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.

The problem usually isn’t the model. It’s the data behind it. When knowledge bases are out of date, fragmented, or inconsistent, even the smartest AI will confidently generate the wrong answer. This is why knowledge base integrity and RAG governance matter more than model size or speed.

The urgency is clear. McKinsey reports that almost all companies are using AI, but only 1% feel they’re at maturity. Many also admit that accuracy and trust are still major barriers. In customer experience, where loyalty is fragile, a single hallucination can trigger churn, compliance headaches, and reputational fallout.

Leading enterprises are starting to treat hallucinations as a governance problem, not a technical one. Without governed data, AI becomes a liability in CX. With it, organizations can build automation that actually strengthens trust.

What Are AI Hallucinations and What Causes Them?

When customer-facing AI goes off-script, it usually isn’t because the model suddenly turned unreliable. AI hallucinations in CX happen when the system fills gaps left by bad or missing data. Picture a bot telling a customer they qualify for same-day refunds when the actual policy is 30 days. That’s not creativity, it’s a broken knowledge base.

Hallucinations tend to creep in when:

• Knowledge bases are outdated or inconsistent, with different “truths” stored across systems.
• Context is missing, for example, an AI forgetting a customer’s purchase history mid-conversation.
• Validation checks are skipped, so the bot never confirms whether the answer is still correct.

The risks aren’t small. 80% of enterprises cite bias, explainability, or trust as barriers to using AI at scale. In CX, inaccuracy quickly turns into churn, complaints, or compliance headaches.

There are proven fixes. Enterprises just need to know what to implement before they go all-in on agentifying the contact center.

The Real-World Impact of AI Hallucinations in CX

The stakes around AI hallucinations in CX translate directly into lost revenue, churn, and regulatory risk. A bot that invents refund rules or misstates eligibility for a benefit doesn’t just frustrate a customer – it creates liability.

Some of the impacts seen across industries:

• Retail: Misleading warranty responses trigger unnecessary refunds and drive shoppers to competitors.
• Public sector: Incorrect entitlement checks leave citizens without services they qualify for.
• Travel: Fabricated policy details can mean denied boarding or stranded passengers.

The financial burden is real. Industry analysts estimate that bad data costs businesses trillions globally each year, and the average cost of a single data-driven error can run into millions once churn and remediation are factored in.

Case studies show the impact, too. Just look at all the stories about ChatGPT, creating fictitious documents for lawyers, or making up statements about teacher actions in education. Every hallucination is a reminder: without knowledge base integrity and RAG governance, automation introduces more risk than reward. With them, AI becomes a growth driver instead of a liability.

Why Hallucinations Are Really a Data Integrity Problem

It’s tempting to think of AI hallucinations in CX as model failures. In reality, they’re usually symptoms of poor data integrity. When the information feeding an AI is out of date, inconsistent, or fragmented, the system will confidently generate the wrong answer.

Knowledge base integrity means more than just storing information. It’s about ensuring accuracy, consistency, and governance across every touchpoint. Without that, CX automation is built on sand.

Common breakdowns include:

• Outdated articles: A policy change goes live, but the bot still cites the old rules.
• Conflicting records: Multiple “truths” for the same customer, leading to contradictory answers.
• Ungoverned logs: Data pulled in without privacy controls, creating compliance exposure.

Some organizations are already proving the value of treating hallucinations as governance problems. Adobe Population Health saved $800,000 annually by enforcing stronger data controls, ensuring agents and AI systems pulled only from validated knowledge sources.

Building the Foundation: Clean, Cohesive Knowledge

Solving AI hallucinations in CX starts with building a solid data foundation. No model, no matter how advanced, can perform reliably without knowledge base integrity. That means every system, from the CRM and contact center platform to the CDP – has to point to the same version of the truth.

A few steps make the difference:

• Unified profiles: Use CDP to connect IDs, preferences, and history across systems. Vodafone recently reported a 30% boost in engagement after investing in unified profiles and data quality.
• Agent-ready records: Golden IDs, schema alignment, and deduplication stop bots from improvising. Service accuracy depends on knowing which record is the right one.
• Data freshness: Expired knowledge is one of the fastest routes to hallucination. Setting SLAs for update frequency ensures AI doesn’t serve answers that are weeks, or years, out of date.
• Governance layers: Microsoft’s Purview DLP and DSPM frameworks, for example, help enforce privacy boundaries and ensure sensitive data is never exposed to customer-facing AI.

Clean, governed data is what allows automation to scale safely. In fact, Gartner notes that automation without unified data pipelines is one of the leading causes of failure in AI deployments.

The lesson is clear: AI only works if the underlying knowledge is accurate and consistent. RAG governance begins not at the model layer, but in how enterprises treat their data.

Choosing Your LLM Carefully: Size Isn’t Everything

When automating CX workflows, the assumption that “bigger means better” often backfires. In fact, purpose-built, smaller language models can outperform broad, heavyweight counterparts, especially when they’re trained for specific customer service tasks.

Here’s what’s working:

• Smaller, tailored models excel at soft-skill evaluations. In contact center hiring, they outperform general-purpose LLMs simply because they understand the nuances of human interaction better.
• Efficiency is a major advantage. Smaller models require fewer computational resources, process faster, and cost less to run, making them ideal for real-time CX workflows.
• They also tend to hallucinate less. Because they’re fine-tuned on targeted data, they stay focused on relevant knowledge and avoid the “overconfident bluffing” larger models can fall into.
• Distillation, teaching a smaller model to mimic a larger “teacher”, is now a common technique. It delivers much of the performance without the infrastructure cost.

Choosing the right model is a strategic decision: smaller, domain-specific models support RAG governance and knowledge base integrity more effectively, without blowing your budget or opening new risks.

RAG Governance: Why Retrieval Can Fail Without It

Retrieval-augmented generation (RAG) has become a go-to strategy for tackling AI hallucinations in CX. Companies like PolyAI are already using RAG to make voice agents check against validated knowledge before replying, cutting down hallucinations dramatically.

Instead of relying only on the model’s training data, RAG pulls answers from a knowledge base in real time. In theory, it keeps responses grounded. In practice, without proper RAG governance, it can still go wrong.

The risks are straightforward:

• If the knowledge base is outdated, RAG just retrieves the wrong answer faster.
• If content is unstructured, like PDFs, duplicate docs, or inconsistent schemas, the model struggles to pull reliable context.
• If version control is missing, customers may get different answers depending on which copy the system accessed.

That’s why knowledge base integrity is critical. Enterprises are starting to use semantic chunking, version-controlled KBs, and graph-RAG approaches to make sure AI agents retrieve the right data, in the right context, every time.

Vendors are also moving quickly. Google Vertex Agent Builder, Microsoft Copilot Studio’s RAG connectors, and open-source projects like Rasa’s extensions are designed to enforce cleaner retrieval pipelines. Companies like Ada are proving that governed RAG can cut down false answers in sensitive workflows like background checks.

RAG is powerful, but without governance, it risks becoming a faster way to spread bad information. Grounding AI in trusted, validated sources, through structured retrieval and strong RAG governance, is the difference between automation that builds trust and automation that erodes it.

The Model Context Protocol for reducing AI hallucination

Even with RAG governance, there’s still a missing piece: how the model itself connects to external tools and data. That’s where the Model Context Protocol (MCP) comes in. MCP is emerging as a standard that formalizes how AI systems request and consume knowledge, adding a layer of compliance and control that CX leaders have been waiting for.

Without MCP, connectors can still pull in unreliable or non-compliant data. With MCP, rules can be enforced before the model ever sees the input. That means:

• Version control: AI agents only access the latest, approved policies.
• Schema validation: Data must meet format and quality checks before it’s used.
• Integrity enforcement: Broken or incomplete records are automatically rejected.

This is particularly relevant in regulated industries. Financial services, healthcare, and the public sector can’t risk AI fabricating eligibility or compliance-related answers. MCP provides a structured way to prove governance at the system level.

Vendors are already moving in this direction. Salesforce’s Agentforce 3 announcement positioned governance and compliance as central to its next-generation agent framework. For CX leaders, MCP could become the difference between AI that “sounds right” and AI that is provably compliant.

Smarter Prompting: Designing Agents to Think in Steps

Even with clean data and strong RAG governance, AI hallucinations in CX can still happen if the model is prompted poorly. The someone asks a question shapes the quality of the answer. That’s where smarter prompting techniques come in.

One of the most effective is chain-of-thought reasoning. Instead of pushing the model to jump straight to an answer, prompts guide it to reason through the steps. For example, in a travel entitlement check, the AI might be told to:

• Confirm eligibility rules.
• Check dates against the customer record.
• Validate exceptions before giving a final response.

This structured approach reduces the chance of the AI skipping logic or inventing details to “sound confident.”

Other strategies include:

• Context restating: Have the model summarize customer inputs before answering, to avoid missing key details.
• Instruction layering: Embedding guard phrases like “If unsure, escalate” directly into prompts.

Better prompting changes how the AI reasons. Combined with knowledge base integrity and retrieval grounding, thoughtful prompt design is one of the simplest, most cost-effective ways to cut hallucinations before they ever reach a customer.

Keeping Humans in the Loop: Where Autonomy Should Stop

AI is getting better at handling customer requests, but it shouldn’t be left to run everything on its own. In CX, the cost of a wrong answer can be far bigger than a frustrated caller. A single AI hallucination in CX around something like a loan decision, a medical entitlement, or a refund policy can create compliance risks and damage trust.

That’s why most successful deployments still keep people in the loop. Routine questions like order status, password resets, and warranty lookups are safe to automate. But when the stakes rise, the system needs a clear off-ramp to a human; no company should try to aim for limitless automation.

There are simple ways to design for this:

• Flagging low-confidence answers so they’re routed to an agent.
• Escalating automatically when rules aren’t clear or when exceptions apply.
• Training models with reinforcement from human feedback so they learn when to stop guessing.

Real-world examples prove the value. Ada’s work with Life360 showed that giving AI responsibility for repetitive queries freed agents to focus on tougher cases. Customers got faster answers when it mattered most, without losing the reassurance of human judgment for sensitive issues.

The lesson is straightforward: automation should extend, not replace, human service.

Guardrail Systems: Preventing AI hallucination

AI can be fast, but it still needs limits. In customer service, those limits are guardrails. They stop automation from giving answers it shouldn’t, even when the data looks clean. Without them, AI hallucinations in CX can slip through and cause real damage.

Guardrails take different forms. Some block responses if the system isn’t confident enough. Others make sure refund rules, discounts, or eligibility checks stay within company policy. Many firms now add filters that catch bias or toxic language before it reaches a customer.

The goal isn’t perfection. It’s layers of protection. If one check misses an error, another is there to catch it. Tucan.ai showed how this works in practice. By adding guardrails to its contract analysis tools, it cut the risk of misinterpreted clauses while still saving clients time.

For CX teams, guardrails aren’t about slowing automation down. They’re about trust. Customers need to know that the answers they get are safe even when they come from a machine.

Testing, Monitoring, and Iterating

AI systems drift. Policies change, data updates, and customer expectations move quickly. Without regular checks, those shifts turn into AI hallucinations in CX.

Strong CX teams treat testing and monitoring as part of daily operations. That means:

• Running “red team” prompts to see how an agent handles edge cases.
• Tracking hallucination rates over time instead of waiting for customer complaints.
• Comparing different prompts or retrieval methods to see which reduces errors.

Enterprises are starting to put this discipline into place. Retell AI cut false positives by 70% through systematic testing and feedback loops. Microsoft and others now offer dashboards that log how models use data, making it easier to spot problems early.

The principle is straightforward. AI is not a one-off project. It’s a system that needs continuous oversight, just like a contact center workforce. Test it, measure it, refine it.

The Future of AI Hallucinations in CX

Customer experience is moving into a new phase. Contact centers are no longer testing basic chatbots. They are rolling out autonomous agents that can manage full interactions, from checking an order to triggering a refund. Microsoft’s Intent Agent, NICE’s CXone Mpower, and Genesys’ AI Studio are early examples of that shift.

The upside is clear: faster service, lower costs, and better coordination across systems. The risk is also higher. A single AI hallucination in CX could mean a compliance breach or a reputational hit that takes years to repair. Regulators are watching closely. The EU AI Act and ISO/IEC 42001 both push for stricter rules on governance, transparency, and accountability.

The market is responding. Salesforce’s move to acquire Convergence.ai and NiCE’s purchase of Cognigy show how major vendors are racing to build platforms where governance is built in, not added later. Enterprises want systems that are safe to scale, not pilots that collapse under risk.

The reality is that hallucinations won’t disappear. Companies will need to learn how to contain them. A strong knowledge base, integrity, tight RAG governance, and frameworks like MCP will differentiate brands that customers trust from those they don’t.

Eliminating AI Hallucinations in CX

The risk of AI hallucinations in CX is not going away. As enterprises scale automation, the cost of a wrong answer grows, whether that’s a compliance breach, a lost customer, or a dent in brand trust.

The good news is that hallucinations are not an unsolvable problem. They’re usually data problems. With a strong knowledge base, integrity, clear RAG governance, and frameworks like MCP to enforce compliance, organizations can keep automation reliable and safe. Guardrails, smarter prompting, and human oversight add further protection.

Together, these measures turn AI from a liability into an asset. Companies that treat governance as central will be able to roll out advanced agents with confidence. Those that don’t risk being left behind.

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.

Christian Draeger, who runs passenger experience there, talks about it in a way that’s surprisingly down-to-earth. “We’re not just starting at the airport door,” he says. “We’re already looking at customers, how they can get prepared for their travel, even days ahead of the actual travel plans.”

That’s a different way of thinking about travel, one where the airport is part of the journey, not a pause in it. Draeger’s rule is simple: “Put the passenger in the center.”

That idea is becoming more important. Around two-thirds of travelers now use AI tools to plan their trips, and most say they want services that adjust to them, not the other way around. Berlin’s answer is to mix technology with empathy, using automation to remove hassle, not humanity, and turn the everyday airport routine into something that actually works for people.

Understanding & Designing for the Modern Traveler

Christian Draeger has spent a lifetime around airports. More than thirty years in aviation have given him a deep sense of how people move, wait, and connect. During his time with Star Alliance, he helped shape what millions of passengers now recognize as the modern travel experience. When he joined Berlin Brandenburg Airport, he came in ready to rethink that experience from the ground up.

Berlin handles around 25 million passengers a year, so it’s big enough to be busy, but small enough to still care. “We also operate our premium services: two business-class lounges and an ultra-premium lounge where you get à la carte dining and a chauffeur service to the aircraft,” Draeger said.

That same care for detail extends to the parts of the journey most people barely notice. The airport also took control of its own security operations, because, as Draeger puts it, “We felt that the mandate of the federal police didn’t provide enough attention towards the passenger experience.”

Now there are 32 security lanes, 24 fitted with advanced CT scanners, so passengers can keep laptops in their bags and carry small amounts of liquid without delay. “It’s about having a consistent experience across the whole area of the airport,” he says.

Every choice is made with the passenger in mind. “It starts really by knowing our customers,” Draeger says. “If we have a family that’s traveling once a year on holiday, their prerogatives are different from a business-class customer focused on getting through as efficiently as possible.”

That balance, efficiency for some, discovery for others, is at the heart of personalization in travel, and it’s essential. A recent study found that 93 percent of travelers now expect some form of tailored service. Berlin’s approach proves those numbers translate into real-world design decisions: better security flow, less queuing, and even duty-free areas reimagined as “specialized marketplaces.”

Dual-Terminal Strategy: Two Philosophies, One Vision

A walk through Berlin Brandenburg Airport reveals something a bit different. Its two terminals don’t just separate airlines; they reflect two completely distinct types of travelers. One is designed for comfort, the other for speed. Together, they show how personalization in travel can be built into the physical space, not only into digital systems.

“The level of automation that you will find with low-cost carriers is more in focus than with a legacy carrier,” says Draeger. Terminal 2 is the efficient, minimalist one: smaller, sharper, and geared toward travelers who value simplicity and price over perks. “Terminal 2 is geared to simplicity and generating additional revenues through add-on services,” he explains.

Think self-service kiosks, intuitive wayfinding, and a layout that helps people move quickly from curb to gate. “The utilization of busses is less, you have more walk boardings,” he adds.

Terminal 1, meanwhile, is a different rhythm altogether. “It’s about efficiency and comfort, both guided by digital tools.” Business and frequent flyers pass through airport automation that’s designed to make the process seamless. Over a hundred self-service kiosks are spread across the terminal, complemented by digital signage and premium lounges.

It’s the physical version of a digital truth: no two passengers want the same thing. Some want to breeze through with a coffee and a boarding pass on their phone. Others want time, space, and a glass of something cold before they fly. Both deserve an experience that feels intentional.

That’s what Berlin is building, a new kind of airport customer experience where infrastructure itself becomes a form of personalization. Different terminals, different tools, same philosophy: know who’s traveling, and design accordingly.

AI and Automation Enhancing Personalization in Travel

Like most airports, Berlin once relied on a traditional call center. It worked, but just barely. “We were looking at our call center and we weren’t completely happy,” says Draeger. “It was limited, inconsistent, and expensive.”

That frustration turned into an opportunity. Berlin decided to replace its call center entirely with a generative AI-powered system. The result was “Berry”, Berlin’s always-on virtual assistant.

“Customers can call the AI hotline and have a conversation just like we’re having right now,” Draeger says. It took just six weeks to build and launch, and within a few months, the results were striking: satisfaction above 85 percent, costs down 65 percent, and service available 24/7.

The human element didn’t vanish; it just found a new home. Instead of waiting in phone queues, travelers get answers right away. Lost something? Need flight details? Berry, the airport’s AI agent, takes care of it and loops in a person if the question needs a human touch. It’s simple to use too: one phone number on Berlin Airport’s website connects straight to Berry.

Building the AI Layer with Berry

Behind the scenes, Berry learns fast. “After six to eight weeks we reached an acceptable level… then you could see week-to-week improvements as GenAI learned,” Draeger explains. His team fed the system with real passenger questions and prioritized the most urgent topics first, like the classic “I left my laptop on the aircraft.” “We prioritized major customer concerns to ensure correct routing from day one,” he says.

Now the airport is preparing for the next step, chat. “We want to also offer the ability to get in touch with our AI agent through chat functionality,” says Draeger. QR codes will soon appear throughout the terminals, linking passengers directly to Berry via chat, integrated into the website and app. “If you’re standing in the arrivals hall, we’ll know based on the QR code where you are, and tailor the information accordingly.”

The idea is to build truly contextual assistance: a passenger in departures might ask about gate directions or restaurants, while someone at baggage reclaim could get help locating transport or lost luggage. “Customers can switch between voice and chat depending on environment or age. My children would prefer to talk; someone in a crowded terminal might prefer to chat,” Draeger says.

Operational AI and the Quest for Seamlessness

A lot of what makes Berlin Brandenburg Airport work isn’t something you can see. It happens on the tarmac between the terminal and the runway, where planes turn around for their next flight, and timing is everything.

“We also have others more on the ramp side,” says Draeger, referring to a system the airport now uses to track ground operations in real time. Cameras watch every stage of the turnaround, feeding data to an AI that predicts how long the process will take and where it might go wrong. “They can predict turnaround durations and steer additional resources if required,” he explains. “If a baggage belt is missing upon arrival, they can autonomously act on that and resolve bottlenecks.”

This is the kind of work that truly shapes the airport customer experience. When flights leave as scheduled, lines move faster, and connections fall into place without drama. Most travelers never think about the coordination behind it all. Yet every new piece of technology adds a layer that must fit perfectly with the rest.

But every new layer of technology brings its own challenge. “We always want to have this seamless experience for our customers,” Draeger says. “As we introduce more technology, we’ll have the challenge of combining it with legacy systems.”

Airports, after all, are built to last, and that means old baggage systems, decades-old software, and miles of wiring that can’t just be swapped overnight. “Traffic is increasing significantly, and we have limited infrastructure,” he adds. “We need simpler processes and better technology to absorb growth.”

Behind the polished front end of any airport automation project lies a balancing act: new tools talking to old systems, innovation working around concrete and cables.

The Future for Personalization In Travel: Digital Handholding

When asked what he thinks the future of travel looks like, Christian Draeger doesn’t mention drones or driverless terminals. He talks about something far simpler: help that is steady, thoughtful, and personal. “We always like to call it digital handholding,” he says. “A digital entity that’s completely informed, taking the customer by the hand and guiding them through the journey.”

Many agree that this is exactly where AI in the travel industry is heading. Gartner predicts that more than 80% of all customer interactions will be AI-assisted by 2029. The difference now is how personal that assistance can become.

“In the future, we see customers having their own personalized digital agents,” Draeger says, “on mobile, VR glasses, or other interfaces.” Those agents will be able to do a lot. “They’ll be able to rebook flights, change hotels, handle issues,” he explains. “We’ll need to provide them with the knowledge base and interconnectivity so they can act.”

He describes a world where these personal assistants talk to each other. “We’ll see a marketplace developing for agent-to-agent interaction,” he says, a network where your digital travel companion can speak directly to an airline, a hotel, or even the airport itself to smooth out the details before you notice them.

Some of that is already visible in small ways. Berlin is already imagining using augmented reality to help people find their way through the terminal. “If you come to Berlin Airport, sometimes you’ll find too many information boards,” Draeger admits. “Imagine augmented reality guiding you through the airport.”

It’s easy to see where this leads: toward an airport customer experience that blends technology with intuition. The idea isn’t to overwhelm passengers with data, but to take away the stress of travel entirely.

Personalization in Travel and Airports as Experience Ecosystems

Christian Draeger talks about air travel the way some people talk about music, not as noise and movement, but as rhythm. Airports, he says, are meant to keep that rhythm steady. When they do, everything else feels effortless.

“It’s all about making travel easier,” he says. “Like when you take a train, you just arrive and go, that’s the overarching ambition.”

Mostly, Berlin Brandenburg Airport is just pushing for a calmer travel experience. From the moment a traveler checks in to the moment they leave the gate, the goal is to take away the small frictions that make airports stressful. Berry, the AI voice agent, is part of that. So are the self-service kiosks, the CT-scan security lanes, and the quiet bits of software that keep aircraft turning on time.

“It’s not about one technology: Gen AI, robotics, biometrics, or AR,” Draeger says. “It’s about combining them to make travel much simpler.”

That line sums up Berlin’s whole approach to personalization in travel. It isn’t about showing off what technology can do; it’s about how little the traveler has to notice it.

That’s the real future of airport customer experience: an ecosystem that looks complicated underneath but feels beautifully ordinary on the surface, the kind of simplicity only achieved when someone’s been obsessing over every detail on your behalf.

A decade ago, CDPs were viewed as a niche tool for marketing ops teams. Today, they’re being re-evaluated as strategic infrastructure for organizations struggling with data chaos, regulatory uncertainty, and fragmented customer experiences.

Customer journeys don’t follow scripts anymore. A buyer reads an email in the morning, taps a push notification at lunch, then calls support before checkout. Every system sees a piece of that journey. Most don’t talk to each other.

That’s the problem CDPs are built to solve.

They pull in customer data from all over and connect it into one place. Marketers use the system to personalize. Analysts use it to predict. Legal teams use it to track consent and data handling.

With the market for CDP platforms now growing at a rate of 39.5% CAGR, there’s never been a better time for enterprises to start exploring their options, or learning how they work.

What Is a Customer Data Platform (CDP)?

A customer data platform is a solution that brings customer data together. It’s not just storing that data, it’s making usable, and useful.

According to the CDP Institute, a true Customer Data Platform is “packaged software that creates a persistent, unified customer database that is accessible to other systems.” That’s the technical baseline. In practice, it means something simpler: less chaos, more clarity.

CDP software collects data from tools already in play: websites, mobile apps, CRM systems, emails, support tickets, even point-of-sale.

The data isn’t trapped in dashboards. It moves across systems, teams, and use cases. A CDP can trigger a personalized message mid-session, route a high-value lead to the right rep, or alert support before a customer churns. Today, there are various types of CDPs:

• Traditional CDPs: The all-in-one platforms that combine ingestion pipelines, identity resolution, segmentation dashboards, integrations, and governance modules. This approach works well for companies looking for speed and simplicity. Everything is pre-integrated
• Composable CDPs: Here the CDP acts more like a control layer. The core data stays in your warehouse and the CDP connects to it. This model separates storage from activation. It gives IT full control over infrastructure, while still letting marketing teams build audiences, launch campaigns, and pull insights from unified profiles.
• Agentic CDPs: Agentic CDPs don’t just unify and activate data, they make decisions. Using AI, they can monitor customer behavior, score intent, generate segments, and trigger journeys automatically. Some route decisions to external systems like CPaaS tools or contact center workflows, adjusting messages or offers in real time based on what a customer does.

The real value of all types? A single customer view that doesn’t sit in theory. It works in real time, across every customer touchpoint, for every team that needs it.

How Does a Customer Data Platform Work?

A Customer Data Platform connects systems that usually don’t speak to each other. It takes scattered data, from marketing, sales, support, product, even in-store, and turns it into a profile that actually makes sense. Then it makes that profile useful.

Most CDPs follow the same core steps.

Step One: Ingest

Data shows up from everywhere. Emails, apps, websites, POS systems, CRMs, ads, chatbots, third-party sources. Even offline events.

A good CDP doesn’t care where it came from or what format it’s in. Structured or messy, real-time or batch, it pulls it in.

Some tools now ingest straight from cloud warehouses like Snowflake. Others tap into event pipelines or use SDKs to capture behavior in-session.

Step Two: Resolve and Unify

Nobody uses one login across channels. Some people shop anonymously, then sign in later. Others change devices or email addresses halfway through a purchase cycle.

That’s why identity resolution matters. CDPs use a mix of deterministic (exact match) and probabilistic (likely match) logic to connect those dots.

Once matched, the CDP builds a unified profile that updates live, and reflects consent preferences, interactions, and history across time.

Step Three: Segment and Orchestrate

Segments can be built using anything: purchase frequency, channel preference, support history, churn risk, lifecycle stage. One system, one view.

Then the orchestration layer kicks in. CDPs push those segments into email platforms, ad tools, mobile apps, contact centers, and more, all from the same profile source.

Some platforms even trigger automations in tools like CPaaS platforms or journey orchestration engines. Others send data back to BI tools via reverse ETL for better modeling.

Step Four: Govern

Consent and traceability are now crucial across customer journeys.

The best CDP platforms handle this too. They align with enterprise security and compliance strategies. Many are tuned for tracking opt-ins, deletions, suppression lists, and lawful basis for processing. Some use AI for automatic data redaction or anonymization.

CDPs don’t just make data available. They make it usable across marketing, support, operations, and analytics.

What is a CDP? The Data CDPs Collect

CDP software only works if the data inside it is complete. That means pulling from every channel a customer touches, across the journey map. This data comes in various forms, such as:

• First-Party Data: Captured directly by the business. Page views, app sessions, email opens, purchases, cart activity. Anything that happens inside owned channels.
• Zero-Party Data: Voluntarily shared by the customer. Survey responses. Preference selections. Form inputs. Chat interactions. Zero-party data has become especially important as cookie-based tracking fades. It’s the most transparent type and often the most valuable.
• Behavioral & Transactional: What people do, and what they buy. Page flows, scroll depth, session time. Order history, product categories, returns. Some platforms also connect browsing to offline purchases, like retail stores or call-in orders, if there’s an identifier.
• Support & Interaction Logs: CDPs often connect to contact center platforms, CRMs, or service desks. That data adds context: what issues came up, how they were resolved, and how long it took.
• Consent & Preference Data: CDPs track consent flags, lawful basis, and versioning of terms, along with how preferences are set and updated. This ensures downstream tools don’t personalize based on outdated or noncompliant data.
• Industry-Specific Signals: Financial institutions might include KYC flags. Retailers often track loyalty tiers or POS metadata. Healthcare CDPs might handle appointment cycles or treatment milestones.

The structure depends on the sector, but the principle stays the same: one profile, built from all corners of the business.

CDP vs CRM vs DMP, and Beyond

There’s a reason people confuse CDPs with other tools. The lines between them aren’t always clear, especially when vendors blend features across categories. But the roles are different. A Customer Data Platform doesn’t replace a CRM, or a data warehouse. It sits alongside them.

CDP vs CRM

CRM systems are designed for managing known contacts. Sales teams use them to log calls, track deals, and monitor pipelines. CDPs, by contrast, pull in both anonymous and known behavior. They track what someone does before they ever fill out a form.

That includes app sessions, web visits, and product usage. Then, once the person is identified, the CDP merges that activity into a unified profile.

CDP vs DMP

DMPs (Data Management Platforms) focus on third-party data. They use cookies and device IDs for anonymous targeting, mostly for ad buying. Data inside a DMP usually expires within 90 days.

CDPs work with first-party and zero-party data. They store it long term, build persistent profiles, and connect it across teams. In a post-cookie world, CDPs are increasingly replacing DMPs in the stack.

CDP vs Data Warehouse

Data warehouses are built for storage and analysis. They handle massive volumes, structured queries, and historical reporting. Think Snowflake, Redshift, BigQuery.

CDPs are built for activation. They act on data. Push it out. Feed downstream systems. Sometimes they pull from warehouses directly, especially in composable CDP setups, but they don’t replace them.

Benefits of CDPs for Enterprise Organizations

Enterprise teams don’t buy technology because it’s trendy. They buy it to fix problems. To simplify complexity, to speed things up, and to prove value.

What is a Customer Data Platform in this context? It’s not just a database. It’s an operating layer that connects marketing, sales, support, analytics, legal, and IT, with one shared view of the customer.

That alone unlocks benefits across the business.

1. One Customer, One Profile

Most companies don’t lack data. They lack alignment. The CRM has email activity. The ad platform tracks sessions, while VOC platforms monitor feedback. Support logs live in a separate system. Data teams work from a warehouse nobody else touches. CDPs bring all of it into one place.

When every team sees the same real-time profile,  purchase history, sentiment signals, consent status, decisions get faster, and friction disappears.

2. Smarter Personalization, Real-Time

Personalization works best when it’s invisible.

CDPs enable that. They update segments as people interact with content. They pass that data into apps, websites, email tools, CCaaS platforms, or journey builders, so experiences stay relevant without manual work.

This isn’t limited to marketing either. Service agents can see churn risk. Sales teams get intent scores. Loyalty programs can trigger personalized rewards instantly.

3. Better Customer Value

When teams operate from a shared view, it’s easier to retain customers, and increase lifetime value. CDPs help predict behavior based on actual usage, not assumptions. They catch patterns that suggest churn, surface cross-sell signals, or prioritize customers who are most likely to convert.

That data flows directly into CRM systems, email sequences, or support prioritization, wherever it’s needed.

4. Privacy Built In

Every profile includes consent status and lawful basis for communication. That’s tracked in real time. If preferences change, the updates hit downstream tools automatically.

That keeps marketing compliant. But it also prevents legal and compliance teams from chasing down mistakes after the fact.

5. Cross-Functional Efficiency

IT controls the infrastructure. Marketing uses the profiles. Support sees key context. Analysts feed models with unified inputs. Instead of everyone pulling from their own source of truth, the CDP creates a foundation everyone can build on, with security and speed.

6. Measurable ROI

CDPs improve time to insight. Time to personalization. Time to campaign. They cut down the number of platforms needed to make a decision.

According to research, 72% of companies using AI-driven CDPs have seen a significant increase in ROI and faster time-to-value across new product launches and CX experiments.

The payback comes not from flashy dashboards, but from fewer delays, better targeting, and less time spent asking, “Where’s that data?”

CDP Use Cases Across Industries

The value of a Customer Data Platform doesn’t stop at marketing. Different industries use CDPs to solve very different problems, from churn to compliance to personalization at scale. What they share is complexity. Multiple systems. Multiple teams. Customers who expect fast, tailored, and consistent interactions. Here’s how CDP software delivers value across sectors.

Retail: Real-Time Offers That Make Sense

Retail CDPs connect in-store purchases with online behavior. Someone clicks a product in an email, walks into a location, and buys something else, the system knows, and the next message reflects it.

Loyalty programs update in real time. Abandon-cart campaigns respond within minutes. Push notifications and CPaaS integrations deliver time-sensitive promotions when they make sense.

In brick-and-mortar environments, CDPs also help with inventory-based personalization, like suggesting alternatives based on local stock.

B2B SaaS: Catching Churn Before It Happens

SaaS companies rely on retention. A CDP combines product usage data with support tickets, CRM notes, and billing flags to spot accounts showing signs of churn.

That profile can trigger alerts inside the sales platform, push tailored content, or route a renewal reminder to the right rep, all before the customer walks away.

This goes beyond MQLs. CDPs can score likelihood to renew, likelihood to expand, and even identify upsell windows based on actual behavior.

Healthcare: Secure, Personalized Experiences

In healthcare, timing and trust are everything.

CDPs in this space help unify patient engagement across portals, apps connected by CPaaS, appointment systems, and follow-ups, while maintaining HIPAA-compliant data handling and strict consent control.

A patient asks a question via chatbot. Books an appointment online. Misses a follow-up. The CDP connects the dots so the care team can act with the right context.

Financial Services: Privacy-First Personalization

CDPs give banks and insurers a way to tailor communications based on customer behavior  without violating consent or crossing compliance lines.

Segmenting by lifecycle stage, account status, or transaction type allows institutions to deliver high-value offers, nudge product adoption, or flag risk, all with the audit trails and data governance regulators expect.

Leading CDP Vendors & Market Insights

The number of companies offering CDP platforms has increased. The 2025 Gartner Magic Quadrant highlights just how diverse the market has become. Some vendors focus on speed and usability. Others lean into composable architecture or embedded AI orchestration. For enterprise buyers, the right fit depends on use case, internal data maturity, and integration needs.

Major players include:

• Salesforce: Salesforce’s CDP is fully integrated with the broader Salesforce stack, including Marketing Cloud, Service Cloud, and Commerce Cloud. It’s a strong choice for orgs already committed to Salesforce. The strength lies in real-time data activation and native AI models tied into Einstein.
• Tealium: Known for its real-time zero-party data capture features, Tealium combines privacy, consent, and flexible insights with a strong pricing model. It also has an expansive partner ecosystem, which makes integration simpler.
• Oracle Unity: Oracle Unity is a traditional, bundled CDP that brings together data from marketing, sales, and service across Oracle’s ecosystem. It’s strong in retail, telecom, and finance, especially for global enterprises with strict governance needs.

Need help choosing? Explore the CDP market map.

How to Choose a CDP: A Strategic Guide

After “what is a CDP” the next question companies ask is often, “How do I pick the right one?”

A Customer Data Platform can do a lot. But not every CDP fits every business.

Choosing the right one starts with clarity, about what problems you’re solving, who owns the process, and how the tech fits into your existing stack.

• Start With Goals: What’s the real driver? Personalization across channels? First-party data strategy post-cookie? Consent and compliance management? Define 2–3 high-value use cases. Use those to shape the requirements.
• Map the Stack: What systems already house customer data? CRM? Data warehouse? Commerce tools? Call center platforms? The CDP should integrate with those, not duplicate them. Composable CDPs might work best if you already use a strong data warehouse like Snowflake or Databricks. Traditional CDPs may move faster for teams starting from scratch.
• Check Scalability and Control: Think long-term. Can the CDP grow with your business? Does it give IT the governance and observability they need? Can it support marketing without engineering bottlenecks? Look for flexible APIs, support for real-time syncs, and user controls that map to your org.
• Run a Cross-Functional RFP: This should involve marketing, data, legal, and IT from day one. Ask about: data model flexibility, consent handling, identity resolution accuracy, pre-built and custom connectors, AI capabilities, and warehouse integrations.
• Plan for Buy-In: Even the best CDP won’t work without adoption. Define who owns the rollout. Who trains teams. Who monitors data quality. Who manages compliance risk. Clarify those roles early, and set expectations accordingly.

What’s Next: CDPs in an AI-Driven Future

The future of the Customer Data Platform isn’t static profiles and batch updates. It’s real-time, responsive, and increasingly autonomous. AI isn’t just a feature anymore. It’s reshaping how CDP software is built, deployed, and used across teams.

Trends include:

• Agentic CDPs: Instead of waiting on fixed rules, agentic CDP systems interpret intent, predict outcomes, and adjust actions in real time. A customer opens an app but doesn’t engage. Instead of a generic retargeting email, the CDP may route them to a conversational bot, change the homepage offer, or pause outreach entirely based on live models.
• Real-Time Infrastructure: Legacy CDPs worked in batch mode. Data was refreshed every few hours, sometimes daily. That no longer works. Modern stacks need streaming ingestion, event-based orchestration, and response times measured in milliseconds, not minutes.
• Composable CX The next evolution isn’t just smarter CDPs. It’s composable CX, AI-enhanced platforms where CDPs work alongside CPaaS, analytics, orchestration tools, and support systems. Every part of the stack shares live customer context. Every system acts with that context. AI helps prioritize what matters most, across every channel.

Discover what’s next with the latest industry research.

What is a Customer Data Platform for Today’s Enterprise?

CDP software gives enterprise teams a way to build real-time, privacy-safe, AI-enhanced experiences on top of a unified customer foundation.

That’s not just a marketing win. It’s operational stability. It’s compliance without complexity, and it’s the ability to personalize every interaction across digital, human, and hybrid touchpoints.

In a time where data privacy is tightening, AI is accelerating, and customer expectations are climbing fast, the need for CDPs has moved from optional to essential.

If the business needs faster decision-making, better engagement, or cleaner data to support AI rollouts, the CDP is where it starts. CX Today brings together the people, vendors, and platforms shaping the next phase of customer experience.

Ready to move forward?

• Join the CX Community: Stay ahead of the curve with insights from global leaders and practitioners in the CX Community.
• Test the Tech: See live demos, walkthroughs, and product briefings from top CDP vendors at upcoming CX events.
• Plan Your Next Investment – Use our CX Marketplace to compare options across the full CX stack, from Customer Data Platforms to CRM, CCaaS, BI, and beyond.

Need a broader view? Visit the full CX Guide for a look at the future of customer experience.

Here, we examine the use cases for AI agents that are transforming efficiency and service quality behind the scenes. We’ll look at how leading service teams are using AI not just to talk to customers, but to support, analyze, and evolve their entire CX infrastructure. 

A Silent Revolution

“Imagine an incredible human advisor who has read everything about your company,” says Alan Ranger, VP at Cognigy. “That’s what these agents can be – fully grounded in any knowledge source, structured or unstructured.” From Word documents to sprawling intranets, AI agents can instantly surface critical information and respond with human-like precision. 

This revolution is not just leading to efficiency gains – it impacts information flows through the enterprise. Instead of searching through wikis or relying on outdated manuals, staff can now query an intelligent assistant that responds with authority and nuance. In organizations like Bosch, which manage vast product catalogs, these AI agents allow any employee to access consistent, up-to-date product knowledge – eliminating silos and speeding up internal service delivery. Cognigy works with Lidl to provide employees on the floor of the supermarket with an earpiece that allows them access to AI Agents for any kind of help. By using voice commands, Lidl workers can quickly access information such as product details or stock levels and perform tasks like opening or closing cash registers. Learn more about that here: https://www.cognigy.com/solutions/ecommerce-retail. 

From Custodian to Architect

AI agents are not only retrieving knowledge – they’re beginning to maintain and evolve it. “Before, everything had to be structured. Now, it can be completely unstructured,” Ranger explains. AI is now keeping a dynamic repository of enterprise knowledge, making it easier for both human and AI agents to retrieve the information they need and stay aligned with the company. 

Though customers may never interact directly with these AI systems, they are increasingly benefiting from them. Ranger cites use cases where internal LLMs (large language models) lead to improvements in consistency, accuracy, and speed – especially when grounded in curated data. “If you’ve grounded it, that’s the only thing it will do,” he notes. “It won’t make things up.” 

By quietly powering back-end workflows, AI agents are ensuring that what customers experience – be it through support, delivery, or product accuracy – is smarter, faster, and more reliable. 

Guardrails and Governance

As AI becomes more deeply embedded into operations across enterprises, the need for guardrails and good governance are more critical than ever. Cognigy’s Ranger advocates for the creation of AI councils to manage this dual workforce – humans and machines alike. “AI shouldn’t be governing AI. You still need human oversight,” he emphasizes. Notably, new roles are emerging, like the Senior VP of People and AI, tasked with ensuring AI systems follow the same standards as their human counterparts. 

The Next Episode

Looking ahead, Ranger predicts that invisible AI agents will play a growing role in customer insight generation. “We’ll have far greater insights into the customer journey – where things are going well, where they’re not,” he says. These insights, distilled from behind-the-scenes data analysis, will empower businesses to refine experiences before customers even articulate their pain points. 

While much of the spotlight remains on customer-facing AI tools, the real game-changer might be what’s happening behind the scenes. These invisible AI agents are reshaping the way organisations operate – streamlining internal processes, unifying fragmented knowledge, and ultimately enabling better, faster, and more consistent customer experiences. 

For CX leaders, the message is clear: the future of exceptional customer experience won’t just come from the tools customers can see – it will be powered by the intelligence working quietly in the background. Those who recognise and invest in this hidden layer of capability will be the ones who raise the bar for service, insight, and operational excellence in the years to come. 

The conversation reveals that many organizations rushing to implement AI, particularly agentic AI, are discovering their data infrastructure isn’t ready. Johnson advocates for the 80/20 rule: tackle the biggest customer-facing problems first, such as warranty claims or support issues, before attempting comprehensive automation.

TechSee’s innovative approach combines visual AI with omnichannel support, enabling customers to simply take a picture of their problem—whether it’s a router setup issue or TV error code—and receive guided resolution steps. This visual verification method reduces support calls from seven to just one, dramatically improving customer satisfaction and operational efficiency.

The key takeaway? Before investing heavily in AI, enterprises must first ensure their data is clean, trusted, and properly organized. As Johnson notes, “We don’t just see the problem, we can see the solution.”

The index connects to a company’s knowledge sources, customer records, call transcripts, and more. It then powers Amazon Q, AWS’s “AI assistant for work”.

Now, the index may also power the Zoom AI Companion.

As a result, Zoom users can pull information from various enterprise applications without having to open them.

Those systems include Box, Dropbox, Google Drive, Salesforce, SharePoint, and many others.

Consequently, when searching for information, employees don’t have to leave the first app they open every morning: Zoom Workspace.

In a blog post, AWS shared an example of a sales rep seeking additional information before a renewal call on Zoom.

With this integration, they could ask the AI Companion: “When is the customer’s contract up for renewal, and who signed the last one?” The AI Assistant would then pull from the index to provide an instant answer.

“AWS and Zoom are changing the future of work with our latest collaboration,” said Swami Sivasubramanian, VP of AWS Agentic AI, when sharing the news on LinkedIn.

“Q Business brings enhanced accuracy of AI responses and a secure framework to implement GenAI assistants,” he continued.

What I think customers are really going to love about this integration is that they can stay within their familiar Zoom interface and reduce the time they spend switching between applications.

Yet, perhaps most exciting is the potential for AWS and Zoom to take this collaboration further.

Consider the Amazon Q use cases the business index supports beyond extracting enterprise knowledge.

Specifically, think about how Amazon Q uses data from the index to generate content and complete multi-step tasks.

For instance, a sales or service leader could ask Q to extract key insights from a customer satisfaction survey and create visualizations to share with the team.

Meanwhile, a marketing rep might ask Q to summarize what was discussed during meetings – across collaborations apps – while they were on vacation.

There are also many use cases far beyond customer-facing functions. For instance, an HR employee could ask Q to create a job posting for a new position before creating and scheduling a social media post to promote it.

By taking the collaboration further, the tech giants could grant employees access to all these capabilities via the Zoom AI Companion.

In this sense, AWS may help Zoom become the operational center point for a business.

From that center point, employees could share cross-platform data and trigger actions across various business systems without ever leaving Zoom.

To pinch a line from Sivasubramanian’s post: “This is what workplace innovation looks like!”

How to Access the AWS-Zoom Integration

Companies should use the Amazon Q Business Data Accessor to connect Zoom’s AI Companion with the Amazon Q Business index.

Essentially, this acts as a gatekeeper, controlling what data Zoom’s AI Companion can access from the Amazon Q index and ensuring everything follows company rules and privacy settings.

Zoom’s AI Companion can connect to the Amazon Q index through this gatekeeper.

In doing so, companies can pull helpful, real-time information from a company’s internal data via Zoom without leaving the platform.

The AWS-Zoom Partnership Gathers Momentum

The partnership between AWS and Zoom is going from strength to strength.

In February, AWS announced its exit from the UCaaS market, sunsetting Chime and moving its internal teams to Zoom.

At the time, the cloud giant noted in a company blog: “When we decide to retire a service or feature, it is typically because we’ve introduced something better or our partners offer a solution that is a good fit for our customers as well as our own employees.”

Zoom is seemingly the partner providing a “good fit” solution.

Meanwhile, the Zoom Contact Center became available on the AWS marketplace late last year alongside Zoom Workplace, Revenue Accelerator, and Workvivo.

AWS made Zoom their 2024 EMEA Alliance Partner of the Year shortly after.

As that collaboration continues, the potential for Zoom to become the operational center point for business further takes shape.

Dashworks integrates with “all” a company’s apps to create a “single source of truth”.

In doing so, it centralizes shared documents, tickets, and databases.

Its AI Assistant then answers employee questions by dipping into that single source of truth and formulating answers.

As such, it can support a marketer asking for the latest brand guidelines. It may also assist a sales rep requesting the status of an account or a service agent trying to solve a customer query.

Yet, the possibilities extend much further.

Given this, Dashworks will help HubSpot bolster Breeze Copilot, the AI assistant that connects customer insights across its hubs to answer user questions and help them complete tasks.

Indeed, Dashworks will expand the breadth of knowledge sources Breeze Copilot accesses, so it sources answers from more enterprise systems, including Google Drive, Slack, and beyond.

“What’s impressive about Dashworks is its simplicity,” said Nicholas Holland, SVP & Head of AI at HubSpot, when announcing the news.

Ask a question, and it instantly pulls information scattered across documents, messages, tickets, teams, and third-party apps. What used to take hours now takes seconds.

“We’re excited to integrate this powerful search into Copilot to create a true go-to-market assistant,” concluded Holland.

Yet, it’s not just Breeze Copilot; Dashworks may also bolster Breeze Agents, HubSpot’s collection of AI agents that automate various front-office tasks.

Indeed, it may ground them in more data, helping them source the latest, most relevant information to reason and act on.

Beyond Breeze, HubSpot also plans to leverage the deep search and reasoning ability of Dashworks to enhance many of the AI features already embedded within its offering.

Moreover, by leveraging Dashworks’ library of 1,800+ integrations, HubSpot hopes to connect to more unstructured data sources and support customers in connecting with tricky workloads.

In doing so, the CRM stalwart can make it easier for brands to unpack where key information lives, understand who knows what, and prioritize.

The Dashworks team will move across into HubSpot’s AI product group to help here and improve the context-gathering capabilities of Breeze.

Excited by this prospect, Prasad Kawthekar, co-Founder of Dashworks, said: “Dashworks and HubSpot share a commitment to making powerful technology accessible to businesses of all sizes.

With HubSpot’s leadership in go-to-market solutions and their advanced work with unstructured data, our next chapter with HubSpot will help even more businesses unlock the full potential of AI in their daily workflows.

Fundamentally, the problem HubSpot is looking to solve is how people spend so much time digging for information across different apps, waiting for co-workers to answer questions, and figuring out where to begin.

After this acquisition closes, Breeze Copilot may offer a silver bullet to this persistent problem.

What Else Is New with HubSpot & Breeze?

During its recent Spotlight 2025 event, HubSpot added another 200 product features and enhancements across its ecosystem.

Those included new Breeze Agents released in beta.

First is a Knowledge Base Agent in Service Hub. It identifies knowledge gaps. From there, it reviews successfully resolved human-handled tickets and automatically drafts knowledge base articles.

In doing so, it scrubs personal information and lets a human review and publish them.

Then, there’s a Content Agent within the Marketing Hub. It references a brand’s identity guidelines, top-performing posts, and target audience to help craft complete articles.

A final example is its Prospecting Agent, which performs deep research on target accounts, drafts personalized emails, and segments audiences.

Currently, Breeze Agents are available to all businesses with “Premium” editions of HubSpot at no additional cost.

“Our priority is adoption,” said Paul Weston, Senior Director of Product & GM of Service Hub at HubSpot, in a recent interview with CX Today. “We’re building these agents, getting them into the hands of customers, and seeing how they’re used.

We’ll monetize down the line, but the aim today is to democratize AI and make it part of the day-to-day without adding extra costs.

Another notable addition to the HubSpot portfolio is its Customer Success Workspace, which launched in beta last year but is now live.

As the name suggests, this offers customer success managers (CSMs) a dedicated home in HubSpot. From there, they may track tasks, monitor pipelines, and create custom customer views.

Another exciting feature is HubSpot’s new Customer Health Rating, which scores customer health based on insights across the CRM giant’s ecosystem.

“Businesses can set up health alerts, so a CSM is notified if a customer submits multiple support tickets in a day or drops a low NPS score,” added Weston. “It’s a big deal for helping teams proactively manage accounts.”

For more on these announcements from Spotlight 2025, check out CX Today’s article: HubSpot Brings AI Agents to SMBs.

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Why? Because analytical skills are the foundation upon which all other competencies are built. In an era where data is king, and every decision is driven by insights, your ability to interpret, analyze, and apply information directly impacts your success.

Without these skills, you’re navigating blind, reacting instead of proactively shaping the customer journey.

What Do We Mean by Analytical Skills?

Analytical skills are defined as more than just crunching numbers or dissecting data charts. It’s broader and far more critical.

Analytical skills encompass the ability to systematically and logically think through complex problems, to break down large datasets into actionable insights, and to foresee potential challenges before they arise.

It’s about understanding patterns, interpreting trends, and making data-driven decisions that lead to tangible improvements in customer satisfaction. These skills enable you to see beyond the surface, identifying underlying issues and opportunities that others might miss.

8 Techniques to Harness Your Analytical Skills in CX

Here are 10 ways to apply analytical skills to improve customer experiences:

1. Through data segmentation, you can tailor customer experiences to specific demographics.

By breaking down your customer base into distinct segments, you can create personalized experiences that meet the unique needs of each group. This precision not only enhances satisfaction but also fosters loyalty.

2. Leveraging predictive analytics allows you to anticipate customer needs before they even realize them.

Using past data trends, predictive analytics allows you to foresee customer behaviors and preferences, enabling you to offer solutions proactively rather than reactively.

3. You can identify the true source of recurring customer issues through root cause analysis

Instead of addressing symptoms, root cause analysis digs deep to uncover the underlying problems, allowing you to implement long-term solutions that prevent issues from reoccurring.

4. A/B testing is the secret to validating the effectiveness of different CX strategies

Experimenting with different approaches and analyzing the results enables you to determine which strategies resonate best with your customers, ensuring that your efforts are data-driven and effective.

5. Thanks to journey mapping, you can visualize the customer experience from start to finish.

This technique helps you to understand the entire customer journey, identifying pain points and opportunities for enhancement at each stage, leading to a more seamless experience.

6. By utilizing sentiment analysis, you can gauge customer emotions in real-time

Analyzing customer feedback, social media posts, and reviews gives you immediate insights into how customers feel about your brand—allowing you to address concerns swiftly and celebrate positive feedback.

7. Cohort analysis is a proven analytical method to track customer behavior

The key is to analyze over time and adjust your strategies accordingly. By grouping customers based on shared characteristics and monitoring their interactions over time, you can identify patterns that inform more effective, targeted CX strategies.

8. By applying regression analysis, you can understand the relationship between different CX variables.

This statistical method helps you identify which factors have the most significant impact on customer satisfaction, allowing you to focus your efforts on what truly matters.

Why Analytical Skills Are a Game-Changer for Customer Experience

In the customer experience domain, your analytical skills are your secret weapon. They enable you to transform raw data into meaningful insights, turning complex challenges into manageable tasks. As you harness these skills, you start to notice patterns and connections that others might overlook. You become more than just a problem solver; you become a strategic thinker who can anticipate issues, identify opportunities, and drive continuous improvement.

These skills are what differentiate the leaders from the followers in the CX field. While others may struggle to keep up with rapidly evolving customer demands, you’ll be at the forefront, setting new standards and delivering exceptional experiences. Analytical skills allow you to be proactive rather than reactive, ensuring that you’re always one step ahead.

Moreover, these skills aren’t just limited to one area of CX. They’re applicable across all facets, from customer service to product development to marketing. Whether you’re optimizing your contact center operations or designing a new customer loyalty program, your analytical prowess is the key to success.

Conclusion: Sharpen Your Analytical Skills to Keep Up with Changing CX Expectations

So, how can you ensure that you’re ready to meet the ever-changing demands of today’s customers? The answer lies in continuously honing your skills. Embrace the techniques that turn data into actionable insights. Stay curious, and never stop learning.

By continuously refining your analytical abilities, you’ll not only keep up with the rapid pace of change but also lead the charge in creating extraordinary customer experiences.

Ultimately, the future of CX belongs to those who can see beyond the data, who can anticipate needs, and who can deliver personalized, seamless experiences that customers crave. Be that CX professional. Harness your analytical skills, and watch as you transform customer experiences, driving satisfaction and loyalty to new heights.

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