I already told the support team. Gosh! Why am I repeating myself to the agents?
If this sounds like your customer feedback, then you’re in. Customer expectations are at an all-time high—demand for faster, more personalized, and frictionless support. In this environment, delivering good customer service isn’t just a nice-to-have; it’s a growth strategy. Businesses that don’t evolve will watch their customers walk straight to those who do.
Prioritizing seamless, personalized, and proactive best customer service will see the biggest gains in customer loyalty and revenue growth. How do you ensure it isn’t just “good” but truly exceptional?
Let’s explore 10 effective strategies to deliver and automate good customer service experience solution, which will set your business apart and drive measurable growth.
How do you define & measure good customer service?
Defining good customer service is not just responding to tickets—it’s about eliminating the need for them in the first place.
“Customer service shouldn’t just be a department—it should be the entire company.”
- Tony Hsieh, Former CEO of Zappos.
Businesses that rely on legacy support systems and a reactive support model are setting themselves up for churn. You must redefine what ‘good customer service’ looks like by implementing smarter ways to improve existing customer service and measure its impact.
What defines good customer service?
Customers expect seamless, intelligent, and proactive assistance, and anything less is a competitive disadvantage. Providing excellent customer service in the AI era should be built on the following:
- Instant, seamless, Context-aware resolutions: Eliminate repetition with AI-driven support that remembers past interactions across channels, ensuring instant, relevant resolutions without the back-and-forth.
- Deflect tickets before they happen: Equip users with AI-powered self-service, semantic search, and RAG-driven answers, ensuring users get instant solutions, reducing tickets, and boosting retention.
- Proactive engagement: Identify friction before users even realize there’s a problem. By using session replays, funnel analytics, and real-time behavioral insights to detect drop-offs, hesitation points, and rage clicks. Automate in-app nudges and contextual cues to guide users before they need support
How do we measure good customer service?
Unlike traditional metrics like CSAT and NPS, which aren’t enough. Measuring the good customer service in this AI-infused era should be on:
- AI-driven ticket deflection: If your self-service doesn’t reduce support volume, it’s just another FAQ page.
- First Contact Resolution (FCR): Customers expect answers in one go, not endless follow-ups.
- Customer Effort Score (CES): The less effort a customer puts in, the better your support system is.
- Sentiment & behavioral insights: Tracking frustration points helps prevent churn before it happens.
- Support-to-product feedback loop: If complaints don’t lead to product fixes, you’re stuck in an endless support cycle.
Here’s an example of how good vs. bad customer service looks like in terms of these measurements:
Modern support isn’t about handling customer complaints—it’s about preventing them. Businesses that unify products and support teams, automate resolutions, and continuously learn from interactions will be the ones driving customer loyalty and long-term growth.
Benefits of good customer service
Increased customer satisfaction and loyalty
AI-native support eliminates friction by instantly resolving and seamlessly escalating to the respective team—reducing wait times, and ensuring every interaction is context-aware. Customers are not only more likely to be satisfied when they receive outstanding customer service but also become repeat customers and brand advocates, promoting positive word-of-mouth recommendations and referrals.
Enhanced brand reputation and trust
Customers trust businesses that deliver on their promises. With real-time insights, support agents gain full context on every interaction, enabling faster resolutions and eliminating the frustration of repeated queries. Consistently resolving customer service issues promptly builds trust, making customers feel confident in choosing your products or services.
Improved retention rates
Loyal customers are a valuable asset for any business, as repeat customers tend to spend more and become brand advocates. A unified knowledge system connects customer interactions, support tickets, and product insights, allowing teams to prioritize recurring issues and product gaps. This approach enhances retention and drives revenue by turning customer service into a proactive growth engine.
Competitive advantage in the market-
Good customer service gives the brand a significant edge. Traditional support models rely on disconnected workflows and reactive responses. A modern, AI-powered system brings support, product, and engineering together, ensuring teams work from a single source of truth while continuously improving customer service experiences.
How to improve good customer service?
To improve customer service, businesses must replace outdated systems with AI-driven automation and proactive support. Users get instant answers via semantic search and predictive analytics, while agents optimize processes with real-time insights for faster resolutions and better experiences.
Businesses that implement AI to enhance good customer service report a 30% increase in efficiency. This shift not only improves response times but also creates a seamless, frustration-free support experience.
10 ways to deliver the best customer service in 2025
1. Automate L1, and L2 resolution with conversational AI
Traditional ticket-based support is slow, repetitive, and frustrating—both for customers and agents. Adopting conversational AI-driven good customer service automation software to the business is the biggest transformation to the manual process by instantly resolving customers’ L1 and L2 queries without human intervention, freeing agents to focus on complex issues. Here’s how AI-customer service software eliminates these inefficiencies:
- Unlike some legacy rigid chatbots, self-learning AI understands intent, adapts dynamically, and delivers human-like, contextual responses without constant rule updates.
- With RAG-powered AI, customer queries are resolved at first contact by pulling answers from internal and external sources—eliminating unnecessary escalations.
- Equips your customer with AI-driven self-service, where semantic search and conversational AI let users find solutions to anything they want instantly.
- Automate the knowledge base, where AI converts real-time customer interactions into fresh, searchable knowledge base content, so your system keeps getting smarter.
2. Turn your support app into a self-service hub customer service
Live chat/ chatbot app shouldn’t just be a product—it should be a self-sustaining excellent customer service hub and a growth engine. Customers expect instant answers without submitting tickets or waiting on agents, and legacy support systems only create delays and frustration.
An AI-powered self-service option-enabled support system eliminates this friction by delivering instant, precise responses directly within the app. With semantic search and RAG-driven AI, users no longer have to sift through outdated FAQs. Instead, they can ask questions naturally—just like a conversation—and get accurate, context-aware answers in seconds.
Beyond just support, no-code banners in the app allow businesses to instantly highlight feature updates, promotions, and critical alerts without waiting on engineering. By keeping users informed, engaged, and supported in real-time, your app becomes the go-to source for answers, not just another support channel.
3. Predict the issues before they happen to provide good customer service
Reactive support is outdated—by the time customers report an issue, frustration has already set in. Great customer service isn’t just about fixing problems—it’s about identifying and preventing them before users even notice them.
Unlike legacy systems, the best customer service software empowered with session 360 and user 360 analytics allows businesses to gain real-time visibility into every user interaction, allowing them to detect friction points before they escalate. Session replays, funnel analytics, and heatmaps expose rage clicks, hesitation points, and drop-offs, giving you the full picture of what’s frustrating users—before they even complain.
4. Equip agents with knowledge graph to provide good customer service
While data silos feed your customer’s frustration, support agents don’t have real-time visibility into customer interactions, product issues, and previous resolutions end up making uninformed decisions—leading to longer resolution times and frustrated customers.
A knowledge graph-powered customer service system eliminates this chaos by connecting data from customer interactions, product insights, and business operations into a unified, AI-driven repository. This gives your support team an instant 360-degree view of the user journey, past interactions, and recurring issues, eliminating blind spots and reducing resolution time from minutes to seconds.
5. Unify customer conversations across the channels
Customers expect seamless, context-aware support no matter where they reach out—email, chat, social media, live chat, or in-app. Outdated, siloed systems force agents to jump between platforms, lose context, and ask customers to repeat themselves—leading to frustration and churn.
Addressing these challenges by consolidating customer interactions across platforms into a single, cohesive interface. Integration ensures that support agents have a comprehensive view of each customer’s history, regardless of the communication medium.
DevRev offers Snap-ins like Slack, Whatsapp, live chat, and email integration, enabling real-time, bidirectional synchronization of conversations across platforms. To provide personalized and efficient support, leading to increased customer satisfaction and loyalty.
6. Provide good customer service by building customer-centric product
Good customer service is the fastest way to build a better customer-centric product. Every ticket, complaint, and interaction with customers in real-time is raw and unfiltered, which businesses can’t afford to overlook. Instead of treating support as a standalone function, embedding it into product development ensures every request is logged, prioritized, and turned into meaningful action to offer great customer service.
To achieve this, businesses need AI-driven automation that auto-clusters recurring issues and instantly exposes product gaps to be fixed. It also streamlines resource allocation, shrinks sprint planning to minutes, and enables development to adapt in real-time—turning support into a revenue-driving force.
7. Intelligently route customer tickets based on the impacts
Not all customer issues carry the same weight—some are minor inconveniences, like L1 and L2, where the resolutions can be automated or addressed by conversational AI. Others can be critical failures, basically L3, which directly impact revenue and customer retention. Businesses relying on outdated, manual ticket triaging risk frustrating high-value customers.
Use an AI-driven customer service system. Instead of routing tickets based on arbitrary rules, AI intelligently assesses sentiment, urgency, and customer impact to prioritize critical issues first. High-impact cases—like recurring product bugs or enterprise-level outages—are automatically escalated to the right teams, ensuring rapid resolution.
8. Focus on personalization in every interaction to deliver great customer service
Customers expect seamless, context-aware support—without repeating themselves or getting passed around like a hot potato. Businesses need an AI-driven support system that treats every customer as an individual, not a ticket number, by:
- Unify customer data with a knowledge graph that connects every interaction, purchase, support ticket, and product usage insight in real-time.
- Beyond personalized service, AI suggests relevant upgrades, recommends features, or provides tailored solutions to queries, increasing adoption and retention.
- Whether a customer contacts support via live chat, email, or phone, the support agents can access the customer’s entire history in one place to provide contextual support.
- Self-learning AI support systems understand customer intent, refine responses over time, and provide human-like, personalized resolutions without forcing users to follow rigid decision trees.
9. Build a customer-centric culture
Fostering a customer-centric culture is essential for delivering excellent customer service consistently. When every employee prioritizes satisfaction and understands its importance, it becomes ingrained in your company’s DNA. Building a customer-centric culture requires shifting from reactive service to proactive, value-driven engagement. Here’s how to make it happen:
- Give your agents more than just a helpdesk. Equip them with knowledge graphs, customer journey analytics, and AI-driven recommendations so they can provide informed, personalized resolutions.
- Instead of waiting for complaints, use AI-powered observability to detect friction points, rage clicks, and drop-offs, allowing teams to act before users even reach out for help.
- Track meaningful customer experience metrics like retention, feature adoption, and lifetime value (LTV) instead of just post-ticket customer surveys.
10. Deliver on what you promised
Nothing erodes trust faster than overpromising and underdelivering. Whether it’s a product feature, resolution timeline, or customer service quality, ensure you meet or exceed customer expectations by;
- Provide accurate resolution timelines, real-time status updates, and proactive communication, preventing customer frustration caused by uncertainty.
- A unified system that connects customer issues to product development ensures feature requests, bug fixes, and service improvements don’t get lost.
- AI can resolve L1 and L2 issues instantly, but for complex cases, seamless agent handoffs ensure customers receive human support without repeating themselves or waiting indefinitely.
- Align support, product, and engineering teams with customer needs to resolve issues faster and turn every interaction into a retention opportunity.
Great customer service examples
Is your support stuck in the past? How Binah.ai transformed customer experience with AI
Binah.ai was ahead of its time in health monitoring technology—turning smartphones into powerful health diagnostic tools. But behind the scenes, their customer support was stuck in the past.
As their customer base grew, so did the chaos. Support tickets piled up, and their agents struggled to keep up with outdated workflows, slow resolutions, and frustrated customers. The system they relied on—Zendesk—was no longer helping them. Every ticket felt like a fire to put out rather than a problem to solve.
Eyal Fein, Binah.ai’s Director of Global Support, knew they couldn’t afford to stay in reactive mode any longer. The more they scaled, the more painful their support experience became—for customers and agents alike.
"We had futuristic technology, but our support felt ancient.”
It wasn’t just inefficiency—it was a ticking time bomb for customer satisfaction. They needed a smarter way to scale—one that wouldn’t burn out agents or alienate customers.
Eyal and his team started searching for a solution—not just another tool, but a real transformation. That’s when they found DevRev.
Unlike traditional help desks, DevRev wasn’t just about managing tickets—it was about eliminating them. With AI-powered automation, deep analytics, and seamless migration tools, it offered exactly what Binah.ai needed.
Still, Eyal had doubts. But when they tried DevRev, something surprising happened.
“DevRev made switching effortless. We typed in our website URL, and within minutes, our entire knowledge base was AI-powered.”
Their customers could now get answers instantly—without waiting on an agent. And when an issue did require support, DevRev’s AI suggested three highly relevant responses, cutting resolution time by 15% right out of the gate.
Within weeks, support felt different. Customers no longer had to hunt for help—it was right there, waiting for them.
How Anand Rathi Digital Wealth transformed customer support and cut issue resolution time by 60%
Anand Rathi Digital Wealth had everything—a sleek, mobile-first platform, cutting-edge digital solutions, and a growing base of High Net Worth Individuals (HNIs) managing millions through their app. But beneath the surface, something was broken.
Customers were dropping off before they even got started. The onboarding journey, the first touchpoint for any investor, was bleeding users. The worst part? No one knew why.
We saw users disappear, but we had no clue what went wrong, says Kuntal Modi, Product Manager, AR Digital Wealth.
The entire team replied on assumptions, scattered customer feedback, and endless guesswork to map user behavior. Every fix felt like shooting in the dark. Meanwhile, support teams were drowning in complaints about app crashes, login failures, and screen freezes—problems they couldn’t even see, let alone solve.
They needed more than another tool—they needed to see what was happening in real time, spot friction points, understand drop-offs, and fix issues. That’s when they turned to DevRe.
With DevRev PLuG, they finally had a clear view of what was happening inside their app. A video replay of user sessions allowed them to watch exactly where users struggled, pinpoint broken touchpoints, and resolve issues quickly. Instead of waiting for users to report problems, DevRev mapped every UI freeze, rage tap, and crash, helping developers diagnose and fix issues before they escalated. For the first time, product, support, and development teams were aligned, working with the same real-time insights, cutting support turnaround time by 60%.
In wealth management, customers don’t tolerate friction. Now, with DevRev, they didn’t have to.
Ready to move good customer service from a function to a growth driver?
Delivering exceptional customer service isn’t just about resolving tickets—it’s about redefining how businesses interact with customers. Companies that integrate AI-driven automation, proactive engagement, and seamless support workflows will set themselves apart.
DevRev Support enables companies to break free from legacy, reactive models by unifying customer conversations, automating L1 and L2 resolutions, seamlessly escalating L3 issues to the respective teams, and bridging the gap between support, product, and engineering for faster, more effective problem-solving.
Airdrop equips agents with real-time context, eliminating the need for back-and-forth searches, while intelligent ticket routing prioritizes high-impact issues—escalating them directly to product and engineering teams for faster resolution.
Experience AI-driven support in action and take the next step toward effortless, intelligent customer experiences.
