April 2025

CSAT and NPS: A Guide to Raising Your Scores

Posted on April 30, 2025May 1, 2025 Cristina Bravo-Olmo

There are lots of customer success metrics floating around the customer service industry—it’s hard to keep them straight! But the two we hear most often are CSAT and NPS®.

You know they’re both important, but what’s the difference?

They’re both short, often one-question surveys that use numerical scales. The big difference? CSAT scores (customer satisfaction) measure one specific interaction, while NPS (Net Promoter Score®) evaluates the overall opinion of your business.

Hint: You need both in your business.

Keep reading to learn how to use CSAT and NPS surveys, and what you can do to raise your scores.

What is a CSAT score?

A customer satisfaction (CSAT) survey asks customers a single question: On a scale from one to five, how satisfied were you with [company/service/product/interaction]?

To get the CSAT score, you take the average number of respondents who answered either fours (satisfied) or fives (very satisfied).

The CSAT formula
Total number if 4 and 5 responses ÷ Total number of responses x 100 = % of
satisfied customers

Simple, right? That’s the beauty of CSAT surveys. They’re easy to answer because they’re multiple-choice and come immediately after the interaction. Customer responses tend to be higher than other forms of surveys.

What makes CSAT scores such powerful metrics is their ability to be used across the organization in a variety of ways. The best way to use it in customer service? Immediately after a customer interaction.

It can also evaluate products and services, the e-commerce experience, a piece of content, and more.

How do you use CSAT scores in your business?

Customer satisfaction scores are a quick and easy way to get immediate customer feedback. And with Zendesk reporting that over 60% of customers admit that the pandemic has raised their customer service expectations, staying on top of customer satisfaction is critical to business success.

CSAT is a numbers game. The more customers you get to answer the survey, the better picture you’ll have of your customer service as a whole. While responses tend to be higher than those of other surveys, customers already show signs of survey fatigue.

Here are a few ways to increase response rates.

Best practices to increase CSAT score response rates

Include the survey in their preferred messaging channel. Don’t rely on an email after the interaction (which comes with a meager open rate and an even lower response rate). Instead, send customers the survey right within the messaging platform they’re already using. If the conversation happened over text messaging, send the survey via text at the end.
Use an AI agent to administer the survey. Automate survey distribution and capture sentiment while it’s still fresh in your customers’ minds. Program your AI agent to jump into the conversation once the customer’s problem is solved.
Make the survey visually engaging. Use rich messaging to make your surveys stand out. Try emojis when appropriate, test out stars vs. a number scale, or even try incorporating GIFs. See what it’ll take to get your customers to click!
Be specific. Make sure you say exactly what you’re asking for. A vague “rate us” won’t elicit a good response, but something like “How did Jenny do on this request?” might.

If you’re thinking, “This is great! But what does it really tell me about our customer service team?”, then it’s time for some deeper questions.

You have a few options. Consider adding an optional question that asks why your customers scored the way they did. This captures in-the-moment information to help you discern the problem or what made that customer service experience stand out.

However, adding additional questions (even optional ones) could keep customers from answering the survey altogether. Maybe they feel like they need to think through their answers a bit more, or feel like it’s just too much.

If that’s the case, you can also let them opt in to receive a follow-up survey that goes into more detail. If they agree, send them an email with questions that dig into the heart of the problem. For severe issues or standout surveys, you can even request an interview (and offer an incentive to participate).

It’s also important to note that you’re more likely to hear from customers on either end of the spectrum. The people who had very positive experiences (fives) and extremely dissatisfying experiences (ones) are the most likely to respond to your surveys. Keep that in mind when assessing your customer service experience.

What can you do to improve your CSAT score?

That depends on what you’re measuring.

Let’s assume you’re measuring your customer service interactions. Every customer wants a few key things when they reach out to your support team.

Quick resolutions: 61% of customers define a good customer service experience as one that solves their problems quickly. Make sure your staff is well-trained and has access to all the information they need to serve your customers.
Timely responses: Customers expect access to support agents 24/7. While this isn’t always possible, there are several options to serve customers when agents aren’t available. Many customers want self-service options, so spend the time and effort to enhance your knowledge base. You can also rely on AI agents to answer common questions and set expectations for when an agent will be available. Relying on asynchronous messaging, like text messaging, will also help with more flexible response times.
A friendly customer service agent: Now more than ever, customers are looking for empathy from your customer service agents. Train your agents to practice patience and kindness (and ensure they can translate those emotions into text), and empower them to flex the rules and do what it takes to make the customer happy.

What is NPS?

NPS stands for Net Promoter Score, and it calculates how likely your customers are to recommend your brand.

An NPS survey asks the question, “How likely is it that you would recommend [brand] to a friend or colleague?” Customers then rate their likelihood from 0–10, with zero being not at all likely and ten being extremely likely.

When calculating your NPS, only customers who select nine or ten are considered your promoters, while passives score seven and eight, and detractors score zero through six. So, calculating your NPS looks a little different than calculating your CSAT score.

The NPS formula
% of promoters — % of detractors = NPS

Pros and Cons of Net Promoter Scores (NPS)

Your NPS identifies overall brand perception rather than a specific transaction. This leads to several pros and cons.

Pros	Cons
There’s a strong correlation between NPS (which measures loyalty) and business growth.	Since NPS measures perception instead of performance, it’s harder to pinpoint specific problem areas.
NPS is standardized across brands, so it’s better at providing benchmark numbers on which to base your business’s performance.	It requires a deep analysis of both industry-wide and internal trends to decipher the results.

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Like CSAT surveys, NPS surveys often need a little help to get usable feedback from your customers. Ask respondents to explain their reasoning in a follow-up question. While asking another question may limit your responses, it’s better to have insights on what matters most to your customers.

So, how often should you measure NPS? Since it’s an assessment of your overall experience, you’ll need to evaluate the best frequency and delivery method for your brand. Opt for at least once a year.

If your customer base is large and you change tactics frequently, you might want to consider sending out surveys once a quarter to get more immediate feedback.

What is considered a good NPS?

Since NPS scores are standardized, it’s easy to identify a benchmark score.

According to Sametrix, the average NPS for online shopping brands in 2021 was 41, and the industry leader’s NPS was 59.

Once you start tracking your own data, pay attention to internal and external trends that influence your score. For example, many brands may be experiencing lower-than-average scores due to supply shortages or long wait times.

How do you increase your NPS?

Once you’ve established your NPS baseline, you have a benchmark for future results. But since you aren’t measuring a specific interaction, it’ll take a little more digging to identify ways to improve it. Here are some ways to get started:

Dive into the data: Instead of looking at your NPS as a standalone metric, compare it to what you know about your customers. Are your promoters Gen Z and your detractors Gen X? Did all your promoters buy a particular service? Look at what other metrics you can pull in so you have a bigger picture of the results.
Look at the internal context: What was going on when you sent out that survey? Had you just released a new product? Was your customer service team understaffed? See what could have influenced your responses. It may not give you the whole picture, but it can help you identify where to start.
Review industry-wide trends: It’s no secret that the pandemic caused net promoter scores to drop due to a variety of factors. But it doesn’t have to be a global problem to impact your customer service. See what external trends may have contributed to the score.

To increase your NPS, you need to do some investigating and then rally your customer service team around the solutions. With the right tools and understanding, it’s absolutely possible to increase your scores.

CSAT and NPS: What’s the difference?

When it comes to measuring customer experience, CSAT and NPS are two of the most widely used customer satisfaction metrics, but they serve different purposes. CSAT, short for Customer Satisfaction Score, typically uses a 1–5 or 1–10 scale to measure how satisfied a customer is with a specific interaction. CSAT is all about gauging immediate satisfaction with a particular service moment.

NPS, or Net Promoter Score, uses a 0–10 scale to measure overall brand sentiment, asking customers how likely they are to recommend your company. Responses are segmented into Promoters, Passives, and Detractors, offering a broader view of long-term loyalty.

If you’re comparing CSAT vs NPS, think of CSAT as a snapshot of individual experiences, while NPS tracks the cumulative impression over time. Both are essential tools for strong contact center management.

Should you use NPS or CSAT to evaluate your customer service?

Ideally, you should use both NPS and CSAT scores to get a full understanding of how your brand is performing. While NPS is great at measuring the overall sentiment around your customer service, product, etc., CSAT surveys will provide specific, actionable insights into support interactions.

Unlock Better Customer Metrics with Quiq

To deliver exceptional customer experiences, you need more than just support—you need smart, real-time insights. Quiq’s agentic AI platform makes it easy to measure and act on the customer experience with automated CSAT and NPS surveys delivered at just the right moments. Whether you’re tracking specific interactions or long-term loyalty, Quiq helps you capture meaningful data that drives better outcomes.

CSAT, short for Customer Satisfaction Score, gives you quick snapshots of customer sentiment after individual touchpoints, while NPS, or Net Promoter Score, reveals how likely customers are to recommend your brand—two critical customer satisfaction metrics that work best in tandem. Still wondering what CSAT stands for or how to compare CSAT vs NPS? Quiq makes it effortless.

With intelligent, multi-channel messaging, asynchronous conversations, and AI-powered automation, you’ll serve more customers with less effort—while gaining the insights you need to continuously improve.

Curious how it all works? Watch our video here.

Top Customer Communication Tips and Strategies

Posted on April 30, 2025May 1, 2025 Cristina Bravo-Olmo

When customer service is at its busiest, it might be time to get back to the basics: Your team’s ability to effectively communicate with customers.

Customer service teams are overwhelmed during this time of year, and shoppers are getting more frantic as the holidays grow closer. Unfortunately, this is when basic customer communication standards can go out the window.

What does customer communication entail? A lot more than you might think. From error messages and initiating returns with customer service to marketing emails and social media, it’s all a part of the customer experience.

But it’s essential to your customer service team. Continue reading to learn how to improve your team’s customer communications.

Why is communication with customers important?

Customer service often boils down to how you make a customer feel. Solving customer problems, listening to complaints, providing answers… It’s all worth more than the sum of its parts.

This customer experience often has a direct impact on sales. It affects loyalty, how likely customers are to recommend your brand, and more.

According to a 2020 XM Institute report, emotion has the largest impact on loyalty behaviors (purchasing more, recommending, forgiving, and trusting). Respondents who gave a high emotion rating were 90% more likely to purchase more and recommend the company to others. 74% were also more likely to forgive the company after a bad experience.

Creating positive emotions has a high impact on customer retention, but negative emotions have the opposite effect. According to the Zendesk Customer Experience Trends report, 50% of customers are likely to switch to a competitor after one bad experience. That number jumps to 80% after two.

Learning to communicate with customers effectively is a vital part of your business’s success.

Start with simple communication skills

In our effort to move quickly and stay up to date with the latest technology, we lose some basic customer service principles.

No matter how you interact with customers, there are a few simple tenets and strategies to follow.

1. Employ active listening techniques when you communicate with customers

Are you ever having a conversation with someone, and you can’t tell if they’re actually listening? Even if they are? That’s likely because they’re not showing you that they hear what you’re saying and are taking it in. This technique looks different over different channels, but can be used no matter how you’re talking with customers.

In-person: Nod when appropriate. Smile or show concern based on the conversation, and make eye contact throughout the discussion.
Over the phone: When you can’t make gestures to show you’re listening, give the person on the other end auditory cues. Use phrases like “I understand” or try repeating back what they just said.
Over messaging: This can be a little trickier with asynchronous messaging. Customers who employ messaging to resolve a problem often want it solved quickly without much conversation. In this scenario, the best way to show you’re listening is by responding quickly and addressing the problem head-on.

2. Mirror your customer’s communication style

A great way to connect quickly with your customer is with mirroring. This is another technique that works well in person but can also be used to connect with your customer through other channels. It relies on empathy and your ability to gauge how your customer wants to interact.

In-person: Rely on body language and facial expressions to tell you how your customer is feeling. Are they fidgety and in a hurry? Relaxed and looking for some small talk? Show your customer you understand them by taking their visual cues and using them to determine whether to give quick, to-the-point answers or spend time chatting about the weather. You can also mirror body language as a way to connect with your customer—just don’t go too overboard. You don’t want to make them feel uncomfortable.

Over the phone: Take your cues from the tone of their voice. If they’re leisurely answering your initial questions (and peppering in some jokes), but you try to rush them through the conversation, that’ll leave them feeling unsatisfied.
Over messaging: Are they writing formally or using shorthand and emojis? Try to match your tone to theirs. Find a balance between the brand communication style and your customers’ expectations to make the strongest connection.

3. Show patience

Practicing patience is tough over the holidays. Everyone’s stressed. Lines are long. And there’s only a short time to get everything done. For your customer service representatives, this is when it’s often the most difficult and most important time of year for patience.

Whether customers are coming to you angry and upset or coming to you confused and in need of direction, it’s important to show understanding. Don’t interrupt, and wait until they’ve completed their thought before jumping in with the solution. Patience will help you solve your customers’ problems and win them over time and time again.

4. Practice conflict resolution

Many customer service teams undergo group conflict resolution training, and we certainly recommend it. Working with angry customers is tough on your team and can lead to burnout if not managed properly.

The best way to solve a conflict is to stay calm, validate your customer’s feelings and work toward a solution. This is an oversimplification, but practice helps. Your team will feel more prepared for when it happens, and they’ll know how you want them to handle it.

Employing these basic communication skills effectively during every customer interaction will go a long way in building brand loyalty and elevating the customer experience.

Get proactive with outbound communications

Many businesses think customer service involves waiting around until customers come to them with a problem. The problem with this type of reactive service? Customers are already facing an issue when they come to your team. They may be agitated, upset, disappointed, and filled with tons of other unpleasant emotions by the time they ask for help. Not only does this make it more difficult for your customers to communicate with you, but it also creates a poor impression of your brand that can have long-lasting effects.

Instead, get proactive with your customer service by using outbound communications.

Getting in front of a customer service issue before it happens can be as straightforward or as complex as your business dictates.

Here are some examples of how you can solve a customer’s problem before it happens:

Software-as-a-service products can include helpful tips to direct customers on how to use the product.
E-commerce retailers can send an SMS message with a track-my-package link to reduce inbound “Where’s my order?” calls.
An online business can send out a notification for planned website maintenance.

And customers appreciate proactive service. 63% of surveyed US consumers have a more favorable view of brands that offer proactive customer service notifications, according to a 2019 Microsoft survey.

Communicating early and often with customers is a great way to get ahead of customer service issues and leave a lasting impression.

Establish a Consistent Brand Voice Across Channels

Customers interact with brands across a variety of channels, whether through chat, email, SMS, or social media, and they expect a consistent experience every time. Inconsistent messaging or tone can quickly break trust and create confusion, making it harder for customers to connect with your brand.

Why Brand Voice Matters

A strong, consistent brand voice reinforces your identity and values, ensuring customers know exactly what to expect no matter where they’re interacting with you. This consistency builds familiarity, which fosters deeper loyalty and better retention. When your messaging aligns across all channels, it creates a cohesive experience that customers can rely on.

Practical Ways to Create Consistency

To maintain a unified brand voice, start by developing a brand voice guide. This guide should outline the tone, vocabulary, and grammar style to use across different forms of communication. It should also provide examples of what to say and what to avoid, helping your team align their messaging. Make sure to train all teams, especially those in customer-facing roles like sales, support, and social media, on these guidelines. Tools like Quiq also allow you to create templates and tone settings, further ensuring that every interaction with customers stays on message.

Know When to Automate vs. When to Humanize

Not all customer communication requires a human touch, but not everything should be automated either. Striking the right balance is essential to providing fast and efficient service without sacrificing empathy.

When to Automate

Automation shines when it comes to routine, repetitive tasks like order status updates, password resets, and appointment reminders. These processes can often be handled efficiently by AI agents or automated workflows, freeing up live agents to focus on more complex interactions. Automation is also perfect for after-hours support, handling FAQs, or triaging requests before passing them off to a human agent. Tools like Quiq’s AI-powered solutions make it easy to automate these interactions without compromising customer satisfaction.

When to Humanize

Human involvement is crucial for complex, emotional, or high-stakes issues, such as billing problems, cancellations, or customer complaints. Live agents should also step in during moments that require relationship-building, like onboarding new customers or providing proactive outreach. These instances demand the empathy and nuanced understanding that only a human can provide.

Why This Matters

Over-relying on automation can frustrate customers who need empathy or more personalized assistance. By thoughtfully combining automation and human interaction, you can deliver both speed and satisfaction, ensuring a seamless experience in all forms of customer communication.

Empower your agents to communicate with customers

Since your customer service agents are at the frontline of customer communications, it’s important to empower them with everything they need to be successful when they communicate with customers.

Ensure easy access to product knowledge: In Microsoft’s 2019 survey, 35% of people stated that a service agent’s lack of knowledge was the most frustrating aspect of a poor customer service experience. Make sure your team knows your products, services, and policies inside and out so they can answer customers’ questions fully and resolve issues in one interaction.
Give them the right tools: The same Microsoft survey said 32% of customers stated having to repeat information was the most frustrating impact of a poor customer service experience. Integrate messaging tools into your current customer relationship management service to give your customer service team access to information. Agents will be able to access customer information quickly to solve problems faster, with fewer inconveniences to your customers.
Embrace their changing roles: The role of your customer service team is changing. The idea of a call center complete with a phone bank full of agents is outdated. Now, AI agents answer simple questions and route customer issues to the right agent.

Your support team can now spend more time solving complex customer issues and even engaging in pre-purchase support and upsells. It’s important to recognize this shift and set new expectations for this role now and in the future.

Build a culture of effective customer communication

What’s the most important thing to remember? No, it’s not that the customer is always right. It’s that the customer is always heard.

Your customer service team is the frontline of customer interactions with your brand. It’s vital that you set expectations and give your team the tools to meet them.

Power your customer service team with Quiq

Ensure your team communicates effectively over any messaging channel with Quiq. Our AI-enhanced conversational platform supports your customer service team with multiple messaging channels, AI agents, CRM integrations, and more.

AI Benchmarking Best Practices: A Framework for CX Leaders

Posted on April 29, 2025April 30, 2025 Greg Dreyfus

Is your AI investment delivering provable value, or is it still operating like a black box?

In today’s rapidly evolving customer experience (CX) landscape, where Artificial Intelligence (AI) promises transformative results, like decreasing service costs by up to 30% and yielding an average ROI of $1.41 for every dollar spent, simply implementing AI isn’t enough. You need to measure its impact. AI benchmarking holds the key.

Effective AI benchmarking is critical for evaluating progress, sustaining momentum, and refining your AI initiatives. By comparing performance internally and against industry standards, organizations ensure their strategies are competitive, effective, and aligned with evolving customer expectations. Robust benchmarking also builds credibility by quantifying success and providing a clear narrative for stakeholders. This is vital, as industry projections suggest AI could handle a significant majority of customer interactions, potentially between 70% (per Gartner) and 95% by 2025.

This article cuts through the complexity to deliver actionable AI benchmarking strategies specifically designed for CX professionals who need to demonstrate tangible results. Whether you’re just beginning your AI journey or looking to optimize existing implementations, you’ll learn how to develop an AI benchmarking framework aligned with your strategic goals. I’ll walk you through selecting the right metrics, establishing meaningful baselines, and creating a continuous improvement cycle that drives CX excellence. By the end, you’ll be equipped with practical tools to quantify AI’s impact, turning data into compelling narratives that secure stakeholder buy-in and position your organization as a CX leader. Let’s get started.

The Role of Benchmarking in Al-Driven CX

AI benchmarking goes beyond measuring outcomes; it establishes a clear context for performance. It highlights where AI initiatives deliver value and identifies gaps that require attention. In an era where AI investment is accelerating (98% of leaders plan to boost AI spending in 2025), benchmarking is vital for several reasons:

Identifying Best Practices: Learning from internal successes or external examples to guide future improvements.
Gaining Buy-In: Demonstrating progress and ROI with data-driven insights helps secure support from leadership and operational teams.
Driving Innovation: Comparing results against industry leaders inspires new strategies and reinforces a commitment to continuous improvement.

Understanding why AI benchmarking matters sets the stage. Now, let’s look at what top performance actually looks like in the current landscape.

What Good Looks Like in 2025

Based on current AI benchmarks and successful implementations, “good” Al-powered CX in 2025 isn’t just about isolated metrics. It’s about a holistic transformation that delivers significant, measurable value across the board. Here’s a snapshot:

Substantial Automation & Efficiency

Leading organizations achieve high AI Deflection Rates, with virtual agents fully resolving significant portions of inquiries without human intervention. Reported rates vary widely based on industry and use case, often ranging from 43% to over 75%.

This translates to significant reductions in Average Handle Time (AHT), sometimes resulting in 5x faster resolutions, and major Agent Productivity gains, often between 15-30%. Operational costs see marked decreases, potentially reaching the significant levels mentioned earlier.

Enhanced Customer Experience

Critically, efficiency gains do not come at the expense of satisfaction. Top performers maintain or even improve CSAT scores, often seeing lifts like Motel Rocks’ 9.44 point increase or Quiq clients like Accor achieving 89% CSAT. This is achieved through faster responses, 24/7 availability, increased personalization, and effective Human-Al Orchestration, ensuring empathy for complex issues. Improved First Contact Resolution (FCR) is key, with reductions in repeat contacts of 25-30% reported.

Tangible Business Outcomes & ROI

Success is measured in clear financial terms. Organizations demonstrate strong ROI, often reaching the average levels noted earlier, and achieve significant cost savings (Gartner projects $80 billion globally by 2026). Furthermore, Al is leveraged for revenue growth through Conversational Commerce, turning service interactions into sales opportunities, as seen with Klarna projecting $40M in additional profit, or Quiq clients attributing 10% of daily sales to chat.

Strategic & Integrated Approach

Excellence involves strategically deploying Al within asynchronous messaging channels (SMS, web chat, etc.) favored by customers. It requires robust Al Governance, seamless integration with existing systems, continuous iteration based on data, and commitment to agent training.

Leveraging Advanced, Accurate Al

Successful implementations increasingly use sophisticated conversational Al, often incorporating Large Language Models (LLMs) enhanced with techniques like Retrieval-Augmented Generation (RAG) for factual accuracy grounded in company knowledge. Agent-Assist tools are widely used to empower human agents.

“In essence, ‘good’ in 2025 means Al is deeply embedded, driving efficiency, enhancing customer satisfaction, delivering clear financial returns, and strategically positioning the organization for future innovation…” – Greg Dreyfus, Head of Solution Consulting at Quiq

Achieving this level of success requires a structured approach to measurement. Let’s look at the different ways you can benchmark your progress.

Types of AI Benchmarking

Internal Benchmarking

Focuses on comparing Al-driven performance within the organization to establish a baseline and track improvements over time.

Example: Compare resolution times and CSAT scores for Al versus human-handled inquiries.
Benefits: Highlights immediate wins, uncovers inefficiencies, and ensures alignment with goals.

Competitive Benchmarking

Involves comparing your organization’s metrics against direct competitors.

Example: Evaluate how your Al adoption impacts NPS or cost-per-interaction relative to others in your sector.
Benefits: Identifies competitive gaps or advantages, informs positioning strategies.

Industry Benchmarking

Assesses performance against general industry standards and best practices.

Example: Use analyst reports to compare your productivity gains (e.g., aiming for the 15-30% range) with sector leaders.
Benefits: Provides a macro view, uncovers broad trends for innovation.

Customer-Centric Benchmarking

Focuses on measuring outcomes that directly impact customer perceptions and loyalty.

Example: Compare Customer Effort Scores (CES) before and after implementing Al.
Benefits: Ensures CX initiatives genuinely improve the customer experience.

With these benchmarking types in mind, how do you build a practical framework for your organization?

Building an Al Benchmarking Framework

1. Establish Al Governance & Define Scope (Foundation)

Before deploying Al widely, create a clear Al Governance framework. Assemble a cross-functional team (CX, IT, Legal, Compliance) to define responsible usage policies, ethical guardrails, and risk protocols. Determine which metrics are most relevant to your goals and tie them to business outcomes like cost reduction, revenue growth, or retention.

2. Set Benchmarks at Multiple Levels

Establish benchmarks evaluating:

Operational Impact: FCR, Deflection Rate, AHT, Agent Productivity.
Customer Impact: CSAT, NPS, CES, Churn.
Financial Impact: ROI, Cost Savings, Revenue Influence.
AI Agent Mechanics: Evaluate core components like routing accuracy (did the right skill get called?), skill/tool correctness (did the skill/tool execute properly?).

3. Leverage Tools and Technology

Use appropriate tools to gather and analyze data efficiently. This includes:

Analytics Platforms: To track KPIs and visualize trends.
Customer Feedback Tools: For CSAT, NPS, CES surveys.
CX Automation Platforms (like Quiq): That often have built-in reporting and facilitate AI deployment, especially in asynchronous messaging channels.
Ensure robust integration with existing systems (CRMs, order management, etc…) to avoid data silos and enable personalized experiences.

4. Regularly Review and Update Benchmarks

Metrics and goals must evolve as AI capabilities mature. Schedule regular reviews (e.g., quarterly) to assess performance and adjust strategies. Stay current with industry reports, as benchmarks change rapidly.

Take our free AI readiness assessment to discover where you are on the AI maturity path.

Now that the framework is outlined, let’s dive deeper into the specific metrics you should be tracking, along with current industry benchmarks.

Key Metrics for Al Benchmarking in CX (with 2024-2025 Benchmarks)

Here are top metrics across key categories, updated with recent industry benchmarks:

1. Al Performance & Adoption Metrics

Al Deflection / Containment Rate: Percentage of inquiries handled or fully resolved by Al without human intervention.
- Benchmark: Highly variable based on industry, use case complexity, and AI maturity.
  - Commonly reported rates range from 43% (e.g., Motel Rocks) up to 70-75% for specific sectors (e.g., AirAsia, some telcos).
  - For routine, high-volume tasks, AI may handle up to 80%.
  - Top-performing implementations can achieve even higher containment, such as Quiq client BODi® reporting 88%.
Self-Service Resolution Rate: Percentage of customer issues fully resolved via AI self-service without any human agent involvement.
- Benchmark: Varies; examples include Sony at 15.9% and Quiq client Molekule achieving a 60% resolution rate for interactions handled via self-service AI. Industry average projections evolve (e.g., ~20% now, projected higher).
Agent Assist Utilization: Frequency agents leverage Al tools. Crucial for measuring adoption of augmentation tools.
Al Adoption / Interaction Handling: Percentage of total interactions involving Al.
- Benchmark: Projected Al handling 70% (Gartner) to 95% of interactions by 2025.
Task Convergence / Reliability: Measures the consistency and predictability of the AI agent in completing a specific task within an expected number of steps or interactions. High convergence indicates a more reliable and less error-prone process.

2. Efficiency Metrics

Average Handle Time (AHT) Reduction: Decrease in average interaction time.
- Benchmark: 25-30% range reported. Specifics: 27% (Agent Assist), 30% (Republic Services), 33-sec absolute drop (Camping World), 5x faster resolution (Klarna).
Agent Productivity Gain: Increase in agent efficiency (e.g., inquiries/hr).
- Benchmark: Avg. 15-30% from GenAl. Agents using Al: +13.8% inquiries/hr. Camping World: +33% efficiency. Quiq client (National Furniture Retailer): 33% fewer escalations.
First-Response Time (FRT): Speed of initial reply. Al excels here for instant answers.
Escalation Rate: Percentage of Al interactions needing human help. Depending on the use case, lower is better however some use cases require human escalation.

3. Customer Experience Metrics

First-Contact Resolution (FCR): Percentage issues resolved on first interaction.
- Benchmark: AI contributes significantly to improving FCR by reducing repeat contacts.
- Examples of FCR Improvement: Klarna reported 25% fewer repeat inquiries (effectively a +25% FCR impact); Republic Services saw 30% fewer repeat calls.
- Note: This differs from AI-specific resolution rates. For instance, while Quiq client Molekule achieved a 60% AI self-service resolution rate for the contacts handled by AI, the impact on overall FCR depends on the percentage of total contacts handled by AI.
CSAT Lift / Score: Change in customer satisfaction.
- Benchmark: Often maintained or improved. Klarna: Parity with humans. Motel Rocks: +9.44 points. Any Al use: +22.3% lift avg. Quiq Clients: Accor (89%), BODi® (75%), Molekule (+42% lift).
Customer Effort Score (CES): Measures ease of resolution. Lower effort = higher loyalty.
Net Promoter Score (NPS): Likelihood to recommend.

4. Financial Metrics

Cost Per Contact / Cost-to-Serve Reduction: Decrease in interaction handling cost.
- Benchmark: Reductions align with AI’s potential for significant operational savings, potentially reaching up to the 30% mark mentioned previously. Gartner projects $80B projected savings globally by 2026.
Return on Investment (ROI): Financial return from Al investment.
- Benchmark: As highlighted earlier, the average ROI often reaches $1.41 per $1 spent, with 92% of early adopters seeing positive ROI.
Revenue Influence / Conversational Commerce: Added revenue via Al assistance.
- Benchmark: Klarna: Projected +$40M profit. Retailers: 5-15% conversion lift. H&M: Higher AOV. Quiq clients: Accor (2x booking click-outs), National Furniture Retailer (10% daily sales via chat).

5. Operational Metrics

Error Reduction Rate: Decrease in mistakes vs. manual processes.
Training Time Reduction: Faster onboarding with Al tools.
Knowledge Creation Efficiency: Speed of turning interactions into reusable knowledge.

While these results are impressive, achieving them requires navigating potential pitfalls. Let’s examine the common challenges.

Common Challenges in Al Benchmarking and How to Overcome Them

While the benefits are clear, organizations face hurdles:

1. Accuracy and “Hallucinations”

Challenge: Generative Al can sometimes produce incorrect answers.
Solution: Implement RAG to ground Al responses in verified knowledge; use hybrid approaches; ensure human oversight.

2. Lack of Consistent Data

Challenge: Comparing performance requires standardized data collection.
Solution: Develop uniform data practices; use centralized dashboards; ensure robust integration with existing systems (CRM, etc.).

3. Bias and Fairness

Challenge: Al models can perpetuate biases.
Solution: Use diverse training data; continuously monitor outputs via observability (clear box); establish clear ethical guidelines; ensure human oversight.

4. Data Privacy and Security

Challenge: Al often needs sensitive data, increasing risks.
Solution: Ensure strict compliance (GDPR, CCPA); anonymize data; vet vendors; work with legal teams.

5. Benchmarking in a Rapidly Changing Landscape

Challenge: Benchmarks quickly become outdated.
Solution: Stay connected with analyst reports; update benchmarks regularly; focus on continuous improvement relative to your baseline.

6. Balancing Internal and External Comparisons

Challenge: Internal focus may miss competitive shifts.
Solution: Use internal benchmarks for initial wins; incorporate external insights as Al matures.

7. Change Management & Skills Gap

Challenge: Implementing Al requires organizational change and new skills.
Solution: Communicate clearly; invest in agent training/upskilling (empathy, complex problem-solving); position Al as augmentation; address job fears proactively.

8. Evaluating Multimodal Interactions:

Challenge: Benchmarking AI that handles complex interactions involving voice, visuals, or other modalities requires specific metrics and approaches beyond text-based analysis (e.g., audio chunk analysis for voice agents).
Solution: Develop modality-specific evaluation criteria; ensure benchmarking tools can capture and analyze multimodal data; maintain focus on the overall user experience across modalities.

Download our comprehensive, 102-page guide on AI change management, AI-Ready CX: A Leader’s Guide for Change, Adoption, and Impact. Get the guide >

Continuous Improvement and Outcome-Based Optimization

Benchmarking is not a static report card; it’s a dynamic tool for driving ongoing refinement. Furthermore, consistent evaluation at multiple levels serves as a crucial diagnostic tool, enabling teams to more effectively debug issues and pinpoint root causes when performance deviates from expectations. Organizations must move beyond measurement to action. This involves:

Regularly analyzing gaps between current performance and benchmarks.
Establishing feedback loops: Use analytics, customer surveys, and agent input.
Iterating continuously: Use insights to update AI training, rules, and workflows. Treat AI as a product that requires ongoing improvement.
Focusing on outcomes: Evolve measurement beyond operational metrics to track key business outcomes (CSAT, LTV, retention, revenue).
Engaging cross-functional teams (including an AI governance team) to implement changes and oversee evolution.

Strategic Recommendations for CX Leaders

Based on 2024-2025 trends and AI benchmarks, consider these strategic steps:

Prioritize Asynchronous Messaging Channels (0-6 Months Start): Embrace channels like web chat, SMS, WhatsApp, etc., where customers prefer to interact and Al integrates effectively. [Impacts: CSAT, Agent Productivity, Deflection Rate]. Quiq specializes in optimizing these channels.
Implement Al Agent Deflection for Tier-1 (0-6 Months Start): Focus Al automation on high-volume, low-complexity inquiries first to achieve quick ROI and free up human agents. [Impacts: Deflection Rate, Cost Per Contact, AHT].
Leverage Agent-Assist Tools (6-12 Months+): Augment human agents with Al suggestions, knowledge surfacing, and task automation. [Impacts: AHT, Agent Productivity, FCR, Training Time].
Master Human-Al Orchestration (Ongoing): Design seamless handoffs between Al and humans, ensuring context is preserved. Define clear escalation rules. [Impacts: CSAT, FCR, Agent/Customer Experience]. Quiq’s platform excels at this.
Invest in Data Integration & Agent Training (Ongoing): Break down data silos for a unified customer view. Upskill agents for complex issues and Al collaboration. [Impacts: Personalization, Agent Effectiveness, CSAT].
Explore Conversational Commerce Responsibly (Ongoing): Use Al to offer relevant recommendations during service interactions, prioritizing problem-solving first. Track conversion and sentiment carefully. [Impacts: Revenue Influence, AOV, CSAT (if done well)]. Quiq supports this blend.
Stay Ahead of Technology (Ongoing): Keep an eye on advancements like RAG for accuracy and Agentic Al for future autonomous task handling. [Impacts: Future-proofing, Accuracy, Advanced Automation].

The Path Forward

Implementing robust AI benchmarking is about embedding a culture of data-driven decision-making and continuous improvement within your CX organization. By setting clear goals, leveraging the right metrics, learning from both internal and external examples, and strategically applying AI through platforms designed for effective orchestration like Quiq, CX leaders can move beyond the hype.

You can demonstrate significant value, enhance customer loyalty, contain costs, and ultimately, drive tangible business results in the evolving landscape of AI-powered customer experience. The time to measure, refine, and prove the impact of your AI strategy is now.

Citations List

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“Future of AI in Customer Service: Its Impact beyond 2025.” DevRev.
“Call Center Reporting: Your Definitive Guide (2025).” CloudTalk.
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“What Call Center Benchmarks Should You Target In 2025?” Nextiva.
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“Superagency in the workplace: Empowering people to unlock AI’s full potential.” McKinsey.
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“The Top 14 SaaS Trends Shaping the Future of Business in 2025.” Salesmate.
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“Tackling the Challenges and Opportunities of Generative AI in Financial Services.” Spring Labs.

Unlock Agent Potential with Quiq’s Real-Time Agent Assist Capabilities

Posted on April 24, 2025April 30, 2025 Max Fortis

Customer service is evolving, and with it, the demands placed on service agents are rapidly increasing. From managing complex inquiries to delivering personalized, high-quality customer experiences, agents are under constant pressure to perform at their best. This is where Quiq’s Real-Time Agent Assist comes into play. With AI-driven insights, real-time guidance, and cutting-edge automation, this powerful tool doesn’t just support agents—it transforms them into top performers.

In this blog, we’ll explore precisely how Quiq’s real-time agent assist capabilities—part of our overall AI contact center offering—can revolutionize your customer service operations by boosting efficiency, reducing costs, and delighting customers.

Transform agent productivity with real-time AI insights

Agents are at the heart of your customer interactions, and giving them the tools they need to succeed can make all the difference. Quiq’s real-time agent assist AI is designed to empower agents with in-the-moment guidance and actionable insights during live interactions. These agent tools mean faster resolutions, greater confidence, and improved productivity for your team.

With Quiq, agents no longer have to second-guess their responses or scramble to find the right information. Instead, AI steps in to provide precise recommendations and cues at just the right time.

Take action today
Experience the future of customer service firsthand. Get a demo of Quiq’s real-time agent assist offering today and see how it can transform your support team.

AI-powered efficiency for every role, every conversation

Whether it’s advising agents on complex issues, streamlining onboarding, or cutting operational costs, Quiq’s real-time agent assist offering delivers impactful benefits across the board.

Here’s how it works for your business:

1. Optimize decision-making

Equip your agents with real-time insights and recommended actions, enabling them to resolve issues with precision. Whether handling a challenging customer inquiry or upselling products,

Quiq ensures that agents make the best decisions in every interaction. Agents get real-time suggested responses as the conversation progresses, which leverage the same underlying knowledge and systems that power AI agents. Think: knowledge bases, product catalogs, CRM data, and any other data sources that might be helpful in the context of agentic AI systems. AI Assistants don’t just suggest responses; they can also act on an agent’s behalf—like automatically starting a warranty claim, or updating a customer’s flight, without making the agent do the work manually.

2. Streamline training and onboarding

AI-powered coaching is a game changer for new agents. With Quiq, your team gains access to on-the-job guidance that accelerates learning. New hires ramp up faster, while experienced agents refine their skills, creating a consistently high-performing team. New agents get the same great suggested responses and actions that a high-performing human or AI agent would have.

It makes a brand-new agent as good as an AI agent, because they’re working off the same datasets, integrations and responses.

3. Reduce operational costs

Achieve more with fewer resources. Quiq automates routine inquiries and streamlines workflows, freeing up your agents to focus on high-value interactions. This means fewer hiring needs and a leaner operational model. In addition, AI Assistants can gather extra key pieces of data during a conversation, add them to specific ticket fields or append them to a case or conversation, reducing the amount of manual entry an agent has to do.

4. Enhance customer satisfaction

Quiq’s agent-facing AI empowers agents to provide accurate, instant, and personalized support, leading to faster resolutions and happier customers. The result? Higher CSAT scores and stronger customer loyalty. This is done through a combination of response suggestions, real time feedback, and taking action on the agent’s behalf.

5. Insights into agent performance

Quiq’s robust agent analytics give contact center leaders deep insight into how human agents are performing. In our experience, this is critical to ensuring that real-time agent assistance does its job and helps agents in the most effective way possible.

Watch this video to learn how it works >

Key features of real-time agent assist with Quiq

At the core of Quiq’s real-time agent assist lies a suite of innovative features designed for seamless customer interactions. See it in action:

1. In-the-moment guidance and coaching

Built in Quiq’s AI Studio, AI assistants can leverage data from any enterprise system and combine that with conversational context to suggest responses and provide recommendations, or coaching, during a conversation. Agents thrive with support that adapts in real time. Quiq provides targeted coaching during live conversations, using AI to deliver hints, reminders, and workflows tailored to each interaction.

For instance, in a case study with an office supply retailer, Quiq’s assist feature was so effective it allowed associates to get immediate answers to questions 2 out of 3 times. This led to a whopping 68% self-service rate resolution rate.

2. Automated post-conversation summary and analysis

After-conversation work can be a major time sink—but not with Quiq. Using AI-generated summaries, agents can cut down on post-interaction tasks, allowing them to focus on the next customer. Customers get faster service, and agents stay productive.

Importantly, summaries are also available for the agent right when they take over a conversation. For example, if the user has been talking with an AI agent, the human agent will get a summary of the conversation, creating a seamless experience for the end customer.

Beyond summarization, Quiq can also extract key pieces of information and automatically update CRMs or other enterprise systems with the appropriate information.

3. Smart routing and prioritization

Not all customer inquiries are created equal. Quiq’s intelligent routing ensures that inquiries are directed to the best-suited agents based on real-time data like expertise, workload, or customer urgency. This minimizes wait times and optimizes outcomes.

Real results with AI assistants: Office supplier case study

When a leading office supply retailer integrated Quiq’s agent-facing AI Assistant, they saw impressive improvements in just a few weeks.

Increase in containment rates: 35% (with a 6-month average containment rate of 65%)
Associates got immediate answers: 2 out of 3 times
Self-service resolution rate: 68%
Associate satisfaction with AI: 4.82 out of 5

The AI ensured that each employee was guided toward resolving customer issues promptly while automating laborious and repetitive inquiries. This created a win-win for both customers and the team itself. Read full case study >

Elevate customer support with Quiq’s real-time agent assist offering

Imagine a team where every agent operates at their peak potential, guided by AI that backs their every move. Quiq’s real-time agent assist isn’t just an upgrade for your service department—it’s a revolution that touches every part of your customer experience.

If you’re ready to unlock your agents’ potential and take your customer service to the next level, now is the time to act.

Key Questions to Anticipate from Stakeholders About the AI Impact on Business

Posted on April 15, 2025 Lauren Winder

As AI transforms business operations across industries, leadership teams face increasing pressure to make informed decisions about technology investments. According to McKinsey’s January 2025 report, Superagency in the workplace: Empowering people to unlock AI’s full potential, the “long-term AI opportunity” represents “$4.4 trillion in added productivity growth potential from corporate use cases.”

Yet, despite these compelling advantages and massive increases in AI investments from companies like yours, that same McKinsey report highlights that “only 1 percent of leaders call their companies ‘mature’ on the deployment spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes.”

This implementation gap shows the crucial need for business leaders to address stakeholder concerns thoroughly and strategically when proposing AI initiatives. And a primary avenue through which to do so is by aligning on metrics early on.

When presenting the business impact of AI, stakeholders will likely have a range of questions based on their priorities and concerns. Preparing thoughtful responses to these inquiries will demonstrate your expertise and build trust in your proposed approach.

Here are some key questions to anticipate:

Revenue and Growth Projections

What specific assumptions are driving the revenue growth projections?
Are these growth metrics sustainable, or are they tied to short-term trends?
How does this initiative impact key revenue drivers like upsell, cross-sell, and average order value?

Customer Impact and Engagement

How will this solution improve the customer journey and address pain points?
What measures will ensure that the personalization efforts resonate with customers across diverse segments?
How will you address privacy concerns related to AI-driven personalization?

Competitive Positioning

How does this AI initiative position us against competitors adopting similar technologies?
What unique capabilities or differentiators does this solution bring to the market?

Scalability and Adaptability

How scalable is this solution as customer needs and expectations evolve?
Can the AI system adapt to support new products, markets, or customer demographics?

Risk and Contingency Planning

What risks could arise if the AI fails to meet customer expectations or delivers subpar results?
How will you manage potential reputational risks tied to AI missteps?

Customer Metrics and Validation

What customer feedback mechanisms will be in place to measure the impact of AI on loyalty and satisfaction?
How will improvements in metrics like CSAT, NPS, and retention directly link to financial outcomes?

Operational Feasibility

How will AI integrate with our existing systems and processes?
What level of disruption can we expect during implementation?
How will this impact current employee roles, and what retraining or re-skilling will be required?

Cost and ROI Clarity

How accurate are the projected cost savings, and what assumptions were used in the calculations?
What is the anticipated timeframe to achieve ROI?
Are there hidden costs or unexpected expenses we should anticipate?

Time to Value

How quickly will customers notice and benefit from these enhancements?
What is the expected timeline to achieve meaningful improvements in retention and loyalty?

Final Thoughts

Anticipating these stakeholder questions isn’t just about having ready answers—it’s about demonstrating comprehensive strategic thinking in your AI implementation approach. Gartner research demonstrates that “organizations where the AI team is involved in defining success metrics are 50% more likely to use AI strategically than organizations where the team is not involved.”

As we touch on in another recent article, the landscape of AI implementation is rapidly evolving. Organizations that proactively address stakeholder concerns while maintaining a clear vision of their AI strategy will be positioned for competitive advantage in the coming years.

The journey to successful AI implementation begins with honest assessment. Is your organization prepared to navigate these complex stakeholder conversations?

Beyond Rules: Agentic AI Orchestration and the Dawn of Emergent Intelligence

Posted on April 9, 2025April 9, 2025 Bill O’Neill

In the world of software and automation, “orchestration” is a familiar term. At its simplest, an orchestration tool says, “do this, then do that, and if something goes wrong, do this other thing.” It’s a manager for computers, software, and automated sequences of tasks, often spanning multiple systems. Think of it like a digital composer in front of an orchestra, ensuring each instrument (or in this case, system) plays its part in harmony to create a cohesive whole. Traditional orchestration tools handle predefined workflows, executing tasks in a set order. But the world of automation is changing, leading to the need for better AI Orchestration.

The Shift to Agentic AI: Autonomous Agents

Enter agentic AI. Instead of following rigid instructions, agentic AI systems consist of agents – individual components capable of autonomous decision-making. Imagine a single agent designed for a specific task. Now, imagine another agent designed for a different task, also acting autonomously, making its own decisions based on its training and the information it receives. Each agent operates independently, yet they need to work together. This brings us to the core challenge: how do you orchestrate all this together? How do you get AI agent orchestration right?

What is AI Orchestration? Coordinating the Agentic

Agentic AI orchestration is the art and science of managing, coordinating, and monitoring multiple autonomous AI agents that make their own decisions. It’s about creating a system where these independent agents can collaborate effectively to achieve a common goal. Key concepts include:

Delegation: The ability of one agent to recognize when a task is best handled by another agent and seamlessly hand it off.
Shared State: Maintaining a common understanding of the situation across all agents. This might involve shared data, context, or goals.
Inter-Agent Communication: Establishing channels for agents to exchange information, requests, and results.
Decision Hierarchies: Defining how agents interact and who (or what) makes final decisions when conflicts arise.
Dynamic Adaptation: The ability to add new agents, retire old ones, or modify existing agents without disrupting the overall system. The system should learn and adapt over time.
Tool/Function Calling: Agents dynamically invoke tools, which wrap APIs to perform specific functions like flight rebooking or baggage fees. This simplifies workflows, while the orchestration platform ensures state and context continuity across tasks.

A Real-World Example: Quiq’s Retail Orchestration

Quiq has built advanced agentic orchestration systems for real-world applications. One example involves a high-end retail customer. The goal was to provide personalized product recommendations, mimicking the experience of interacting with expert salespeople. The solution? A multi-agent system:

The Problem: The retailer needed a way to offer highly personalized recommendations to online customers.
The Solution: A system of interacting AI agents, managed by an AI Orchestration platform.
Agent 1 (OpenAI-based): This agent listens to the customer’s requests and preferences and, based on that understanding, searches a “virtual back room” to suggest relevant items (in this case, jewelry).
Agent 2 (Gemini-based): This agent performs the same task as Agent 1, but utilizes a different large language model (LLM). This provides a different perspective and potentially different recommendations.
Supervisor Agent (Different LLM): This agent receives the recommendations from both Agent 1 and Agent 2. It analyzes these suggestions and, using its own LLM, makes a final, consolidated recommendation to the customer.

This system demonstrates the power of agentic orchestration. By combining the strengths of multiple LLMs and specialized agents, the system delivers a richer, more nuanced experience than any single agent could provide on its own. This is an example of “simple” orchestration, where the interaction pathways are relatively well-defined by an AI orchestration platform.

Moving from Simple to Complex: Orchestrating Chains of Agents

Agentic AI orchestration isn’t just about simple, isolated interactions between a few agents, it’s about building systems that tackle increasingly complex challenges. This involves creating chains of agents, where tasks are dynamically delegated across multiple autonomous components.

Unlike traditional workflows with rigid pre-defined steps, these systems operate based on the autonomous nature of the agents. Each agent decides when to delegate a task, and the agent receiving the task is also autonomous, making its own decisions based on its specific capabilities and real-time information. These interconnected, agentic chains allow organizations to break down complex workflows into manageable, intelligent components.

Dynamic tool calling is also central to enabling complex agent chains. Tools serve as functional abstractions around APIs, providing capabilities that bridge agent needs with external systems or processes. These tools offer the agent flexibility—not only access to functions but the ability to decide how and when to use them.

For example, if a customer-facing agent receives a request to reschedule a flight, the agent evaluates the user’s goal, analyzes the available tools (e.g., a flight rebooking tool, a pricing calculation tool, and a baggage fee tool), and autonomously decides which tools to invoke and in what sequence. Instead of being explicitly told which steps to follow, the agent uses its own judgment to determine how the task should be fulfilled based on context and real-time information. The delegated agent then calls the relevant functions to accomplish each subtask, while maintaining a shared state to ensure the results are presented cohesively to the user. This modular and context-driven orchestration eliminates unnecessary repetition, allowing agents to collaborate effectively while offering dynamic and highly personalized solutions to tasks.

However, as systems grow more intricate, AI orchestration becomes critical to ensure smooth coordination, robust communication, and predictable outcomes. Managing these systems requires an orchestration platform capable of providing visibility, control, and alignment with the organization’s goals.

Let’s take another example, this time in the airline industry, and illustrate a multi-hop chain:

Customer: “I need to change my flight to next Tuesday and add a checked bag.”
Customer-Facing Agent: Recognizes two distinct tasks: flight change and baggage addition.
Customer-Facing Agent: Delegates the flight change request to the Rebooking Agent.
Rebooking Agent: Searches for available flights on the new date, calculates any price differences.
Rebooking Agent: Recognizes the need to handle baggage, and delegates this sub-task to the Baggage Agent.
Baggage Agent: Calculates the baggage fees based on the airline’s policies.
Baggage Agent: Returns the baggage fee information to the Rebooking Agent.
Rebooking Agent: Combines the flight change information and baggage fees, returning a complete price quote to the Customer-Facing Agent.
Customer-Facing Agent: Presents the options and the total price to the customer.

This demonstrates how a single customer request can trigger a cascade of interactions between multiple specialized agents. The power comes from the agents’ ability to dynamically delegate tasks based on their capabilities and the context of the request. This illustrates the complexity and need for specialized tools in AI agent orchestration.

Delegation Strategies

The “intelligence” of an agentic system often lies in how it chooses to delegate. Sophisticated strategies include:

Capability-Based Routing: Agents advertise their skills (e.g., “I handle baggage”), and tasks are routed accordingly.
Resource-Based Biding: Find the least expensive agent to complete the task if multiple agents are available, offering similar services.
Load Balancing: Distributing work to the least-busy agents to ensure efficiency.
Context-Aware Delegation: Choosing the best agent based on the specific details of the request.
Learned Delegation: The system learns over time which agents are best suited for different tasks, optimizing delegation based on past performance.
Hierarchical Delegation: Agents delegating to sub-agents forming a tree of task execution.

Key Components of an Agentic AI Orchestration System

The “plumbing” that enables agentic agents to communicate and work together is critical. This involves several foundational components, and our platform provides a best-of-breed AI Orchestration platform designed to unify these elements.

Communication Layer: Agentic systems rely on robust communication mechanisms to connect agents internally and externally. APIs are fundamental to this process, allowing agents to interact with other systems, external data sources, and business processes. APIs enable agents to access the information they need to make decisions and seamlessly integrate their outputs into workflows.

State Management: Effective state management ensures agents maintain context and consistency across interactions. Whether handling customer preferences in retail or flight details in an airline system, shared information must be stored and accessed efficiently. This can involve shared databases, distributed caches, or more sophisticated state management systems tailored for agentic AI.

Decision-Making Logic: Decision logic governs how agents determine when to delegate and to whom, exemplifying the emergent intelligence of agentic systems. In addition to and in place of static decision trees and hard-coded logic, agents make dynamic judgment calls based on the state of the conversation. These decisions are driven by real-time inputs, statistical probabilities, and predicted outcomes. For example, an agent might weigh context, historical data, and immediate priorities to select the best course of action. External inputs through APIs and enterprise integrations further enrich these decisions, providing the necessary data for agents to optimize outcomes.

Monitoring and Analytics: Monitoring tools provide visibility into agent operations and interactions, ensuring workflows remain aligned to business value and high-performing as systems evolve. In many cases, an observer agent, such as an external LLM-based system, plays a crucial role in this orchestration. This agent analyzes ongoing conversations, identifies interesting or alarming behaviors, and generates insights such as customer sentiment, emerging topics, and other actionable analytics. This is yet another case of what it means to have AI orchestration, where agents—including observer agents—collaborate not only to act, but also to monitor, interpret, and provide valuable feedback to improve the system. By leveraging observer agents, businesses gain deeper visibility and ensure their systems continuously adapt to real-world complexity.

Deployment and Management: Deployment tools simplify the lifecycle of agent operations. Agents can be deployed, updated, and retired while integrating with external systems and business processes. This ensures seamless orchestration and alignment between agentic systems and enterprise needs.

The Importance of Observability, Debugging, and Guardrails

In a complex system with multiple interacting autonomous agents, things may go wrong. This is where deep analytics, observation tools, and robust guardrails become absolutely essential. We need to understand not just how each individual agent behaved, but also how the data flowing between agents and the shared state influenced their actions. We also need mechanisms to keep the system operating within defined boundaries. These aspects are critical in any effective AI Orchestration strategy.

Observability and Debugging

Effective observability requires:

Individual Agent Monitoring: Detailed logging of each agent’s decisions, actions, and internal state. Our platform provides comprehensive analytics for understanding agent behavior.
Inter-Agent Communication Tracing: Visualizing the flow of data and requests between agents. This helps pinpoint bottlenecks and identify unexpected interactions.
Shared State Analysis: Understanding how changes in the shared state (e.g., customer preferences, inventory levels) affect agent behavior.
Root Cause Analysis: Tools to quickly identify the underlying cause of unexpected behavior or errors. This is crucial for debugging and improving the system. Our platform offers advanced diagnostic capabilities to streamline troubleshooting.
Explainability: The system should be designed in a way that allows a human to understand, in detail, how the agentic AI agents are making their decisions and how they interact with each other. Our platform prioritizes transparency and provides tools to explain agent behavior.

Emergent Behavior and Guardrails

As agentic systems become more complex, they can exhibit emergent behavior, characteristics that arise from the interactions of agents but were not explicitly programmed. Emergent behavior can be a powerful asset, such as discovering novel solutions or optimizing workflows in unexpected ways. However, it can also result in negative outcomes, such as oscillations, inefficiencies, or harmful outputs. Establishing robust guardrails is therefore critical to ensure agents stay aligned with organizational goals while minimizing risks.

Effective platforms must provide a comprehensive suite of tools for managing emergent behavior and ensuring system safety. These include mechanisms for enforcing boundaries, maintaining operational integrity, and addressing sensitive data concerns.

Explicit Constraints:
Platforms must establish clear operational constraints to control agent behavior and resource consumption.

Role-Based Access Control (RBAC): Limit what actions each agent is allowed to perform based on its role and capabilities. This ensures agents only operate within their intended boundaries.
Resource Limits: Constrain resources such as computational power or LLM tokens that each agent can consume, preventing excessive use that could destabilize the system.
Rate Limiting: Prevent agents from making too many requests or delegations within a short time frame, protecting the system from overload or abuse.
Validation Rules: Define rules that all agent outputs must satisfy to ensure alignment with business aims and compliance requirements.

Sensitive Data Handling and Isolation:
Agentic systems often deal with sensitive data, making it critical to enforce policies for its usage and sharing.

Authentication Between Agents: Require agents to authenticate their identities when communicating, ensuring secure exchanges and preventing unauthorized actions.
Policy Enforcement for Sensitive Data: Enable policies that dictate how sensitive data—such as customer information or proprietary business details—is accessed, processed, and shared by agents. This helps control data exposure and aligns with regulatory standards.
Data Isolation: Provide mechanisms to isolate certain types of data between agents to ensure information is only accessible to agents specifically authorized to see it. This enables hierarchical or segmented workflows where certain agents are excluded from viewing or interacting with datasets containing sensitive information.

Monitoring and Intervention:
Monitoring and intervention systems offer real-time oversight of agent activities and allow for swift intervention in case of unexpected behaviors.

Anomaly Detection: Use advanced algorithms to detect unusual agent behavior that may indicate operational issues or risks.
Circuit Breakers: Automatically halt or slow interactions between agents when errors or anomalies exceed predefined thresholds, mitigating cascading failures.
Human Override: Provide mechanisms for human operators to intervene and manually take control of key systems when necessary.
Kill Switches: Offer immediate shutdown capabilities for individual agents or subsystems to address emergencies or critical failures.

Governance and Policy:
Governance tools ensure agentic systems operate within defined organizational, regulatory, and ethical constraints.

Policy Engines: Implement rules that enforce compliance across all agent activities, ensuring alignment with legal, regulatory, and ethical standards.
Auditing and Compliance: Enable detailed auditing and reporting tools to maintain transparency, document actions, and ensure compliance with industry standards or regulations.

By combining these guardrails with robust authentication, sensitive data policies, and monitoring, organizations can unlock the full potential of AI Orchestration while controlling risks and maintaining alignment with operational goals. Proper safeguards allow agentic systems to operate dynamically and intelligently while remaining constrained by the principles of safety, security, and compliance.

Challenges and Considerations

Agentic AI orchestration, while powerful, presents several challenges:

Complexity: Managing the interactions of many autonomous agents can quickly become overwhelming. Careful design and modularity are essential. Our services team can help you architect and implement robust, scalable solutions.
Explainability: As mentioned above, understanding the emergent behavior of a complex agentic system is a major challenge. Our platform and expertise provide the tools and guidance needed to address this.
Debugging: Identifying and resolving issues in a distributed system of autonomous agents requires specialized tools and techniques. We provide the necessary debugging capabilities for AI orchestration.
Security: Protecting against malicious agents or vulnerabilities in the communication and state management layers is crucial. Our platform incorporates robust security measures.
Ethical considerations: Ensuring responsible use of agentic systems, particularly in areas like decision-making and autonomy, is paramount. We are committed to helping you build ethical and responsible AI solutions.

Benefits of Implementing AI Orchestration

AI orchestration unlocks intelligent responsiveness, moving beyond simple automation. By embracing AI Orchestration, organizations gain:

Dynamic Adaptability and Emergent Solutions: Systems dynamically adapt and discover emergent solutions. Autonomous agents respond to the unexpected, innovating beyond pre-programmed systems, orchestrated by AI Agent orchestration.
Increased Resiliency Through Decentralization: Decentralization enhances resilience. Agents continue operating during failures, re-routing tasks for robust reliability.
Optimized Resource Utilization Through Autonomous Allocation: Dynamic resource allocation optimizes resource utilization, minimizing waste and driving cost savings.
Enhanced Agility Through Flexible Integration: Modular systems allow seamless integration of new components, quickly adapting to evolving needs. This agility is powered by an AI Orchestration platform.
Improved Scalability Through Agent Replication and Delegation: Distributed architecture enables seamless scaling. New agents handle growing workloads through dynamic delegation.
More Efficient Task Distribution Through Agentic Task Selection: The orchestrating agent ensures the most efficient sub-agent is selected for each task.
Empowered Innovation Through Decentralized Decision-Making: Decentralized decision-making fosters innovation. Autonomous agents explore new approaches, accelerating iteration and creative solutions. Our platform provides guardrails and safety checks for AI Orchestration, keeping everything aligned with business policies.

Other Use Cases (Brief Examples)

Beyond retail and airlines, agentic AI orchestration has potential applications in many areas:

Customer Service: Multi-agent chatbots that can handle complex inquiries, escalating to different specialized agents as needed.
Data Analysis: Collaborative agents that can analyze different aspects of a dataset and combine their insights to generate a more comprehensive understanding.
Process Automation: Automating complex workflows that involve multiple AI systems, each performing a specific task.

The Future of Agentic Orchestration – Towards Self-Healing and Self-Optimizing Systems

Agentic AI orchestration offers tremendous potential for building more scalable, resilient, adaptable, and efficient AI systems. By allowing autonomous agents to collaborate and specialize, we can tackle complex problems that were previously intractable.

Our vision is to create systems that are not just intelligent, but also self-healing and self-optimizing – able to automatically detect and recover from failures, and continuously improve their performance over time. We aim for a future where agentic systems become “invisible infrastructure,” seamlessly automating tasks and empowering businesses to achieve new levels of efficiency and innovation. The “composable AI” capabilities are a game changer.

However, realizing this potential requires careful attention to the challenges of observability, explainability, and the need for robust guardrails. Our platform and services team are dedicated to providing the tools, expertise, and support you need to build and deploy these next-generation AI systems successfully and responsibly. Contact us to learn more about how we can help you harness the power of AI Orchestration.

Empowering Businesses with Agentic AI Orchestration

At Quiq, delivering exceptional customer experiences has always been our mission, and we’ve been building these experiences for years. Over time, we’ve learned what it takes to design, deploy, and scale intelligent agentic systems that seamlessly blend automation, autonomy, and efficiency.

These hard-won insights led us to develop AI Studio, a next-generation AI Orchestration tool that encapsulates everything we’ve learned. With AI Studio, businesses can harness the full potential of agentic AI to achieve scalable, intelligent automation while simplifying the complexity and overhead traditionally associated with orchestration systems.

We aim to give our customers the tools they need to succeed:

Build sophisticated agents that are dynamic and adaptable to your workflows.
Debug issues with clarity, ensuring agents perform consistently and reliably.
Monitor agent behavior, inter-agent workflows, and emerging trends with powerful observability tools.
Improve and iterate agents in real-time with transparency and confidence.

AI Studio is more than just a platform. It is the orchestration engine that empowers your team to move beyond rigid automation and build next-generation systems that dynamically respond to your business needs. By enabling your agents to communicate, collaborate, and scale, we help businesses unlock truly transformative customer experiences.

When your team has the right tools, there is no limit to what you can build. Let’s evolve the future of orchestration together. Contact us today to see how AI Studio can transform the way your business approaches intelligent automation.

What is Omnichannel Messaging?

Posted on April 1, 2025 Allyson Scott

Effortless communication is the backbone of today’s leading businesses, especially in industries like eCommerce and retail. Customers expect quick, personalized interactions that fit their needs and, more importantly, their preferences and busy schedule. That’s where omnichannel messaging comes in—a game-changer for businesses looking to nurture meaningful customer relationships, solve queries faster, and deliver exceptional experiences across all touchpoints.

What does omnichannel messaging mean, and how can it reshape how companies connect with their customers? This comprehensive guide explores its definition, benefits, and strategies—and touches on how Quiq’s omnichannel messaging platform can turn it into a competitive edge.

What is omnichannel messaging?

At its core, omnichannel messaging is a strategy that integrates communication channels—including SMS, email, live web chat, social media, and more—into a unified platform. Unlike multichannel messaging, where interactions across channels are siloed, omnichannel messaging ensures customer interactions are seamless, connected, and context-aware no matter which channel they use or move to while trying to resolve an issue.

For example, a customer might start a conversation on Instagram Messenger, continue it via email, and then complete their inquiry over SMS—all while a single thread of past messages and intent follows them. Omnichannel messaging keeps everything cohesive, eliminating repetition or confusion during customer interactions.

Omnichannel messaging has become a vital tool for creating exceptional customer experiences, offering businesses a way to engage customers directly on their preferred platforms while maintaining a consistent and unified voice.

Key benefits of omnichannel messaging

With customer expectations soaring and new channels seeming to pop up each day, omnichannel messaging offers businesses tangible advantages that impact everything from customer satisfaction to long-term loyalty.

1. Seamless customer experience

The greatest strength of omnichannel messaging lies in providing a frictionless experience for customers.

By unifying communication across platforms, businesses ensure conversations are not interrupted, even when customers switch from one platform to another.
Integration with customer support tools, such as CRMs, centralizes interactions, offering teams a full view of previous conversations. This leads to higher clarity and faster resolutions.

Imagine a shopper reaching out via web chat with a query about your eCommerce selection. Later, when they message again through WhatsApp, your support team already knows their query history—saving time for both the customer and your team.

2. Personalized interactions

Personalization has become the gold standard for successful customer engagement. Omnichannel messaging takes it to the next level by utilizing collected customer data to craft highly relevant and timely messages.

Tailor communications based on customer preferences, buying habits, or interaction history. For example, send product suggestions and reminders relevant to their recent purchases.
Maintain message context across channels to avoid frustrating scenarios where customers have to repeat their queries.

This kind of personalization doesn’t just benefit customer satisfaction—it also improves lead conversion and builds layers of trust.

3. Enhanced customer satisfaction

Customer satisfaction relies heavily on businesses’ ability to respond quickly and resolve issues efficiently.

With omnichannel messaging, faster response times are possible through features like AI automation, smart ticket routing, and quick action suggestions for human agents.
Seamless resolutions across any platform create a lasting impression of reliability and commitment toward customer care, further building loyalty.

When customers feel heard and valued, they are more likely to become repeat buyers and brand advocates.

4. Streamlined communication

Managing numerous communication channels often feels overwhelming for customer service teams. Omnichannel platforms rectify this chaos.

By centralizing interactions on a single platform, teams simplify their workflows and enhance communication efficiency.
AI-powered automation handles repetitive tasks—like sending order confirmations or appointment reminders—enabling your agents to focus on higher-value conversations.

Efficiency leads to reduced operational costs without sacrificing quality service—something no forward-thinking business can afford to overlook.

5. Improved customer retention

Research consistently shows that satisfied customers are more likely to stay with a brand.

Omnichannel messaging fosters emotional loyalty by delivering consistent, valuable experiences.
Proactive engagement, such as personalized birthday discounts sent via the customer’s favorite channel, keeps your brand top of mind.

Over time, this combination of trust and tailored care translates to greater lifetime customer value.

6. Consistent brand voice

Your brand’s voice is the essence of its identity. Omnichannel messaging ensures it remains unified across all platforms.

Whether communicating on Instagram, through email, or via SMS, the tone, style, and messaging are consistent.
This consistency reinforces your brand identity, making it recognizable and reliable.

Large-scale enterprises and startups can harness this benefit to build a stronger market presence and leave no room for mixed messaging.

7. Competitive advantage

Adopting omnichannel messaging separates your business from competitors still relying on siloed, fragmented communication strategies.

A seamless, personalized approach encourages stronger customer relationships and establishes your business as innovative and customer-focused.
Businesses leveraging platforms with real-time solutions and AI tools are seen as more responsive and resourceful—a clear differentiation in today’s crowded market.

For example, incorporating communication options like WhatsApp in regions where SMS usage is limited makes your business accessible to untapped customer bases.

Tackle omnichannel messaging with Quiq

Creating an omnichannel messaging strategy is easier with modern tools like Quiq. Designed to integrate seamlessly across your ecosystem, Quiq offers several standout features:

Easy integration with platforms like Salesforce, SAP, and Shopify, uniting customer touchpoints under one interface.
AI-powered agents and automation to handle repetitive queries and speed up response times while improving accuracy.
Unmatched scalability for growing businesses ready to expand to more channels without increasing complexity.

With Quiq, businesses access a world-class omnichannel communication platform that simplifies messaging while amplifying customer satisfaction.

How to Automate Customer Service – The Ultimate Guide

Posted on April 1, 2025May 1, 2025 Michael Hartsog

From graph databases to automated machine learning pipelines and beyond, a lot of attention gets paid to new technologies. But the truth is, none of it matters if users aren’t able to handle the more mundane tasks of managing permissions, resolving mysterious errors, and getting the tools installed and working on their native systems.

This is where customer service comes in. Though they don’t often get the credit they deserve, customer service agents are the ones who are responsible for showing up every day to help countless others actually use the latest and greatest technology.

Like every job since the beginning of jobs, there are large components of customer service that have been automated, are currently being automated, or will be automated at some point soon.

That’s our focus for today. We want to explore customer service as a discipline and then talk about how Agentic AI can automate substantial parts of the standard workflow.

What is Customer Service?

To begin with, we’ll try to clarify what customer service is and why it matters. This will inform our later discussion of automated customer service and help us think through the value that can be added through automation.

Customer service is more or less what it sounds like: serving your customers – your users, or clients – as they go about the process of utilizing your product. A software company might employ customer service agents to help onboard new users and troubleshoot failures in their product, while a services company might use them for canceling appointments and rescheduling.

Over the prior few decades, customer service has evolved alongside many other industries. As mobile phones have become firmly ensconced in everyone’s life, for example, it has become more common for businesses to supplement the traditional avenues of phone calls and emails by adding text messaging and chatbot customer support to their customer service toolkit. This is part of what is known as an omni-channel strategy, in which more effort is made to meet customers where they’re at rather than expecting them to conform to the communication pathways a business already has in place.

Naturally, many of these kinds of interactions can be automated, especially with the rise of tools like large language models. We’ll have more to say about that shortly.

Why is Customer Service Important?

It may be tempting for those writing the code to think that customer service is a “nice to have”, but that’s not the case at all. However good a product’s documentation is, there will simply always be weird behaviors and edge cases in which a skilled customer service agent (perhaps helped along with AI) needs to step in and aid a user in getting everything running properly.

But there are other advantages as well. Besides simply getting a product to function, customer service agents contribute to a company’s overall brand, and the general emotional response users have to the company and its offerings.

High-quality customer service agents can do a lot to contribute to the impression that a company is considerate and genuinely cares about its users.

What Are Examples of Good Customer Service?

There are many ways in which customer service agents can do this. For example, it helps a lot when customer service agents try to transmit a kind of warmth over the line.

Because so many people spend their days interacting with others through screens, it can be easy to forget what that’s like, as tone of voice and facial expression are hard to digitally convey. But when customer service agents greet a person enthusiastically and go beyond “How may I help you” by exchanging some opening pleasantries, they feel more valued and more at ease. This matters a lot when they’ve been banging their head against a software problem for half a day.

Customer service agents have also adapted to the digital age by utilizing emojis, exclamation points, and various other kinds of internet-speak. We live in a more casual age, and under most circumstances, it’s appropriate to drop the stiffness and formalities when helping someone with a product issue.

That said, you should also remember that you’re talking to customers, and you should be polite. Use words like “please” when asking for something, and don’t forget to add a “thank you.” It can be difficult to remember this when you’re dealing with a customer who is simply being rude, especially when you’ve had several such customers in a row. Nevertheless, it’s part of the job.

Finally, always remember that a customer gets in touch with you when they’re having a problem, and above all else, your job is to get them what they need. From the perspective of contact center managers, this means you need periodic testing or retraining to make sure your agents know the product thoroughly.

It’s reasonable to expect that agents will sometimes need to look up the answer to a question, but if they’re doing that constantly it will not only increase the time it takes to resolve an issue, but it will also contribute to customer frustration and a general sense that you don’t have things well in hand.

Automation in Customer Service

Now that we’ve covered what customer service is, why it matters, and how to do it well, we have the context we need to turn to the topic of automated customer service.

For all intents and purposes, “automation” simply refers to outsourcing all or some of a task to a machine. In industries like manufacturing and agriculture, automation has been steadily increasing for hundreds of years.

Until fairly recently, however, the technology didn’t yet exist to automate substantial portions of customer service worth. With the rise of machine learning, and especially large language models like ChatGPT, that’s begun to change dramatically.

Let’s dive into this in more detail.

Examples of Automated Customer Service

There are many ways in which customer service is being automated. Here are a few examples:

Automated questions answering – Many questions are fairly prosaic (“How do I reset my password”), and can effectively be outsourced to a properly finetuned large language model. When such a model is trained on a company’s documentation, it’s often powerful enough to handle these kinds of low-level requests.
Summarization – There have long been models that could do an adequate job of summarization, but large language models have kicked this functionality into high gear. With an endless stream of new emails, Slack messages, etc. constantly being generated, having an agent that can summarize their contents and keep agents in the loop will do a lot to boost their productivity.
Classifying incoming messages – Classification is another thing that models have been able to do for a while, and it’s also something that helps a lot. Having an agent manually sort through different messages to figure out how to prioritize them and where they should go is no longer a good use of time, as algorithms are now good enough to do a major chunk of this kind of work.
Translation – One of the first useful things anyone attempted to do with machine learning was translating between different natural languages (i.e. from Russian into English). Once squarely in the purview of human beings, this is now a task that machines can do almost as well, at least for customer service work.

Should We Automate Customer Service?

All this having been said, you may still have questions about the wisdom of automating customer service work. Sure, no one wants to spend hours every day looking up words in Mandarin to answer a question or prioritizing tickets by hand, but aren’t we in danger of losing something important as customer service agents? Might we not automate ourselves out of a job?

Because these models are (usually) finetuned on conversations with more experienced agents, they’re able to capture a lot of how those agents handle issues. Typical response patterns, politeness, etc. become “baked into” the models. Junior agents using these models are able to climb the learning curve more quickly and, feeling less strained in their new roles, are less likely to quit. This, in turn, puts less of a burden on managers and makes the organization overall more stable. Everyone ends up happier and more productive.

So far, it’s looking like AI-based automation in contact centers will be like automation almost everywhere else: machines will gradually remove the need for human attention in tedious or otherwise low-value tasks, freeing them up to focus on places where they have more of an advantage.

If agents don’t have to sort tickets anymore or resolve routine issues, they can spend more time working on the really thorny problems, and do so with more care.

Strategies for Implementing Automated Customer Service

Once you’ve decided to bring automation into your customer service strategy, the next step is implementation. Here are some key strategies to help you get started and ensure a smooth transition that benefits both your team and your customers.

Assess Your Current Customer Service Needs

Start by reviewing your support data. Which questions pop up most often? Where do your agents spend the most time? Identifying these patterns will help you pinpoint which tasks can—and should—be automated. Look for high-volume, repetitive inquiries that don’t require much nuance. These are prime candidates for automation that won’t sacrifice the quality of your customer experience.

Choose the Right Automation Tools

Not all automation tools are created equal. Consider solutions like AI agents, automated ticket routing, or self-service portals. The key is to choose platforms that work well with your existing CRM and communication tools, so everything stays connected. Look for tools that are flexible, scalable, and easy for your team to manage over time.

Develop a Knowledge Base and Self-Service Options

A well-organized knowledge base can deflect tickets before they ever hit your queue. Build out FAQs, how-to articles, and video tutorials that answer your customers’ most common questions. Use AI-powered search features to surface the right content quickly. And don’t forget to update your content regularly based on feedback and emerging issues—your knowledge base should evolve alongside your customers.

Set Up Automated Responses and Workflows

Automation isn’t just about answering questions—it’s about streamlining entire workflows. Set up automated messages for order updates, appointment reminders, or common troubleshooting steps. Use branching logic and triggers to guide customers through resolutions, and ensure these flows are intuitive. The goal is to help customers solve issues faster, without needing to wait on hold.

Balance Automation with Human Support

Even the best bots have their limits. Make sure customers can easily escalate to a live agent when necessary—especially for complex or sensitive issues. Train your human support team to step in smoothly when automation reaches its edge. And whenever possible, personalize the experience by using data to greet customers by name or tailor responses based on their history.

Monitor Performance and Continuously Optimize

The work doesn’t stop after launch. Keep an eye on key metrics like resolution time, deflection rate, and customer satisfaction scores. Collect feedback from users to understand where automation is helping—or where it might be falling short. With the right data, you can train your AI and machine learning models to recognize patterns, refine workflows, and improve response accuracy—so your automated service keeps getting smarter with every interaction.

Moving Quiq-ly into the Future!

Where the rubber of technology meets the road of real-world use cases, customer service agents are extremely important. They not only make sure customers can use a company’s tools, but they also contribute to the company brand through their tone, mannerisms, and helpfulness.

Like most other professions, customer service agents are being impacted by automation. So far, this impact has been overwhelmingly positive and is likely to prove a competitive advantage in the decades ahead.

If you’re intrigued by this possibility, Quiq has created a suite of industry-leading agentic AI tools, both for customer-facing applications and agent-facing applications. Check them out or schedule a demo with us to see what all the fuss is about.

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Your Guide to Live Chat Support Services

Posted on April 1, 2025May 1, 2025 Michael Hartsog

When customer experience directors float the idea of investing more heavily in live chat for customer service, it’s not uncommon for them to get pushback. One of the biggest motivations for such reticence is uncertainty over whether anyone will actually want to use such support channels—and whether investing in them will ultimately prove worth it.

An additional headwind comes from the fact that many CX directors are laboring under the misapprehension that they need an elaborate plan to push customers into a new channel. However, one thing we consistently hear from our enterprise customers is that it’s surprising how customers naturally start using a new channel when they realize it exists. To borrow a famous phrase from Field of Dreams, “If you build it, they will come.” Or, to paraphrase a bit, “If you build it (and make it easy for them to engage with you), they will come.” You don’t have to create a process that diverts them to the new channel.

Why is Live Chat Important for Contact Centers?

60% of customers indicate that they’re more likely to visit a website again if it has live chat for customer service, and a few more (63%) say that a live chat widget will increase their willingness to make a purchase.

But that still leaves the question of how live chat stacks up against other possible communication channels. Well, nearly three-quarters (73%) are more comfortable using live chat for customer service issues than email or phone—and a high fraction (61%) are especially annoyed by the prospect of being put on hold.

If this isn’t enough, there are customer satisfaction (CSAT) scores to think about as well. This is perhaps the strongest data point in support of customer live chat, as 87% of customers give a positive rating to their live chat conversations.

Agents also prefer live chat over the phone because regularly dealing with angry and upset customers via phone can take an emotional toll. Live chat contributes to agent job retention—a big, expensive issue that many CX leaders are constantly trying to grapple with.

The data is clear, and it makes sense for all the reasons we’ve discussed: Live chat for customer service shows every indication of being a worthwhile communication channel, both now and in the future.

Benefits of Live Chat Support Services

Real-Time Support

When customers need help, they don’t want to wait. Live chat support services provide instant solutions, cutting down resolution times and getting customers the answers they need—fast. No more sitting on hold or waiting hours for an email response. Whether it’s a quick question about a product or an issue with an order, live chat keeps the process smooth and stress-free. And when customers get quick answers, they stick around. Faster replies lead to higher satisfaction, increased trust, and more repeat business. The quicker the response, the better the experience—and that’s a win for both customers and businesses.

Increased Customer Satisfaction

Today’s customers expect immediate support, and live chat support services deliver exactly that. Instant responses mean they don’t have to wait, call, or go searching for answers—help is right there when they need it. This level of convenience keeps frustration low and satisfaction high. Beyond just speed, a great chat experience builds trust. When customers know they can rely on your support team for quick, clear, and helpful answers, they feel confident in your brand. That confidence translates into loyalty, repeat purchases, and positive word-of-mouth—turning a one-time buyer into a long-term customer.

Efficiency

Live chat isn’t just better for customers—it’s a game-changer for support teams too. Unlike phone calls, where agents can only help one person at a time, live chat lets them handle multiple conversations at once. That means fewer bottlenecks, faster resolutions, and better overall efficiency. Plus, fewer phone calls = lower costs. With live chat, businesses can reduce phone expenses, optimize staffing, and minimize hold times—all without sacrificing customer experience. It’s a smarter way to support customers, making teams more productive while keeping costs in check. More efficiency for your team, better service for your customers—everyone wins.

Omnichannel Integration

Customers don’t just stick to one channel—they bounce between email, social media, SMS, and your website. Live chat support services integrate seamlessly into this mix, creating a unified experience. Whether a customer starts a conversation on social media and follows up via chat or asks a question through SMS, they get the same consistent service. Even better, integrating chat across channels keeps all customer interactions in one place, so your team has a complete history of past conversations. That means no more repeating issues, fewer dropped interactions, and a smoother customer journey from start to finish.

Live Chat Support Best Practices

Prompt Response Times

Speed matters when it comes to live chat support services. The faster you respond, the more valued customers feel—and that leads to higher satisfaction and loyalty. Nobody likes waiting, especially when they have a quick question standing between them and a purchase. A prompt answer at checkout can eliminate doubts and reduce cart abandonment before it happens. Whether a customer is asking about shipping costs, return policies, or product details, meeting them in the moment with real-time support keeps them engaged. The result is more completed sales, fewer lost opportunities, and a reputation for being responsive and customer-focused.

Professional Communication

Live chat is fast, but that doesn’t mean it should feel rushed. A professional and friendly tone makes all the difference in building trust and keeping conversations productive. Customers want clear, concise, and helpful responses—not robotic scripts or vague answers. Miscommunication can create frustration, so keep things simple, polite, and to the point. Use proper grammar, avoid jargon, and personalize interactions with the customer’s name. A great chat experience feels like talking to a knowledgeable friend—someone who understands the problem and knows exactly how to help. The smoother the conversation, the more confident customers feel about your brand.

24/7 Availability

Customers shop on their own time—whether that’s during a lunch break, late at night, or halfway across the world. Offering live chat support services 24/7 means you’re always there when they need help. This is especially valuable for global businesses, ensuring customers in different time zones get real-time answers instead of waiting for office hours. Plus, round-the-clock availability isn’t just about support—it’s a sales booster too. A shopper with a question at 2 AM might just leave if they can’t get an answer. But if live chat is available? That hesitation disappears, and the sale happens.

6 Tips for Encouraging Customers to Use Live Chat

1. Make Sure People Know You have Live Chat Services

The first (and probably easiest) way to get more customers to use your live chat is to take every step possible to make sure they know it’s something you offer. You can get a lot of mileage out of promoting live chat through your normal marketing channels–a mention on your support page, on your social feeds, and at the bottom of your order confirmation emails, for example.

First, use your IVR to move callers from phone to messaging. You can also mention that you support live chat for customer service during the phone hold message. We noted above that people tend to hate being put on hold. You can use that to your advantage by offering them the more attractive alternative of hopping onto a digital messaging channel instead—including WhatsApp, Apple Messages for Business, and SMS. For example, this might sound as simple as: “Press 2 to chat with an agent over SMS text messaging, or get faster support over live web chat on our website.”

From your perspective, an added benefit is that your agents can easily shuffle between several different live chat conversations, whereas that isn’t possible on the phone. This means faster resolutions, a higher volume of questions answered, and more satisfaction all the way around.

Similarly, include plenty of links to live chat when communicating with your customers. After they make a purchase, for example, you could include a message suggesting they utilize live chat to resolve any questions they have. If you’re sending them other emails, that’s a good place to highlight live chat as well. Don’t neglect hero pages and product pages; being able to answer questions while talking directly to current and future buyers is a great way to boost sales.

2. Minimize the Hassle of Using Live Chat

One of the better ways of boosting engagement with any feature, including live chat, is to make it as pain-free as possible.

Take contact forms, for example, which can speed up time to resolution by organizing all the basic information a service agent needs. This is great when a customer has a complex issue, but if they only have a quick question, filling out even a simple contact form may be onerous enough to prevent them from asking it.

There’s a bit of a balancing act here, but, in general, the fewer fields a contact form has, the more likely someone is to fill it out.

The emergence of large language models (LLMs) has made it possible to use an AI agent to collect information about customers’ specific orders or requests. When such an agent detects that a request is complex and needs human attention, it can ask for the necessary information to pass along to an agent. This turns the traditional contact form into a conversation, placing it further along in the customer service journey so only those customers who need to fill it out will have to use it.

Though they may seem minor in isolation, there’s an important truth here: if you want to get more people to use your live chat for customer service, make it easy and pain-free for them to do so. Every additional second of searching or fiddling means another lost opportunity.

3. Personalize Your Chat

Another way to make live chat for customer service more attractive is to personalize your interactions. Personalization can be anything from including an agent’s name and picture in the chat interface displayed on your webpage to leveraging an LLM to craft a whole bespoke context for each conversation.

For our purposes, the two big categories of personalization are brand-specific personalization and customer-specific personalization. Let’s discuss each.

Brand-specific personalization

For the former, marketing and contact teams should collaborate to craft notifications, greetings, etc., to fit their brand’s personality. Chat icons often feature an introductory message such as “How can I help you?” to let browsers know their questions are welcome. This is a place for you to set the tone for the rest of a conversation, and such friendly wording can encourage people to take the next step and type out a message.

More broadly, these departments should also develop a general tone of voice for their service agents. While there may be some scripted language in customer service interactions, most customers expect human support specialists to act like humans. And, since every request or concern is a little different, agents often need to change what they say or how they say it.

Setting rules for tone of voice and word choice ensure the messaging experience is consistent no matter which agent helps a customer or what the conversation is about.

Customer-specific personalization

Customer-specific personalization, which might involve something as simple as using their name, or extend to drawing from their purchase history to include the specifics of the order they’re asking about.

Among the many things that today’s LLMs excel at is personalization. Machine learning has long been used to personalize recommendations (think: Netflix learning what kinds of shows you like), but when LLMs are turbo-charged with a technique like retrieval-augmented generation (which allows them to use validated data sources to inform their replies to questions), the results can be astonishing.

Machine-based personalization and retrieval-augmented generation are both big subjects, and you can read through the links for more context. But the high-level takeaway is that, together, they facilitate the creation of a seamless and highly personalized experience across your communication channels using the latest advances in AI. Customers will feel more comfortable using your live chat feature, and will grow to feel a connection with your brand over time.

4. Include Privacy and Data Usage Messages

By taking privacy very seriously, you can distinguish yourself and thereby build trust. Customers visiting your website want an assurance that you will take every precaution with their private information, and this can be provided through easy-to-understand data privacy policies and customizable cookie preferences.

Live messaging tools can add a wrinkle because they are often powered by third-party software. Customer service messaging can also require a lot of personal information, making some users hesitant to use these tools.

You can quell these concerns by elucidating how you handle private customer data. When a message like this appears at the start of a new chat, is always accessible via the header, or persists in your chat menu, customers can see how their data is safeguarded and feel secure while entering personal details.

5. Use Rich Messages

Smartphones have become a central hub for browsing the internet, shopping, socializing, and managing daily activities. As text messaging gradually supplemented most of our other ways of communicating, it became obvious that an upgrade was needed.

This led to the development of rich messaging applications and protocols such as Apple Messages for Business and WhatsApp, which use Rich Communication Services (RCS). RCS features enhancements like buttons, quick replies, and carousel cards—all designed to make interactions easier and faster for the customer.

Using rich messaging in live chat with customers will likely help boost engagement. Customers are accustomed to seeing emojis now, and you can include them as a way of humanizing and personalizing your interactions. There might be contexts in which they need to see or even send graphics or images, which is very difficult with the old Short Messaging Service (SMS).

In the final analysis, rich messaging offers another powerful opportunity to create the kind of seamless experience that makes interacting with your support enjoyable and productive.

6. Separating Chat and Agent Availability

Once upon a time, ‘chat availability’ simply meant the same thing as ‘agent availability,’ but today’s language models are rapidly becoming capable enough to resolve a wide variety of issues on their own. In fact, one of the major selling points of AI agents is that they provide round-the-clock service because they don’t need to eat, sleep, or take bathroom breaks.

This doesn’t mean that they can be left totally alone, of course. Humans still need to monitor their interactions to make sure they’re not being rude or hallucinating false information. But this is also something that becomes much easier when you pair with an industry-leading conversational AI for CX platform that has robust safeguards, monitoring tools, and the ability to switch between different underlying models (in case one starts to act up).

Having said that, there are still a wide variety of tasks for which a living agent is still the best choice. For this reason, many companies have specific time windows when live chat for customer service is available. When it’s not, some choose to let customers know when live chat is an option by communicating the next availability window.

Employing these two strategies means that your ability to service customers is decoupled from operational constraints of agent availability, and you are always ready to seize the opportunity to serve customers when they are eager to engage with your brand

Creating Greater CX Outcomes with Live Web Chat is Just the Start.

Live web chat with customers remains an excellent way to resolve issues while building trust and boosting the overall customer experience. The best strategies for increasing engagement with your live chat is to make sure people know it’s an option, make it easy to use, personalize interactions where possible—and make the most out of AI to automatically resolve routine inquiries while filling in live agent availability gaps.

If you’re interested in taking additional steps to resolve common customer service pain points, check out our ebook on the subject. It features a number of straightforward, actionable strategies to help you keep your customers as happy as possible!

What Is Conversational Commerce?

Posted on April 1, 2025May 1, 2025 Max Fortis

By now, you may have heard the term conversational commerce. Aside from some catchy alliteration that, honestly, just makes it fun to say, you may not know much about why everyone’s talking about it. Let’s clear things up a bit.

Conversational commerce refers to the transactions that take place through digital conversations consumers have with brands on messaging apps like web chat, Apple Messages for Business, text messaging, and even Facebook Messenger– often powered by a conversational commerce platform.

The interactions that take place are powered primarily by chatbots that process consumer messages and offer relevant responses. Conversational commerce takes on many forms and can take place across a multitude of channels.

Here are a few conversational commerce examples in real life:

Consumers shopping and completing transactions within a messaging conversation
Brands helping consumers shop by find a store location or online promotions and deals
Customer experience agents Interacting with a customer to reschedule an appointment or a delivery

These are just a few ways messaging has greased the commerce wheel, making it easier and faster for business to get done. In this article we’ll take a close look at conversational commerce and what companies need to do to fully leverage its benefits.

How Does Conversation Commerce Work?

Consider the smartphone — you know, the thing that is essentially glued to everyone’s hands now-a-days — and reflect on how much searching, browsing, buying, and texting takes place on any given device on any given day. Even before it was egged on by mandates, when our normal was just normal, consumers leaned on the ease, convenience, and speed that shopping from their smartphone offered.

As the world still works through “new normal” restrictions on physical locations, we’ve seen a surge in use as more consumers have looked to online shopping, delivery, and curbside pickup to get through their days. According to comScore consumers spend 85% of their smartphone time using only 5 apps, which tend to be either social media, messenger or other media apps. It makes sense that businesses would marry the power of messaging with consumers preferences.

He predicted these real-time conversations would facilitate more convenient online sales. Mr. Messina has yet to comment on where he picked up that incredibly accurate crystal ball of his, but looks like he was pretty spot on. Now, let’s talk about how your company can benefit from an investment in conversational commerce.

Why Invest In Conversational Commerce

Consumer expectations of speed and convenience have birthed new innovations that are opening up a seamless communication between brands and customers. Gone are the days where customers were satisfied with dialing an 800 number or having to write an email to get help.

Whether it’s a chatbot on Messenger, someone managing direct messages on X, or taking orders via text message, consumers will choose brands that go that extra mile to make their experience personalized and efficient. Needless to say, businesses are investing to make that happen.

Consider these statistics by Gartner:

At least 60% of companies will use artificial intelligence to support digital commerce.
30% of digital commerce revenue growth will be attributable to artificial intelligence technologies, such as those that power conversational commerce.
5% of all digital commerce transactions will come from a bot, such as those that power conversational commerce.

All of the major trends in commerce over the past couple of decades have been in moving to where customers are. Rather than forcing customers to come to you, you go to where they are. The next generation of that is conversational commerce.

Investing in conversational commerce also unlocks tangible business benefits that can elevate your customer experience and bottom line:

Improved Customer Experience

With AI and data-driven insights, conversational commerce allows brands to deliver more personalized interactions. These tools can understand customer preferences and intent, allowing for real-time, contextually relevant responses that feel tailored to the individual. Whether it’s personalized product recommendations via a chat widget, a voice assistant helping narrow down options, or live customer support through messaging apps, these conversational commerce examples help deepen engagement and drive loyalty. The interactions feel more natural and human—making the customer feel seen, heard, and supported throughout the buying journey.

24/7 Availability

AI-powered systems and chatbots enable brands to offer round-the-clock support, meeting rising expectations for always-on service. This is especially valuable for global brands, allowing them to deliver consistent, timely assistance across time zones—without the overhead of a 24-hour support team.

Cost Reduction

Conversational commerce solutions are designed not only to improve customer satisfaction but also to reduce operational costs. By automating routine tasks and frequently asked questions, businesses can scale without heavily increasing headcount. Human agents are freed up to handle higher-impact conversations, improving overall efficiency and productivity.

Create A More Natural Brand-Consumer Relationship With Conversational Commerce

Conversational commerce, much like regular conversations, are meant to build relationships. Conversational commerce is an opportunity to move beyond email blasts, promotional posts on social media, and other communication methods that provide just one-way communication. SMS and other messaging apps enable you to keep your customers informed with updates, and allows them to respond on the same message thread. A real-time exchange of information? Now that’s a conversation.

Creating engaging experiences on these channels are better and easier if your agents have a single view of your customers. Quiq makes it easy for companies to manage conversations with an intuitive agent desktop and native integrations with Salesforce.com, Zendesk, Shopify, and Oracle Service Cloud.

Quiq is architected with an “API First” strategy which means we seek to work in harmony with your existing systems. With Quiq’s integration frameworks you can customize our UI to include data from your internal operations systems or synchronize conversation data into your reporting, customer and other backend systems.

Get Started with Conversational Commerce

Getting started with conversational commerce isn’t complicated. Start with one new channel that customers visit every day such as WhatsApp or Facebook Messenger. Better still, start with text messaging, a universally accepted way to engage with consumers.

If you’re ready to put a conversational commerce platform to work so that your business can thrive, schedule some time to speak with one of our conversation experts today.

Rich Communication Services – A Guide for CX Leaders Weighing the Benefits

Posted on April 1, 2025May 1, 2025 Max Fortis

If by chance you haven’t heard of this new frontier in text-based customer communication, your first question is probably, “what is rich messaging?”

Well, you’re in luck! We wrote this piece specifically to get to the bottom of this subject. Here, we offer a deep dive into rich messaging, the capabilities it unlocks, and its implications for CX. By the time they’re done, CX directors will better understand why rich messaging should be central to their customer outreach strategy and the many ways in which it can make their job easier.

What Is Rich Messaging?

Rich messaging aims to support person-to-person or business-to-person communication with upgraded, interactive messages. Senders can attach high-resolution photos, videos, audio messages, GIFs, and an array of other media to enhance the receiver’s experience while conveying a lot more information with each message.

Before going any further, we should specify that “rich messaging” refers to an umbrella of modern messaging applications, but it’s not the same as rich messaging protocols on offer. Google’s Rich Communication Services (RCS), for example, is one approach to rich messaging, but it is not the same thing as rich messaging in general.

That said, you might still be wondering what is a rich communication service message, exactly? As you can guess, a rich communication service message is just a rich message sent over some appropriate protocol with all the advantages that it offers.

For a number of reasons, rich messaging applications have supplanted SMS in both personal and professional outreach. SMS messages simply do not support many staples of modern communication, such as group chats or “read” receipts. What’s more, the reach of SMS will remain limited because it requires a cellular connection, whereas rich messages can be sent over the internet.

Though SMS will probably be around for a while, rich messaging is becoming increasingly popular as companies have been trending toward greater use of applications like WhatsApp. Armed with these and similar channels, CX directors can now:

More easily capture new customers with compelling outreach.
Resolve customer issues directly via text, chat, or social media messaging (a huge advantage given how obsessed we’ve all become with our phones);
Interact with customers in real-time, which is a capability more and more people are looking for when seeking help.
Gather and act on analytics.
Scale their communications while simultaneously reducing the burden on contact center agents.

Given these facts, it’s no surprise that more and more CX leaders are making texting a key component of building lasting customer relationships.

What is Rich Messaging on Different Platforms?

Now that you have more perspective on what rich messaging is and what it offers, let’s spend some time talking about which platforms you should focus on.

There are a few major providers of rich messaging, but we’ll focus on Apple and WhatsApp. Apple has long been a communication giant, but with billions of users worldwide, Meta’s WhatsApp has certainly earned its spot at the table.

The sections below provide more details about how rich messaging works on each platform.

What is Rich Messaging on Apple?

Through Apple Messages for Business, contact centers can offer their customers a direct line of communication. This allows for far greater speed and convenience, to say nothing of the personalization opportunities opened up by artificial intelligence (more on this shortly).

For more information, check out our dedicated article on rich communication with Apple messages for Business.

What is Rich Messaging on WhatsApp?

WhatsApp is a widely-used application that uses rich messaging for texts, voice messages, and video calling for over two billion users worldwide. Utilizing a simple internet connection for its services, WhatsApp allows users to bypass the traditional costs associated with global communication, making it a cost-effective choice.

Given its vast user base, many international brands are adopting WhatsApp to connect with their customers. WhatsApp Business is an extension of WhatsApp, and it offers enhanced features tailored for business use.

It supports integration with tools like the Quiq agentic AI platform, which can automatically transcribe voice messages and allows for the export of these conversations for analysis using technologies like natural language processing.

For more information, check out our dedicated article on WhatsApp Business.

RCS vs. SMS vs. MMS vs. OTT: Understanding the Key Differences in Messaging

As consumer expectations have grown, so has the need for messaging technology to evolve. Businesses can no longer rely solely on traditional SMS to deliver standout experiences—especially when customers are used to rich, app-like interactions. That’s where understanding the differences between messaging types comes in.

From basic texts to interactive messages with buttons, images, and videos, here’s how SMS, MMS, RCS, and OTT messaging stack up:

What is SMS?

SMS (Short Message Service) is the most basic form of text messaging. It has a 160-character limit and doesn’t support media like images or videos. Despite its simplicity, it’s still widely used for short, timely notifications and reminders.

What is MMS?

MMS (Multimedia Messaging Service) expands on SMS by allowing users to send pictures, videos, and audio. It relies on mobile data and carrier support, and can be more expensive to send—especially at scale.

What is RCS?

RCS (Rich Communication Services) takes messaging to the next level with features like read receipts, typing indicators, carousels, and high-resolution media. It’s carrier-dependent and still gaining traction, but it offers a powerful upgrade for business-to-customer communication.

What is OTT Messaging?

OTT (Over-the-Top) messaging apps—like WhatsApp, Facebook Messenger, and Apple’s iMessage—work over the internet and bypass traditional carriers altogether. These platforms offer end-to-end encryption, rich media, and global reach, making them a go-to for brands looking to meet customers where they are.

The Benefits of Rich Messages for Businesses

Engaging with consumers in more meaningful ways is one of the keys to driving sales and repeat purchases. Whether on Apple, WhatsApp, or another channel, rich messaging is one of the best ways of interacting with customers; it’s convenient and powerful enough to help a CX leader rise above their competition.

Below, we will get into more specifics about the advantages to be had from using rich messaging.

1. Cost-Effectiveness

It may be called “the bottom line,” but let’s face it, your budget probably ranks pretty highly on your list of priorities. Because it works over the internet, rich messaging is a great way for CX directions to connect with customers without breaking the bank.

But it can also help your organization save money by reducing customer support costs. When consumers need to talk to someone at your business, they can speak to knowledgeable agents (or a large language model trained on those agents’ output) through your rich messaging platform. You will reduce the need to provide the hardware and staffing required to run a full contact center, and you will be able to use those savings to invest in other areas of your business.

In this same vein, rich messaging makes it far easier to engage in asynchronous communications. This means agents are able to handle multiple conversations at the same time, resulting in further savings.

Finally, rich messaging is far more scalable than almost any other approach to customer outreach, especially when you effectively leverage AI. Once you’ve figured out what you want your message to be, communicating it to ten times as many people is relatively straightforward with rich messaging.

2. Real-Time Insights

When they integrate rich messaging with a platform offering excellent support for real-time analytics, companies gain access to conversation analytics that provide the insights they need to improve contact center performance.

They can generate reports on click rates and other helpful interaction metrics, for instance, giving CX leaders a feedback loop they can use to test changes and see what improves customer satisfaction, loyalty, and lifetime value.

3. Rich Messaging is Native to the Devices Customers are Already Using

You could pay for the most compelling billboard in the history of marketing, but if it’s on the moon where no one will see it, it’s not going to do you much good. For this reason, we’ve long pointed out that it’s important to meet your customers where they are – and these days, they’re on their phones.

When combined with the statistics in the following section, we think that the case for rich messaging as a central pillar in the CX director’s communications strategy is very strong.

4. Increased Engagement

When developing a customer communication strategy, it’s important to evaluate the potential engagement level of various channels. As it turns out, text messaging consistently achieves higher open and response rates compared to other methods.

The data supporting this is quite strong: in a 2018 survey, fully three-quarters of respondents indicated that they’d prefer to interact with brands through rich messages.

This high level of engagement demonstrates the significant potential of text messaging as a communication strategy. Considering that only about 25% of emails are opened and read, it becomes clear that investing in text messaging as a primary communication channel is a wise decision for effectively reaching and engaging customers.

5. The Human Touch (but with AI!)

Customers expect more personalization these days, and rich messaging gives businesses a way to customize communication with unprecedented scale and sophistication.

This customization is facilitated by machine learning, a technology at the cutting edge of automated content customization. A familiar example is Netflix, which uses algorithms to detect viewer preferences and recommend corresponding shows. Now, thanks to advancements in agentic AI generative AI, this same technology is being integrated into text messaging.

Previously, language models lacked the necessary flexibility for personalized customer interactions, often sounding mechanical and inauthentic. However, today’s models have greatly enhanced agents’ abilities to adapt their conversations to fit specific contexts. While these models haven’t replaced the unique qualities of human interaction, they mark a significant improvement for CX directors aiming to improve the customer experience, keep customers loyal, and boost their lifetime value. What’s more, when used over time, these innovations will help a CX leader stand out in a crowded marketplace while making better decisions.

To make use of this, though, it helps to partner with a platform that offers this functionality out of the box.

6. Security

In addition to streamlining connections between your organization and its consumers, rich messaging may offer dependable security and peace of mind.

Trust and transparency have always been important, but with deepfakes and data breaches on the rise, they’re more crucial than ever. Some rich messaging applications, like WhatsApp, support end-to-end encryption, meaning your customers can interact with you knowing full well that their information is safe.

But, to reiterate, this is not the case for all rich messaging services, so be sure to do your own research first.

What is Rich Messaging? It’s the Future!

Businesses across every industry need to update their approach to messaging to remain relevant with consumers, but that’s especially true for CX leaders. Significant data shows that traditional customer communication channels, like phone, email, and web chat, have already fallen to the bottom of the preference list, and you need a plan in place that allows you to react to changes in customer desires.

Rich messaging is the technology that makes this possible, and it’s even more impactful when you partner with a platform like Quiq that enables personalization, analytics, and better engagement with your customers. Read more here to learn about the communication channels we support!