surveillance tool.

Walk agents through real scenarios where the tool steps in, such as flagging rising frustration, suggesting a missing step, or surfacing key product info before the customer even asks. Once agents see how it helps them work smarter, they’re far more likely to rely on it.

It’s also important to keep the feedback loop open. Encourage agents to share what’s working and what isn’t. If something feels off or distracting, it should be adjusted.

Step #4: Measure impact

Once real-time agent assist is live, track what’s changing. Focus on call center metrics that reflect both performance and experience.

Start with these:

• First call resolution (FCR): Higher resolution rates show that agents are handling queries more effectively during the first interaction, reducing the need for callbacks or escalations.
• Average handle time (AHT): If calls become more efficient while maintaining quality, agents are clearly benefiting from timely prompts and faster access to answers.
• Customer satisfaction (CSAT): An increase in satisfaction scores reflects smoother conversations and stronger outcomes from the customer’s perspective.
• Agent retention: When agents feel supported and less overwhelmed, they’re more likely to stay, especially in high-volume or high-pressure environments.

These indicators work together to reveal the system’s true impact.

With Plivo, all of this data is accessible through a single interface, allowing teams to track trends, identify gaps, and adjust as needed without adding operational complexity.

Future-ready your contact centers with Plivo

Plivo CX gives enterprises a simpler, more flexible way to manage customer service. Its cloud-based platform brings everything — voice, chat, and messaging into one place. This helps your team focus on solving problems instead of switching tools.

Here’s what it brings to your contact center:

• AI-powered live chat that answers common questions instantly using your knowledge base
• Custom knowledge base integration so agents always have accurate, up-to-date information
• Unified Agent Desktop to manage conversations across channels without losing context
• Smart routing based on agent skills and predictive insights to speed up resolutions
• Workflow builder to automate customer journeys across all communication channels
• API support to customize workflows and connect with your existing tools

Get started for free and bring speed, clarity, and consistency to every customer conversation with Plivo CX.

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Accenture’s 2024 report indicates that 74% of organizations say their investments in generative AI and automation have met or exceeded expectations. Even more telling, 63% plan to increase that investment by 2026.

This sends a clear signal: AI is paying off.

Now the question is, are you using it to its full potential? Or are outdated systems slowing your team down?

To help you get a full overview of the current landscape, we have handpicked top customer service stats from McKinsey, PwC, Gartner, and other credible sources. You’ll see exactly where AI agents deliver results and how Plivo helps you act on them.

Top AI agent statistics 

AI agents are changing how you deliver customer service. But how much impact are they really making? 

To help you make smarter decisions, we’ve broken down the latest statistics into seven key categories. Each one highlights where AI agents impact, from adoption and performance to ROI, customer preferences, and challenges.

Growth of AI agents in customer service

More companies are deploying AI agents to reduce wait times, cut support costs, and scale faster. Let’s look at the numbers showing how fast AI adoption is accelerating:

1. The AI agents market was valued at $3.7 billion in 2023 and is projected to reach $103.6 billion by 2032, growing at a CAGR of 44.9% from 2024 to 2032 (S&S Insider)
2. 63% of top-performing companies plan to increase cloud budgets by 6% or more (PwC)
3. 85% of enterprises are using AI in 2025 (Wiz)
4. By 2025, 80% of companies will have adopted or plan to adopt AI-powered chatbots to support their customer service operations (Gartner)
5. As of early 2025, 78% of organizations are using AI in at least one business function, up from 72% in early 2024, showing steady growth in AI adoption across industries (McKinsey)
6. 78% of respondents report using AI in at least one business function, a rise from 72% in early 2024 and 55% the year before, showing rapid growth in AI adoption(McKinsey)

7.  The share of companies running fully AI-led operations jumped from 9% in 2023 to 16% in 2024. These businesses are seeing 2.4 times higher productivity and  developing more effective retention strategies (Accenture)
8.  By 2029, AI agents will autonomously resolve 80% of common customer service issues, eliminating the need for human intervention in most routine cases (Gartner)
9.  63% of retail organizations report using generative AI to enhance their existing customer service efforts (Capgemini)
10.  Organizations are turning to AI to drive top-line growth and stand out from competitors. 42% aim to improve product or service quality, while 39% are focused on  boosting revenue. At the same time, they’re using AI to enhance operations—40% to increase workforce productivity, 41% to improve IT efficiency, and 39% to  speed up innovation (Weka)

Use cases: where AI agents are creating impact

If you think AI has limited use, these stats will shift your perspective:

11. Automating just up to 20% of support tickets can lead to an 8-point increase in repeat purchase rates within 28 days (Gorgias)
12. Automated responses resolve some tickets instantly and help teams respond faster overall. On average, merchants using automation reply 37% faster than those who don’t (Gorgias)
13. Merchants who automate customer tickets resolve them 52% faster than those who don’t, showing how AI significantly speeds up support (Gorgias)
14. AI agents will cut the time needed to exploit account exposures by 50%, emphasizing the importance of AI in both defense and risk response by 2027(Gartner)
15. Organizations using Gen AI–enabled customer service agents saw a 14% increase in issue resolution per hour and a 9% reduction in time spent handling issues (McKinsey)

Performance metrics that matter

AI agents now match or exceed human benchmarks in speed, accuracy, and resolution rates. But how do they actually perform under pressure? These numbers tell you what to expect and where to set your goals:

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16. 72 % of top-performing companies have seen increased productivity by implementing AI (PwC)
17. Using generative AI tools boosts user performance by 66% on average. The biggest gains come from complex tasks, with less-skilled workers seeing the most improvement (Nelisen Norman Group)
18. Conversational AI in contact centers will cut agent customer service operations costs by $80 billion by 2026. In 2022, global end-user spending on these solutions reached $1.99 billion, showing substantial investment in automation (Gartner)
19. Among leading generative AI adopters, the majority report a high or very high impact on key business areas: 79% say it boosts innovation, 76% say it supports new product launches, and 76% see faster time to market—all contributing to stronger competitive positioning  (Weka)
20. Customer support agents using a generative AI assistant boosted their productivity by 14% on average (NBER)
21. Workers are, on average, 33% more productive during each hour they use generative AI (Federal Reserve Bank of St. Louis) 

AI agent ROI and cost efficiency

Businesses are saving millions in overhead while improving support outcomes. Want to know how much ROI you could unlock with AI agents? These stats break it down:

22. Support agents using AI tools can manage 13.8% more customer inquiries per hour, boosting productivity without the need to hire more staff (Nielsen Norman Group)
23. Generative AI and related technologies could automate tasks that currently take up 60 to 70% of employees’ time, significantly changing how work gets done (McKinsey)
24. AI-powered systems have led to a 31.5% boost in customer satisfaction scores and a 24.8% increase in customer retention, showing clear gains in both experience and loyalty (ResearchGate)
25. Companies using generative AI are 35% less likely to report that human agents feel overwhelmed by the amount of information during customer calls (Deloitte)
26. 9 in 10 of organizations using AI report saving both time and money. Service operations professionals are especially optimistic, nearly all believe generative AI will improve their company’s customer service (Salesforce)
27. 1 in 3 companies with omnichannel integration tools resulted in 9% lower cost per assisted contact (Deloitte)

AI agents + human agents: The hybrid advantage

The best support teams blend human empathy with AI efficiency. AI agents handle volume. Human agents step in for nuance. These figures show what’s working and where companies get it wrong:

28. 71% of customers believe it is important for a human to validate the output of AI (Salesforce)
29. Today, 54% of workers worldwide trust humans and AI to work together on most tasks, highlighting the need for businesses to implement AI responsibly and in ways that earn user trust(Salesforce)
30. Among organizations using generative AI, 27% say their employees review all AI-generated content before it’s used, whether it’s a chatbot response for a customer or an image for marketing (McKinsey)
31. While 68% of people have used automated customer service chatbots, 88% still prefer speaking with a human when they need support, highlighting the ongoing importance of human interaction (Ipsos)
32. 87% of executives believe generative AI will augment jobs rather than replace them, suggesting most roles will evolve with AI support instead of being automated away (IBM)
33. Executives can drive AI adoption by showing employees that the goal is to add value, not just cut costs. Companies that foster a culture of experimentation and don’t penalize failure see a 10% boost in revenue growth during tech adoption. Among AI users, those with this open mindset achieve a 22% higher revenue growth rate than those who don’t (IBM)

AI Agents and Customer Preferences

Customers care about fast, helpful answers, not who gives them. But preferences still matter. You’ll want to see what real users are saying before making your next CX decision:

34. 28% of respondents have used AI-powered visual search to find products that look like what they’re interested in purchasing (BCG)
35. Nearly 24% of consumers are already comfortable with AI agents making purchases on their behalf, and that number rises to 32% among Gen Z shoppers (Salesforce)
36. About one in three consumers prefer to buy products through automated or digital channels, such as AI agents, rather than interacting with a person (Salesforce)
37. Some of the top reasons for customers interacting with AI include better availability (41%), faster issue resolution (37%), and more accurate information (30%) (SurveyMonkey)
38. 52% of consumers are interested in using AI to guide them through a product, website, or feature. 47% want personalized deals based on their purchase history, and 42% are interested in AI-driven product recommendations (SurveyMonkey)
39. 89% of customers say it’s important to know whether they’re interacting with a human or an AI, highlighting the need for transparency in customer support (Salesforce)

Challenges & considerations

If you’re thinking about scaling AI, you need to know what could go wrong and how others navigate it. These stats show the friction points worth planning for: 

40. 92% of analytics and IT leaders say that the demand for trustworthy data is at an all-time high, especially as AI becomes more central to decision-making (Salesforce)
41. 61% of workers are open to using generative AI, but many still lack access to reliable data and the security skills needed to use it effectively (Salesforce)
42. Nearly 64% of organizations express concern about how AI and machine learning projects affect their energy consumption and carbon footprint. 25% say they are very concerned (Weka)

43. 35% of organizations cite storage and data management as the top infrastructure barriers to AI adoption. However, those with widespread AI implementation report fewer issues in these areas (Weka)
44. 37% of the U.S. IT leaders see data quality as a major obstacle to AI success (Hitachi Vantara)
45. 40% of employees will need retraining due to AI adoption, underscoring the need for ongoing learning and strong human oversight (IBM)

Experience next-gen AI-driven engagement with Plivo 

The data speaks for itself—AI agents are no longer optional. They mean faster resolutions, lower costs, and higher satisfaction. But success depends on choosing the right partner.

Plivo is an all-in-one omnichannel customer service platform that  helps you deploy open AI-powered AI agents that actually perform. No overpromises. Just faster responses, fewer escalations, and better CX.

With Plivo, you get:

• AI agents for instant support: Handle FAQs, status checks, and call routing automatically, reducing agent load and response times 24/7
• Metrics & reporting: Get live insights into call volume, handle time, and team performance
• Smart, context-rich escalations: Transfer complex issues to human agents with full conversation history. No repeated questions, no friction
• Easy integrations: Connect Plivo to your CRM, helpdesk, and other tools without heavy dev work
• Flexible scalability: Whether you’re supporting 10 users or 10,000, Plivo adapts to your scale and needs
• Workflow automation: Automate follow-ups, ticket routing, and updates to keep customers informed without manual effort
• Enterprise-grade security: Built with SOC 2 and GDPR compliance to keep your data safe and your business trustworthy

Want to see how AI agents transform your support strategy? Book a demo now.

Organizations are struggling to do more with less when it comes to customer success. Many organizations are facing tight budgets. As TSIA’s The State of Customer Success 2025 webinar showcases, in this money crunch, they are moving towards AI in customer success to automate repetitive tasks. 

In fact, 40% of organizations have already deployed AI in customer success management operations. AI-powered customer success agents are a promising solution in this area. 

In this post, we will look at what a customer success AI agent is, its top use cases, and how to implement one. 

What is a customer success AI agent?

A customer success AI agent uses machine learning and artificial intelligence to automate the routine work of customer success managers (CSMs) such as upselling, cross-selling, promotions, engagement, etc. It operates like a virtual assistant to CSMs but is an automated one. 

How do customer success AI agents differ from AI-powered chatbots?

AI-powered chatbots typically answer customers' queries and help with customer service. On the other hand, customer success AI agents are capable of doing more advanced functionalities in marketing, customer engagement, and customer service.

How does an AI customer success agent work?

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An AI-powered customer success agent typically works in four steps:

1. Data collection: It connects with your existing CRM platforms, support portals, and marketing platforms to create a complete database of customers (website activity, support queries, orders, etc.).

2. Data analysis: Once the data is collected, AI agents analyze it using natural language processing and machine learning. These interpretations can be stored as customer profiles, which can then be used to personalize any further customer interactions.

3. Task automation: AI agents can automate specific tasks in custom engagement/service and use the interpreted data to personalize communication. 

4. Learning and adaptation: AI agents also have self-learning capabilities, which means they can continuously improve by analyzing past interactions and responses.

Top AI as customer success agent use cases

Some top tasks of CSMs where AI customer success agents can assist are: 

1. Personalized communication

AI customer success agents analyze past customer interactions (mail, chat, calls, and support tickets) and send personalized communication. This communication could be related to onboarding, predictive alerts, automated check-ins, nudges, follow-up messages, feedback surveys, etc.

2. Customer engagement

AI customer success agents can further run customer campaigns for engagement, such as welcome series, feedback surveys, and more. These campaigns help improve customer retention/loyalty and increase production adoption.

3. Upselling and cross-selling

AI customer success agents analyze past customer data (activity, orders, etc) and run:

• Upselling campaigns: Recommending higher-tier products
• Cross-selling campaigns: Suggesting complementary products. 

4. Customer support

AI customer success agents can also take away some manual customer support tasks off the CSM's task list, such as:

• Ticket routing: It analyzes the ticket, categorizes it, and assigns it to the relevant department for resolution.
• Call routing: It analyzes the customer’s query and automatically routes calls to the next best executive available for the customer call.
• Customer support: It acts as a virtual support agent and handles some repetitive customer queries.  

AI agents transform customer support with automation.

5. Customer health scoring

AI customer success agents analyze data from existing systems (CRM, marketing, support tickets, etc) and build customer profiles with a score. These profiles can help identify which customers are more likely to come back and which are more likely to churn. You can further build marketing campaigns using these scores.

6. Call quality monitoring

AI customer success agents automatically analyze customer call recordings to assess the following:

• Call quality: Is the audio clear, volume consistent ,and connection stable?
• Conversation quality: Does the tone of voice and choice of words by customers show they were happy with the solution?

This information further helps you improve the customer experience and quality of customer calls.

7. Customer success agent training 

AI customer success agents provide live cues to CSMs in case of any issues, like a real-time virtual assistant. It can also simulate customer interactions to train new CSMs who have just been onboarded. Overall, it significantly reduces the team's training efforts.

How to implement an AI customer success agent

You can integrate and implement an AI customer success agent successfully in five simple steps:

1. Understand your use cases

A simple strategy to identify the right use cases for AI agents in customer success is to go into your CSM’s task list and identify tasks that are:

• Repetitive with the scope of automation
• Manual with the scope of error

You can further look into business priorities and look for any automation scope (example: upselling, cross selling, etc) that leads to long-term benefits. 

2. Identify current data sources

The next step is to identify where your data currently is, such as:

• CRM records
• Support tickets
• Emails
• Call recordings
• Survey responses

3. Choose the right technology

Once you have the base information ready, you can choose a provider that can:

• Integrate with your existing data sources
• Support the required use cases
• Support all your communication channels
• Handle scale as your business advances
• Meet your budget with growth

4. Create customer workflows

After taking a subscription of an AI customer success agent, identify all the touchpoints where these agents can handle the interactions and where human intervention is required. 

For example, an AI customer success agent can handle level-1 and level-2 queries and need CSM help for level-3 queries. 

5. Continuously improve workflows

Using AI customer success agents requires regular monitoring and experimentation. You can identify new use cases that can be implemented in your AI customer success platform and opportunities for further workflow enhancements. 

The reporting and analytics modules of the AI customer success platform also provide necessary KPIs to track implementation.

Use Plivo for customer success

Plivo is an advanced AI agent helping you to provide:

• 24/7 support: Provides round-the-clock assistance on SMS, Voice, and WhatsApp.

Conversational AI: Use company data (by connecting with CRM, billing, and support systems) and provide precise answers to customers.

• Omnichannel support: Engages customers via voice, Email, SMS, WhatsApp, live chat, and more.
• Sales & engagement boost: Sends AI-driven cart reminders, offers, upsell, cross-sell, and proactive messages
• Real-time insights: Monitors resolution rates, pain points, and customer satisfaction.

Start building better customer experiences with AI. Book a demo today.

Short message service (SMS) might not make headlines in 2025, but it continues to outperform flashier channels where it matters most — reach and response. With 90% of texts read within three minutes and returns hitting $8.11 per message, it remains one of the most dependable tools for customer communication.

What’s changed is the intelligence behind the message.

AI-powered SMS bots can now understand intent, personalize responses, and hold real conversations. Whether it’s appointment reminders or sales follow-ups, these bots handle it with speed, context, and clarity.

In this blog post, you’ll learn how to build a powerful SMS chatbot using the latest artificial intelligence (AI) tools and messaging platforms.

What is an SMS chatbot?

An SMS chatbot is an automated software application that simulates human conversation over SMS using text-based interactions. Instead of requiring human agents to respond manually to every message, an SMS chatbot uses pre-programmed rules or AI to interpret and respond to incoming text messages in real time.

These chatbots are commonly used in customer service, marketing, appointment scheduling, lead generation, and other business functions where quick, scalable, and consistent communication is needed.

How does an SMS chatbot work?

At its core, an SMS text bot follows a structured workflow that enables two-way communication between users and businesses. Here's a breakdown of the process:

The user sends a message

The interaction begins when a user sends a text message to a designated phone number, typically a short code or a virtual number. For example, a customer might text “Check alance” or “Book Appointment.”

The SMS gateway forwards the message to the chatbot

The incoming SMS is routed through a gateway or messaging platform (e.g., Plivo), which bridges mobile networks and the chatbot’s backend. The gateway receives the user’s text and passes it to the chatbot server or engine for processing.

The chatbot processes the input

Once the chatbot receives the message, it evaluates the content using one of the following methods:

• Keyword recognition: Looks for specific predefined keywords or phrases. For example, “balance,” “schedule,” or “cancel.” This is a rule-based system suitable for straightforward use cases.
• Predefined conversation flows: Follows a set of scripted responses and branching logic based on the user’s input. These flows simulate a conversation and can include multiple decision points and replies.
• Natural language processing (NLP): Uses NLP and machine learning (ML) to understand user intent beyond keywords. This allows bots to interpret variations in sentence structure, spelling, or phrasing (e.g., "I'd like to check my account balance" vs. "balance").

The processing logic maps the input to an appropriate intent (i.e., what the user wants) and retrieves information from a database, customer relationship management (CRM) system, or knowledge base to form a relevant response.

The bot sends a response

Once the input is processed, the chatbot composes a response, either static (predefined text) or dynamic (based on real-time data, such as account status or appointment slots).

It sends it back through the SMS gateway to the user’s mobile phone. This entire interaction typically occurs within a few seconds, providing a seamless user experience.

Step-by-step guide to creating an SMS bot

Here’s a straightforward guide to help you build an SMS bot from scratch.

Step #1: Choose an SMS platform

Start by picking a platform that can reliably send and receive SMS. It’s what enables your bot to respond in real time, manage message delivery, and scale conversations without manual effort.

Plivo provides a scalable infrastructure, global reach, and features such as message delivery reports, two-way messaging, and more. To get started with Plivo:

1. Create an account: Head to Plivo's website and sign up for an account.
2. Get your API credentials: After logging in, access the "Dashboard" to retrieve your Auth ID and Auth Token. These credentials will be necessary for authenticating your API requests.
3. Purchase a phone number: You will need a phone number capable of sending and receiving SMS messages. Plivo offers a variety of numbers to choose from based on your region.
4. Configure your number: Finally, set up the number for SMS functionality in the Plivo Console to ensure it's ready to receive incoming messages.

With Plivo set up, you’re ready to begin designing the backend of your SMS bot.

Step #2: Set up your development environment

Next, you must set up your development environment to integrate the Plivo SMS API and build the bot. Here’s how:

1. Choose a programming language: Select a programming language that you're comfortable with that also supports Plivo's SDK. Common choices include Python, Node.js, or Ruby.
2. Install the necessary dependencies:

1. For Python: Install Python if it has not already been installed. Use pip to install Plivo's SDK by running pip install plivo.
2. For Node.js: Install Node.js from the official website, then Plivo’s SDK using npm: npm install plivo.

3. Set up your IDE: Choose your integrated development environment (IDE) for writing code. Popular options include VSCode, PyCharm, or any editor of your choice. Ensure that you have version control to manage your codebase.
4. Create a basic API request: Before diving into actual bot development, test to make sure you can send an SMS via Plivo’s API.

Step #3: Design the bot flow

A well-structured bot flow helps your SMS bot respond clearly and stay on track during a conversation. Below is a simple way to plan:

1. Understand the text bot's purpose: Determine what the bot is supposed to do. Is it for customer support, order tracking, or information retrieval? Clearly define the use case.
2. Map out user interactions: Create a flowchart or wireframe of the conversation. Identify key touchpoints, such as greetings, user queries, responses, and potential follow-up actions.

For example, a simple order tracking bot might start with a welcome message, ask for an order number, and then return the order status.

Define response logic: Think through how the bot will respond at each stage. Consider the different types of responses: simple text, questions, or actions (like triggering a function or fetching data from a database).

The bot flow will serve as a blueprint for development, ensuring a smooth user experience.

Step #4: Develop the text bot

Now, it's time to write the code that brings your AI SMS chatbot to life. Let’s see how:

1. Authenticate with Plivo: At the start of your bot script, import the Plivo library and authenticate using your API credentials:

2. Create messaging logic: Write the logic for sending and receiving messages. For example, to send a message, use the following code:

3. Handle incoming messages: Set up a web server (with Flask or Django, for example) to receive and respond to SMS messages. You'll need to design an endpoint that Plivo can call to notify you of incoming SMS messages.

Example using Flask:

4. Add bot logic: Based on the user’s incoming message, define a series of if-else conditions or use more advanced techniques like ML or AI to interpret and respond to the user's queries. For instance:

Step #5: Test your SMS bot

Once the bot is built, thorough testing is key to making sure it works as intended. It helps catch bugs, validate message formatting, and confirm that conversations play out smoothly across different scenarios. You must test:

• Response accuracy: Simulate various user inputs to see how the bot responds. Test both expected and unexpected messages. For example, test valid order numbers, invalid inputs, and edge cases, such as what happens if the user sends an empty message.
• Edge cases: Ensure the bot can handle edge cases, such as special characters, long messages, or incorrect inputs, in a graceful manner. You may need to implement input validation and error handling to manage these cases.
• Different scenarios: Run through all possible conversation paths based on your bot flow. Ensure the bot can handle various user scenarios and provide helpful feedback.

Step #6: Deploy and monitor

Once your SMS bot is working as intended, it’s time to deploy it and start using it in a live environment. Deployment involves making the bot available to real users and monitoring its performance to ensure it functions correctly.

Below are a few key areas to focus on during and after deployment to keep things running smoothly:

• Host the web server that processes incoming messages on a cloud platform like Amazon Web Services (AWS), Google Cloud, or Heroku. Ensure your server is publicly accessible so Plivo can reach it. Set the webhook URL in Plivo to point to your server’s endpoint (e.g., https://yourdomain.com/receive_sms/).
• Use Plivo’s built-in analytics to monitor your bot’s performance. Keep an eye on crucial metrics like message delivery rates, response times, and error rates.
• After deployment, continually analyze user interactions to identify opportunities for enhancing the bot’s performance. Collect user feedback to refine your chatbot’s SMS responses and add new features over time.

How AI enhances SMS bot performance

AI significantly enhances SMS bot performance by making conversations more intelligent, personalized, and efficient.

Traditional SMS bots rely on predefined scripts, but AI-powered bots apply NLP to understand user intent, even with typos or slang. This results in faster, more accurate responses and increased customer satisfaction.

Let’s understand this better.

In fact, AI SMS text bots are expected to save businesses over $11 billion annually through reduced costs and improved customer service efficiency.

The technology also lets bots analyze past interactions and personalize messages, improving engagement rates. For example, an AI SMS chatbot can recommend products based on previous purchases or browsing behavior. Furthermore, machine learning empowers continuous improvement as the bot learns from every interaction.

Challenges when building an AI SMS bot

AI makes SMS bots smarter, but it also raises the bar. Here’s what can trip you up and what to plan for.

Ensuring data privacy and regulatory compliance

When your SMS bot handles user data — even something as simple as a name and phone number — you're entering regulated territory.

Frameworks like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) apply strict rules on how that data is collected, stored, and used. SMS interactions are no exception.

Compliance shapes how your bot is designed. That means building in consent prompts, limiting data collection to what’s absolutely necessary, and giving users a clear way to opt out or request data deletion. It also means being thoughtful about integrations — if your bot connects to third-party CRMs, analytics tools, or cloud storage, you're still accountable for how that data is handled downstream.

Don’t treat privacy as an afterthought because regulators won’t either.

Balancing automation with human support

Automation can improve efficiency, but relying too heavily on it can alienate users, especially when the conversation requires empathy or complex reasoning.

Program your bot to recognize its limitations and offer seamless escalation to human agents when needed. Be transparent with users about when they’re chatting with a bot and when a human is taking over.

It’s also important to define clear handoff points within your bot flow. For example, if a user repeats the same question twice, expresses frustration, or types something your NLP model can’t categorize, that’s a signal to escalate. You don’t need complex sentiment analysis to make this work — just a few well-placed triggers can keep conversations from going off the rails.

On the backend, make sure your support team has context when they step in. Passing along the full conversation history, user metadata, or selected intents can help human agents respond faster and more accurately.

Training AI with low-quality message data

An AI SMS bot is only as good as what it’s trained on, and most teams get this part wrong. They either feed the model with perfect, internally written scripts (“What are your store hours?”), or synthetic training data generated by people who don’t text like actual users.

The result? A bot that looks smart in testing but crumbles when it meets real messages like:

“u open today?”
“store open sat??”
“hey r u guys taking returns rn”

SMS language is messy. It’s informal, typo-heavy, and full of shorthand or implied meaning. If your AI model hasn't seen this kind of input before, it won't know what to do with it, or worse, it will respond with confidence to the wrong intent.

To avoid this, train on anonymized real user messages whenever possible. Include edge cases, abbreviations, slang, and even common customer frustrations. Augment your dataset with diverse tones, sentence structures, and intents.

And if you're fine-tuning large models, don’t overtrain: it’s better to build fallback logic for unclear queries than to have a bot that responds confidently to something it clearly didn’t understand.

How Plivo helps build an AI-powered SMS bot

Plivo is a leading Communications Platform as a Service (CPaaS) that enables businesses to build intelligent SMS bots easily. Its robust APIs and SDKs allow seamless integration with AI models, making it simple to automate conversations and streamline customer interactions.

The platform supports advanced features, including message scheduling, interactive SMS, and multi-language capabilities. Businesses can utilize AI-driven conversation management tools to deliver more intelligent responses, while built-in analytics and reporting tools provide in-depth insights into message performance.

Plivo tool also prioritizes security and compliance, providing encryption and data privacy controls to protect customer information.

Talk to us about launching an AI-powered SMS bot today.

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Manus AI emerges as a groundbreaking solution for autonomous task execution in a world where efficiency and automation drive innovation. Unlike traditional chatbots that merely assist, Manus AI independently executes tasks, offering a new paradigm in AI technology.

The Autonomous Edge of Manus AI

Manus AI stands out by performing real-world functions autonomously, setting it apart from conventional AI tools like DeepSeek. While many AI systems require human intervention to complete tasks, Manus AI initiates and executes them from start to finish. Imagine an AI that screens resumes, organizes complex data sets, conducts in-depth research, and even builds digital assets—all without human oversight.

Real-World Applications

Manus AI's capabilities extend across various domains, making it a versatile tool for businesses and individuals alike. For instance, Manus AI handles property research, providing comprehensive market analysis and valuation insights. In the financial sector, it performs stock analysis, offering timely and accurate data-driven decisions. Moreover, Manus AI develops websites and generates entire courses or presentations, showcasing its broad utility.

Seamless Task Management

Users assign tasks to Manus AI and receive notifications upon completion, ensuring a seamless workflow. This feature benefits entrepreneurs and small businesses that need to maximize productivity without expanding their workforce. By accessing data through APIs and writing code to solve complex problems, Manus AI automates processes that traditionally required significant human resources.

Setting New Benchmarks in AI

Manus AI's capabilities underwent rigorous testing on the GAIA benchmark, where it achieved state-of-the-art results. This benchmark evaluates AI systems on reasoning and tool use, areas where Manus AI excelled. Its ability to autonomously navigate complex tasks and provide solutions demonstrates its advanced reasoning skills and practical utility.

A Competitive Edge

In a market crowded with AI agents like Anthropic Computer Use and OpenAI Operator, Manus AI offers a compelling alternative. These competing systems often prove costly or complex, limiting their accessibility to large corporations. Manus AI aims to democratize AI-powered task execution by offering a more affordable solution. This approach broadens access to advanced AI technology and encourages innovation across different sectors.

Open-Source Innovation

The developers behind Manus AI plan to open-source parts of the model, further enhancing AI accessibility. By doing so, they invite developers and researchers to contribute to its evolution, fostering a collaborative environment for AI advancement. This initiative could lead to new applications and improvements, benefiting a wider audience and accelerating the pace of technological progress.

Redefining AI-Assisted Automation

Manus AI has the potential to redefine AI-assisted agentic automation, making advanced task execution widely available. By bridging the gap between AI potential and practical application, Manus AI sets a new standard for what autonomous systems can achieve. Its ability to perform tasks independently not only enhances productivity but also frees up human resources for more strategic and creative endeavors.

Manus AI represents a significant leap forward in AI technology, offering unprecedented capabilities in autonomous task execution. As it continues to evolve and expand its reach, Manus AI could transform how we approach work and productivity. How do you envision AI like Manus AI changing the landscape of your industry?

Are you measuring the right set of call center metrics that actually improve performance?

This is important because a 2023 survey by TCN revealed that 73% of U.S. consumers would abandon a brand after a single negative customer service interaction. It also marks a significant increase from 42% in 2021.

In this context, monitoring metrics like Average Handle Time (AHT) can reveal inefficiencies that cost both time and money. 

However, all seasoned CX leaders know that not every metric deserves your attention.In fact, focusing on the wrong numbers wastes resources and can lead to adverse decisions.

In this blog post, you’ll learn about 12 expert-vetted call center metrics you should track. Each includes a clear definition, why it matters, and actionable tips to help you perform better.

Why is tracking call center metrics important?

Metrics defeat their purpose if you don’t act on them, or promptly use them to spot delays, fix weak spots, and improve how your team works. However, when you do, customers definitely notice. They get faster answers and better support. As a result, they come back, and they tell other prospects about your brand.

As Maya Angelou once put it,

According to a 2024 Salesforce report, 88% of customers say good customer service makes them more likely to purchase again, and 80% consider the company's experience as necessary as its products and services.  

To meet these rising expectations, businesses must ensure their call centers aren't just functional but truly effective. Here is how measuring call center success can help you:

Boost customer satisfaction

Widely used metrics like Customer Satisfaction Score (CSAT) and First Call Resolution (FCR) tell you if you meet customer needs. Regularly tracking these metrics ensures alignment with customer expectations and helps reduce churn.

Enhance agent performance

Metrics such as Average Handle Time (AHT) and Agent Utilization Rate provide insights into agent efficiency. Monitoring these can help optimize schedules and identify areas for targeted training.

Optimize operational costs

Efficiency directly impacts operational expenses. Metrics like average handle time and call abandonment rate can highlight process inefficiencies, allowing businesses to optimize labor budgets and improve overall cost-effectiveness.

Top 12 call center metrics 

When hold times exceed 2 minutes, customer satisfaction takes a hit. To maintain high-quality service and stay ahead of customer expectations, it’s crucial to monitor performance closely. That’s where these 12 key call center metrics come in:

Customer-focused metrics

Let us go over the key call center metrics that show how well you’re meeting customer expectations. You’ll learn which numbers reflect satisfaction, loyalty, and effort so you can act fast when things slip. 

1. First Call Resolution (FCR)

FCR shows how often your team solves customer issues on the first try. It’s a strong indicator of customer satisfaction and loyalty. If customers need to follow up, frustration and costs build up.

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A reasonable FCR rate is 70–79%. World-class call centers aim for 80% or more. According to SQM Group, improving your FCR by just 1% can save you over $280,000 annually.

Tips to improve FCR:

• Define what counts as a repeat or escalated call
• Set a clear time window to track follow-ups
• Exclude customer errors (like calling the wrong department) when evaluating FCR
• Give agents quick access to knowledge bases and internal tools
• Run regular call audits to spot common resolution gaps
• Train agents to ask the right questions early to avoid callbacks
• Use call summaries or confirmations to reduce confusion

2. Customer Satisfaction Score (CSAT)

How do your customers feel after they interact with your team? CSAT measures their satisfaction and shows whether you’re meeting their expectations.

How to measure CSAT:

Ask customers a simple post-interaction question: “How satisfied were you with your experience?” Provide a scale (e.g., 1 to 5 or 1 to 10).

CSAT Best Practices:

• Set clear, realistic goals based on your current score and industry benchmark
• Personalize every customer interaction using their history and preferences
• Tackle negative feedback head-on. Spot trends and offer tailored fixes
• Build a QA checklist to monitor support quality regularly
• Train agents with internal resources and provide omnichannel support
• Speed up replies with AI chatbots that collect info before routing to agents
• Offer in-app customer service, FAQs, and video guides for self-service

3. Net Promoter Score (NPS)

NPS measures how likely your customers are to recommend your business. It’s a quick way to gauge loyalty and identify issues that hurt your customer experience.

How to calculate NPS:

Ask your customers: “How likely are you to recommend us to a friend or colleague?” They respond on a scale of 0 to 10.

• Scores 9–10 = Promoters
• Scores 7–8 = Passives
• Scores 0–6 = Detractors

Formula:

NPS = % of Promoters – % of Detractors

Example: If 60% are Promoters and 20% are Detractors, your NPS is +40

How to improve operations with NPS:

• Reach out to Detractors promptly. Ask what went wrong. Be empathetic and offer specific solutions
• Share it with support, sales, and product teams. Recognize employees who move the score up
• Let teams review real customer feedback. Talk through common complaints. Spot trends early
• Use actual NPS responses in training. Help agents learn what creates Promoters and what doesn’t
• Are poor scores tied to specific agents, products, or workflows? Investigate. Then fix it
• After you make updates, monitor NPS again. Did it rise? If yes, build on it. If not, dig deeper
  

4. Average Handle Time (AHT)

AHT measures the total time it takes to resolve a customer issue, from the start of contact to the end of it. It’s a core KPI for call centers that want to monitor efficiency without sacrificing service quality.

How to calculate AHT:

The formula differs slightly by channel. Here’s how to break it down:

• For Calls:
  AHT = (Talk Time + Hold Time + Follow-Up Time) / Total Number of Calls
  
  
• For Email:
  AHT = (Total Time Spent + Customer Wait Time) / Total Number of Emails
  
  
• For Live Chat:
  AHT = Total Handle Time / Total Number of Chats
  
  

AHT varies by industry and complexity, but for many contact centers, a solid benchmark is around six minutes per interaction.

How to reduce AHT (without hurting CX):

• Host ongoing training, call reviews, and performance feedback to keep skills sharp and reduce unnecessary delays
• Build an easy-to-navigate help center or self-service chatbot. Let customers solve simple issues on their own and reduce ticket volume
• Send alerts, how-to guides, and status updates before customers reach out. Fewer incoming issues mean faster response times for complex ones
• Use skill-based routing to match customers with the right agent the first time
• Deploy chatbots or virtual assistants to handle FAQs, triage issues, or collect context before the agent steps in

Operational Efficiency Metrics

Managing time, reducing hold times, and resolving issues quickly all come down to how efficiently your team operates. These contact center KPIs show how well your team manages time, call volume, and resources.

5. Call Abandonment Rate

This metric highlights how efficiently calls are managed, especially regarding wait times. Call abandonment refers to the percentage of inbound calls that disconnect before a caller speaks to a live agent. It’s often a sign of long hold times, confusing IVR menus, or poor customer experiences.

Formula:

Call Abandonment Rate = (Total Calls - Completed Calls) / Total Calls

Example:

Suppose your call center received 1,000 calls daily, but only 850 were completed (i.e., connected to an agent).

Call Abandonment Rate = (1,000 - 850) / 1,000 = 150 / 1,000 = 0.15

Abandonment Rate = 15%

This means 15% of your callers hung up before getting assistance.

How to reduce abandonment rates:

• Give agents caller context to resolve issues faster 
• Use data to identify when and why callers hang up
• Deploy AI voice agents for fast, automated help
• Announce real-time wait times to set expectations
• Optimize IVR flows for clarity and quick routing
• Offer a self-service help center for common queries
• Analyze abandonment points across the caller journey
• Train agents on soft skills and product knowledge

6. Service Level

Service levels measure how quickly and effectively your support team responds to customer inquiries. They reflect your ability to meet agreed expectations and keep customers satisfied.

Main types of service level agreements (SLAs):

• Customer-based SLA: Tailored for one customer with terms unique to their needs
• Service-based SLA: Applies the same terms for one service across all customers
• Operational SLA: Tracks internal performance metrics like uptime and maintenance
• Multi-level SLA: Combines elements of the above, covering company-wide, customer-specific, and service-based aspects
  
  

How to monitor and maintain service levels:

• Set realistic, data-backed targets
• Predict peak times using historical trends
• Staff agents based on skill, not just availability
• Cross-train agents for flexible coverage
• Track real-time performance and adjust instantly
• Cut handle time without sacrificing quality
• Route simple queries to self-service tools
• Offer call-backs during high-volume periods

7. Average Speed of Answer (ASA)

ASA tracks how long, on average, it takes for a customer to reach a live agent after entering the call queue. It doesn’t include time spent navigating IVR menus but only the wait time once the caller is in line to speak to someone. 

It is a direct indicator of your team’s responsiveness. A high ASA often signals understaffing or inefficient routing, damaging your brand reputation and hurting CSAT scores.

How to improve ASA:

• Set a benchmark ASA target using historical call data
• Staff appropriately based on forecasted peak volumes
• Use skill-based routing to get calls to the right agent faster
• Monitor queue lengths in real time and reallocate agents as needed
• Encourage the use of IVR or self-service for simple requests
• Offer call-back options when wait times spike

8. Call Volume

This refers to the total number of incoming and outgoing calls handled by your support team over a specific period. It gives a clear picture of demand and helps measure your contact center’s workload.

Monitoring call volume trends reveals valuable patterns, such as peak hours, seasonal spikes, or campaign-driven surges. This insight helps managers plan and avoid service bottlenecks.

Common triggers for spikes:

• Seasonal demand: Holidays like Christmas or sales events like Black Friday lead to a surge in customer inquiries
• Promotions and launches: Marketing campaigns or new product releases generate more interest and questions
• Service issues: Outages or disruptions push customers to call for updates or support
  
  

Smart ways to handle the load:

• Use call data to predict peaks, hire part-timers if needed, and prevent agent burnout
• Direct callers to the right agent or department automatically to reduce wait time and improve handling
• Provide updated FAQs, knowledge bases, and AI chatbots so customers can resolve issues on their own
• Integrate CRM and call systems so agents can access caller history instantly and resolve issues faster
• Track metrics to identify patterns, plan better, and continuously optimize your capacity and staffing
  
  

Agent Performance Metrics

These metrics help you assess how effectively your agents resolve customer issues, manage time, and contribute to overall service quality. 

9. Agent Utilization Rate

Are you making the most of your agents’ time on the clock? AUR measures how much of an agent’s paid time is spent handling calls or doing related work. It’s a direct indicator of how efficiently your team is being used.

Tracking this metric helps you balance productivity and burnout. Underutilized agents waste the budget, while overutilized agents burn out and make mistakes. 

Formula:

Agent Utilization Rate = (Total Handle Time / Total Logged-in Time) × 100

Example:

If an agent spends 6 hours handling calls during an 8-hour shift:

Utilization Rate = (6 / 8) × 100 = 75%

This means the agent was actively engaged in work 75% of their shift.

How to  improve agent utilization without overloading your team:

• Use call volume forecasts to align staffing levels with demand
• Automate repetitive tasks so agents focus on high-value conversations
• Cross-train agents to cover multiple roles or channels when needed
• Monitor in real time and adjust breaks or shift lengths on the fly
  
  

10. Agent Occupancy Rate

A key efficiency metric, agent occupancy rate, measures how much time agents spend handling calls or after-call work compared to their total available time.

Occupancy vs. Utilization:

While utilization includes all logged-in activities (including breaks and training), occupancy focuses strictly on time spent on customer-related tasks.

Formula:

Occupancy Rate = (Talk Time + After-Call Work) / (Available Time) × 100

Example:

• Talk Time: 5 hours
• After-Call Work: 1 hour
• Logged-In Time: 8 hours

Occupancy Rate = (5 + 1) / 8 = 0.75 or 75%

This means the agent was actively engaged with work 75% of their shift.

Practical tips to improve Agent Occupancy Rate without burning out your team:

• Match staffing levels to peak and low demand hours using historical data
• Mix inbound, outbound, and non-call tasks to keep agents engaged without overloading
• Automate repetitive wrap-up tasks like call tagging or follow-up email templates
• Send the right calls to the right agents to reduce handling time and increase occupancy
• Let supervisors and agents track occupancy levels and make quick adjustments

11. Quality Scores (QA Scorecards)

Quality Scores (QA Scorecards) measure how well your agents handle customer interactions based on pre-set criteria.

These evaluations cover tone of voice, product knowledge, script adherence, resolution accuracy, and compliance. For instance, you might score a call out of 100 based on these areas, each weighted according to importance.

Tracking quality scores helps you spot coaching opportunities, reward top performers, and ensure consistent service. When used with regular feedback and training, QA scorecards become a tool for continuous performance improvement.

12. Adherence to Schedule

Sticking to assigned shifts is critical to maintaining service levels. Adherence to Schedule measures how closely agents follow their assigned work schedules, such as logins, breaks, and logout times.

Even a few agents going off-schedule can lead to longer wait times and missed SLAs. For example, if 5 out of 20 agents take unplanned breaks during peak hours, your call queue could double.

Tips to improve adherence to the schedule:

• Communicate start times, breaks, and end-of-shift rules upfront
• Track adherence live and address deviations immediately
• Allow short flexibility between tasks to reduce lateness from call overruns
• Use system alerts to notify agents before shift or break changes
• Review adherence reports during one-on-ones and offer support when patterns emerge
• Incentivize punctuality with public praise or small rewards

Improve your call center metrics with Plivo 

If you want faster resolutions, lower wait times, and better visibility into performance, you need more than just basic reporting. You need a solution built for modern support teams, one that works as hard as your agents do.

As an all-in-one platform for omnichannel customer service, Plivo gives you that edge. It comes with OpenAI-powered AI agents and specific actionable insights on its console. As a result, you can run a smarter, faster, and more efficient call center.

Here’s how Plivo improves your operational efficiency with its tailored features:

• AI agents for instant resolutions: Automate routine queries and reduce agent workload. Let AI handle FAQs, status updates, and call routing 24/7
• Context-aware escalation: Automatically create tickets, escalate complex issues from AI agents to expert agents with complete context, reducing transfer friction and improving resolution times
• Easy integrations: Plug Plivo into your CRM, helpdesk, or other tools with minimal effort
• Scalable and flexible: Whether you’re a team of 10 or 1,000, Plivo grows with you
• Workflow automation: Manages follow-ups, ticket routing, and status updates without manual input, keeping customers informed at every step
• Real-time analytics: Track call volume, handle time, and agent performance as it happens. No more guessing
• Enterprise-grade security: Complies with SOC 2 and GDPR requirements to protect customer data and maintain trust.

Ready to reduce wait times, boost agent productivity, and delight your customers at every touchpoint? 

See how Plivo can transform your call center. Book a demo now.