Yet many organisations still rely on traditional automation systems that struggle to meet these demands.  

Rigid IVR flows, generic chatbot scripts and siloed customer data often create more frustration than value, leaving customers repeating themselves and brands losing control of the customer journey. 

According to Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, many companies suffer from what he calls the “customer journey black hole” – a gap where context and customer history fall through the cracks, resulting in broken experiences and unnecessary friction. 

“Think about a typical banking interaction,” Anand says.  

“A customer fills in a loan application online, then calls the contact centre for support, only to be asked to provide the same information again. It’s no surprise that customers become frustrated.  

The consequence isn’t just dissatisfaction – 92 percent of customers say they’ll leave a brand after two or more poor experiences.

The Limits of Traditional Automation 

Even as businesses invest in automation to manage scale, traditional systems are increasingly showing their age.  

Script-based chatbots struggle to interpret nuanced intent.  

IVR systems force customers into predefined paths that rarely reflect what they actually want.  

And behind the scenes, data remains fragmented across CRM systems, ticketing platforms, and communication channels. 

“Legacy automation solves tasks, not outcomes,” Anand explains. “It might complete a form or look up an account, but it doesn’t understand the end goal of the interaction. It doesn’t collaborate with other systems.  

“It doesn’t adapt when the customer deviates from the script. Ultimately, it can’t orchestrate a full journey.” 

As customer journeys become more complex and decentralised, these limitations are becoming untenable.  

Organisations are now looking for a more intelligent and adaptive approach that can engage customers in real time, maintain continuity, and drive tangible results. 

Agentic AI in Action 

This is where agentic AI comes into play.  

Unlike traditional automation, agentic AI is built around autonomous, outcome-driven agents that can reason, collaborate and take contextual decisions.  

These agents can be trained for specific use cases such as cart abandonment recovery, KYC completion, proactive service notifications or multi-step issue resolution. This helps brands transition from basic automation to autonomous actions and AI decisioning.  

“Agentic AI is purpose-built,” Anand says. “Each agent understands the goal it needs to achieve, but it also knows how to work with other agents throughout the journey.  

“So you may have one agent focused on customer onboarding, another handling verification, and another coordinating follow-ups – all sharing context in the background.” 

This type of orchestration is increasingly essential for large enterprises. In e-commerce, for example, an agentic AI flow can detect a customer abandoning a cart, trigger hyper-personalised reminders across SMS, WhatsApp or email, and follow up based on engagement. If the customer expresses confusion or dissatisfaction, the agent can switch channels or escalate to a human agent with full context. 

“You’re no longer relying on one-size-fits-all automation,” Anand adds.  

You’re creating a dynamic loop that adapts to each customer’s needs and behaviours.

Voice AI: Transforming Real-Time Interactions 

The rise of voice AI is taking things a step further.  

Advanced speech-to-speech models now enable natural, human-like interactions that go far beyond traditional voice bots.  

These systems can understand real intent, detect emotion, and respond conversationally – making voice channels significantly more efficient and engaging. 

“For many customers, voice is still the channel of choice,” Anand notes.  

“But the experience has often been painful because legacy IVR is so restrictive. With voice AI, customers can speak normally and get real-time problem solving without navigating menus or waiting for an agent.” 

Tata Communications is seeing growing demand for voice AI in sectors such as banking, utilities, retail and travel, where customers frequently need rapid support with complex queries.  

When combined with agentic AI, voice agents can collaborate with other AI systems, retrieve information, complete tasks and escalate with full context when human support is required. 

“The beauty of voice AI is that it doesn’t break the flow,” Anand says. “If an escalation is needed, the human agent gets the full transcript, sentiment analysis and journey history. The customer never has to start again.” 

A Unified Approach 

Tata Communications has integrated these capabilities into a unified platform that connects multiple AI agents, voice systems and human support teams through powerful APIs and data connectors.  

The goal is to create a single interaction fabric that ensures continuity across every channel. 

“When an AI agent hands over to a human, or vice versa, all context is preserved,” Anand explains.  

“This is critical. If customers have to repeat themselves, the customer feels unheard and the journey becomes painful. Our platform eliminates that friction by ensuring that every agent – human or AI – understands the full picture.” 

The company has already seen strong results.  

One electric vehicle brand achieved a 25 percent increase in customer follow-through after deploying agentic AI-driven outreach.  

A large e-commerce marketplace reduced return-to-origin orders by 45 percent following the introduction of AI-powered WhatsApp workflows. 

“These are not incremental improvements,” Anand highlights. “They are major operational gains driven by intelligent automation that understands the customer’s intent.” 

Human-First, Outcome-Driven CX 

Despite the advances in AI, Anand emphasises that human expertise remains essential.  

Tata Communications’ approach is intentionally hybrid – using AI to handle repetitive tasks, streamline journeys and provide real-time insights, but ensuring humans remain central to complex, high-empathy interactions. 

“The best CX strategy is human-first,” he says. “AI should enhance human capability, not replace it. When AI and humans collaborate, you deliver outcomes that are personalised, proactive and genuinely valuable. That’s the future of customer experience.” 

As enterprises look to modernise their digital engagement, agentic AI and voice AI are emerging as critical technologies that can close the customer journey black hole and deliver the seamless, context-aware experiences customers expect. 

To explore how your organization can overcome the customer journey black hole and create seamless, unified experiences, contact Tata Communications to learn more about their integrated CX platform capabilities.   

For this writer, as much as it pains me to admit, it was the trademark side-swept fringe and uncomfortably tight jeans of an emo teenager.   

For younger readers, it might be the current, inexplicable obsession with Labubus, which one day you’ll look back on with confused nostalgia.   

Whatever your vice, the good news is that some mortifyingly embarrassing photos and a small amount of wasted cash are probably all you have to worry about.   

Unfortunately, for major enterprises delivering customer experiences that matter, deciding to hitch their wagon to the wrong trend can have far more damaging results.  

Right now, there is no bigger CX trend than AI. Be it chatbots, agent-assist tools, or QA, enterprises are experimenting with AI wherever and however they can.  

Of course, this isn’t to say that AI should be ignored; the technology’s potential to drastically alter and enhance CX is undeniable. But despite the hype, not every AI deployment delivers the results businesses expect.  

For Joseph Kelly, Solutions Architect at Miratech, part of the issue is the ubiquitous nature of the tech, as he explains:  

Everything is AI. But is it just AI for AI’s sake?  

“Customers really need to hone in on the right strategy to start with. In the CCaaS space, in customer experience and employee experience, first getting strategy right will help cut through a lot of the clutter and get to the heart of how AI can really help.”   

Kelly’s point hits at a real challenge: how to separate genuine AI value from marketing spin. 

Vendors are quick to slap ‘AI-powered’ on everything, from natural language understanding to speech recognition; the trick is knowing what will actually move the needle.  

The Hype vs. Reality  

When organizations are hype-driven, they run the risk of deploying technology without a defined goal, which often results in overspending.   

Kelly notes that even established tools like NLU IVRs have been ‘AI-powered’ in marketing terms for years, without fundamentally improving the experience.  

“It’s about cutting through the marketing and sales speak on what is really AI, and what’s not,” he says.  

“Then, you look at where you want to start to make real change. Are you looking to enhance your customer experience with AI? Or your agent experience with AI? That’ll help guide you where you’re trying to get to.”  

Enterprises that clarify their objectives – whether it’s reducing call volumes, boosting first-contact resolution, or improving agent workflow – are far more likely to see tangible benefits from their AI deployments.   

Start Small, Solve Real Problems  

For organizations just starting with AI, Kelly believes the best approach is to take things step-by-step. 

For example, he highlights practical pilots like agent-assist, smarter routing, and call deflection as good examples of “seeing how the technology can help agents provide more informed and efficient answers to customer inquiries.”  

Small-scale projects reduce risk and can produce immediate wins against clear goals to build on. Routing customers correctly the first time reduces wait times; agent-assist tools speed up complex resolutions. These early wins build momentum and justify wider adoption.  

However, in order for these pilots to be successful, he emphasizes the need for “good, accurate data that the AI can access.”  

Once pilot projects show value, scaling AI requires alignment with broader business goals. Efficiency, personalization, and agent experience must stay front and center. But again, data is at the heart of it all.  

“Where am I going to house all of this information?” Kelly asks.  

“Does it have to be in the CCaaS vendor’s platform? Do I need a way to connect these things so a change in one system propagates to another?”  

Kelly also cautions that adding AI won’t fix a weak foundation.  

 If you don’t have a really stellar customer experience today, adding AI is not going to provide the benefits you’re probably thinking it can. 

Consolidating data, optimizing knowledge management, and improving processes must come first.  

Avoiding AI for AI’s Sake  

For Kelly, the major contributors to AI project failures are vanity projects, poor integration, and a lack of adoption by agents and customers.   

To combat this, change management is critical.  

Agents need confidence in new tools, and customers must feel automation improves – not hinders – the experience. Without this, even advanced AI can underperform.  

This is where Miratech can help. By grounding AI projects in business needs and guiding enterprises through data strategy, integration, and adoption, the company turns AI investments into tangible, measurable business outcomes. 

This all means AI doesn’t have to be just a buzzword or trend. When used with clear goals, it can truly transform customer experience – improving efficiency, personalization, and agent empowerment.  

The key is to have a purpose: and then start small, scale strategically, and let AI serve the business, not the other way around.  

You can learn more from Joseph Kelly on how to maximize your CCaaS migration by checking out this article.

You can also discover Miratech’s full suite of solutions and services by visiting the website today. 

Yet, many enterprises are struggling to deliver on this promise, falling victim to what experts call the “customer journey black hole.”  

This phenomenon occurs when customer data, context, or information are lost between interactions, creating fragmented and frustrating experiences that can cost brands both loyalty and revenue. 

Gaurav Anand, VP and Head of Customer Interaction Suite at Tata Communications, describes the black hole as the exact opposite of what brands intend to deliver.  

“Your data – whether it’s for an inquiry, a product question, or a complaint – is going in one channel and then getting lost. That’s the black hole side of it,” he explains.  

And the impact of this journey black hole is severe. It’s not just that the customer is unhappy. Your actual business numbers are getting impacted. 

Why It Happens: Silos, Channels, and Technology Gaps 

The consequences of a fragmented customer journey are stark. A study by PwC found that 92% of customers would abandon a brand after two or more negative interactions. 

In other words, every dropped call, unresolved query, or repeated customer statement represents a potential lost sale, diminished brand trust, and decreased customer lifetime value. 

The causes of the black hole are multifaceted. Enterprises often operate in silos, with marketing, product, billing, and support teams holding pieces of the customer puzzle in separate systems that do not communicate with each other.  

Misaligned channels exacerbate the problem, as customer preferences differ by demographic, time of the day, their stage in the journey etc. For example a younger consumer may prefer messaging apps, while older generations often prefer phone calls. 

Technology gaps, such as disconnected AI solutions and multiple vendor systems, along with operational inefficiencies, further worsen the situation.  

And in today’s world, if the hand-off from AI to humans loses context and customers have to repeat themselves, then this black hole can be pretty deep.  

The Cost of Ignoring the Black Hole 

The impact extends beyond customer experience metrics. Anand points out that “closing these gaps is just not about improving customer experience. It’s about protecting the brand value and the value of the businesses that these brands are in.”  

Fragmented customer interactions can cost enterprises significantly in lost sales, operational inefficiencies, and increased churn, underscoring why addressing the black hole is not just a CX priority, but a business imperative. 

When customer data is lost or mismanaged, brands risk reduced sales, operational inefficiency, and reputational damage, as negative experiences can spread quickly in the age of social media. 

Compliance and privacy requirements can also unintentionally introduce friction, making it more challenging to provide smooth, personalized interactions. Ignoring the black hole may result in a slow erosion of customer trust, which competitors can capitalize on. 

Closing the Gap: Unified CX and AI Solutions 

Addressing the black hole requires a holistic approach.  

Enterprises need to unify their data and orchestrate interactions across all touchpoints to streamline operations. This includes ensuring that every channel communicates with the others, maintaining context between interactions, and empowering human agents with the right information at the right time.  

AI can help, but only if fed with contextual & comprehensive data and deployed thoughtfully, maintaining continuity between automated and human-led experiences. 

The payoff is significant. Brands that achieve seamless, data-driven experiences can reduce churn, increase engagement, and extract actionable insights from end-to-end journey analytics. In a competitive marketplace, providing frictionless experiences is no longer optional – it’s a differentiator.  

As Anand warns, “It sounds simple, but the customer has high expectations. And if their expectations aren’t met, they will leave. The black hole is real, and brands cannot afford to ignore it.” 

As more enterprises confront the customer journey black hole, many are turning to unified CX strategies that bring together communications, data, and AI.  

Companies like Tata Communications are helping organizations reimagine this space – integrating CPaaS, CCaaS and CXP technologies to create more connected, context-rich interactions.  

It’s a shift from managing channels to orchestrating journeys – a move that could define the next era of customer experience. 

To explore how your organization can overcome the customer journey black hole and create seamless, unified experiences, contact Tata Communications to learn more about their integrated CX platform capabilities.

In this exclusive interview, we sit down with Martin Taylor, deputy CEO and co-founder of Content Guru, to explore how CX leaders can successfully navigate the challenges of data ownership while embracing the transformational potential of AI and automation. 

Watch the Full Interview on Youtube 

The Data Explosion: From Calls to Connected Everything 

The scale of data generation in modern contact centers is staggering. “We’re creating billions of records a year just as Content Guru, as is everybody else,” Taylor explains in the interview. “So you’ve got increasingly information not just from calls anymore but from all the digital channels.” 

But it’s the emergence of what Taylor calls the “digital customer” that’s truly transforming the landscape. With predictions of 39 billion Internet of Things devices by 2030, each representing a human in the eyes of regulators, the volume and variety of personal data flowing through CX environments is exploding exponentially. 

” The UK Information Commissioner’s Office considers data generated by IoT devices  – be it a movement or somebody’s temperature detected by a smart health device or a smart fridge  reporting it is  empty – as a piece of personal data,” Taylor notes. 

The Jurisdiction Maze: Where Geography Meets Governance 

One of the most complex challenges facing global organizations is navigating the intersection of geography and regulation. As Taylor explains, “Data is being produced at large volume all over the world, every day, every second. So how that is generated and the rules under which it is being generated vary by geography and by market segment.” 

The implications go far beyond GDPR. While the regulation establishes that “the data subject is ultimately the owner of the data,” the practical responsibilities for processors and vendors create a complex web of obligations that vary by jurisdiction and sector. 

“The EU want it to take place within the EU. The UK want it to take place within the UK, the US in the US,” Taylor explains. “And then if you go to the actual sectors themselves like medical or financial, they’ve got a load of rules of their own and those are done per country as well.” 

Breaking Down Silos: The New Collaborative Imperative 

The complexity of modern data governance is forcing unprecedented collaboration between traditionally separate functions. Taylor shares a real-time example from his own organization: “An example from here today is about something that’s come to my desk  about live sentiment analysis and its legality within an EU AI Act context.” 

This seemingly straightforward CX enhancement required input from legal, product, and information security teams – a pattern that’s becoming the norm rather than the exception. “Those sorts of conversations are happening throughout all levels of the value chain from the provider of a service right through to the vendor,” Taylor observes. 

The Death of “Public Cloud” Assumptions 

Perhaps one of the most significant shifts in thinking has been the abandonment of the idea that being “in the cloud” absolves organizations of data responsibility. 

“I think we’ve seen the death of this idea of there being such a thing as a public cloud,” Taylor states emphatically. “Everyone can see that clouds belong to organizations now and that they reside in specific jurisdictions.” 

The AWS outage in Virginia that affected organizations worldwide serves as a stark reminder of this reality. Many affected companies didn’t even know they had connections to that specific location, highlighting the importance of understanding not just whose cloud you’re using, but where it physically resides and under what jurisdictions it operates. 

Balancing Innovation with Responsibility 

As organizations rush to embrace AI and automation, the challenge becomes maintaining innovation velocity while ensuring responsible data handling. Taylor uses an oil refinery analogy to explain this balance: “I think of raw data as like crude oil and then the refining process, fractional distillation. You’re looking now for that kind of high quality racing fuel  that we use to feed the AI.” 

Not every implementation needs to become more complex, but those involving AI require higher-quality data and more sophisticated governance. “In some cases, it’s AI, it needs that richer fuel. You can’t feed it the heating oil, because it won’t work,” Taylor explains. 

Looking Ahead: Preparing for 2026 and Beyond 

As we look toward the year ahead, Taylor predicts continued growth in both opportunity and complexity. “We’ve all heard  a lot about agentic AI  during 2025. I think 2026 is when it starts to get applied,” he notes, while emphasizing that this won’t mean wholesale replacement of human agents. 

Instead, organizations should prepare for “more data, more automation, and that means more data handling challenges.” This will require: 

• Enhanced Security Postures: Moving beyond perimeter defense to comprehensive data protection throughout its lifecycle 

• Geographic Strategy: Making deliberate choices about data processing locations based on customer needs and regulatory requirements 

• Vendor Due Diligence: Evaluating partners not just on technical capabilities but on jurisdictional alignment and compliance frameworks 

The Trust Dividend 

Ultimately, the organizations that succeed in this complex landscape will be those that can demonstrate they’re worthy stewards of customer data. As Taylor concludes, “There’s going to be a lot more scrutiny of how all of this wonderful new processing is going to happen.” 

The complexity brings opportunity for those willing to invest in getting it right. By building transparent, responsible data governance practices, organizations can turn compliance obligations into competitive advantages – earning customer trust while enabling innovation. 

The question isn’t just who owns your customer data – it’s whether you’re prepared to prove you deserve that ownership. 

Continue the Conversation 

For more insights on navigating these challenges, visit contentguru.com 

For enterprises, that’s both a challenge and an opportunity. Handled properly, feedback reveals exactly where things are and aren’t working. It tells support teams which moments frustrate. It tells product teams what’s missing, and it tells the C-suite what customers value enough to fight for.

That’s the real job of customer feedback management, turning scattered input into structured insight, then routing it to the teams that can actually do something with it.

The best CFM systems don’t just capture data. They:

• Map feedback across the full journey, not just surveys
• Spot trends early, before they show up in churn
• Connect insight directly to actions: faster support, better products, clearer messaging

In short, modern customer feedback management platforms give enterprises a new kind of muscle: the ability to listen deeply, move early, and improve continuously

What is Customer Feedback Management?

Customer feedback management is the discipline of collecting, interpreting, and acting on customer sentiment – not just from surveys, but from every channel where customers leave a mark.

That might mean tracking a drop in CSAT after a product update, combing through live chat logs, or decoding a two-star review on Trustpilot. In most enterprise settings, it means building a feedback loop that crosses teams: product, marketing, service, and operations all relying on the same source of truth.

The best customer feedback management software doesn’t just store responses. It translates them into structured insight, surfacing trends, routing complaints, and pushing alerts to the right place, fast. It’s the glue between listening and resolution.

To work at scale, feedback systems typically include:

• Multichannel ingestion: Web forms, support tickets, NPS, app reviews, even social DMs. Every signal matters, even if it’s unstructured.
• Theme detection and prioritization: Tools flag repeat issues or keyword clusters before they become reputational risks.
• Workflow integration: A refund complaint can notify finance. A delivery bug can trigger a ticket in product ops.
• Dashboards and reporting: With the help of AI systems, leaders get a filtered view of real insights by product line, geography, or channel.

Leading companies aren’t collecting feedback in a vacuum. They’re wiring it directly into CRM systems, contact center tools frontline workflows, so the right people can act without delay. The tighter the integration, the faster teams can respond, fix what’s broken, and strengthen customer relationships that last.

Where Feedback Fits: Feedback Management, VoC, and EFM

Feedback is only useful if it leads somewhere. That’s where terminology starts to matter. Voice of the Customer (VoC), customer feedback management, and enterprise feedback management (EFM) are often used interchangeably. They shouldn’t be.

Customer feedback management is the engine room. It handles collection, sorting, tagging, and routing. Think of it as the operational layer that turns raw input from surveys, ratings, and comments into tasks and decisions. This is where data moves from inboxes and dashboards into action plans.

Voice of the Customer (VoC) goes broader. It doesn’t just listen to what customers say, it listens to how they feel, how they behave, and where they’re frustrated or delighted without necessarily saying it outright. A good VoC program blends direct feedback with behavioral signals and sentiment analysis. It’s about seeing the full picture.

Enterprise feedback management (EFM) stretches even further. It includes employee and partner insight, compliance triggers, internal process reviews, and often sits closer to risk management than CX. In highly regulated or distributed organizations, EFM is essential infrastructure.

At enterprise scale, feedback management isn’t just a support tool. It’s part of the system of record: connected to customer data platforms, CRMs, business intelligence tools, and employee engagement systems (WEM tools).

Each of these frameworks adds something. The most mature organizations use all three as parts of one loop: listen, understand, and act.

What is Customer Feedback Management? Feedback Types

Customer feedback isn’t always a form or a star rating. It’s often informal, unstructured, or buried in systems where no one’s looking. Recognizing the different types is the first step toward building something that works across departments and channels.

• Direct Feedback: The most visible kind. Surveys after support calls. CSAT and NPS prompts. Product reviews submitted through apps or portals. It’s usually structured, timestamped, and easy to analyze. But it’s also the most filtered. The people who answer tend to be at the emotional extremes, either thrilled or annoyed. Everyone else stays quiet.
• Indirect Feedback: This is what customers say when they’re not talking to you directly. Tweets. Public forum threads. Online reviews. Complaints posted to third-party sites. In many organizations, this insight slips through the cracks. But today’s customer feedback management platforms use NLP and sentiment tools to bring these comments into view before they become brand problems.
• Inferred Feedback: This is the feedback customers don’t say out loud, but show in what they do. Dropping out halfway through checkout. Asking the same question in three different places. Bouncing between help pages without finding what they need.

On their own, these signals can be easy to miss. But together, they reveal patterns of frustration that direct surveys might never surface.

Why Customer Feedback Management Matters

There’s no shortage of dashboards in a modern enterprise. But few of them speak with the voice of the customer. That’s what feedback management changes. It shifts insight from lagging reports to live reality, focusing on the real-time pulse of what customers need, want, and expect.

For enterprise leaders focused on customer experience, this isn’t a soft metric. It’s operational. According to Bain & Company, companies that excel at customer experience grow revenues 4%–8% above their market. But growth doesn’t come from tracking satisfaction scores alone. It comes from turning those scores into action.

Here’s where feedback becomes a business driver:

• Alignment Across Teams: Sales hears one thing. Support hears another. Product has a third backlog entirely. When feedback lives in separate systems, teams solve different problems. When it’s centralized, patterns emerge, and teams move in the same direction.
• Early Signal Detection: A broken link on a signup form. A billing process that’s confusing in one region. A surge in cancellation requests. Customer feedback management platforms surface these issues before they hit churn reports. The earlier the fix, the lower the cost.
• Smarter Roadmapping: Feedback isn’t just a support signal, it’s a product roadmap tool. Tracking customer insights, linking them to outcomes, and activating responses leads to strategic action. Teams can prioritize features that drive loyalty.
• Competitive Advantage: Every brand says it listens. Few can prove it. Companies that consistently close the loop visibly earn trust. In a market where switching costs are low, trust is often the only real moat.

The case for customer feedback management software isn’t just about efficiency. It’s about agility, spotting the next risk or opportunity while competitors are still guessing.

How to Build a Customer Feedback Management System That Works

Enterprises don’t lack feedback. They’re swimming in it. The challenge isn’t collection, but coordination. Scattered responses, siloed ownership, and no clear plan for what happens next. That’s where customer feedback management becomes a system, not just a task.

1. Start with What You Already Have

Before adding new tools or channels, map what’s in play. Most enterprise teams already gather feedback across:

• Post-interaction surveys
• Help desk conversations
• Social and review platforms
• Product feedback forms
• Sales and account notes

But it’s often fragmented, or locked in spreadsheets, CRM fields, and third-party platforms. Start by listing every touchpoint where customers leave a trace. Then identify who owns that data, how it’s reviewed, and whether it drives action.

2. Build a Shared System, Not Just a Repository

A true customer feedback management system isn’t just a bucket. It’s a hub. One place where cross-functional teams can view, analyze, and act on insights. That requires more than storage. It needs structure. Look for tools that:

• Integrate with your CRM system and CDP
• Tag feedback by source, product line, sentiment, urgency
• Offer role-specific dashboards for ops, product, CX, compliance
• Allow for routing, escalation, and response tracking

Consider other integrations that might be helpful too, such as connections to your ERP and business intelligence platforms, or workforce management tools.

3. Design a Feedback-to-Action Pathway

Without clear ownership, feedback dies in the backlog. Teams need to agree on what gets prioritized, who responds, and how it loops back into service design, training, or product fixes.

The strongest systems:

• Flag urgent or high-impact issues automatically
• Route insights to the right teams (with deadlines)
• Track outcomes, not just volume
• Communicate resolution back to the customer

When that loop works, feedback becomes part of how the business runs.

How to Use Feedback to Improve Business Results

Most companies collect feedback. Fewer actually do something meaningful with it. In mature organizations, feedback isn’t just a sentiment report, it’s a driver of change. Done right, it informs strategy, sharpens execution, and reduces churn.

• Prioritize patterns over outliers: It’s easy to get caught up in the latest complaint or viral review. But high-performing teams step back. They look for volume, frequency, and trends, not just anecdotes. That could mean mapping repeat issues to product features, or tracking common service pain points over time.
• Feed insight to the right systems: Don’t keep customer feedback on a CX dashboard. Use it to inform product roadmaps, workforce planning, pricing models, training strategies, and anything else that impacts the customer experience.
• Expand your metrics: Go beyond NPS and CSAT. Think about customer effort scores, overall retention rates and churn. Determine the KPIs you want to keep track of in advance, and make sure everyone is watching them, including the C-Suite.

Choosing Customer Feedback Management Software

Customer feedback is everywhere. What separates good companies from great ones is what they do with it. That’s where the right customer feedback management software comes in, to make insights actionable, accountable, and accessible across the enterprise.

Start With the Business, Not the Tool

Software selection should begin with the problems it’s meant to solve. Are customers dropping off after onboarding? Or are service complaints slipping through the cracks? Are product teams getting insight too late to act?

Clear goals tend to point to the right tool:

• Real-time alerts for contact center agents?
• Text analytics for unstructured NPS comments?
• Trend reporting to inform product roadmaps?

Once those use cases are clear, it becomes easier to separate the platforms built for scale from those that just tick boxes.

Integration Over Isolation

In a modern tech stack, no system should sit alone, especially not feedback.

Customer insights gain power when connected to:

• CRM platforms, where individual records tell a full customer story
• Contact center solutions, where timing and channel matter
• CDPs, which consolidate behavioral and transactional data
• BI tools, for deeper cross-functional reporting
• Broader ERP, WEM, and business management tools

Make sure your platforms feed the systems powering decisions.

Think Long-Term: Governance, Scalability, and Fit

Even the most powerful platform will struggle without strong foundations. For enterprise buyers, that means focusing on operational readiness:

• Can the system support multiple teams and regions with clear permissions?
• Are escalation workflows and approvals built in?
• Does the vendor offer strong uptime guarantees and compliance controls?
• Is the reporting flexible enough to satisfy both executive leadership and front-line teams?

Ease of use matters too. If agents, analysts, and leaders can’t find value in it quickly, feedback won’t flow where it’s needed most.

Discover the best customer feedback management solutions:

Customer Feedback Management Best Practices

Technology may capture customer sentiment, but it’s what companies do next that separates good intentions from real improvement. At the enterprise level, feedback shapes products, and defines brand reputation, retention, and revenue.

Here’s what the most effective teams get right.

• Track consistently: Feedback isn’t a file to review later. It’s a feed that’s active and ongoing. Companies need to review regularly, discuss in depth, and build around it.
• Make feedback cross functional: Operations needs visibility into service complaints, marketing needs to know where messaging misses, and HR should see how poor feedback is affecting teams. Get everyone involved.
• Close the loop: Replying to feedback, or acting on it, is crucial. Customers want to know their input mattered, and teams want confirmation their fix was felt. Ensure that your action is clear, powerful, and visible.
• Read between the lines: Surveys are useful, but raw behavior can say more. Combine behavioral insights, structured survey data, and conversational analytics for a comprehensive view of what customers really feel, not just what they say.
• Make it easy to act: Help teams fix issues quickly. Check if workflows are in place for feedback routing, and whether CX agents can escalate recurring problems. Give people the tools they need to act.

Customer Feedback Management Trends

Customer expectations haven’t just shifted, they’ve splintered. Channels have multiplied. Responses move faster. The tools used to manage it all are catching up. Here’s what’s defining feedback management right now:

The Rise of AI-Powered Analysis

Enterprise teams spent years circling AI as a concept. Now it’s operational. The strongest feedback systems today don’t just categorize responses, they break them down by tone, urgency, and underlying cause.

Platforms like Medallia, NICE, and Sprinklr are using natural language processing and conversational analytics to surface issues before they mutate. Instead of waiting for quarterly survey analysis, teams can spot sentiment drops and recurring themes as they happen.

Feedback Is Becoming Embedded

Feedback used to live in standalone forms: a survey here, a rating box there. That’s changing. Leading platforms now capture signals from everyday interactions: chat logs, call transcripts, even app usage.

Feedback is moving closer to the moment. A delivery delay triggers a quick prompt. A cancelled subscription opens the door to ask why. Systems are listening all the time, and they’re getting smarter about what to listen for.

Structured Feedback Loses Traction

It’s not just about ticking boxes. The most valuable insights often show up in open comments, social threads, or long-form email replies. That unstructured data used to be hard to sort. Now, it’s where the action is.

Enterprises are investing in platforms that can handle nuance: that can understand sarcasm, spot emotion, and cluster feedback without a human reading every line. Forrester calls this shift “human insight at scale”, and it’s showing up as a core capability in nearly every customer feedback management platform leader.

Everything Connects Or It Doesn’t Work

Feedback is most valuable when it flows. Into support platforms, product roadmaps, agent scripts, and CX dashboards. But that only happens when systems talk to each other.

Leading tools now integrate out-of-the-box with CRMs, contact center systems, VoC platforms, and enterprise resource planning (ERP) solutions. That allows customer concerns to influence decision-making across the business, not just in service.

Privacy Remains Crucial

The line between “listening” and “surveilling” is thin, and enterprise buyers know it. In a post-GDPR, opt-out-default world, customer feedback strategies need to include transparency.

That means clear consent prompts. Data handling disclosures. Anonymization features. Especially in regulated sectors, ethics now sit beside analytics in the buyer’s checklist.

What is Customer Feedback Management? The Voice of CX

Customer feedback management It affects product decisions, shapes brand reputation, and drives loyalty at scale.

Done well, it connects dots across departments, from support and sales to marketing and operations. It puts real-time customer truth in front of the people who can do something about it.

But it only works when the systems are connected, the insights are trusted, and the loop is truly closed. That’s why enterprise teams are investing in modern customer feedback management platforms to operationalize input.

For companies focused on loyalty, innovation, and experience, the question isn’t whether to invest in customer feedback tools. The only real question is: which one will help you act faster, and smarter? CX Today is here to help:

• Join the Community: Be part of a dynamic CX-focused network. Swap ideas with thought leaders and elevate your feedback strategy.
• Test the Tech: Discover the top-rated platforms, meet vendors, and explore trends at live and virtual events.
• Plan Your Next Investment: Use our CX Marketplace to explore top vendors in feedback, VoC, CDP, and contact center tech.

Or visit the ultimate CX guide for enterprise experience leaders, for insights into how to build a better CX strategy, one step at a time.

Scorecards, consistency checks, and compliance boxes have shaped the bulk of QA activity – and while these remain essential, their impact on customer experience often stops at the agent level.  

Many leaders still struggle to answer bigger questions: What’s driving call volumes? Where are customer expectations shifting? Which friction points keep resurfacing across journeys?  

The traditional model simply wasn’t built for that.  

As Derek Corcoran, CEO and Founder of Scorebuddy, puts it, those older processes are “baked into operational QA teams” and are “legacy in some ways.”  

Manual evaluation was always time-consuming, and teams were typically “only covering 2% of total conversations – and that’s on a good day.”  

With such a limited sample, it’s no surprise that QA often surfaced issues but rarely connected them to broader CX patterns.  

Instead of operating with a telescope, Corcoran says, teams were forced to work “with a microscope.” They were focused on agent performance rather than systemic insight. And until very recently, the technology didn’t allow for anything different.  

The Shift: From Passive Scoring to Meaningful Discovery  

AI and modern analytics are rapidly changing the QA equation.  

Today, QA leaders can step out of the purely operational view and into a far more strategic one. But the shift only happens when teams move beyond scoring into meaning-making.  

According to Corcoran, the first leap many customers take is simply getting out of spreadsheets and “streamlining” evaluation work. Immediately, this unlocks visibility:  

“Suddenly they’ve visibility they didn’t have before, and the agents are now directly engaged in the interaction with the evaluators and supervisors.” 

But the step-change occurs when that structured data meets analytics.  

Scorebuddy connects to multiple data sources – voice, chat, bots, and more – aggregating everything into a single picture.  

This lays the groundwork for a deeper analysis, including tagging, filters, dashboards, sentiment analysis, and custom forms that reflect what leaders actually want to understand, rather than just what they can tally.  

The key is scale. A manual team might evaluate a small percentage of conversations, but analytics sits “across a much wider set” and introduces what Corcoran calls a “multiplier effect.”  

Agents can no longer argue that the wrong calls were chosen, and leaders can finally see meaningful patterns in customer behavior.  

Most importantly, this resolves one of CX’s biggest pain points: explaining why customers are making contact.  

With analytics, “you almost have an inbuilt business analyst,” Corcoran explains. Leaders can see exactly when contact drivers spike, how they correlate to product changes, and what should be escalated beyond the contact center.  

Spotlight: When QA Data Reveals What No One Else Saw  

Take the example of a retail client navigating a Black Friday surge.  

As Corcoran recalls, the QA and analytics engine detected a sharp increase in calls linked to a specific delivery provider.  

“They could see that one particular service delivery company was missing its SLAs,” he explains.  

“This insight allowed the business to launch an action plan to which the provider responded, allowing them to drive down that cause of contact.” 

That ability to correlate contact drivers with operational issues outside the CX team creates direct, measurable impact on CSAT and customer effort – something that would have been nearly impossible using traditional QA alone.  

Another emerging hotspot is bot governance. As more companies deploy GenAI-driven virtual agents, they often assume these systems perform flawlessly unless complaints surface.  

But as Corcoran warns, bots “can go off script,” and a conversation that appears resolved “may not be the case.”  

Without structured QA on bot interactions, businesses risk unseen CX damage.  

One Scorebuddy customer now audits 50,000 bot conversations per week and compares bot performance side-by-side with human agents, providing a powerful lens for identifying where automation shines and where it creates risk.  

Building the QA–VoC Insights Loop  

In order to move from insights to analytics, QA teams must thread those insights back into operational decisions, product teams, and executive conversations.  

The strongest programs combine QA and VoC input into a single continuous loop, where quality signals highlight friction, VoC confirms customer impact, and analytics quantify scale.  

This integrated view is exactly where Corcoran sees QA heading:  

“We’re now getting visibility of other aspects in the business that we can then move into the C-suite if necessary.”  

From Problems to Priorities  

Scorebuddy’s evolution mirrors this broader industry shift.  

The goal is no longer to simply review calls but to understand why things go wrong, what’s driving customer effort, and what to fix first.  

QA becomes not just a safeguard for consistency but a discovery engine – one capable of shaping cross-functional decisions.  

In a world where contact centers must deliver more with less, this is where QA proves its worth: not as a reporting tool, but as a source of truth for customer reality.  

You can watch and listen to more of Derek Corcoran’s QA insights by checking out this exclusive interview with CX Today.   

You can also discover Scorebuddy’s full suite of services and solutions by visiting the website today.   

With global enterprises like BT, Deutsche Telekom, and EE already seeing results, the discussion explores how hybrid human-AI teams, composable experience design, and data-driven automation are shaping the contact center of 2026 — and how a unified CX platform can future-proof operations and drive measurable business value.

In this CX Today interview, Rob Scott sits down with Shrenik Jain, VP of Product Management for CCaaS at Sprinklr, to explore what’s next for contact centers and how Sprinklr is taking a platform-first approach to transform customer experience.

Jain explains why Sprinklr didn’t follow the legacy voice-first route, how unification enables smarter AI, and what the shift from assistant AI to agentic AI means for tomorrow’s CX workforce. If you’re rethinking your contact center strategy for 2026, this one’s worth a watch.

Key Discussion Points:

• The real reason CCaaS is saturated: Why traditional cloud migration is no longer enough, and what customers now expect.
• Connected intelligence over channel sprawl: How Sprinklr integrates social, voice, messaging, and AI into a single platform.
• The rise of agentic AI: What moving from reactive assistants to autonomous agents means for skills, roles, and workforce planning.
• Actionable insights, not just dashboards: How AI is shifting from descriptive to prescriptive, and even autonomous decision-making.

To explore Sprinklr’s unified CXM platform and how it enables smarter, scalable, AI-powered customer engagement, visit sprinklr.com

A loyal customer isn’t just someone who comes back. They spend more. Stay longer. Recommend faster. They open emails, tolerate hiccups, and ignore your competitors’ ads. They’re also far cheaper to retain than any lead your sales team is chasing right now.

Loyalty isn’t a lucky break. It’s the outcome of moments that go right consistently, and often quietly. A first experience that flows without friction. A support interaction that resolves more than just the issue. A product that keeps its promise. Each of these moments builds equity in the relationship.

When those touchpoints connect  across teams, systems, and time something stronger than repeat business takes shape. Customers begin to trust. They stick around, not because it’s the easiest option, but because the experience earns it.

What is Customer Loyalty?

Customer loyalty reflects a decision: the conscious choice to stay with a brand when alternatives are just a click away. It’s not just about satisfaction, plenty of satisfied customers churn. Loyalty runs deeper. It’s emotional, earned through consistency, value, and trust built over time.

In practical terms, loyalty shows when customers return after a poor experience, because they believe it’s the exception, not the norm. It shines when existing buyers refer peers, opt into updates, or upgrade without needing a discount.

But for enterprises, this isn’t a soft metric. It’s measurable, in retention rates, customer lifetime value, and referral growth. In fact, increasing customer retention by just 5% can boost profits by 25% to 95% depending on the industry. Loyalty doesn’t just pay off; it compounds.

Now, it matters more than ever. With CX as a key battleground, loyalty becomes a lead indicator of business resilience, and a hedge against rising acquisition costs.

The ROI of Customer Loyalty

Customer loyalty used to be a feel-good metric. Now it’s a board-level priority.

Retaining a customer isn’t just cheaper than winning a new one, it’s smarter. The cost of acquisition has spiked over 60% in the last five years, especially across digital channels. Meanwhile, repeat customers spend more, refer faster, and support brands longer, even when things go wrong.

The return is measurable:

• CAC Down, Margins Up: Brands with strong loyalty programs don’t need to outspend rivals on ads. Their customers come back organically. Acquisition costs are up to 7x higher than retention costs, and rising. Loyalty brings those numbers down.
• Predictable Revenue: Returning customers are more consistent. They know the product, trust the brand, and often skip the comparison stage altogether. That makes forecasting easier, pipelines more stable, and marketing spend more efficient.
• Loyalty = Resilience: In downturns, loyal customers stick. They’re more forgiving of glitches and slower to churn. A loyalty strategy isn’t just about growth, it’s about survival when market headwinds hit.
• Better Intelligence: Good loyalty tools are also listening tools. They track not just transactions, but behavior: redemptions, preferences, referrals, and feedback. That kind of data can feed customer journey strategies and help pinpoint why loyalty is rising or falling.
• Cross-Functional Buy-In: Loyalty isn’t a marketing-only game anymore. When programs sync with CRMs and support channels, they empower every team that touches the customer and help break down the silos that usually hurt CX.

What is Customer Loyalty Management?

Loyalty isn’t a byproduct of good service; it’s the result of managing relationships with intent. For enterprises, customer loyalty management is the discipline of designing and maintaining systems that keep the right customers coming back, staying longer, and contributing more value over time.

Loyalty doesn’t come from running rewards programs on cruise control. It starts with clarity; knowing who your most valuable customers are, what keeps them engaged, and how to stand out even when competitors promise more for less.

The best loyalty strategies don’t operate in a silo. They’re part of the broader customer experience engine, connected to feedback, support, product usage, and behavioural cues. Managed well, these strategies turn loyalty into a dynamic input, not just a passive output. It’s not a metric at the end of a funnel, it’s something built and reinforced at every stage of the journey.

Loyalty Management Tools and Platforms

The strongest tools today aren’t just managing point balances or sending birthday emails. They’re helping organizations understand loyalty as a behavior, not a program.

At a basic level, these platforms centralize loyalty data: engagement patterns, redemption activity, repeat purchase signals, and more. But the more advanced systems go further. They apply machine learning to spot early signs of churn, flag disengaged segments, and recommend next-best actions in real time.

What sets the leading loyalty management platforms apart is their ability to fit inside a broader CX tech stack. That means:

• Integrating with CRM to unify customer context
• Connecting to feedback loops for real-time insight
• Embedding in messaging infrastructure like CPaaS to deliver hyper-personalized moments that actually land

Many also support predictive analytics, using behavioral data to calculate loyalty risk scores, tailor rewards dynamically, or prompt human intervention when relationships are at risk.

How to Measure Customer Loyalty

Loyalty isn’t a single number. It’s a pattern, and like most patterns in enterprise CX, it takes a mix of metrics to see the full picture.

Behavioral signals still lead the pack. Metrics like repeat purchase rate, frequency of interaction, average order value, and churn give a direct read on what customers are doing, and where that behavior changes over time.

Behavioural signals often say more than surveys. A customer who slows their spending, skips repeat purchases, or stops logging in is sending a message. Something has shifted, in the experience, the product fit, or the perceived value.

Behaviour tells you what happened. But it won’t tell you why. That’s where customer sentiment comes into play.

Tools like Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) dig beneath the surface, giving teams a clearer sense of how customers actually feel about their experience. When behavioural dips show up, they offer the context needed to act fast, and fix the root cause before it costs more.

For many organizations, this layer is captured across touchpoints with VoC tools, then analyzed over time to correlate sentiment with spend or attrition.

What’s changing now is the rise of emotional loyalty metrics. These tools look beyond direct feedback, using conversational analysis, sentiment trends, and inferred emotional cues to understand attachment, not just satisfaction. It’s especially useful for brands competing on experience, not price.

Taken together, these data points create a more reliable model. Not just who’s loyal today, but who’s likely to stay, spend, and advocate tomorrow.

How to Choose Loyalty Management Software

The wrong loyalty platform won’t break a business, but it will stall progress. What looks slick in a demo can crumble under pressure if it can’t sync with existing systems, surface usable insights, or grow with you.

Enterprise teams evaluating loyalty management software need more than a feature checklist. They need to know how the tool will hold up six months in, with multiple departments relying on it.

Here’s what separates the useful from the disruptive:

True Integration

No platform works in isolation. If loyalty data sits in a separate bucket from customer service, CRM, or analytics tools, there’s a problem.

That means:

• Real-time syncing with CRM systems
• Alignment with VoC platforms
• Integrations with Business intelligence solutions

Most loyalty management platforms also seamlessly connect with CCaaS platforms, conversational analytics tools, and ERP software.

Dashboards That Get Used

Too many platforms surface metrics. Fewer tell you what they mean.

The strongest systems flag what matters: declining engagement from a once-loyal segment, a regional drop in redemption rates, churn triggers hiding in feedback. Ideally, these insights feed into broader customer intelligence tools.

Ask the vendor: When loyalty starts to dip, how will your platform show it, and who will know?

Scalability

Will it handle loyalty across multiple brands? Markets? Languages? Can it adapt to tiered models, emotional loyalty, partner programs?

Look for:

• Configurable logic, not hard-coded structures
• Clean admin interfaces for rule management
• Role-based controls that keep compliance teams comfortable

If it takes a developer to adjust a points rule, it’s not enterprise-ready.

Discover who’s driving results in the loyalty management software market here:

Best Practices for Improving Customer Loyalty

Loyalty doesn’t just emerge from a points program or a fun campaign. For enterprises, it’s a byproduct of consistent, intentional experience design, built into service flows, product strategy, data models, and frontline decision-making.

Build Feedback Loops That Actually Close

The fastest way to erode loyalty? Ignoring input – or worse, asking for it and doing nothing.

Instead of measuring feedback volume, measure action: How many product updates were driven by complaints? How often are support teams looped in to resolve themes emerging from surveys? Connect your loyalty program to customer feedback management tools that can drive real changes, not just reporting.

Use Tiering: But Don’t Let It Turn Transactional

Tiered loyalty still has its place, but only when it’s designed with purpose. Value shouldn’t just reflect spend. It should acknowledge engagement in all its forms. Early adopters, advocates, testers, even those who provide consistent feedback – they’re all part of the loyalty equation.

In B2B especially, tiers work best when they reflect mutual success. Think retention milestones, shared KPIs, or collaborative innovation, not just contract size.

Let AI Do More Than Segment

Yes, AI can slice customer cohorts faster. But real value comes when it flags what’s slipping before it shows up in churn.

Modern loyalty management tools increasingly come with predictive features: surfacing customers at risk of disengagement, nudging reps to check in, or adjusting loyalty offers based on sentiment and behavior patterns. Don’t just use AI to automate, use it to alert.

Tie Service Quality to Loyalty Outcomes

When loyalty starts to dip, it’s often not marketing’s fault, it’s a missed service expectation, or a support gap that never got escalated.

Bring loyalty and service metrics closer together. Track whether NPS dips after a long resolution time. Monitor whether loyalty program members get faster assistance, and whether that’s noticed.

Reward the Behavior You Want More Of

Discounts create habits, and not always good ones. If you reward spend alone, you build deal-seekers, not advocates.

Instead, reward the moments that drive growth:

• Referrals
• Feedback submitted
• Community contributions
• Self-service engagement
• Event participation

Loyalty isn’t a transaction, it’s a signal. Recognize the signals that drive real business value.

Localize Where It Matters

For multinational brands, loyalty can’t be global by default. Preferences shift by market, so should campaigns.

Consider:

• Local holiday-based promotions
• Regional tier naming conventions
• Local influencers or ambassadors

Global strategy. Local flavor. That balance keeps loyalty human.

Customer Loyalty Management + Service: The Critical Link

Loyalty doesn’t just live in a dashboard or a rewards app. It’s won or lost in moments that often feel small: a delivery delay, a billing dispute, a misunderstood policy. The way a brand responds in these moments is often more influential than any discount or points tier.

And that makes customer service a cornerstone of customer loyalty management.

When Service Is Seamless, Loyalty Feels Earned

Customers don’t demand flawlessness. But they do expect clarity, speed, and respect when things go wrong. Loyalty isn’t tested during moments of delight, it’s tested when something breaks. Support teams who can see a customer’s history, loyalty status, and previous interactions don’t just fix problems faster. They solve them with more context, more care, and often, more impact.

This is where integration matters:

• CRM systems should surface loyalty data
• CPaaS platforms can enable proactive outreach
• Ticketing systems can reflect VIP status or churn risk

Proactive Service = Preventative Loyalty Loss

The best loyalty moves aren’t reactive. They’re invisible, because the problem was handled before the customer noticed.

For example:

• Flagging shipping delays and sending apologies before the complaint
• Alerting high-value customers when products they love are low in stock
• Following up after negative sentiment is detected in chatbot interactions

This requires orchestration. But the payoff is reduced escalation volume, increased trust, and loyalty built on more than transactions.

Empower Agents Like They’re Brand Ambassadors

Loyalty lives or dies with the agent experience. If the frontline team feels unsupported, overworked, or stuck with legacy tools, they can’t deliver the kind of service that loyalty depends on.

Modern workforce engagement platforms are helping here, giving agents better training, clearer knowledge bases, and visibility into customer journeys. This isn’t just an ops upgrade, it’s a loyalty investment.

Customer Loyalty Management Trends to Watch

Enterprise loyalty strategies evolve with the customer, and the customer continues to change.

Over the past two years, loyalty has shifted from tactical marketing add-on to boardroom-level priority. Why? Because retention has become the fastest route to stable revenue.

Here’s what’s changing right now.

• Loyalty Is Getting Smarter: Rather than shouting about rewards, top brands are building invisible loyalty, systems that work behind the scenes, adjusting experiences based on behavior, purchase history, and product use. The loyalty isn’t in the point balance. It’s in the recognition. AI and predictive analytics are playing a bigger role here, helping teams act on churn signals before the customer ever says a word.
• Emotional Loyalty Takes the Lead: Price cuts don’t build loyalty. They build expectations. Enterprise buyers are shifting from transactional incentives to emotional loyalty strategies, things like exclusive experiences, consistent service, and values-based alignment. In B2B markets, that might look like strategic co-development, VIP access to product roadmaps, or account-based reward systems.
• Loyalty Hardwired Into CX: The strongest loyalty programs don’t operate in isolation. They’re woven into the wider customer experience stack, touching CRM, CPaaS, contact center platforms, and data systems. This allows brands to reward customers in real time, based on meaningful actions, not just spend.
• Consent-First Design: The days of collecting data “because we can” are over. Modern loyalty programs are being rebuilt around trust and transparency. That means clear value exchanges, upfront permissions, and control for the customer. Loyalty is no longer about how much data you can gather, it’s about how responsibly you use what you have.

Customer Loyalty Management Beyond the Transaction

Customer loyalty isn’t a finish line. It’s an ongoing, intentional outcome earned across every interaction, reinforced with every decision, and protected by every system put in place.

For enterprise teams, managing that loyalty means more than launching a rewards program. Managing loyalty well means making it easier for customers to stay than to leave. That’s not about discounts or perks, it’s about designing experiences that feel effortless, relevant, and personal.

Whether the goal is improving retention, boosting lifetime value, or gaining a clearer view of customer behaviour, the right strategy starts with the right tools, and the right insights.

CX Today offers a range of resources to help enterprise teams build loyalty systems that actually move the needle:

• Explore the Marketplace: Compare top loyalty management vendors with features tailored for growth, data integration, and security at scale.
• Join the Community: Learn how CX and marketing leaders across industries are evolving loyalty strategies in the CX Community.
• Track What’s Changing: Follow new developments in AI-powered loyalty, cross-channel experience design, and customer journey intelligence with research reports.

See how loyalty fits into the broader CX ecosystem. Visit our Ultimate CX Guide for a practical deep dive into the people, platforms, and processes driving customer-led growth.

Will Salesforce’s Informatica Acquisition Make Agentforce Unstoppable?

Salesforce has announced the acquisition of Informatica.

First reported back in May, the purchase has now officially been confirmed for approximately $8BN.

The deal will see Salesforce leverage Informatica’s AI-powered cloud data management capabilities to improve its agentic AI offerings – most notably, the Agentforce platform.

Alongside the data catalog, Salesforce will also gain access to Informatica’s integration, governance, quality and privacy, metadata management, and Master Data Management (MDM) services.

The end goal is to use these capabilities to build a unified data foundation for agentic AI, enabling safe, responsible, and scalable AI agents across the enterprise.

Indeed, in discussing the news, Salesforce Chair and CEO Marc Benioff described data and context as the “true fuel of Agentforce.”

“When companies get their data right, they get their AI right, and Agentforce becomes unstoppable.”

In terms of the specifics, Salesforce also detailed how Informatica’s capabilities will sharpen its Data 360 feature… (Read more).

Cloudflare Outage Disrupts Major Platforms, Payments, and Black Friday Plans

It’s becoming a familiar story: A technical glitch at Cloudflare, one of the biggest internet infrastructure providers, knocked a number of websites and services offline for a few hours on November 18, disrupting customer access and merchant payments.

X (formerly Twitter), ChatGPT, Claude, Perplexity, Spotify and payment giant Square were among those caught up in the fallout.

The trouble began just before 11:48 GMT, when Cloudflare posted that it was dealing with an “internal service degradation” causing intermittent outages across its service network. Users saw error pages, stalled logins, broken APIs, and sites claiming connections were blocked. There were a few conflicting signals about the restoration progress, as at one stage the company reported that services were beginning to recover, but then around 15 minutes later reverted to “continuing to investigate this issue.”

By 13:04 GMT, Cloudflare admitted that one of its fixes involved disabling WARP access in London entirely, temporarily cutting off users from its WARP performance-boosting and VPN service that helps secure and accelerate internet connections:

“During our attempts to remediate, we have disabled WARP access in London. Users in London trying to access the Internet via WARP will see a failure to connect.”

Cloudflare announced a fix five minutes later, but continued to receive “reports of intermittent errors” until close to 17:00 GMT… (Read more).

Five9 Targets CX Inefficiencies with New Genius AI Upgrades

Five9 has introduced a fresh wave of Genius AI updates designed to push the company’s “Agentic CX” vision further into the contact center core.

Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

So, let’s take a closer look at Five9’s newest features… (Read more).

UJET Acquires Spiral to Address Customer Data Analysis Roadblocks

UJET has announced its acquisition of Spiral to bolster its AI capabilities.

The AI startup will allow UJET to continue its AI roadmap for enhanced customer service solutions.

This partnership will also address customer data analysis issues for UJET’s enterprise customers.

This acquisition is set to further UJET’s AI roadmap vision by bolstering the company’s AI capabilities and addressing customer experience concerns.

By highlighting these issues of visibility between customer and leader, organizations will be able to improve their customer issues before they reach escalation.

In fact, UJET has reported that organizations that are unaware of these individual customer problems are losing approximately $5MN-$30MN in customer churn revenue.

This can be linked to ignored or forgotten negative customer experience complaints, with organizations reportedly gathering only five percent of reported customer issues.

According to UJET CEO, Vasili Triant, customer churn remains a blind spot for many enterprises, arguing that customer interaction analysis is not done effectively… (Read more).

Announced at the company’s CX Summit in Nashville, the new capabilities span routing, quality management, analytics, and digital engagement, tying them more closely together to help organizations extract greater value from AI at scale.

As many enterprises attempt to take AI from pilot projects into day-to-day operations, fragmentation continues to slow progress.

Disconnected data, inconsistent reporting, and standalone AI experiments often make it difficult to achieve the continuous improvement leaders expect.

Five9’s latest releases aim to combat these challenges by treating AI not as an add-on but as the connective layer running across the environment.

Five9 Chief Product Officer Ajay Awatramani framed the shift as a more fundamental rethinking of how AI should function inside the contact center:

“Our Agentic CX vision is about creating systems that don’t just respond but also help teams better understand and anticipate customer needs.”

“With these innovations, AI moves from the periphery to the core of the contact center – linking data, people, and processes into a system more closely embedded with contact center operations in ways intended to support continuous learning, adaptation, and more efficient and meaningful customer experiences.”

So, let’s take a closer look at Five9’s newest features.

Four New Innovations in the Genius AI Suite

Agentic Quality Management (AQM)

Five9’s new quality management approach is designed to evaluate up to 100% of customer interactions.

The model focuses on generating insights that can inform routing decisions, coaching programs, and ongoing performance improvement.

Rather than operating as a separate analysis tool, AQM feeds data directly into other parts of the Genius AI ecosystem.

Genius Routing

This updated routing engine uses defined customer attributes, agent skills, and proficiency levels to determine the best match for each interaction

Because it can draw on real-time inputs from AI performance systems and self-service applications, it becomes easier for organizations to deliver faster resolutions and more personalized experiences.

OneVUE Analytics

OneVUE builds on Aceyus VUE technology but packages it in a more approachable, self-service analytics experience. Users can assemble custom dashboards with flexible metrics that cover both traditional CCaaS operations and multi-vendor environments.

It is built with AI-ready reporting in mind, giving teams a clearer view of the KPIs shaping customer interactions.

Adaptive Digital Engagement

The digital engagement update introduces a no-code Dynamic Web Messenger Configurator, allowing teams to launch or adjust webchat experiences instantly.

Five9 also revealed a new partnership with Meta, enabling native WhatsApp connectivity with template support, broadcasts, and AI Agents. The broader goal is to give organizations modular digital channels that plug easily into Five9’s AI layer.

Early Customer Feedback

Northwestern Mutual is among the customers already experimenting with the new capabilities.

Eric Schanno, the company’s Principal Solutions Engineer, explained how leveraging Five9 had allowed Northwestern Mutual to “move faster from insight to action.”

“We are very excited about the potential of Five9 AQM to help us elevate coaching across 100% of interactions. And OneVUE gives us a single source of truth for the metrics that matter.

“The combination is raising the bar on performance and helps deliver more consistent, high-quality experiences for our customers.”

A Step Toward Fully Agentic CX

Five9’s message seems to be that the next stage of CX transformation isn’t just about adopting AI tools, but weaving AI into the operational fabric of the contact center.

These Genius AI enhancements move the platform further in that direction, giving organizations a more unified foundation for delivering adaptive, insight-driven customer experiences.

More News from Five9

Afiniti has teamed up with Five9 to bring its AI Pairing technology into the Five9 Intelligent Cloud Contact Center, signalling a step forward for more sophisticated, behind-the-scenes AI in CX.

Rather than placing AI directly in front of customers, Afiniti analyses behavioural and contextual signals in the background to match each caller with the agent most likely to deliver a successful outcome. In effect, it acts as a matchmaking engine for the contact center.

IDC’s new MarketScape points to rapid momentum in Europe’s CCaaS sector, forecasting growth from $1.5BN in 2024 to $3.7BN by 2029 – a 20 percent CAGR.

Five9 is highlighted as a market leader, backed by its AI-driven platform, solid compliance credentials, and strategy tailored to European requirements.