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How to Improve Customer Retention: 12 Proven Tactics

Posted on Lauren Winder

Key Takeaways

  • Acquiring new customers costs five to seven times more than retaining existing ones, yet most companies still allocate the majority of resources to acquisition rather than retention.
  • Customer retention rate is calculated as ((Customers at End of Period – New Customers Acquired) / Customers at Start of Period) × 100, providing a clear metric to track loyalty performance.
  • The 12 proven retention tactics center on three core drivers: delivering fast and effective customer service, personalizing interactions at every touchpoint, and using predictive analytics to identify at-risk customers before they churn.
  • AI enables customer retention strategies to scale by handling routine inquiries instantly, maintaining consistent experiences across all channels, and identifying churn risk patterns for proactive intervention.

Acquiring a new customer costs five to seven times more than keeping an existing one. Yet most companies still pour the majority of their resources into customer acquisition while retention gets treated as an afterthought.

The math doesn’t add up—and the businesses that figure this out tend to outperform those that don’t. Below, we’ll cover how to calculate your retention rate, the key metrics that matter, and 12 tactics that actually move the needle.

What is customer retention?

Customer retention is a business’s ability to keep existing customers over a specific period. Put simply, it measures how many people stick around versus how many leave.

The connection to customer experience is direct: when customers feel valued and supported, they stay. When interactions feel frustrating or impersonal, they look elsewhere.

Every touchpoint either strengthens or weakens that relationship.

Why is customer retention important?

One way to answer is with another question: How much repeat business do you want to drive?

Keeping existing customers costs far less than finding new ones. Retained customers also tend to spend more over time and refer others without being asked, which creates a compounding effect on revenue.

Here’s why retention deserves attention:

  • Lower acquisition costs: Selling to someone who already knows your product takes less effort and marketing spend than convincing a stranger.
  • Higher lifetime value: Loyal customers often expand into additional products or services as the relationship deepens, increasing customer lifetime value over time.
  • Organic growth: Satisfied customers tell colleagues and friends, bringing in new business without referral incentives.
  • Repeat business: Customers who stay become repeat customers, generating purchase frequency that compounds over time.

A strong customer retention strategy also creates predictable revenue, which makes planning and business growth far more manageable.

How to calculate your customer retention rate

The formula is straightforward:

Customer Retention Rate = ((Customers at End of Period – New Customers Acquired) / Customers at Start of Period) × 100

For example, if you started the quarter with 1,000 customers, acquired 150 new ones, and ended with 1,050, your calculation would be: ((1,050 – 150) / 1,000) × 100 = 90%. That tells you 900 of your original 1,000 customers stayed, while 100 churned.

Tracking your repeat customer rate alongside this figure gives a fuller picture of how well your customer retention efforts are working.

What is a good rate for retaining customers?

A good customer retention rate varies by industry, but falls between 35-84%.

What matters more is that you increase customer retention over time and understand why customers are lost in the first place.

Benchmarking your customer rate against industry peers helps set realistic targets, but the goal should always be to reduce customer churn quarter over quarter.

Key customer retention metrics to track

Retention rate alone doesn’t tell the whole story. A few additional metrics round out the picture.

Customer churn rate

Churn rate is the flip side of retention—the percentage of customers who leave during a given period. If retention is 90%, churn is 10%.

Tracking when churn happens matters as much as how much, and measuring customer effort can reveal underlying causes. A spike after onboarding points to a different problem than churn at renewal time.

Customer lifetime value

Customer lifetime value (CLV) measures total revenue a customer generates over their entire relationship with you. Someone who stays five years and expands their account is worth far more than someone who leaves after six months.

Customer lifetime value CLV helps prioritize where to focus retention efforts. If your highest-value customers share certain characteristics, you can concentrate resources on keeping similar customers engaged.

Customer satisfaction

Customer satisfaction (CSAT) measures how well your product or service meets customer expectations at specific moments in the relationship. Customers rate their experience—typically on a scale of 1 to 5—after key interactions like a support conversation, onboarding session, or feature launch.

Unlike NPS, which captures overall loyalty, CSAT zeroes in on individual touchpoints. A low score after a support interaction can flag a process problem before it compounds into broader dissatisfaction and eventual churn.

Net promoter score

Net Promoter Score (NPS) measures customer loyalty based on one question: how likely are you to recommend us? Scores range from -100 to 100.

NPS often acts as a leading indicator. Drops in NPS frequently show up before customers actually leave, giving you early warning to intervene before poor customer service becomes a pattern.

Purchase frequency rate

Purchase frequency rate tracks how often customers return to buy within a given period. A rising purchase frequency rate signals strong customer engagement and brand loyalty, while a declining rate can be an early warning sign of disengagement.

12 effective customer retention strategies

The tactics below address the core drivers of loyalty: service quality, personalization, and proactive engagement. Together, they form a set of effective customer retention strategies that work across industries.

1. Deliver fast and effective service

Speed and resolution quality form the foundation of retention. Customers who get issues resolved quickly and completely are far more likely to stay than those who wait days for partial answers.

Meeting expectations here doesn’t mean rushing through interactions. It means having the right information, context, and authority to actually solve problems. AI-powered support can help by handling routine inquiries instantly while routing complex issues to the right human agent with full context intact.

2. Offer omnichannel support across every channel

Customers expect to reach you on their preferred channel—voice, chat, SMS, or social—without repeating themselves when they switch. The phrase “without repeating themselves” is key.

True omnichannel support maintains context across channels.

A customer who starts on chat and moves to phone shouldn’t have to re-explain their issue. Platforms that maintain continuous conversation context make this possible, and customers notice the difference. A seamless customer experience across every touchpoint is one of the strongest signals that you value their time.

One often overlooked factor in customer retention is experience consistency across touchpoints. When interfaces, flows, or messaging feel disjointed, even strong products can become frustrating to use. Superside’s research into customer experience design shows that consistent UI patterns, predictable interactions, and clear visual hierarchy reduce friction and build trust over time, especially as products scale and teams grow.

3. Personalize customer interactions at every touchpoint

Generic responses feel impersonal. Tailored ones feel like you’re paying attention.

Personalization includes remembering customer history, making relevant recommendations, and customizing communications based on past behavior. Even small touches—using a customer’s name, referencing previous purchases—signal that you see them as an individual rather than a ticket number. Personalized experiences and personalized support are among the most effective ways to keep customers coming back.

When you personalize customer interactions consistently, customers feel seen, which builds the kind of long term loyalty that drives repeat purchases.

4. Use predictive analytics to identify at-risk customers

Data patterns can signal churn before it happens. Declining engagement, support ticket spikes, and usage drops all suggest a customer might be considering alternatives.

Acting early on warning signs is what makes the difference.

When you identify customers who may churn proactively, a check-in when engagement drops can address concerns before they become deal-breakers. Using customer data this way turns a reactive process into a proactive one.

5. Self-service resources that actually resolve issues

Effective self-service resources empower customers to solve problems on their own timeline. Knowledge bases, AI agents, and well-designed FAQs all contribute.

The emphasis here is on “actually resolves.”

Self-service that deflects customers without solving their problems creates frustration, not satisfaction. The goal is resolution, not ticket avoidance.

6. Reduce friction across the customer journey

Long wait times, complicated processes, and having to repeat information all create friction. Every unnecessary step is an opportunity for frustration.

Audit your customer journey for friction points:

  • How many clicks does it take to get help?
  • How often do customers re-explain their situation?
  • Where do customers interact with your brand and encounter unnecessary barriers?

Reducing barriers makes staying with you easier than leaving.

7. Create a strong onboarding experience

Customers who understand how to get value from your product stay longer. Those who struggle during onboarding often never reach the point where your product becomes indispensable.

Effective onboarding includes tutorials, proactive guidance, and early wins. The goal is helping customers succeed quickly so they experience value before frustration sets in.

When a customer experiences early success, they’re far more likely to remain loyal.

8. Gather and act on customer feedback

Soliciting customer feedback is only half the equation. The other half is implementing changes and telling customers what you changed based on their input.

When customers see their feedback reflected in product updates or service improvements, they feel invested in your success. Closing the loop matters—and it’s one of the clearest ways to demonstrate that customer satisfaction drives your decisions.

9. Maintain proactive customer communication

Reaching out before problems arise—with updates, check-ins, or relevant information—demonstrates investment in the relationship.

There’s a line between valuable communication and spam, though. The test is whether your outreach helps the customer or just promotes your products. Keeping customers engaged through genuinely useful communication is what separates strong retention programs from noise.

10. Build customer loyalty programs that reward repeat customers

Tiered loyalty programs with exclusive perks give customers tangible reasons to stay. Loyalty incentives such as early access to products, free shipping, or personalized discounts all create switching costs.

Exclusive access to new features or events can also reward repeat customers in ways that feel meaningful rather than transactional.

Rewards work best when they feel genuinely valuable. A meaningful discount beats a points system that requires a spreadsheet to understand. Your most loyal customers should feel that status is worth maintaining.

11. Stay transparent and build customer trust

Honesty about issues, clear pricing, and visibility into decisions build lasting relationships. Customers stay with brands they trust, even when competitors offer lower prices.

And transparency extends to how you handle mistakes. Acknowledging problems and explaining how you’re fixing them often strengthens customer relationships more than pretending nothing went wrong.

12. Be a partner, not a vendor

The shift from transactional to relational changes everything. Partners understand customer goals, offer guidance, and invest in customer success beyond the immediate sale.

Prioritizing customer retention means treating every interaction as an opportunity to deepen the relationship.

Proactively sharing relevant industry insights, connecting customers with resources they didn’t ask for, and treating their success as your success all signal that you’re invested for the long haul.

Customer retention examples: What good looks like in practice

Seeing customer retention programs in action makes them easier to apply. Here are a few customer retention examples that illustrate the principles above:

  • Proactive outreach: A SaaS company notices a drop in product usage and sends a personalized check-in email before the customer considers canceling. The customer achieves a resolution before churn ever becomes a possibility.
  • Closed-loop feedback: A retailer surveys customers after purchase, identifies a recurring complaint about shipping, fixes it, and emails affected customers to let them know. Customer satisfaction improves and repeat purchases increase.
  • Loyalty tiers: A subscription service creates tiered loyalty programs that reward customers with exclusive access to new features based on tenure. The most loyal customers feel recognized, and churn among that segment drops significantly.
  • Community building: A brand builds an online community around its product, creating a forum where users share tips, and connect. Building a community around your brand turns customers into advocates.

How to build a strong customer community

A strong customer community gives customers a reason to stay that goes beyond the product itself. Online forums, user groups, and brand-hosted events all contribute to a sense of belonging.

When customers engage with each other and with your team in a shared space, they develop connections that make switching feel like a loss—not just of a product, but of a community.

Referral programs can also grow naturally from a strong community. Satisfied customers who feel connected to your brand are far more likely to refer others, turning your loyal customer base into a growth engine.

How AI improves customer retention

AI enables many of the tactics above at scale. What once required large teams can now happen automatically, consistently, and around the clock.

  • Faster resolution: AI agents handle routine inquiries instantly, freeing human agents for complex issues that require judgment and empathy.
  • Consistent experience: AI delivers the same quality regardless of volume or time of day, helping meet customer expectations at every interaction.
  • Proactive engagement: AI identifies patterns that signal churn risk before customers leave, enabling early intervention and keeping customers engaged.
  • Personalization at scale: AI uses customer data to tailor every interaction without requiring manual effort, which increases CLV and drives repeat business.

The key is AI transparency and governance. Brands that can see how their AI makes decisions maintain control over the customer experience. Those operating with black-box AI risk inconsistent or off-brand interactions that erode customer trust.

Build a customer retention plan that scales

Retaining customers improves when service, personalization, and proactive engagement work together across channels.

No single tactic works in isolation—the combination creates an experience customers don’t want to leave, resulting in fewer customers lost.

A complete customer retention plan should address every stage of the customer journey, from onboarding through renewal, and should be revisited regularly as customer expectations evolve. Proven customer retention strategies share one trait: they treat retention not as a department, but as a company-wide commitment.

For enterprise CX leaders ready to improve customer retention with AI that stays transparent and on-brand, book a demo with Quiq.

FAQs about improving customer retention

What is the difference between client retention and customer retention?

Client retention and customer retention refer to the same concept. “Client” is typically used in B2B or professional services contexts, while “customer” is more common in B2C and retail.

Which customer retention strategy delivers the fastest results?

While results vary by industry, prioritizing quick response times and omnichannel support often yields immediate impact. Customers notice when you’re easy to reach and proactive in resolving issues – on the channels they prefer using to contact you. Acknowledging their pain points promptly can quickly build trust and prevent customer churn.

How long does it take to see improvements in customer retention rates?

Most businesses see measurable retention improvements within three to six months of implementing new approaches. Building lasting loyalty, though, is an ongoing effort rather than a one-time project.

What are the 4 pillars of customer retention?

The four pillars typically cited are service quality, personalized experiences, proactive communication, and loyalty programs. Each addresses a different driver of why customers stay or leave.

The Ultimate eCommerce Customer Service Software Guide For Enterprise Teams

Posted on Max Fortis

Key Takeaways

  • The expectation gap is your opportunity. 91% of support teams say customer expectations have increased, yet Forrester found CX quality hit an all-time low in 2024. Businesses that invest in the right tools can significantly improve satisfaction and build lasting brand loyalty.
  • A single platform beats a patchwork of add-ons. The best enterprise solutions unify ticketing systems, AI, omnichannel support, and data in one place—reducing complexity and giving support agents easy access to the context they need.
  • Agentic AI delivers real financial returns. Unlike basic chatbots, agentic AI can handle end-to-end requests autonomously, driving down cost per contact and freeing your support team to focus on complex, high-value interactions.
  • Omnichannel support is no longer optional. Today’s customers expect to connect across multiple channels—SMS, WhatsApp, chat, and more—without repeating themselves. Meeting shoppers where they are is foundational to a great shopping journey.
  • Data and analytics are your competitive edge. Platforms that integrate data, real time reporting, and conversation analytics give you deeper insights to optimize every step of the support experience—from reducing shipping delays to improving agent coaching.

eCommerce customer service software is the technology ecosystem that enables brands to resolve shopper inquiries, manage returns, and provide product support.

This guide is for customer experience leaders at enterprise organizations who manage high interaction volumes and need a safe, reliable partner to scale their operations.

eCommerce customer service takes more than a team of hardworking agents. It takes a deliberate technology strategy to turn customer needs into fast resolutions. We will outline the essential features, implementation steps, and evaluation criteria you need to choose the right platform for your company.

Why eCommerce customer service software matters

eCommerce customer service software directly impacts your revenue and retention by resolving shopper friction before it leads to cart abandonment. It allows your company to meet modern demand while optimizing operational expenses and strengthening brand loyalty across every touchpoint.

Shopper expectations continue to rise—and most businesses are falling behind. 91% of support teams report that customer expectations have increased over the past year. Yet, rather than meeting those expectations, overall CX quality is getting worse: Forrester’s 2024 US Customer Experience Index found that CX quality among US brands fell to an all-time low for the third consecutive year, with 39% of brands declining in quality.

In fact, only 3% of companies are currently customer-obsessed—meaning the vast majority are leaving a clear opening for competitors willing to invest.

For every eCommerce business, from enterprise retailers to small businesses scaling fast, that gap between online shoppers’ rising expectations and declining delivery is the single greatest opportunity to capture loyalty and grow revenue through better customer support.

Core features of customer service software for eCommerce businesses

Conversational commerce for customer service

The best eCommerce customer service platforms combine customer relationship management, ticketing systems, analytics, and conversational engagement into a single platform. These functional areas give your customer service agents the context they need to resolve customer issues quickly and deliver a seamless experience.

Five must-have features define a modern enterprise stack for businesses:

  1. Customer Relationship Management to store historical data and personalize every interaction.
  2. Ticketing systems to organize, prioritize, and track customer inquiries across support channels.
  3. Agentic AI to autonomously automate rote tasks, handle routine customer queries, and guide human agents in real time.
  4. Omnichannel support to communicate with customers across their preferred channels—SMS, chat, WhatsApp, email, and phone support—through one unified interface.
  5. Integrations with commerce platforms and external systems to keep data synced and give agents easy access to order history.

Agentic AI And AI Agents

Conversational AI uses artificial intelligence to interpret natural language and execute specific tasks on behalf of the customer. Agentic AI takes this a step further by making contextual decisions and persisting through multi-step processes without human intervention—enabling your customer service team to handle far more customer needs with the same headcount.

Agentic AI adapts to your workflows instead of forcing you into rigid templates.

For example, an AI Agent can locate an order, process a return, and issue a refund independently—resolving customer issues end-to-end without escalating to a human agent.

This is where businesses stand to save real money: fewer repetitive tasks means your human agents can focus on the complex, high-empathy conversations that require a human touch.

If you want to see this in action, request a Quiq demo to observe how our model-agnostic platform lets you orchestrate custom AI solutions safely and transparently.

Omnichannel support and business messaging

Omnichannel support unifies communications from SMS, WhatsApp, Apple Messages for Business, and other channels into a single agent interface for your business. This ensures that conversation history never breaks when a customer switches channels—a critical feature for maintaining trust throughout the shopping journey.

As an added benefit, messaging is a more efficient and affordable form of customer engagement than traditional phone support. Asynchronous messaging allows customers to pause and resume conversations as their schedule permits, while intelligent queues and automated routing rules direct messages to the most appropriate agent based on topic and priority.

For eCommerce businesses handling high volumes, connecting customers across multiple channels through a single platform dramatically reduces operational overhead.

Knowledge base and self-service

A knowledge base is a centralized library of articles and frequently asked questions that gives shoppers easy access to answers without contacting support.

A well-organized self-service portal reduces inbound ticket volume and improves satisfaction by letting customers resolve simple issues on their own schedule.

Structure your articles with clear headings and concise answers. Optimize your knowledge base for search by including natural language terms your customers actually use.

Agentic AI can use this content to provide real-time autosuggest answers to shoppers during chat interactions—turning a static resource into a dynamic support tool. Self service done well is one of the highest-ROI investments a team can make, but ongoing knowledge base management is critical.

Ticketing, workflow automation, and agent productivity

Workflow automation removes repetitive manual tasks from your agents so they can focus on resolving complex issues. Automated routing rules assign incoming tickets based on agent skill and current capacity, ensuring every customer inquiry reaches the right person quickly.

Asynchronous digital channels allow customer service agents to handle multiple conversations at once—significantly improving efficiency without sacrificing quality. You can configure escalation triggers and service level agreements (SLAs) to ensure high-priority customers receive immediate attention when an AI Agent transfers a complex case to a human.

For enterprises managing large care teams, this kind of automation is essential to scaling customer support and meeting customer demands without proportionally increasing headcount.

Analytics, reporting, and data

Analytics software tracks customer behavior and agent performance to help your company identify operational bottlenecks and surface deeper insights into your CX. You need clear, real time visibility into your data to make informed decisions.

Key metrics to track include CSAT scores, average handle time, and automated resolution rates.

Enterprise platforms deliver customizable dashboards and export options to analyze this data across your support channels. Linking your analytics to external data sources—including your CRM and ecommerce platform—provides a complete view of the customer journey and helps you identify where customers are dropping off or experiencing friction.

More data, used well, translates directly into more money saved and more sales retained.

Quality management and conversation analysis

Quality management tools evaluate support interactions to ensure customer service agents adhere to brand standards and regulatory requirements.

AI-driven quality scoring evaluates conversations at scale, replacing slow and inconsistent manual sampling with automated insights.

For example, Quiq’s Conversation Analyst reviews 100 percent of your customer interactions to identify trends in sentiment and service quality. You can establish automated sampling rules to replace manual reviews, giving supervisors actionable insights for coaching and performance improvement—without adding headcount to your management layer.

Collecting and acting on customer feedback

When it comes to eCommerce customer service, customer feedback highlights exactly where your support experience succeeds or fails. You must collect this data consistently to refine your automated and human workflows.

Use in-conversation triggers to capture feedback while the experience is fresh. Collect customer satisfaction and Net Promoter Score data immediately after an issue is resolved. Use these insights to drive workflow changes, improve AI accuracy, and demonstrate the ROI of your customer support investment to leadership.

Using customer data to personalize support

Personalized support relies on instantly accessible customer data. Shoppers expect you to know who they are, what they purchased, and what support they’ve already received.

Sync your customer service software with your main CRM platform so agents can access order history, loyalty status, and prior interactions without switching tools. This allows you to personalize automated messages and deliver a customer experience that feels genuinely attentive rather than transactional.

You must also detail privacy and compliance steps within your organization to protect consumer data and adhere to global security regulations.

Aligning your customer service team with eCommerce goals

Your service team should understand how their daily actions impact broader ecommerce metrics like conversion rates, retention, and brand loyalty. Alignment begins with clear communication and role definition.

Define specific roles for AI supervisors and traditional agents or management. Provide comprehensive training on how to use AI tools as assistants rather than viewing them as replacements. Track productivity KPIs in your reporting, like cost per contact to measure the financial impact of your team and make the case for continued investment in automation and tooling.

Meeting customer expectations in eCommerce

Customers expect fast responses, easy access to information, and the ability to communicate on their preferred channels. Meeting these expectations requires a proactive approach to customer service.

Send proactive notifications for shipping updates or appointment reminders before the customer has to ask. Communicate your policies for returns and refunds clearly through automated updates.

Shipping delays are one of the most common drivers of inbound inquiries—an automated text message sent at the moment a delay is detected prevents frustration and reduces ticket volume before it spikes. For businesses using 3PL providers, that delay data lives in a third-party system, making a direct integration between your 3PL and customer service platform essential to triggering those notifications in real time.

Proactive communication is one of the fastest ways to improve customer satisfaction without adding agents.

Implementation roadmap for eCommerce businesses

A successful software implementation requires careful planning and a phased approach. Don’t attempt to launch every channel and feature simultaneously.

Propose a phased rollout that begins with a single use case. Run a pilot test with limited support channels like SMS or web chat. Focus heavily on change management and agent onboarding so your staff understands how to operate the new tools confidently.

For both small businesses and large enterprises, a disciplined rollout reduces risk and accelerates time to value.

Integrations and technical considerations

Digital_Conversations_Pricing_Integrations_Business_Messaging_Channels

Customer service software must connect with the tools your team already uses to be effective.

Pre-built integrations reduce deployment time and technical debt, and they make it far easier for support agents to access the data they need without leaving their primary workflow.

List required integrations for your commerce platforms and CRM systems. Use APIs and webhooks to connect proprietary internal databases. Advise your IT team to enforce single sign-on and conduct thorough security checks prior to deployment.

When evaluating solutions, your company should prioritize vendors who offer native integrations over those requiring expensive add-ons.

Migration of customer data and messages

Data migration moves your historical customer records from your old platform to your new system. This process must be handled carefully to prevent data loss and protect the continuity of your customer experience.

Use a comprehensive data mapping checklist to align old fields with the new software architecture.

Preserving chat conversation history is critical—returning customers should never have to repeat themselves to a support agent. Test data integrity thoroughly after migration before you go live, and run a parallel period if possible to catch discrepancies before they affect real customers.

Pricing, ROI, and Total Cost Of Ownership (TCO)

Enterprise software pricing models vary, but they typically include licensing fees, usage costs, and professional services. You must look beyond the initial price tag to understand the total cost of ownership.

Compare pricing models that charge per agent against those that charge per interaction. Be cautious of platforms that layer core functionality behind add-ons—what looks affordable at first can become significantly more expensive at scale. Agentic AI, on the other hand, drastically reduces your cost per contact over time.

For example, one of the nation’s largest medspas achieved a 5.2x return on investment in less than six months by routing leads through agentic AI.

How to evaluate and choose the right platform

Selecting the right platform requires a rigorous evaluation of the vendor’s technology and their approach to partnership. Your company needs a solution that scales with your ambition and integrates cleanly with your existing stack.

Provide your team with an evaluation checklist that prioritizes model-agnostic AI and deep integration capabilities.

Create a vendor comparison table to evaluate legacy solutions against AI-native platforms. Include specific RFP questions asking vendors how they verify AI claims, prevent hallucinations, and ensure their automation can handle the full range of customer requests your business receives.

Case studies and use cases

Real-world examples prove the financial and operational benefit of businesses using AI in the enterprise sector. High-volume brands use these tools to achieve massive efficiency gains and improve both customer satisfaction and brand loyalty.

Case in point:

  • Spirit Airlines achieved a 40% automated resolution rate by implementing an AI agent across its digital messaging channels.
  • Brinks Home reduced its call volume by 30% in three years and increased its Net Promoter Score by over 90 points.

Brian Lunseth from Brinks Home noted that these improvements led to an 18 percent increase in customer satisfaction scores in just 12 months—a direct result of deploying the right eCommerce customer service tools and empowering support agents to focus on what they do best.

Getting started with Quiq for eCommerce customer support

Don’t just respond; resolve. Quiq is the trusted agentic AI partner that turns customer needs into reliable resolutions. We combine your brand intelligence with transparent AI systems to deliver a customer experience your shoppers will remember.

Our platform supports conversational and agentic AI, robust reporting, and omnichannel support across web chat, SMS, WhatsApp, and more—giving your care team a single platform to manage all inquiries in real time.

If you are ready to scale your customer experience without losing control, request a Quiq demo today.

Frequently Asked Questions (FAQs)

How does agentic AI differ from a standard chatbot?

Traditional chatbots follow rigid, predefined conversation flows and often fail when asked complex questions. Agentic AI understands multi-part customer requests, makes contextual decisions, and independently takes action across external systems to resolve customer issues—making it far more effective for enterprise ecommerce support.

Can we keep our existing CRM if we implement a new customer service platform?

Yes. Modern solutions integrate directly with popular CRM systems like Salesforce, Oracle, and Zendesk. This allows your support team to sync data bi-directionally and manage conversations directly from your existing interfaces—no need to rebuild from scratch.

How does customer service software support sales and revenue growth?

Customer service software plays a direct role in sales performance by reducing churn, recovering abandoned carts, and building the loyalty that drives repeat purchases. When your company uses reporting tools to identify friction points in the customer journey, you can proactively address issues before they impact conversion. Businesses that invest in a high-quality service experience consistently see stronger retention and higher customer lifetime value—making customer service a revenue driver, not just a cost center.

What reporting capabilities should enterprise teams look for in a customer service platform?

Enterprise customer service platforms should offer real-time reporting dashboards, customizable KPI tracking, and the ability to export data for deeper analysis. The most valuable reporting tools go beyond basic ticket counts—they surface trends in service quality, agent performance, and customer sentiment across every channel.

Look for platforms that integrate reporting directly to your CRM and commerce systems so your company can make fully informed decisions. Strong reporting is also essential for demonstrating the ROI of your customer service investment to leadership and aligning your team around shared goals.

Asynchronous Messaging: How to Use it to Deliver Exceptional Customer Service

Posted on Michael Hartsog

Key Takeaways

  • Asynchronous messaging lets people send and respond to messages on their own schedule without needing to be online at the same time.
  • Asynchronous messaging helps teams minimize context switching and stay focused by eliminating the pressure to respond immediately.
  • Async communication works especially well across time zones, ensuring progress continues even when schedules don’t overlap.
  • It’s ideal for feedback, updates, documentation, and questions that don’t require a real-time discussion.
  • Asynchronous messaging works best with clear communication. Setting response-time norms ensures async messaging stays efficient and avoids unnecessary delays.

Messaging is good. Asynchronous messaging is better.

Let’s face it. Customers have little tolerance for inconveniences of any kind. Whether that’s waiting around for a response, repeating information, or finding an immediate solution to their problem.

Customer service teams aim to serve, so having the available channels to give customers the exact experience they want is crucial to increasing customer satisfaction.

What is asynchronous messaging?

Asynchronous Messaging is a communication method where two parties don’t need to be present or active at the same time for the conversation to continue. Messages are sent and received on each person’s own schedule, allowing for flexible, delayed responses like email, SMS, or chat systems, where replies can come minutes or hours later.

What does asynchronous messaging actually look like? Imagine you’re the customer. You’re busy but need help returning a pair of boots (that just aren’t your style) that your well-meaning dad bought you for your birthday. A completely random example…

You reach out to the live chat service on the eCommerce website to initiate the return, but you’re interrupted midway through the conversation. There’s an immediate work problem that needs your attention. The kids are fighting. The sky is falling. Whatever it may be, you have to start the process all over again.

Frustrating, right? It’s just a simple return!

Well, asynchronous messaging (sometimes called async messaging or asynchronous chat) takes the stress out of that conversation. It doesn’t require both parties to be present at the same time to complete the interaction. You can simply jump back in once you’ve taken care of life’s responsibilities. This is asynchronous messaging at its best.

SMS/text messages, WhatsApp, and Facebook Chat are all prime examples of asynchronous messaging in action. Conversations can start, stop, and resume whenever either person is available.

Synchronous vs Asynchronous Conversations

At the other end of the spectrum is synchronous messaging. It’s typically a live, one-to-one chat between a customer and a customer service representative.

What makes it so different? There’s usually a clear beginning and end to a synchronous chat. A customer reaches out with a specific question or to find a solution to their problem, and the conversation ends once those needs are met.

Think of it like a typical phone interaction—just using messaging instead of voice. And since synchronous messaging is so similar to phone interactions, it often comes with the same drawbacks.

  • Customers have to wait for a live agent.
  • If the agent can’t answer a question, the customer has to be rerouted.
  • Agents can only serve one person at a time.
  • Complex problems take up more of your agents’ time.
  • If a customer gets interrupted, the chat ends without a resolution.

Think of asynchronous messaging somewhere between live chat and email. Customers typically expect a quick,—but not instant, —response. This flexibility allows your team to deliver exceptional customer service atin a time that works for the customer and your team.

But that’s not the only benefit. Here are seven benefits of adding asynchronous messaging to your customer service arsenal.

  1. Customers can fit you into their busy days. Life is busy. We’re always multitasking. There are too many distractions—it’s a lot. Asynchronous messaging gives customers the flexibility to fit you into their schedules. They don’t have to block time out of their day for a lengthy live chat or wait for business hours to get someone on the phone. Instead, they can get their support requests taken care of on their own time, at their own pace.
  2. Less wait time. Since agents can jump in and out of multiple conversations—as many as 30 at a time—customers spend less time “on hold” waiting to connect with a live agent. And that is really important to customers. Zendesk reports that over 60% say getting their issues resolved quickly is the most important aspect of good customer service. With asynchronous messaging, customers can get answers while going about their day.
  3. No repeated information. One of the things that frustrates customers the most is having to repeat their problem. No matter whether they get disconnected from a live chat or transferred to multiple people before they get an answer to their problem, repeating themselves almost always leads to a bad experience. With asynchronous messaging and a conversational platform behind it, customers (and agents) can pick up the conversation right where they left off. There’s no information lost between sessions. Their conversation history is available for agents to reference at any time.
  4. Resolve problems in less time. While asynchronous messaging potentially drags out conversations (depending on how quickly your customers respond), agents often spend less working time per interaction. Quiq clients can reduce work time by 25–40% when converting calls to messaging. This is because agents can quickly address those frequently asked questions that don’t necessarily require a phone call (think password reset process and hours of operation). Simple problems get solved faster, while more complex problems have the breathing room to come to a thorough resolution.
  5. Prioritize customer requests. During peak times, when your team is truly overwhelmed, asynchronous messaging helps your agents triage customer requests. Collect customer information, sentiment, and problem upfront to determine how quickly the problem needs to be addressed. A customer service agent can immediately help an angry customer with a simple problem and close out the ticket quickly. A neutral person with a more complex question can wait a little longer for your agents to figure out the right response.
  6. Asynchronous messaging agent efficiency customer engagement performance channels sms facebook instagram whatsapp conversationsMeet support demands with fewer agents. Much like phone calls, synchronous messaging requires one agent per customer interaction. To meet demand and avoid long wait times, you need a higher volume of staff members at all times. This also means that you likely have to hire extra team members to support peak times. Asynchronous messaging can help with that. Since your support team can take part in multiple conversations at once, you can serve more customers with fewer agents. This is particularly helpful now when baseline support ticket requests have gone up 20% since the beginning of the pandemic, according to Zendesk.
  7. Get more opportunities to initiate a conversation. Since conversations are more flexible, customers are more likely to engage with customer service reps at different stages within the the customer lifecycle. Customers don’t have to set aside big chunks of time for conversations and your team will have more context to help serve them better. From starting a conversation from Maps with Apple Messages for Business or using Facebook Messenger to ask about size options, there’s ample opportunity to serve customers and increase revenue.

How to Make the Most Out of Asynchronous Messaging

Messaging as a whole has significantly grown in popularity since the pandemic began, and it has done so at a faster rate than any other channel. Support tickets coming in from messaging channels rose by 48%, compared to a 15% increase from live chat.

If you haven’t embraced asynchronous messaging yet, we have a few best practices to shape your approach and help you get started.

  • Design your asynchronous messaging strategy around your agents waiting for the customer—not the other way around. While it gives your agents the ability to manage multiple conversations, the benefit should really be for the customers’ flexibility. If you use asynchronous chat to spread your team too thin, the experience can end up feeling like email, which no one likes.
  • One way to improve response times and decrease the time agents spend per interaction? Use a chatbot to welcome customers and collect pertinent information beforehand. This way, customers get served quickly, and agents can spend their time problem-solving instead of gathering information.
  • Want to stand out? Don’t treat messaging like email. In 2020, Zendesk reported that it takes more than 11 hours, on average, to close messaging tickets. That’s compared to 30 minutes for voice and live chat and 11.5 hours for email or web form tickets.
    While messaging gives your team more flexibility to respond, customers still expect a response time in under 5 minutes. Try staffing it as you would with voice and live chat to start. Then, adjust as your team becomes more efficient and you invest in other ways to streamline service.
  • Remember to track actual work time. Overall, asynchronous messaging will have high-resolution times since you can resolve issues in two minutes, two hours, or two days. Giving customers the freedom to respond at their own convenience can superficially elevate those numbers. But remember: if a customer is responsible for the delay, a two-day conversation can result in a lower work time and a higher customer satisfaction rate. So take resolution times with a grain of salt. A conversational platform like Quiq can help you measure actual work time.

Start Using Asynchronous Messaging to Deliver Stellar CX

With customers flocking to messaging channels, it’s a great time for your customer service team to adopt asynchronous messaging. The best way to set your team up for success? With an Agentic AI platform, like Quiq.

Turn any messaging channel into an asynchronous experience. With Quiq, you can:

  • Manage conversations across multiple channels
  • Serve customers based on sentiment
  • Increase agent efficiency and boost customer satisfaction

Sign up for a Quiq demo and see how it can help you deploy asynchronous messaging and elevate your customer service.

Frequently Asked Questions (FAQs)

What is asynchronous messaging?

Asynchronous messaging is a communication method where messages are sent and received without requiring both parties to be present or active at the same time. It allows people to respond on their own schedule.

How is asynchronous messaging different from synchronous messaging?

Synchronous messaging requires real-time interaction (like phone calls or live chat), while asynchronous messaging supports delayed responses (like email or Slack messages).

What are common examples of asynchronous messaging?

Email, SMS/texting, project management comments, voicemail, collaborative tools like Slack or Teams, and ticketing systems all use asynchronous communication.

Why is asynchronous messaging important for teams?

It helps reduce interruptions, supports global or distributed teams across time zones, and allows people to respond more thoughtfully rather than immediately.

When should I use asynchronous messaging instead of synchronous communication?

Use it for non-urgent questions, feedback, updates, documentation, or anything that doesn’t require an immediate back-and-forth discussion.

Does asynchronous messaging hurt productivity?

Not when managed well, it’s often more productive. It reduces context switching, allows deep work, and creates a written record of decisions and conversations.

Can asynchronous messaging work for customer support or sales?

Yes. Many support and sales workflows now use async channels like email, in-product messaging, or chat with delayed response, offering flexibility for both reps and customers.

Are there downsides to asynchronous messaging?

If expectations aren’t clear, delays can happen. It’s helpful to set response-time norms so communication stays efficient.

How does asynchronous messaging improve documentation?

Everything is written, searchable, and trackable, making it easier for teams to revisit decisions, share knowledge, and onboard new members.

Can asynchronous and synchronous messaging be used together?

Absolutely. Many teams blend both asynchronous for everyday communication and synchronous methods (meetings, calls) for urgent or complex discussions.

AI in Customer Service: Interactions and Strategies

Posted on Max Fortis

AI is one of the most exciting new developments in customer service. But how does customer service AI work, and what does it make possible? In this piece, we’ll offer the context you need to make good decisions about this groundbreaking technology. Let’s dive in!

What is AI in Customer Service?

AI in customer service means deploying innovative technology–generative AI, custom predictive models, etc.–to foster support interactions that are quick, effective, and tailored to the individual needs of your customers. When organizations utilize AI-based tools, they can automate processes, optimize self-service options, and support their agents, all of which lead to significant time and cost savings.

What are the Benefits of Using AI in Customer Service?

There are myriad advantages to using customer support AI, including (but not limited to):

  • Quicker Response Times: AI swiftly manages both simple and complex questions, minimizing wait times and enhancing the overall customer experience.
  • Round-the-Clock Support: With AI in place, customers receive continuous support, no matter the time or day, ensuring help is always available when needed.
  • Reduced Costs: Automating repetitive tasks with AI reduces the need for large teams, which helps lower operational costs while maintaining service quality.
  • Increased Agent Productivity: AI takes care of mundane tasks, freeing up agents to focus on more strategic efforts, like cross-selling and delivering personalized solutions.
  • Tailored Interactions: Using customer data, AI personalizes responses and suggestions, making every interaction feel unique and relevant to the customer’s needs.
  • Effortless Scalability: As your business grows, AI can seamlessly manage a growing number of customer requests without adding additional resources.
  • Emotion Recognition: AI can assess customer sentiment in real-time, adapting its responses to ensure more positive and satisfying interactions.
  • Reliable Accuracy: AI ensures that all customer interactions are consistent and precise, based on your company’s guidelines and information, minimizing errors.
  • Agent Empowerment: By automating routine tasks, AI empowers agents to focus on more meaningful, high-impact work, making their roles more rewarding.
  • Optimized Processes: AI streamlines operations by identifying which tasks can be automated, allowing your support team to work more efficiently.

9 Applications for AI in Customer Service

Is AI right for your customer service operations? Here are some common ways companies are adopting it.

  1. AI Agents: AI-powered bots manage both routine and complex tasks, automating customer interactions and allowing agents to focus on higher-value work.
  2. Agent Assistance: AI provides real-time guidance and response suggestions, helping agents work more efficiently and confidently.
  3. Automated Workflows: AI streamlines workflows by intelligently routing tickets, suggesting responses, and summarizing conversations, improving efficiency.
  4. Workforce Management: AI predicts staffing needs, optimizes schedules, and personalizes shifts, helping reduce overtime and improve team management.
  5. Service Quality Assurance: AI speeds up quality assurance by reviewing customer interactions and providing actionable insights to improve agent performance.
  6. Call Management: AI helps manage calls by transcribing interactions, summarizing calls, and offering real-time support, reducing wait times and enhancing training.
  7. Help Center Optimization: AI improves knowledge base performance by identifying content gaps and automating the creation or updating of articles.
  8. Revenue Generation: AI drives upselling and cross-selling by integrating with backend systems, offering personalized product recommendations during customer interactions.
  9. Insights for Improvement: AI analyzes customer conversations to uncover patterns, trends, and areas for improvement, helping businesses refine their support strategies.

Things to Consider When Using AI in Customer Service

Now that we’ve covered some necessary ground about what customer support AI is and why it’s awesome, let’s talk about a few things you should be aware of when weighing different solutions and deciding on how to proceed.

Augmenting Human Agents

Against the backdrop of concerns over technological unemployment, it’s worth stressing that generative AI, AI agents, and everything else we’ve discussed are ways to supplement your human workforce.

So far, the evidence from studies done on the adoption of generative AI in contact centers have demonstrated unalloyed benefits for everyone involved, including both senior and junior agents. We believe that for a long time yet, the human touch will be a requirement for running a good contact center operation.

CX Expertise

Though a major benefit of customer service AI service is its proficiency in accurately grasping customer inquiries and requirements, obviously, not all AI systems are equally adept at this. It’s crucial to choose AI specifically trained on customer experience (CX) dialogues. It’s possible to do this yourself or fine-tune an existing model, but this will prove as expensive as it is time-intensive.

When selecting a partner for AI implementation, ensure they are not just experts in AI technology, but also have deep knowledge of and experience in the customer service and CX domains.

Time to Value

When integrating AI into your customer experience (CX) strategy, adopt a “crawl, walk, run” approach. This method not only clarifies your direction but also allows you to quickly realize value by first applying AI to high-leverage, low-risk repetitive tasks, before tackling more complex challenges that require deeper integration and more resources. Choosing the right partner is an important part of finding a strategy that is effective and will enable you to move swiftly.

The best way to ensure the time to value is there is by using automatic time tracking to find out how your real agents stack up to AI ones.

Channel Enablement

These days, there’s a big focus on cultivating ‘omnichannel’ support, and it’s not hard to see why. There are tons of different channels, many boasting billions of users each. From email automation for customer service and Voice AI to digital business messaging channels, you need to think through which customer communication channels you’ll apply AI to first. You might eventually want to have AI integrated into all of them, but it’s best to start with a few that are especially important to your business, master them, and branch out from there.

Security and Privacy

Data security and customer privacy have always been important, but as breaches and ransomware attacks have grown in scope and power, people have become much more concerned with these issues.

That’s why LLM security and privacy are so important. You should look for a platform that prioritizes transparency in their AI systems—meaning there is clear documentation of these systems’ purpose, capabilities, and limitations. Ideally, you’d also want the ability to view and customize AI behaviors, so you can tweak it to work well in your particular context.

Then, you want to work with a vendor that is as committed to high ethical standards and the protection of user privacy as you are; this means, at minimum, only collecting the data necessary to facilitate conversations.

Finally, there are the ‘nuts and bolts’ to look out for. Your preferred platform should have strong encryption to protect all data (both in transit and at rest), regular vulnerability scans, and penetration testing safeguard against cyber threats.

Observability

Related to the transparency point discussed above, there’s also the issue of LLM observability. When deploying Large Language Models (LLMs) into applications, it’s crucial not to regard them as opaque “black boxes.” As your LLM deployment grows in complexity, it becomes all the more important to monitor, troubleshoot, and comprehend the LLM’s influence on your application.

There’s a lot to be said about this, but here are some basic insights you should bear in mind:

  • Do what you can to incentivize users to participate in testing and refining the application.
  • Try to simplify the process of exploring the application across a variety of contexts and scenarios.
  • Be sure you transparently display how the model functions within your application, by elucidating decision-making pathways, system integrations, and validation of outputs. This makes it easier to model how it functions and catch any errors.
  • Speaking of errors, put systems in place to actively detect and address deviations or mistakes.
  • Display key performance metrics such as response times, token consumption, and error rates.

Brands that do this correctly will have the advantage of being established as genuine leaders, with everyone else relegated to status as followers. Large language models are going to become a clear differentiator for CX enterprises, but they can’t fulfill that promise if they’re seen as mysterious and inscrutable. Observability is the solution.

Risk Mitigation

You should look for a platform that adopts a thorough risk management strategy. A great way to do this is by setting up guardrails that operate both before and after an answer has been generated, ensuring that the AI sticks to delivering answers from verified sources.

Another thing to check is whether the platform is filtering both inbound and outbound messages, so as to block harmful content that might otherwise taint a reply. These precautions enable brands to implement AI solutions confidently, while also effectively managing concomitant risks.

AI Model Flexibility

Finally, in the interest of maintaining your ability to adapt, we suggest looking at a vendor that is model-agnostic, facilitating integration with a range of different AI offerings. Quiq’s AI Studio, for example, is compatible with leading-edge models like OpenAI’s GPT3.5 and GPT4, as well as Anthropic’s Claude models, in addition to supporting bespoke AI models. This is the kind of versatility you should be on the look out for.

What is the Future of AI in Customer Service?

The future of customer service lies in AI and humans working together to provide personalized and empathetic experiences. AI agents, powered by natural language processing and sentiment analysis, will handle complex inquiries and offer proactive, tailored solutions. Automation will streamline workflows, reducing response times and allowing agents to focus on higher-value tasks like upselling. AI-driven insights will continuously refine customer service strategies, improving efficiency and satisfaction, all while prioritizing data privacy and ethical AI use.

Where to Get Started with AI in Customer Service

To successfully integrate AI in customer service, begin by identifying key pain points like long response times or repetitive inquiries. Start small by automating areas such as self-service or support ticketing, and gradually expand as you refine its effectiveness.

It’s important to consider challenges like data privacy and AI bias. Ensure robust data protection measures are in place and train AI on diverse datasets to avoid unfair outcomes. Early-stage AI implementation may face issues with data quality and accuracy, but these can be addressed through data cleaning and continuous refinement.

When selecting AI tools, prioritize systems that balance functionality with ease of use. Ensure smooth integration with existing CRM systems and maintain clear escalation paths for more complex issues.

By starting small, monitoring AI performance, and continuously optimizing, businesses can successfully integrate AI in customer service with human agents to enhance efficiency. Ultimately, with thoughtful planning and continuous improvements, AI can help businesses create a more responsive, personalized, and efficient customer service experience that complements human capabilities and meets evolving customer expectations.

8 Customer Experience Metrics Every CX Leader Should Be Tracking

Posted on Cristina Bravo-Olmo

Delivering a remarkable customer experience (CX) is no longer optional—it’s essential. It can be the defining factor that sets your business apart, fosters loyalty, and drives growth. To truly understand and elevate your CX, tracking the right customer experience KPIs is critical.

Customer experience metrics offer clear and quantifiable insights into how your customers perceive your business, empowering you to identify strengths and address gaps effectively. But what are these key metrics, and how can they guide your strategy?

This guide will explore eight essential customer experience metrics, unpack their significance, and show you how to leverage them to improve satisfaction, loyalty, and overall business success.

What are customer experience metrics?

Customer experience metrics are quantifiable indicators that reflect the success of your business in meeting, and preferably exceeding, customer expectations. They go beyond traditional customer service metrics to evaluate every touchpoint of the customer journey, offering a comprehensive view of satisfaction, loyalty, and engagement.

Unlike operational metrics, which measure backend efficiency, CX metrics focus on the customer’s perception of interactions with your brand—both emotional and rational. When tracked effectively, measuring customer service metrics highlights gaps in your service and offers actionable insights to refine your strategies.

Why CX metrics matter

Metrics aren’t just numbers—they’re a reflection of your customers’ thoughts, feelings, and behaviors. Focusing on CX metrics allows you to:

  • Boost retention by building stronger relationships with your customers.
  • Optimize processes to reduce bottlenecks and frustrations.
  • Drive revenue by improving loyalty and attracting referrals.

Key customer experience metrics

Every organization needs to assess CX from multiple angles. Here are the eight metrics every CX professional should be tracking to create measurable and meaningful improvements.

  1. Customer Satisfaction Score (CSAT) measures a customer’s overall happiness with a specific product, service, or interaction on a scale of 1-5.
  2. Net Promoter Score® (NPS) measures customer loyalty and willingness to recommend a company to others using a scale of 0-10.
  3. Customer Effort Score (CES) measures the ease of a customer’s experience with a company or specific task.
  4. Customer Churn Rate measures the percentage of customers lost over a specific period.
  5. Customer Retention Rate measures the percentage of customers a company retains over a specific period.
  6. Customer Lifetime Value (CLV) predicts the total revenue a customer is expected to generate throughout their relationship with a company.
  7. First Response Time (FRT) measures the time it takes for a customer to receive an initial response to their inquiry.
  8. Average Resolution Time (ART) measures the average time it takes to completely resolve a customer’s issue.

Let’s take a look at them one by one.

1. Customer Satisfaction Score (CSAT)

A Customer Satisfaction Score (CSAT) measures how satisfied customers are with a specific interaction, product, or service. It offers a direct look into how your brand meets immediate customer needs.

How to measure CSAT

Customers are typically asked, “How satisfied were you with your experience?” and rate their satisfaction on a scale of 1 to 5. The CSAT formula is simple:

CSAT (%) = (Number of Satisfied Responses / Total Responses) × 100

For instance, if 80 out of 100 customers rate their experience as satisfied (4-5), your CSAT is 80%.

Why CSAT is important

Tracking CSAT lets you pinpoint issues right away and focus on areas where customers expect immediate improvements. For example, customer service teams can use CSAT to evaluate agent performance and streamline workflows.

qualtrics graph

Source: Qualtrics

How to improve CSAT

  • Immediacy: Address customer feedback on the spot. If there’s an issue with an order, for example, resolve it as quickly as possible to the customer’s satisfaction.
  • Ask for feedback in the context of the experience: Use surveys directly after an experience and within the channel it occurred in to capture the customer’s sentiment on the highest, most honest note possible.
  • Proactive support: Anticipate issues through data-driven analytics.
  • Employee training: Equip your team with the skills to deliver exceptional service.

    Learn how BODi® achieved a 75% CSAT rating with Quiq’s AI. See case study >

2. Net Promoter Score (NPS)

Net Promoter Score® (NPS) reveals how likely customers are to recommend your business to others, serving as a long-term loyalty indicator.

How to measure NPS

nps example

Source: Lumoa

Ask your customers, “How likely are you to recommend [brand/product/service] to a friend?” Customers respond on a scale of 0-10. Responses fall into three categories:

  • Promoters (9-10): Likely to recommend.
  • Passives (7-8): Neutral.
  • Detractors (0-6): Unlikely to recommend.

Calculate NPS as follows:

NPS = % of Promoters – % of Detractors

Why NPS is crucial

A rising NPS indicates growing customer loyalty, while a low or declining score signals dissatisfaction that needs urgent attention.

How to enhance NPS

  • Engage promoters: Encourage them to share referrals or write reviews.
  • Address detractor concerns: Reach out to unhappy customers to understand issues and resolve them.
  • Build real connections: Use insights to deepen customer relationships.

“BRINKS has been a happy Quiq customer since November 2017. We started by implementing two-way, asynchronous messaging for sales and customer support, which reduced our call volume YoY, including 30% in just the past 3 years. In that same timeframe, we had increased our NPS scores by a staggering 90+ points.” —Brian Lunseth, Director, Digital Customer Experience & Dev at BRINKS

3. Customer Effort Score (CES)

Customer Effort Score measures how easy it was for customers to complete a specific action, such as resolving an issue or making a purchase.

How to measure CES

A common CES survey asks, “How easy was it to accomplish [specific task]?” Responses typically range from 1 (very difficult) to 5 (very easy). Calculate an average CES by dividing the total score by the number of responses. For instance:

CES example

Source: Responsly

Why CES matters

Effortless experiences lead to higher satisfaction and loyalty. Studies show that reducing customer effort has a direct impact on repeat business.

How to improve CES

  • Streamline navigation: Simplify the process for high-friction actions like payments or returns, e.g., by using high risk merchant accounts.
  • Invest in automation: Self-service tools like AI agents can make problem-solving quicker.
  • Proactive customer service: Reach out before issues escalate. Proactive AI can do this for you on your website, using information about the customer’s previous orders, shopping behaviors, and more.

4. Customer Churn Rate

Churn Rate tracks the percentage of customers who stop doing business with you during a given period.

How to measure churn

Calculate churn by dividing the number of customers lost during a specific period by the total number of customers at the beginning of that period, then multiply by 100.

Why reducing churn is key

Churn directly impacts revenue. Retaining existing customers is far more cost-effective than acquiring new ones, making churn reduction a high priority for CX professionals.

How to minimize churn

  • Identify pain points: Use surveys to understand why customers leave.
  • Deliver value: Ensure customers feel they’re getting more than they paid for.
  • Reward loyalty: Offer exclusive benefits or personalized outreach to high-value customers.

5. Customer Retention Rate

Retention Rate measures your ability to keep customers over time, reflecting satisfaction and trust.

How to measure retention

Retention Rate = ((# of Customers at End – # of New Customers) / # of Customers at Start) × 100

Why retention matters

A high retention rate drives repeat purchases, referrals, and long-term profitability.

How to improve retention

  • Personalized communication: Use customer data for tailored messaging.
  • Loyalty programs: Reward continued engagement with meaningful incentives.
  • Listen & adapt: Act on feedback to show customers their voice matters.

6. Customer Lifetime Value (CLV)

CLV estimates the total revenue a customer will bring to your business throughout their relationship with your brand.

How to measure CLV

CLV = (Average Purchase Value × Purchase Frequency) × Customer Lifespan

Why CLV is critical

CLV provides insights into the long-term value of different customer segments, helping you allocate resources more effectively.

How to increase CLV

  • Upsell opportunities: Introduce complementary products.
  • Exceptional CX: Maintain service quality at every touchpoint.
  • Proactive retention: Address issues that could lead to churn.

7. First Response Time (FRT)

FRT measures the average time it takes for customer service teams to respond to inquiries.

How to measure FRT

Divide the total time to first response by the number of support tickets answered.

Why FRT matters

Customers expect fast responses. A quick first response fosters trust and improves customer sentiment.

Tips to improve FRT

  • Automate responses: Use AI to acknowledge tickets instantly.
  • Efficient routing: Ensure tickets reach the right teams quickly.
  • Track trends: Identify recurring delays and resolve the root cause.

8. Average Resolution Time (ART)

ART measures the average time needed to resolve customer issues fully.

How to measure ART

Total resolution time / Total number of cases resolved = ART

Why ART is essential

Highly efficient resolutions ensure a smooth customer experience, demonstrating your service team’s competence.

How to reduce ART

  • Incorporate AI to handle routine questions: Use artificial intelligence to automatically solve more Tier 1 inquiries.
  • Comprehensive training: Equip agents to solve issues faster, boosting their capabilities with technology that helps them do their jobs more efficiently.
  • Knowledge bases: Offer customers easy access to self-help resources.
  • Cross-team collaboration: Enable teams to share insights to address complex issues efficiently.

Learn how Molekule achieved 60% resolution rates with Quiq’s AI. See case study >

Improving CX metrics one step at a time

Knowing how to measure customer experience metrics and tracking them is not enough—you need to act on what the data reveals. Each CX metric shines a light on specific aspects of the customer journey, from satisfaction (CSAT) to service efficiency (FRT and ART).

No single metric paints the full picture. Combine insights from various metrics to assess your customers’ needs holistically.

Using platforms like Quiq, you can simplify the process by uniting analytics from multiple channels. This allows you to analyze customer sentiment, improve inefficiencies, and empower teams with real-time insights.

How a Leading Office Supply Retailer Answered 35% More Store Associate Questions with Generative AI

Posted on Mike Zinne

In an era where artificial intelligence is rapidly transforming various industries, the retail sector is no exception. One leading national office supply retailer has taken a bold step forward, harnessing the power of generative AI to revolutionize their in-store experience and empower their associates.

This innovative approach has not only enhanced customer satisfaction but has also led to remarkable improvements in employee efficiency. In fact, the company has experienced a 35% increase in containment rates (with a 6-month average containment rate of 65%) vs. its legacy solution.

We’re excited to share the details of this groundbreaking initiative. Keep reading as we examine the company’s vision, their strategic approach to implementation, and the key objectives that drove their AI adoption. We’ll also discuss their GenAI assistant’s primary capabilities and how it’s improving both customer experiences and employee satisfaction. By the end, you’ll see how much potential lies in applying this use case to additional employees—not just in-store associates—as well as customers. There’s so much to unlock. Ready? Let’s dive in.

The Vision: Empowering Associates with GenAI

This company is dedicated to helping businesses of all sizes become more productive, connected, and inspired. Their team recognized the immense potential of GenAI early on. The vision? To create a GenAI-powered assistant that could enhance the capabilities of their store associates, leading to improved customer service, increased productivity, and higher job satisfaction.

Key objectives of the GenAI initiative:

  • Simplify store associate experience
  • Streamline access to information for associates
  • Improve customer service efficiency
  • Boost associate confidence and job satisfaction
  • Increase overall store associate productivity

Charting the Course to Building a GenAI-Powered Assistant

By partnering with Quiq, the national office supply retailer launched its employee-facing GenAI assistant in just 6 weeks. Here’s what the launch process looked like in 9 primary steps:

  1. Discovery of AI enhancements
  2. Pulling content from current systems
  3. Run a Proof of Concept with Quiq team
  4. Run testing through all categories of content
  5. Approval to Pilot with Top Associate Group
  6. Refine content based on associate feedback for chain rollout
  7. Run additional testing through all categories
  8. Starting chain deployment to larger district of stores
  9. Maintain content accuracy and refine based on updates

Examining the Office Supplier’s Phased Approach to Adoption

Pre-launch, the teams worked together to ensure all content was updated and accurate. Then they launched a phased testing approach, going through several rounds of iterative testing. After that, the retailer shared the GenAI assistant with a top internal associate team to test and try and break it. Finally, the internal team utilized a top associate group to share excitement before launch.

At launch, the office supplier created a standalone page dedicated to the assistant and launched a SharePoint site to share updates for the internal team. They also facilitated internal learning sessions and quickly adapted to low feedback numbers. Last but not least, the team made it fun by branding the assistant with a fun, on-brand name and personality.

Post-launch, the retailer includes the AI assistant in all communications to associates, with tips on what to search for in the assistant. They also leverage the assistant’s proactive messaging capabilities to build excitement for new launches, promotions, and best practices.

Primary Capabilities and Focus

Launching the GenAI assistant has been transformative because it is trained on all things related to the office supply retailer, which has simplified and accelerated access to information. That means associates can help customers faster, answering questions accurately the first time and every time, regardless of tenure. Ultimately, AI is empowering associates to do even better work—including enhanced cross and upselling with proactive messages.

Proactive messaging to associates helps keep rotating sales goals top of mind so they can weave additional revenue opportunities into customer interactions. For example, if the design services team has unexpected bandwidth, the AI assistant can send a message letting associates know, inspiring them to highlight design and print services to customers who may be interested. It also provides a fun countdown to important launches, like back-to-school season, and “fun facts” that help build up useful knowledge over time. It’s like bite-size bits of training.

GenAI Transforms the In-Store Experience in 4 Critical Ways

Implementing the GenAI assistant has had a profound impact on in-store operations. By providing associates with instant access to accurate information, it has:

  1. Enhanced Customer Service: Associates can now provide faster, more accurate responses to customer questions.
  2. Increased Efficiency: The time it takes to find information has been significantly reduced, allowing associates to serve more customers.
  3. Boosted Confidence: With a reliable AI assistant at their fingertips, associates feel more empowered in their roles. Plus, new associates can be as effective as experienced ones with the assistant by their side.
  4. Improved Job Satisfaction: The reduced stress of information retrieval has led to higher job satisfaction among associates. Not to mention, the GenAI assistant is there to converse and empathize with employees who experience stressful situations with customers.

Results + What’s Next?

As a result of launching its GenAI assistant with Quiq, our national office supply retailer customer has realized a:

  • 68% self-service resolution rate, allowing associates to get immediate answers to questions 2 out of 3 times
  • Associate satisfaction with AI 4.82 out of 5

And as for next steps, the team is excited to:

  • Launch a selling assisted path
  • Expand to additional departments within stores
  • Add more devices in store for easier accessibility
  • Integrate with internal systems to be able to answer even more types of questions with real-time access to orders and other information

The Lesson: Humans and AI Can Work Together to Play Their Strongest Roles

The office supply retailer’s successful implementation of GenAI serves as a powerful example of how the technology can transform retail operations by helping human employees work more efficiently. By focusing on empowering associates with AI, the company has not only improved customer service but also enhanced employee satisfaction and productivity.

Interested in Diving Deeper into GenAI?

Download Two Truths and a Lie: Breaking Down the Major GenAI Misconceptions Holding CX Leaders Back. This comprehensive guide illuminates the path through the intricate landscape of generative AI in CX. We cut through the fog of misconceptions, offering crystal-clear, practical advice to empower your decision-making.

Email AI Webinar Recap: Quiq Announces AI for Email Responses, Triage, Insights & More

Posted on Max Fortis

Quiq is dedicated to helping enterprises move to the next generation of CX through customer-centric AI. Our journey began with an AI assistant that can improve your CX, grow revenue, and manage your costs. This is powered by Generative AI and Large Language Models (LLMs)—it all started in digital messaging, your customers’ most preferred channel, but has grown to encompass voice and email, too.

I sat down with my colleague Greg Dreyfus to take a deep dive into Email AI, explain why we decided to build it here at Quiq, show how it works, and explore the most effective ways to use it to drive CX outcomes.

Applying AI to Email Creates an Omnichannel Approach

Earlier this year, we launched Voice AI, applying the latest AI to your most expensive channel: phone. This delivers multimodal interactions with Voice and text messaging. It’s built with Large Language Models, state-of-the art language processing, text-to-speech, and fits with your existing telephony infrastructure.

Omnichannel AI that you can leverage across all channels

And now Quiq is delighted to announce Email AI! With the addition of AI for email responses through Quiq, you can now maximize your AI investment across your most meaningful channels, increasing the impact and time to value for your organization.

Our AI assistants use the same underlying platform, which means you can build once and run everywhere. You can also reuse all of your data, business logic, and system integrations across touchpoints. We’ve proven that these AI assistants can drive outcomes with the dozens of live examples we have in the market.

Why Apply AI to Email?

For a lot of businesses, email is their most voluminous channel. This is a statistic from the Connected Customer report from Salesforce, that cites that customers use multiple channels sometimes 8 or more.

Salesforce Connected Customer Report

Source: Salesforce Connected Customer Report

Other channels are growing quickly, but email continues to rank #1 for popularity with customers.

Traditional Email Challenges are Well-Known

Email has a few problems that have plagued it as a channel from day one. During the webinar, I asked attendees:

Which of these is your biggest challenge with email?

  1. High inbound email volumes & constant backlog mean high turnaround times & costs
  2. Poor CSAT & CX
  3. Low agent satisfaction

65% of respondents chose High inbound email volumes & constant backlog mean high turnaround times & costs, which reinforces the fact that email is persistently expensive and hard-to-tackle for brands.

But beyond the high costs, which are driven by volume, email has other challenges, the biggest of which are:

1. Poor CX

According to Deloitte, 88% of companies are prioritizing the customer experience created from their contact centers. Yet, 26% of consumers feel that contacting customer support, especially through email, has become more challenging over time.

There’s a clear disconnect between the amount of time, effort, and money enterprise businesses are investing in CX and the experiences their customers are having on the other side of that—particularly via email.

2. Low agent satisfaction

Agent morale gets impacted with high ticket volumes and seasonal surges. Constant backlogs and broken threads further complicate the channel for agents, making it a frustrating experience for your contact center employees.

Email AI Solves Stubborn Channel Challenges

Quiq’s Email AI works across every stage of your email journey. It uses AI to triage, draft, respond to and analyze emails efficiently and quickly, ensuring consistent and personalized communication to your customers.

How Email AI works in nutshell

 

Let’s say a customer email comes in. With Email AI, we can improve agent efficiency by routing that to the right agent. You can resolve routine issues through generative responses or drafts for your agent. If you implement customer account specific information, you can extend this to answer even more complex questions. And finally, you can use AI and automation to analyze existing emails to identify opportunities to improve.

The best part is that it is easy to deploy: Email AI works with your existing email management platform, so no need to rip and replace.

Email AI Demo #1: Using AI to Respond to a Customer Inquiry

My colleague, Greg Dreyfus, Head of Solutions Consulting, showed how Quiq’s Email AI works with Zendesk to respond back to a customer inquiry with information about the order.

Check out how this all works, ultimately improving not only the customer experience but also the ticket record left behind in the email management platform thanks to spot-on AI categorization.

 

As you can see, the AI-powered categorization is extremely useful for ticket routing and analytics, too. After showing this first use case, we polled the audience to understand where they could see the generative component of Email AI being the most valuable in their contact center operations. Here is what they said.

Audience Poll #2

This made a lot of sense to us because, of course, your agents are the ones responding to customer emails. So, to the extent that you can use AI to provide responses for them to edit or review, you can make email incrementally more efficient with a phased approach to introducing AI into your existing workflows.

Email AI Demo #2: Using AI to Flag a Customer Inquiry as Sensitive

Knowing how to best use AI is critical to capitalizing on the efficiency gains it offers while minimizing risk. Quiq’s Email AI can deal with sensitive inquiries differently than those deemed not sensitive. Watch Greg demonstrate exactly how this works and how our AI handles sensitive communications differently.

Of course, we wanted to get a sense from attendees about how often they think their teams would need sensitive conversations treated differently by AI, so we polled them a third time after demonstrating the use case.

Audience Poll #3

Poll #3

Unsurprisingly, 90% of those who responded indicated that they need AI to handle sensitive communications differently—which is exactly why we’ve approached Email AI with the customizability that we have.

To recap, Greg went over Triage and Response use cases for Email AI. While we didn’t cover a demonstration of Insights, that’s a third area of major business impact that we’d love to talk to you about if you’re interested.

Email AI Uses

5 CX Outcomes You Can Expect from Email AI

Now with our application of GenAI to email, we’re super excited to impact the CX outcomes that matter to your business. Here’s the top 5 benefits you can expect to realize first when you adopt Quiq’s AI for email responses, triaging, insights, and more:

  1. Faster response times
  2. Personalization at scale
  3. Boosts customer retention
  4. Enhanced data insights
  5. Improves bottom line

Watch the Full Webinar On-Demand

If you attended the webinar live, thank you for joining us! If not, we invite you to experience the complete walkthrough directly: Watch the webinar on-demand. You can learn more about AI Studio, the environment we use to build all AI assistants, right here.

How to Automate Email for Customer Service – Complete Guide

Posted on Max Fortis

In our last article, we made the case for email automation for customer experience by pointing out that it’s easy to use, reduces work for your agents, and allows you to utilize the personalization that customers today have come to expect.

But that still leaves the ‘how’ of choosing and setting up a platform to automate email for customer service, which is what we’ll focus on today. By the end, you’ll have a much clearer picture of what to look for and how to make the overall process run as smoothly as possible.

Let’s dive in!

An Overview of Email Automation for Customer Service

First, let’s briefly recap what email automation for CX actually is. Email automation in customer service refers to using technologies like generative AI to automate and personalize email communications, positively impacting the speed of your agents’ response times, how satisfied your customers are, and how efficiently your business runs. It helps businesses manage large volumes of inquiries while still retaining high standards for the quality of customer interactions.

Chances are, you’re already using email to communicate with customers, but by leveraging AI for email responses, you can rapidly address queries about warranties or recent purchases, thus dramatically decreasing the inbound issues requiring your agents’ attention. This is a net win for everyone–you, your team, and your customers.

How to Automate Email for Customer Service

Now that you’re sufficiently excited by the possibilities represented by robust email AI assistants, let’s walk through what’s actually involved in using them.

In the next few sections, we’ll cover two big topics: how to select an email automation for CX platform, and what to do with it afterward.

Decide on a Good Email Automation Solution

Any attempt to implement email automation for customer experience has to revolve around a certain set of features. When you’re shopping around, check that your preferred tool has them.

1. One of the first things you’ll want to check is that a platform supports email AI assistants based on large language models (LLMs).

We’ve alluded to these assistants a few times already, and that’s because they play a major role in effective automation. AI for email responses is what drives the ability to personalize each email to the recipient, for example, but that’s hardly all. These days, customer-facing AI assistants can often answer questions directly (in text and voice), without the need of hassling a human agent, freeing them up to laser in on those interactions requiring more judgment.

Moreover, they can intelligently classify and route issues by topic, urgency, etc. You will want to ensure your software provider is using the latest generation of AI and large language models because it is much better at reasoning over the nature of your customer inquiry and generating a response.

Plus, in some cases, email automation for customer experience platforms support native data analytics (even if they don’t support it currently, it’s important to make sure it’s on the roadmap since analytics is an important feature). There’s a reason they’re calling data the new oil, and that’s because it can unlock dramatic insights into your customers’ preferences and how your business is operating. When a platform makes it easy to collect and utilize this data, you can make far more informed decisions.

2. Next, you want to make sure that your preferred email automation solution integrates well with other software.

Obviously, it’ll need to play well with your email client, but at minimum, you’ll also want to verify that it’ll plug into your CRM and any knowledge management systems you use (such as databases of customer purchases).

A related advantage is to pick a tool that allows you to deploy once and run everywhere. Integration is part of this, because it requires the platform to be built on business logic that allows it to utilize the same data as your other applications (i.e., to fit seamlessly into your data layer.)

3. Then, you’ll want to assess its adaptability and channel support.

In the beginning, you may only need the barest functionality—in fact, it’s often best to test these tools with simple proofs of concept that don’t make use of any fancy bells and whistles. But, as you grow, your needs will change, and you want a platform that will grow along with you.

Migrating later is always an option, but that will require a certain expense in time and money that could be avoided by making sure you’re using the right platform in the first place.

In a similar vein, when you automate email for customer service you want something with support for multiple channels. Even when a conversation begins with a customer emailing you it might quickly move to voice or text, and you want something that can move seamlessly over all these channels with the least friction possible.

“Ground” Your Email Automation Software

Once you’ve got this lined out, you’ll need to ensure that the AI email assistant lying at the heart of the software only references your company’s trusted knowledge and data. The most recent large language models work quite well out of the box, but even the best of them will still need to be programmed to your specific circumstances.

There are a few parts of this cycle. First, you must gather the relevant data, with a particular focus on two things:

  1. Company-specific data that helps capture your brand, vision, and approach;
  2. User-specific data that helps your email AI assistant to generate better replies.

The former is necessary to strike the right tone (are you going for ‘playful’ or ‘professional’, for example) and converse with customers in a way that reflects your basic values. One way to do this is to offer a model brand voice examples in instructions or prompts.

Less abstractly, the second is needed to make sure models are responding in the most helpful possible ways, by drawing on your data. This could involve using techniques such as retrieval augmented generation (RAG) or fine-tuning. RAG involves pointing a model to a CRM, a Notion table, etc., so that it can utilize this data in answering a customer inquiry.

Fine-tuning is another technique that could involve something like showing a large language model many instances of your best agents helping a customer, then using that to have the model make suggestions to more junior agents as they resolve an issue or respond directly on its own.

3 Best Practices for Using Automated Email for Customer Service

Now, we’ll spend a few sections explaining some of the best ways to successfully automate email for customer service, in a way that makes your life easier and pleases your customers.

1. Personalize your Emails

The first is straightforward but very important: make sure you’re personalizing your communications to your customers. This is one of the great use cases for modern AI email assistants. Unlike prior generations of AI tools that relied entirely on templates and brittle rules, good email AI assistants use powerful language models to grasp the context of each email and generate responses customized to a given customer.

This means that every customer feels seen in a way that’s not possible with generic boilerplate responses. For you, this drives positive outcomes like reducing response times, increasing issue resolution, and boosting customer satisfaction.

2. Make Privacy and Safety a Priority

Another thing that’s important is emphasizing customer privacy and safety. On the one hand, consumers today are accustomed to providing credit card information or their social security online (to make purchases or to file taxes), but they also see endless examples of data breaches compromising millions of people and putting them at risk of identity theft.

There’s no easy way to guard against this, but our purpose here is to highlight how crucial this part of your enterprise is. Your customers will trust you more if they believe that you’re doing everything you can to keep their confidential information from prying eyes. Learn more about security and compliance in the context of email automation for CX here.

3. Always be Testing and Monitoring

Finally, settling on a platform to automate email for customer service and deploying it are the start of your journey, not its end.

One reason for this is that the underlying language model technology that powers AI for email responses is stochastic. There are successful methods which make the models much more reliable. With prompt chaining, for example, you might check an incoming user email, reformat it for better results, generate an answer (making sure it’s included citations to its sources), then fact check it.

Nevertheless, you still have to monitor its output over time to make sure it’s still providing timely, helpful responses to your customers. A good platform will handle a lot of this monitoring while providing you a way to provide feedback if you’re seeing outputs that are problematic in some way.

Make The Most Use of Email Automation for Customer Service

Automating the workflows involved in addressing customer queries is one of the exciting new frontiers in customer service—and it’s already supercharging agents in contact centers.

For a broader look at automation across all support channels, not just email, check out our guide on what automated customer service is and how to implement it

You can learn more about the unique benefits of Quiq’s email AI right here!

The Truth About APIs for AI: What You Need to Know

Posted on Lauren Winder

Large language models hold a lot of power to improve your customer experience and make your agents more effective, but they won’t do you much good if you don’t have a way to actually access them.

This is where application programming interfaces (APIs) come into play. If you want to leverage LLMs, you’ll either have to build one in-house, use an AI API deployment to interact with an external model, or go with a customer-centric AI for CX platform. The latter choice is most ideal because it offers a guided building environment that removes complexity while providing the tools you need for scalability, observability, hallucination prevention, and more.

From a cost and ease-of-use perspective this third option is almost always best, but there are many misconceptions that could potentially stand in the way of AI API adoption.

In fact, a stronger claim is warranted: to maximize AI API effectiveness, you need a platform to orchestrate between AI, your business logic, and the rest of your CX stack.

Otherwise, it’s useless.

This article aims to bridge the gap between what CX leaders might think is required to integrate a platform, and what’s actually involved. By the end, you’ll understand what APIs are, their role in personalization and scalability, and why they work best in the context of a customer-centric AI for CX platform.

How APIs Facilitate Access to AI Capabilities

Let’s start by defining an API. As the name suggests, APIs are essentially structured protocols that allow two systems (“applications”) to communicate with one another (“interface”). For instance, if you’re using a third-party CRM to track your contacts, you’ll probably update it through an API.

All the well-known foundation model providers (e.g., OpenAI, Anthropic, etc.) have a real-world AI API implementation that allows you to use their service. For an AI API practical example, let’s look at OpenAI’s documentation:

(Let’s take a second to understand what we’re looking at. Don’t worry – we’ll break it down for you. Understanding the basics will give you a sense for what your engineers will be doing.)

The top line points us to a URL where we can access OpenAI’s models, and the next three lines require us to pass in an API key (which is kind of like a password giving access to the platform), our organization ID (a unique designator for our particular company, not unlike a username), and a project ID (a way to refer to this specific project, useful if you’re working on a few different projects at once).

This is only one example, but you can reasonably assume that most protocols built according to AI API best practices will have a similar structure.

This alone isn’t enough to support most AI API use cases, but it illustrates the key takeaway of this section: APIs are attractive because they make it easy to access the capabilities of LLMs without needing to manage them on your own infrastructure, though they’re still best when used as part of a move to a customer-centric AI orchestration platform.

How Do APIs Facilitate Customer Support AI Assistants?

It’s good to understand what APIs are used for in AI assistants. It’s pretty straightforward—here’s the bulk of it:

  • Personalizing customer communications: One of the most exciting real-world benefits of AI is that it enables personalization at scale because you can integrate an LLM with trusted systems containing customer profiles, transaction data, etc., which can be incorporated into a model’s reply. So, for example, when a customer asks for shipping information, you’re not limited to generic responses like “your item will be shipped within 3 days of your order date.” Instead, you can take a more customer-centric approach and offer specific details, such as, “The order for your new couch was placed on Monday, and will be sent out on Wednesday. According to your location, we expect that it’ll arrive by Friday. Would you like to select a delivery window or upgrade to white glove service?”
  • Improving response quality: Generative AI is plagued by a tendency to fabricate information. With an AI API, work can be decomposed into smaller, concrete tasks before being passed to an LLM, which improves performance. You can also do other things to get better outputs, such as create bespoke modifications of the prompt that change the model’s tone, the length of its reply, etc.
  • Scalability and flexibility in deployment: A good customer-centric, AI-for-CX platform will offer volume-based pricing, meaning you can scale up or down as needed. If customer issues are coming in thick and fast (such as might occur during a new product release, or over a holiday), just keep passing them to the API while paying a bit more for the increased load; if things are quiet because it’s 2 a.m., the API just sits there, waiting to spring into action when required and costing you very little.
  • Analyzing customer feedback and sentiment: Incredible insights are waiting within your spreadsheets and databases, if you only know how to find them. This, too, is something APIs help with. If, for example, you need to unify measurements across your organization to send them to a VOC (voice of customer) platform, you can do that with an API.

Looking Beyond an API for AI Assistants

For all this, it’s worth pointing out that there’s still many real-world AI API challenges. By far the quickest way to begin building an AI assistant for CX is to pair with a customer-centric AI platform that removes as much of the difficulty as possible.

The best such platforms not only allow you to utilize a bevy of underlying LLM models, they also facilitate gathering and analyzing data, monitoring and supporting your agents, and automating substantial parts of your workflow.

Crucially, almost all of those critical tasks are facilitated through APIs, but they can be united in a good platform.

3 Common Misconceptions about Customer-Centric AI for CX Platforms.

Now, let’s address some of the biggest myths surrounding the use of AI orchestration platforms.

Myth 1: Working with a customer-centric AI for CX Platform Will be a Hassle

Some CX leaders may worry that working with a platform will be too difficult. There are challenges, to be sure, but a well-designed platform with an intuitive user interface is easy to slip into a broader engineering project.

Such platforms are designed to support easy integration with existing systems, and they generally have ample documentation available to make this task as straightforward as possible.

Myth 2: AI Platforms Cost Too Much

Another concern CX leaders have is the cost of using an AI orchestration platform. Platform costs can add up over time, but this pales in comparison to the cost of building in-house solutions. Not to mention the potential costs associated with the risks that come with building AI in an environment that doesn’t protect you from things like hallucinations.

When you weigh all the factors impacting your decision to use AI in your contact center, the long-run return on using an AI orchestration platform is almost always better.

Myth 3: Customer-Centric AI Platforms are Just Too Insecure

The smart CX leader always has one eye on the overall security of their enterprise, so they may be worried about vulnerabilities introduced by using an AI platform.

This is a perfectly reasonable concern. If you’re trying to choose between a few different providers, it’s worth investigating the security measures they’ve implemented. Specifically, you want to figure out what data encryption and protection protocols they use, and how they think about compliance with industry standards and regulations.

At a minimum, the provider should be taking basic steps to make sure data transmitted to the platform isn’t exposed.

Is an AI Platform Right for Me?

With a platform focused on optimizing CX outcomes, you can quickly bring the awesome power and flexibility of generative AI into your contact center – without ever spinning up a server or fretting over what “backpropagation” means. To the best of our knowledge, this is the cheapest and fastest way to demo this API technology in your workflow to determine whether it warrants a deeper investment.

Leveraging Agent Insights to Boost Efficiency and Performance

Posted on The Quiq Team

In the ever-evolving customer service landscape, the role of contact center agents cannot be overstated. As the frontline representatives of a company, their performance directly impacts the quality of customer experience, influencing customer loyalty and brand reputation.

However, the traditional approach to managing agent performance – relying on periodic reviews and supervisor observations – has given way to a more sophisticated, data-driven strategy. For this reason, managing agent performance with a method that leverages the rich data generated by agent interactions to enhance service delivery, agent satisfaction, and operational efficiency is becoming more important all the time.

This article delves into this approach. We’ll begin by examining its benefits from three critical perspectives – the customer, the agent, and the contact center manager – before turning to a more granular breakdown of how you can leverage it in your contact center.

Why is it Important to Manage Agent Performance with Insights?

First, let’s start by justifying this project. While it’s true that very few people today would doubt the need to track some data related to what agents are doing all day, it’s still worth saying a few words about why it really is a crucial part of running a contact center.

To do this, we’ll focus on how three groups are impacted when agent performance is managed through insights: customers, the agents themselves, and contact center managers.

It’s Good for the Customers

The primary beneficiary of improved agent performance is the customer. Contact centers can tailor their service strategies by analyzing agent metrics to better meet customer needs and preferences. This data-driven approach allows for identifying common issues, customer pain points, and trends in customer behavior, enabling more personalized and effective interactions.

As agents become more adept at addressing customer needs swiftly and accurately, customer satisfaction levels rise. This enhances the individual customer’s experience and boosts the overall perception of the brand, fostering loyalty and encouraging positive word-of-mouth.

It’s Good for the Agents

Agents stand to gain immensely from a management strategy focused on data-driven insights. Firstly, performance feedback based on concrete metrics rather than subjective assessments leads to a fairer, more transparent work environment.

Agents receive specific, actionable feedback that helps them understand their strengths and which areas need improvement. This can be incredibly motivating and can drive them to begin proactively bolstering their skills.

Furthermore, insights from performance data can inform targeted training and development programs. For instance, if data indicates that an agent excels in handling certain inquiries but struggles with others, their manager can provide personalized training to bridge this gap. This helps agents grow professionally and increases their job satisfaction as they become more competent and confident in their roles.

It’s Good for Contact Center Managers

For those in charge of overseeing contact centers, managing agents through insights into their performance offers a powerful tool for cultivating operational excellence. It enables a more strategic approach to workforce management, where decisions are informed by data rather than gut feeling.

Managers can identify high performers and understand the behaviors that lead to success, allowing them to replicate these practices across the team. Intriguingly, this same mechanism is also at play in the efficiency boost seen by contact centers that adopt generative AI. When such centers train a model on the interactions of their best agents, the knowledge in those agents’ heads can be incorporated into the algorithm and utilized by much more junior agents.

The insights-driven approach also aids in resource allocation. By understanding the strengths and weaknesses of their team, managers can assign agents to the tasks they are most suited for, optimizing the center’s overall performance.

Additionally, insights into agent performance can highlight systemic issues or training gaps, providing managers with the opportunity to make structural changes that enhance efficiency and effectiveness.

Moreover, using agent insights for performance management supports a culture of continuous improvement. It encourages a feedback loop where agents are continually assessed, supported, and developed, driving the entire team towards higher performance standards. This improves the customer experience and contributes to a positive working environment where agents feel valued and empowered.

In summary, managing performance by tracking agent metrics is a holistic strategy that enhances the customer experience, supports agent development, and empowers managers to make informed decisions.

It fosters a culture of transparency, accountability, and continuous improvement, leading to operational excellence and elevated service standards in the contact center.

How to Use Agent Insights to Manage Performance

Now that we know what all the fuss is about, let’s turn to addressing our main question: how to use agent insights to correct, fine-tune, and optimize agent performance. This discussion will center specifically around Quiq’s Agent Insights tool, which is a best-in-class analytics offering that makes it easy to figure out what your agents are doing, where they could improve, and how that ultimately impacts the customers you serve.

Managing Agent Availability

To begin with, you need a way of understanding when your agents are free to handle an issue and when they’re preoccupied with other work. The three basic statuses an agent can have are “available,” “current conversations” (i.e. only working on the current batch of conversations), and “unavailable.” All three of these can be seen through Agent Insights, which allows you to select from over 50 different metrics, customizing and saving different views as you see fit.

The underlying metrics that go into understanding this dimension of agent performance are, of course, time-based. In essence, you want to evaluate the ratios between four quantities: the time the agent is available, the time the agent is online, the time the agent spends in a conversation, and the time an agent is unavailable.

As you’re no doubt aware, you don’t necessarily want to maximize the amount of time an agent spends in conversations, as this can quickly lead to burnout. Rather, you want to use these insights into agent performance to strike the best, most productive balance possible.

Managing Agent Workload

A related phenomenon you want to understand is the kind of workload your agents are operating under. The five metrics that underpin this are:

  1. Availability
  2. Number of completions per hour your agents are managing
  3. Overall utilization (i.e. the percentage of an agent’s available conversation limit they have filled in a given period).
  4. Average workload
  5. The amount of time agents spend waiting for a customer response.

All of this can be seen in Agent Insights. This view allows you to do many things to hone in on specific parts of your operation. You can sort by the amount of time your agents spend waiting for a reply from a customer, for example, or segment agents by e.g. role. If you’re seeing high waiting and low utilization, that means you are overstaffed and should probably have fewer agents.

If you’re seeing high waiting and high utilization, by contrast, you should make sure your agents know inactive conversations should be marked appropriately.

As with the previous section, you’re not necessarily looking to minimize availability or maximize completions per hour. You want to make sure that agents are working at a comfortable pace, and that they have time between issues to reflect on how they’re doing and think about whether they want to change anything in their approach.

But with proper data-driven insights, you can do much more to ensure your agents have the space they need to function optimally.

Managing Agent Efficiency

Speaking of functioning optimally, the last thing we want to examine is agent efficiency. By using Agent Insights, we can answer questions such as how well new agents are adjusting to their roles, how well your teams are working together, and how you can boost each agent’s output (without working them too hard).

The field of contact center analytics is large, but in the context of agent efficiency, you’ll want to examine metrics like completion rate, completions per hour, reopen rate, missed response rate, missed invitation rate, and any feedback customers have left after interacting with your agents.

This will give you an unprecedented peek into the moment-by-moment actions agents are taking, and furnish you with the hard facts you need to help them streamline their procedures. Imagine, for example, you’re seeing a lot of keyboard usage. This means the agent is probably not operating as efficiently as they could be, and you might be able to boost their numbers by training them to utilize Quiq’s Snippets tool.

Or, perhaps you’re seeing a remarkably high rate of clipboard usage. In that case, you’d want to look over the clipboard messages your agents are using and consider turning them into snippets, where they’d be available to everyone.

The Modern Approach to Managing Agents

Embracing agent insights for performance management marks a transformative step towards achieving operational excellence in contact centers. This data-driven approach not only elevates the customer service experience but also fosters a culture of continuous improvement and empowerment among agents.

By leveraging tools like Quiq’s Agent Insights, managers can unlock a comprehensive understanding of agent availability, workload, and efficiency, enabling informed decisions that benefit both the customer and the service team.

If you’re intrigued by the possibilities, contact us to schedule a demo today!

Request A Demo

Top 3 Things to Know About Apple Messages for Business

Posted on Max Fortis

Key Takeaways

  • Apple Messages for Business offers a native, seamless channel. It allows customers to message a business directly via the iOS/macOS Messages app – accessible from Maps, Search, Safari, Siri, or within your app.
  • It supports rich, interactive experiences. Beyond plain text, you can send images, links, lists, schedule appointments, accept payments via Apple Pay, use augmented reality, and more.
  • It enables both automated and human-powered interactions. You must have live agents available, but you can also layer in AI agents to handle common tasks to provide 24/7 service.
  • There are strategic advantages to adoption. Use Message Suggest to shift traffic from calls to messaging, enhance your brand presence in Apple’s ecosystem, and reduce friction by meeting customers in a familiar channel.

How much do you know about Apple Messages for Business?

We know what you’re thinking. Not *another* messaging platform.

But hear us out. Business Messages is one of the most organic messaging experiences you can offer your customers.

There’s no denying that Apple knows how to create a great customer experience. From their Genius Bars to their MacBook packaging, there’s care and attention paid to every small detail.

And that extends to their Business Messages feature. We’re answering three basic questions to help you discover what it is and how to use it to improve your customer experience below.

What is Apple Messages for Business?

Apple Messages for Business allows users to connect with your business through Apple’s native Messaging app on their iOS- or macOS-enabled devices. With more than 1 billion iPhone users, you have a simple way to connect with your customers.

When you enable Apple Messages for Business, users get one-on-one access to your representatives directly in Maps, Safari, Search, Siri, Spotlight, and even within your iOS app. All they have to do is click the messaging icon, and they’re taken to their messaging apps.

Your customers don’t need to navigate through an automated phone tree, search for an email address on your website, or download an app—they can simply open up the Messages app and start chatting with a customer service representative right away.

What can you do with Apple Messages for Business?

Besides enabling customers to connect with you as easily as they do with their favorite people, Apple Messages for Business offers a variety of features you can leverage to improve your customer experience.

Rich messaging

So much more than a text. Rich messaging allows you to send images, links, share a location, read receipts and more.

Send a rich link that displays website information from within the message. Customers can verify it’s the information they’re looking for before tapping the link, and they’re provided an easy way to get back to the conversation after they’ve visited the site.

Retailers can use messaging to send product images, insurance companies can ask for pictures of car damage, or you can share nearby brick-and-mortar locations.

Appointment scheduling

Let customers schedule appointments right from within messaging. Customers can see if it conflicts with their schedule, automatically add the appointment to their iCal, and get reminders to ensure they don’t miss it.

Augmented reality

Help customers decide if a product fits in their home. Using their iPhone camera and augmented reality, customers can visualize a product in their homes.

List picker

Simplify customer choice by letting them pick from a pre-populated list. Use it to help them pick locations, item size, color, or service.

pay to chat apple pay

Accept payments with Apple Pay

Eliminate abandoned carts by completing sales right within Messages. You can send payment requests with Apple Pay and make it so much easier for customers to complete a transaction. No more entering in extra information, and no getting up from the couch to get your credit card… Just instant gratification.

Collect (secure) customer information

What’s one thing customers hate most about customer service? Having to repeat themselves and give out their information to multiple people. Apple Business Chat removes this obstacle by providing the customer information you need. Plus, messages are long-lived. No matter when a customer reaches out, their previous conversation will live in the messaging platform so you can pick up right where you left off. Just like messaging a friend.

What are the benefits of using Apple Messages for Business?

Apple Messages for Business provides many opportunities to delight customers while still streamlining your customer support process. Benefits of Apple Messages for Business include:

  1. The ability to encourage messaging with Message Suggest.
  2. Delivery of a seamless customer experience.
  3. Incorporation of both live agents and chatbots.

Encourage messaging with Message Suggest

Typically, customers can message you by tapping a messaging icon. But you can boost messages and reduce your call volumes with Apple Message Suggest. Customers can tap on your business phone number from anywhere within iOS or macOS, including websites, social media, business directories, etc., and they’ll be given the option to message instead of call.

Adding Message Suggest can help reduce call volume and drive traffic to messaging, which is often more efficient and cost-effective.

Deliver a seamless customer experience

Think of Apple Messages for Business as an extension of your brand experience. You control your business contact info the user sees when they search your name, including logo, contact information, and more.

You can even customize the call-to-action text for Apple Suggest. Instead of the standard, “Why call when you can message?”, consider a simple “Text us” or an encouraging “Get faster service when you send a text.”

Incorporate both live agents and chatbots

One of the requirements for having an Apple Messages for Business account is having live agents available to respond to customer inquiries. But that doesn’t mean you have to have someone at the ready at all times. You can still use AI agents at specific points of the interaction to ensure instant service at all times.

Use bots to:

  • Welcome customers
  • Automate the checkout process
  • Gather customer information
  • Troubleshoot simple customer issues
  • Collect feedback

Since messaging can happen at any time, you can use bots to service customer questions 24/7, such as:

  • Checking order statuses
  • Canceling an order
  • Scheduling appointments
  • Confirming account balances

You can still utilize bots to help you deliver the best experience to your customers while tapping in live agents to provide that one-on-one support.

Take a deep dive into Apple Messages for Business.

We’ve just scratched the surface on what you can do with Apple Messages for Business to surprise and delight your customers. By giving customers a way to connect with your business that they know, love, and use daily, you’re creating a comfortable and inviting experience for them. P.S. To use Apple Messages for Business, you’re required to use a messaging service provider like Quiq.

Frequently Asked Questions (FAQs)

What is Apple Messages for Business?

It’s a native iOS and macOS feature that lets customers message your business directly through Apple’s Messages app – from Maps, Safari, Siri, or your app without needing to call or email.

What features does Apple Messages for Business offer?

Businesses can send rich messages, schedule appointments, collect secure customer information, enable augmented reality previews, and accept Apple Pay payments within chat.

How does Apple Messages for Business improve customer experience?

It streamlines support by removing friction – customers can start conversations instantly and pick up where they left off, similar to texting a friend.

Can Apple Messages for Business integrate live agents?

Yes. Companies must have human agents available, but can also automate common requests using AI agents to provide 24/7 support.

What are the main benefits for businesses?

It helps reduce call volumes with Message Suggest, builds trust through Apple’s secure ecosystem, and enhances brand perception with a seamless, familiar messaging experience.