2025 CX Trends: 5 ways customer experience ushers in a new era

This article was originally published in the Customer Strategist Journal. Read the issue here.

The customer experience (CX) landscape is constantly evolving with changing technologies, customer behaviors, challenges, and opportunities. That’s always been the case — but as we head into 2025, things feel different.

Now that AI has fully made the leap from theoretical buzzword to a cornerstone of contact center operations and brands are seeing its benefits firsthand, the year ahead is poised to be truly transformative.

Brands are equipped with incredibly powerful analytics and insights they’ve never had before, which brings exciting opportunities to revolutionize how they think about and deliver CX on a much broader scale.

As we head in 2025, here are 5 major trends that will reshape the way CX is delivered in the contact center.

1. CX sheds its borders

The CX world will keep expanding (literally) as more locations across the globe become hubs for CX excellence.

AI-powered tools like real-time translation, accent localization, and voice enhancement will empower brands to break down traditional barriers and deliver amazing customer support from anywhere in the world. With these technologies, associates and customers can communicate easily with each other, no matter where they’re located.

With borderless CX, brands can escape the confines that previously limited the contact center. Seize the opportunity to look at new geographies, technologies, and strategies that can transform your CX operation.

Amid this changing landscape, companies will increasingly seek to do business in regions where they can make a lasting social impact. Areas like South Africa, Rwanda, and others are poised to emerge as top CX destinations thanks to their highly skilled and digitally savvy workforces, robust infrastructure, competitive cost benefits, and impact opportunities.

2. Data insights break through barriers

Data’s role in CX will grow more crucial, so it’s important to have systems in place that allow data to flow seamlessly between brands and customers across all channels. Contact centers traditionally tend to be very segmented, but in 2025 those silos will start breaking down.

Insights will become more powerful, and AI will get even better at predicting the best methods for resolving customer issues based on behavior and sentiment. Real-time data, innovative AI analytics, and experts who can put those insights to work will be foundational to CX success in the coming year.

The term “omnichannel” has been part of the CX lexicon for years, but in 2025 advanced channel orchestration will begin to dominate the contact center landscape in ways it hasn’t before.

Use AI-enhanced quality and insights tools to listen to all interactions, across any channel, and identify trends, challenges, and opportunities. Then, layer on quality experts who know how to cull actionable insights from your data to truly transform the contact center.

3. AI agents make their mark on self service

With first-generation chatbots firmly in the rearview mirror, AI-powered autonomous “agents” are set to transform customer experience. They’re smarter, more predictive, and easier than ever to integrate into CX systems.

AI will evolve from supporting human associates during interactions to collaborating with them in real time – offering suggestions, context, and sentiment analysis as interactions are happening.

Not only will AI agents help make associates much more efficient, but they’ll also let customers become more self-sufficient. Customers will increasingly resolve their own issues on their own time, without an associate, as AI-powered tools become increasingly accurate and tailored to individual customer preferences.

As this trend unfolds, resist the urge to adopt technology simply for technology’s sake. Rather, make sure your AI strategy is always guided by your customer journey, and seek to solve the most pressing customer pain points first to make the biggest impact.

4. A new CX workforce emerges

One of the many byproducts of AI that will rapidly evolve is the role of contact center associates. With routine tasks taken off their hands by automation, in 2025 associates will need to be equipped to handle more complicated and nuanced interactions.

Brands will look for associates who possess different types of skills. Soft skills like empathy will take on new importance, and associates will need to be more technically proficient to work in tandem with AI-powered tools.

A new workforce means a new approach to learning. In 2025, employees will expect training and coaching to be experiential, tailored to their specific needs, take place in the channel of their choice, and be available on-demand.

Lean into AI to help revolutionize your associate onboarding, training, and coaching. AI-enhanced training tools let associates role play realistic customer scenarios and deliver real-time feedback to help improve performance. And AI can listen to 100% of customer interactions to help inform training and identify immediate coaching opportunities.

5. Value grows in your customer base

When brands focus solely on resolving the customer issue at hand, they could miss out on opportunities to grow loyalty and revenue.

In 2025 and beyond, brands will become better at harnessing the full value of every interaction. Digging into data and insights will help them integrate sales efforts into more customer touchpoints, understand the optimal amount of effort to put into each interaction, and gain deeper knowledge around individual customer value. Companies will be better equipped to identify and prioritize their most valuable customers.

AI should play a leading role in this, too. Use AI-enhanced insights and analytics to listen to interactions, incorporate service-to-sales opportunities, and identify customers who have more potential value. Dig into data to get the best sense of where there are opportunities to maximize value, then ensure associates are trained and ready to meet the moment.

Embrace the new CX era

As 2025 approaches, brands have unprecedented tools, insights, and capabilities at their disposal to reshape their customer experiences. It’s an exciting time to make a mark on contact center operations and strategy.

At the same time, CX increasingly lives at the intersection of the contact center, customer relationship management (CRM), and AI and analytics. So be sure to connect the dots among those three key components with a holistic CX tech stack.

To deliver seamless experiences in 2025 and beyond, brands must strike the right balance between humans and automation — and have a strong, customer-centric strategy in place to guide them.

Decoding the Feedback Dilemma: A Strategic Framework for Evaluating Customer Requests

By Ricardo Saltz Gulko

Guest article as part of our partnership with the European Customer Experience Organization. See original post here.

In the dynamic world of B2B customer experience, balancing responsiveness to feedback with long-term strategy can feel like walking a tightrope. Every feature request represents a signal—sometimes an isolated need, sometimes a broader trend—but responding to every demand can lead to resource strain, product dilution, and missed strategic opportunities.

The secret lies in transforming customer feedback into a structured decision-making framework. This ensures not only that your customers feel heard but also that your organization retains its strategic focus. Below is a deeper, more analytical take on the original framework, enhanced with actionable strategies and insights.

Assess Strategic Alignment: The Backbone of Decision-Making

The first and most crucial filter for evaluating feedback is determining how well it aligns with your company’s strategic goals. A feature may seem promising in isolation but could detract from your long-term objectives.

Key Questions to Ask:

Does this request support our core mission and value proposition?
Will it enhance our competitive advantage or dilute it?
Is this feature central to solving the most critical customer pain points identified in our strategic roadmap?

Framework for Analysis:
Use a strategic alignment matrix to classify requests based on their impact and feasibility. For example:
High impact, low feasibility: Requires prioritization but warrants resource adjustments.
Low impact, high feasibility: Reassess against opportunity costs.

Analytical Challenge:
Strategic alignment is particularly difficult with high-value customers, whose influence can skew priorities. Misaligned decisions often manifest as scattered product features, eroding overall coherence.

Example:
Consider how SAP addresses feature requests. By adhering strictly to its ERP roadmap, SAP ensures every update fits its vision while solving broad customer pain points. A seemingly small UX improvement rolled out in Europe ultimately boosted satisfaction across industries.

Broader Market Demand: Data-Driven Validation

While an individual request might reflect one customer’s unique need, assessing whether it signals a broader market demand is critical. This requires moving beyond anecdotal evidence into data-driven territory.

Action Steps:

Conduct customer cohort analysis: Identify patterns across demographics and verticals.
Use quantitative tools: Leverage surveys, CRM data, and market analytics to determine whether a request is a widespread need.

Challenges:
Vocal customers often overshadow silent majority preferences. Chasing niche demands risks alienating your broader user base.

Example:
When Salesforce received requests for deeper CRM integration, it didn’t simply respond to the enterprise client asking for it. Instead, it studied data from multiple sectors, realizing that an API enhancement would benefit its global customer base. The result: a scalable solution that strengthened Salesforce’s ecosystem.

Join ECXO.org, the only open-access CX professional network connecting practitioners, leaders, companies and executives to shape the future of customer experience! Become a member or learn more here: https://ecxo.org/

Technical Feasibility: Beyond the Surface Complexity

Understanding the technical feasibility of a feature requires collaboration across teams. Even a seemingly simple request can mask hidden complexities that strain infrastructure or delay critical updates.

Steps to Evaluate Feasibility:

Engage R&D, engineering, and operations teams early to map out potential challenges.
Calculate the development cost-to-value ratio: Compare estimated hours against the potential benefits of the feature.
Prioritize technical debt avoidance: Features that complicate future scalability should be deprioritized.

Insights:
Feasibility isn’t just about engineering effort—it’s about whether implementation will introduce inefficiencies or misalignments with your technology stack.

Example:
Siemens rejected an overly complex analytics request that required re-architecting their IoT platform. Instead, they developed a modular analytics solution, balancing feasibility with market relevance.

ROI Analysis: Calculating Value Beyond Costs

A feature’s return on investment is not limited to direct financial gains. It encompasses customer retention, market competitiveness, and operational efficiency.

ROI Indicators to Measure:

Will the feature reduce churn or attract new customers?
Can it create cross-sell or upsell opportunities?
Does it reduce the total cost of ownership for your clients?

Challenges:
ROI is inherently speculative, especially for innovative features. A clear hypothesis supported by test cases can mitigate risks.

Example:
Hitachi’s decision to invest in modular IoT analytics was backed by pilot tests in industrial automation. These tests confirmed broader applicability, justifying the investment.

Scalability as a Differentiator

Scalable features amplify returns by serving a broad customer base rather than individual clients. They minimize maintenance costs and strengthen product consistency.

Key Considerations:

Can the feature be modularization to fit different customer needs?
Will it simplify or complicate your overall product ecosystem?

Example:
Samsung SDS developed enhanced cloud security protocols after identifying overlapping demands across several industries. By deploying a scalable solution, they ensured that resources were utilized efficiently.

Resource Management: Balancing Ambition with Reality

Even strategically sound and feasible features can fail without adequate resources. Teams must evaluate whether they can support the project without sacrificing existing priorities.

Resource Allocation Model:

Fixed vs. variable resources: Determine whether additional budgets or temporary staffing can address resource shortages.
Phased development: Deliver the feature incrementally, ensuring manageable workloads while demonstrating progress.

Example:
When Siemens faced resource constraints, it scheduled their IoT updates in phases, ensuring timely delivery without disrupting other projects.

Urgency Evaluation: Separating Critical from Cosmetic

Urgency often pushes companies to prioritize features that may not align with strategy. While time-sensitive requests can be important, they must be weighed against other factors.

Actionable Insights:

Assign urgency scores: Rank features based on their potential to capture time-limited opportunities.
Evaluate market timing: Certain trends justify expedited action, but others may fade before completion.

Transparent Customer Communication: Building Trust

Communicating decisions effectively—whether a request is approved or declined—is essential for preserving trust. The rationale must be clear, rooted in data, and delivered empathetically.

Best Practices:

Use data-driven explanations to validate your decision.
Provide timelines for accepted features and propose alternative solutions for declined ones.

Example:
Salesforce’s structured communication templates allow their teams to manage customer expectations effectively, often proposing alternative workflows or near-term updates.

Conclusion: Transforming Feedback into Strategic Action

Customer feedback represents both opportunities and challenges. To act wisely, businesses must adopt a robust, analytical approach that balances responsiveness with foresight. By evaluating requests through a structured lens—assessing alignment, feasibility, market demand, and scalability—companies can ensure that every decision strengthens their competitive edge.

Feature requests are not just data points; they’re stepping stones to innovation. The art lies in knowing when to act and when to say no, always guided by strategy, scalability, and vision.

Join ECXO.org, the only open-access CX professional network connecting practitioners, leaders, companies and executives to shape the future of customer experience! Become a member or learn more here: https://ecxo.org/

CCMA report: Data analytics reinvents the contact center, starting with the front line

“With great power comes great responsibility.” That proverb may be traced to Voltaire and Spider-Man, but it’s contact center leaders who see the impact data and analytics are already having on the front line — and the obligations that come with it.

Forward-thinking leaders who prioritize the customer experience across a wide range of business sectors shared their insights, experiences, and some cautionary tales with CCMA earlier this year. The resulting new report, “Bringing the power of data and analytics to the front line,” captures best practices for leveraging data analytics to transform the contact center.

Particularly noteworthy is the consensus among CX leaders that contact center associates’ role cannot be overstated. Powerful tools hold great potential for frontline associates to improve the experience but add risk if not carefully thought through and delegated to the appropriate frontline and leadership roles.

The report’s respondents represent an array of business sectors, from banking and financial services to technology, transportation, insurance, hospitality, travel, entertainment, retail, and the public sector. Despite the diversity, the findings led to five discoveries relevant to all business verticals heading into 2025.

The five discoveries highlighted in the CCMA report are centered around responsible and ethical deployment of data analytics — where it can yield the greatest benefits while resisting temptation to overreach into unproven use cases.

Empower associates with dynamic dashboards
Frontline associates are not merely cogs in a wheel; they are eager to track achievements. Accessing their own KPIs through personal dashboards provides the visibility they seek — in real time. It’s imperative that the interpretation of data, however, is left to leaders, not front-line workers, survey respondents emphasized.

“When you use lagging metrics at the end of the month, it’s very hard for agents to truly understand what drives the score. Now you can have agents self-correcting,” said Peter Tubb, head of Global Trading Services, IG Group.

Associates welcome real-time feedback, said Daniel Nield, head of bike operations and live chat at insurance broker Atlanta Group. “People want to know their quality scores without waiting for their coaching session or scheduled update. ‘What do I need to work on?’ They can use that data to start eliciting conversations with their peers and with their managers, whereas at the moment it’s very one-way.”

Keep contact center KPIs simple
Information overload is a very real risk when leveraging data and analytics. CX leaders need to be selective about which KPIs to share with frontline associates. Too much data can be head-spinning and put focus on the wrong areas. When leaders correctly interpret an associate’s KPIs, they can coach that individual in meaningful ways that lead to an improved experience for customers.

“Make it simple and make it short. Not having to look at many different numbers to get to what you need to know. Surface the things that are important,” said Nick Coleman, senior manager of customer care at Dunelm, a home furnishings retailer.

One report participant recalled the data overload he confronted: “My teams were presented with every data element they could possibly have: Case file closures, AHT, call answer rates — whatever you could think of. It drove a focus on the numbers, not service,” said Luke Squires, operations director, Sykes Holiday Cottages. Reprioritizing KPIs — an 18-month process — made service quality associates’ responsibility while management was accountable for service levels. As a result, NPS rose to the 70s while CSAT and answer rates broke records at the vacation rentals company.

Use speech analytics to inform training, strategy
Speech analytics are low-hanging fruit too juicy to resist, and no industry vertical is priced out. With large language models (LLMs) to analyze contact center conversations, organizations get an early warning system that flags emerging issues before they snowball, identifies at-risk customers before they’re lost for good, and highlights types of interactions that require skillful handling and perhaps extra training.

“We’ve been able to understand a lot more in the short time that we’ve had speech-to-text,” said Sharon Oley, customer services director at software company Sage Group. “If a customer is showing some signs of dissatisfaction and potentially going to leave us, we’re able to then delve into those calls or chats and understand why.”

The Very Group, an online retailer and financial services provider, leverages speech analytics to better understand difficult topics that warrant extra time to handle with empathy. The system also cues associates to take a break after a taxing, emotionally charged interaction.

“We can go to our agents who are our best performers on certain topics and say, ‘What are you doing differently?’ That means you can give great outcomes to the customers and reduce AHT for these particular topics. And we can inform our training teams who onboard new agents to focus on these difficult topics,” said Luke Ollerhead, senior insight manager, The Very Group.

Speech analytics enables Atlanta Group to be proactive. “Now, if we see a surge in customers stating something, it’s automatically flagged so we can start analyzing and fixing at it straight away,” said Nield.

Deliver better, faster resolutions with AI-powered knowledgebases
Wide variance in how organizations leverage — or don’t use — knowledgebases defies logic. AI-driven tools like agent assist have proven their value interpreting and anticipating customer needs and serving up the right answers for faster resolution.

Agent assist prompts contact center associates as to what they might do next, depending on what the customer is saying, said Nicola Mayers, senior customer contact manager at railway Network Rail Ltd.

“About 80% of your calls will be memorable from an agent’s perspective in terms of providing the right answer. But it’s the 20% that you can’t remember that are really important,” said Michael Sherwood, head of brand and experience, Atom Bank. It’s that 20% of interactions that tend to be more complex and merit more time to deliver quality CX.

“Our knowledgebase also captures the reason for contact. You can see the top reasons for customers ringing in,” Sherwood added.

Embrace the changes technology brings
The final, perhaps most compelling discovery unearthed by CCMA’s report is not just how the role of associates is changing but how the contact center is being reinvented.

“You have to be prepared for the job role and the skill sets to fundamentally change,” said Dunelm’s Coleman. “If I’ve employed people in the past to click buttons and follow processes, but now I’m asking them to deliver what the machine has generated rather than generate it themselves, I’m now employing communications people — not people who follow process maps.”

Report participants recognize that change is not easy. Implementations can fail. Resistance to new technology, processes, and roles is to be expected. Concerns about jobs lost to AI and automation is very real. That means contact center leaders are obligated to reassure and educate.

“We have to take away the fear that the technology is going to replace people,” said Joe Burke, former vice president partner and customer care, Go City, a travel company. “It will shift people toward value-added tasks, things that the bot or an AI engine can’t do very well, for example conversations requiring empathy, highly complex, or emergency situations.”

Cut through the AI noise by focusing on the “who”

Improve CX and drive ROI with a roles-based strategy

The AI landscape is evolving so quickly, it seems like nearly every day there’s another “shiny new object” promising to revolutionize customer experience (CX). As brands try to stay on the cutting edge of technology – and one step ahead of their competitors – the compulsion to adopt AI, and the sheer number of tools available, can be overwhelming.

But embracing new technology just for technology’s sake won’t work in the long run. Unless they’re part of a broader, cohesive strategy, investments won’t deliver the results you expect, and they’ll likely cost your brand time and money.

There’s a long list of failed attempts where brands tried to force the wrong AI solution into their CX. Chili’s ended up pausing a pilot program where robots seated guests and ran food to tables because the return didn’t outweigh the investment, and McDonald’s ended a test run of AI-powered chatbots at its drive-throughs when the bots repeatedly botched orders, just to name a couple examples.

It’s time to reframe how brands think about AI. Instead of getting lost in the sea of AI tools available, center your AI strategy around the people in your CX operation who stand to benefit from AI. It’s time to shift perspective, from the “what” to the “who.”

The main stakeholders in the contact center are associates, team leaders, CX leaders, and customers. Each has unique objectives and challenges, but they’re united as part of one cohesive CX ecosystem. And AI can benefit each persona in different, but connected, ways.

For associates, AI is a companion
New hires in the contact center want to excel in customer service and genuinely care about people. They want to help customers by giving them the answers they need and brightening their day. But their lack of experience could get the better of them; they have high-pressure jobs and could get tripped up by nerves and inexperience.

Associates have many challenges to juggle: high stress levels, a steep learning curve, measuring up to performance and quality expectations, a lack of confidence, and pressure to communicate clearly and accurately.

AI’s greatest benefit to associates is its ability to help them do their jobs better, with less stress, better results, and greater ease. Associates shouldn’t fear AI as something that will replace them; they should view it as a companion or assistant.

AI-powered tools can make many facets of their jobs easier. AI training simulations offer hands-on practice in a safe environment, real-time knowledge assist can instantly put information at an associate’s fingertips during interactions, intelligent summarization can make sense of complicated interactions, and accent softening and noise reduction can make interactions less frustrating.

Simply put, AI can tackle the parts of associates’ jobs they find the most frustrating and make them quicker and easier. This makes for a better associate experience and saves brands time, money, and headaches.

For team leaders, AI is an ally
Team leaders are usually customer service pros, having been star associates earlier in their career. They command respect in the contact center but genuinely care about their employees and want to see them thrive. But they often need to handle multiple issues simultaneously and can sometimes be a little impatient. They know what makes a great customer experience, so they expect associates to be efficient, knowledgeable, and customer-focused – and they look for ways to make their jobs easier.

Team leaders have a lot on their plates. Their biggest challenges are balancing administrative duties, maintaining team performance, staying motivated in their own job, and training and coaching each team member in a personalized way that resonates.

AI can be an ally that empowers these employees to be better leaders. Customized training simulations and performance dashboards allow them to create tailored training scenarios and identify areas for improvement. Leadership development programs can equip them with the tools and know-how they need to become more effective mentors. And AI-powered administrative support can automate routine tasks so they can focus on more strategic work.

With AI by their side, team leaders can spend more time on the parts of their job that matter most, making them more engaged in their work and driving more career growth for their team.

For CX leaders, AI is an advisor
Higher-level strategists and CX leaders are all about building the optimal customer experience and recognize the impact CX has on the company’s bottom line and brand loyalty. They’re no strangers to budget cuts and have the tough task of figuring out how to deliver the best possible CX without breaking the bank.

Their decisions can only be as good as the data they’re given, so access to actionable insights is crucial to their success. They also need to get department leaders to work together if they’re going to drive ROI.

These employees are tasked with turning data into insights, identifying patterns and outliers, managing fraud, and growing contact center revenue. Each objective brings unique challenges and obstacles.

AI should act as an advisor to CX leaders, helping them make more informed business decisions. AI-powered insights can uncover new ways to innovate, identify cost reduction opportunities, enhance collaboration, and optimize performance. AI tools can also provide strategic workforce planning and automation to lower costs without sacrificing customer satisfaction.

For customers, AI is a concierge
When customers have problems, they reach out to the contact center and expect a resolution – fast. Their demands typically aren’t unreasonable; they just want a quick solution and get frustrated when things are harder than expected. They can become easily frustrated by inefficient delays and issues that require more than one call.

There are many things can aggravate an already-frustrated customer trying to get support: overly complicated processes that make it too hard to get answers, inconsistent information depending on which associate they speak with, long wait times, language barriers when communicating with customer support, and the inability to resolve issues.

AI should be a concierge for customers, helping them connect to answers more easily and quickly. Self-service tools can empower them to resolve their own issues (on their own time and in their preferred channel) and seamlessly connect them to an associate when needed. And the customer data that AI can capture and analyze across all touchpoints can help brands deliver a more personalized and proactive experience that grows satisfaction and loyalty.

Prioritize people in AI investments
Choosing the best AI tools for your employees, customers, and bottom line may seem daunting. A people-focused approach will help inform strategize, prioritize investments, and ensure you’re getting the most from the AI tools you choose and the people using them.

In addition to making your contact center more efficient and reducing overall cost to serve, the right AI solutions will make your associates, team leaders, CX leaders, and customers happier – and more likely to remain loyal to your brand.

What it takes to create “effortless” experiences

Effortless. Seamless. Easy. These are the customer experiences that brands of all types aspire to achieve. But as you know if you’ve ever tried something without preparation and practice, just because something looks effortless doesn’t mean it is.

A few years ago, I signed up for a 10K run before I realized it was the morning after my best friend’s 30^th birthday party. After riding the party bus and imbibing in the free-flowing food and drinks the previous night, I was woefully unprepared to manage the unexpected hills and the endurance needed for the race. I finished next to last, just besting an octogenarian. I was unprepared and exhausted, with little outcome to show for it.

The winners of that race made it look easy as they crossed the finish line. Effortless. Simple. They trained, prepared, and probably skipped a casino trip the night before. For customer experiences, it’s the same thing.

A good experience is explicitly designed to reduce effort on the part of customers, but so much goes on under the surface within a CX organization to make it happen. Technology integration. Well-trained employees. Ongoing data analysis for actionable insight. Clear processes.

This issue celebrates the detail, hard work, and minutiae companies work with to craft “seamless” experiences for their customers and employees.

Explore how to tackle the uphills within CX efforts – siloed information and action, unreliable or unavailable data, and untrained employees, just to name a few. Check out how Avelo Airlines competes against the big guys with simplified experiences. And get tips on how to outsmart failure points within CX innovation projects.

Let’s get moving.

Sincerely,
Liz Glagowski
Editor-in-Chief

Conversational AI vs. conversation AI: What’s the difference?

AI is evolving so quickly it can be hard to keep all the different types straight. That’s especially true when it comes to conversational AI and conversation AI. Not only do both use AI to enable natural-sounding conversations, but their names are so similar they’re often (mistakenly) used interchangeably, adding to the confusion.

But there are important differences when it comes to conversational AI vs. conversation AI, and understanding those distinctions is key to ensuring you’re getting the most out of each.

Conversational AI helps in the moment

Conversational AI is the set of technologies that enable machines to simulate conversations. At its core, it delivers real-time voice or text assistance to people.

Conversational AI powers tools that help customers (or associates) in the moment, by quickly delivering the answers or information they need. These include chatbots, speech-based assistants, voice bots, and other self-service options.

Not all conversational AI tools are the same. AI-powered chatbots, for instance, are automated software that simulate a chat conversation with a user in natural language. They’re very useful for automating simple tasks and enabling 24/7 access for customers, but limited because they’re primarily text-based and scripted to answer only specific questions.

Intelligent virtual assistants (IVAs), on the other hand, use conversational AI to learn from each interaction and get smarter over time. They’re chat assistants that can generate more personalized responses by combining analytics and cognitive computing. They consider – in real time – individual customer information, past conversations, and location, and are more advanced than simple chatbots.

Conversation AI improves the future

Unlike conversational AI that’s used to facilitate seamless interactions in the moment, conversation AI (also known as conversation intelligence) analyzes large volumes of data from conversations to cull insights and trends over time and improve future decision making and interactions.

Your contact center collects huge amounts of data; conversation AI harnesses its potential by making sense of it all. Technologies like speech and predictive analytics let you listen in on every interaction happening in the contact center.

In addition to helping identify what works and what doesn’t during interactions, these tools quickly comb through large quantities of data and identify trends, patterns, and anomalies. You’ll get to know your customers much better, enabling you to serve them faster.

Armed with those insights, you can eliminate guesswork and make data-backed decisions that improve customer experience (CX), employee experience (EX), and your bottom line going forward. Speech analytics, for instance, helped a global social media company grow customer conversions by 233% and call volume analysis helped a national energy company reduce call volume by 60%.

Simply put, conversation AI speeds up your ability to put insights to work.

Both are keys to CX success

While there are definite differences when it comes to conversational AI vs. conversation AI, both can play important roles in a successful CX operation.

Embrace conversational AI where automating simple tasks makes sense. Customers will appreciate the ability to resolve basic inquiries in their own time and in their preferred channels. And using chatbots and other tools frees your associates up to focus on tasks where they can add more value – improving CX, EX, customer satisfaction, and loyalty.

Use conversation AI tools on a deeper level, to get the most from your data. Your contact center is a treasure trove of insights, but you’ll never uncover them without the right tools. Conversation AI can help you to get know your customers (and your contact center) better and make more-informed decisions that drive the results you need.

But don’t implement either type of AI merely for technology’s sake. Neither will deliver the ROI or results you want if they aren’t part of a thoughtful AI strategy. A “set it and forget it” approach doesn’t work for AI.

Brands need to continually check on how tools are performing and making adjustments as needed. If you lack that know-how in house, working with a CX partner that specializes in conversational and conversation AI is a great way to tap into proven best practices and expertise.

Redefining Customer Feedback: Embracing Comprehensive Metrics for Accurate Sentiment Analysis

By Ricardo Saltz Gulko

This article is published through a partnership with the European CX Organisation (ECXO). Read the original here.

Introduction

The Net Promoter Score (NPS) has long been a widely used metric for assessing customer loyalty, satisfaction, and the potential for customer churn as a relationship and transactional metric. Despite its widespread use across various industries, NPS has come under scrutiny for not providing a holistic view of the customer experience. This article examines the critiques of NPS, its performance in different business contexts, and emerging global trends in customer feedback strategies. By adopting a multifaceted approach, organizations can gain a thorough understanding of customer sentiment, leading to better decision-making and enhanced customer satisfaction.

The Inadequacies of NPS

NPS is centered on a single question: “How likely are you to recommend us?” This provides a limited and momentary glimpse into customer sentiment. Gartner predicts that over 75% of organizations will move away from using NPS as a primary metric for customer service and support by 2025. The simplicity of NPS fails to capture the complexities of customer relationships and experiences, which are vital for improving satisfaction. Companies like Toyota and Samsung in Asian markets have found that while NPS gives a quick snapshot, it doesn’t delve deeply into changing customer expectations and perceptions. A more continuous and longitudinal approach is needed to truly understand customer behavior and preferences.

The Broader Critique of Singular Metrics

The issue with NPS is not unique. In various fields, relying on a single metric can lead to incomplete conclusions. In customer experience (CX), metrics like CSAT and CES face similar limitations. In accounting, metrics such as Net Operating Profit need additional context to be meaningful. In healthcare, a single heart rate measurement is insufficient without considering other factors like activity level. No single metric can provide a complete picture; a combination of metrics is necessary for a true understanding.

The Need for Comprehensive Metrics in B2B and B2C Contexts

In B2C environments, where interactions are more transactional, NPS can be a useful indicator of customer advocacy. Companies like Unilever and Siemens use NPS to assess consumer sentiment and identify product improvement areas. However, in B2B settings, characterized by complex decision-making and long-term relationships, NPS often falls short. Sony and LG in South Korea exemplify the difficulties of applying NPS in contexts that require sustained service excellence and relationship management. A comprehensive approach that integrates multiple feedback sources, including Voice of the Customer (VOC) metrics, data analytics, and AI, is essential for a complete understanding.

Global Adoption of NPS and Its Limitations

Despite its limitations, many global companies like Alibaba and SAP continue to use NPS within broader customer experience strategies. These companies complement NPS with qualitative insights and additional metrics to create a complete picture. Platforms like Medallia support NPS implementation across diverse markets, emphasizing the need for localized customer feedback approaches. Metrics such as CSAT, CES, and others are most valuable when integrated into broader strategies.

The Impact of Data Analytics, AI, and Real-Time Feedback

Advancements in data analytics and real-time feedback mechanisms are transforming how companies gather and use customer insights. Companies like Rakuten and L’Oréal leverage AI-driven analytics to monitor sentiment across digital platforms, enabling proactive responses. This approach challenges the retrospective nature of NPS surveys, offering immediate insights that inform strategic decisions and enhance satisfaction. AI can mitigate survey biases by covering all customers and using operational data to generate insights, facilitating communication within the company.

Diverse Metrics for Holistic Customer Insights

To achieve a comprehensive understanding of customer sentiment, organizations are adopting various metrics alongside NPS:

Customer Effort Score (CES): Measures the ease of interaction and issue resolution.
Customer Satisfaction (CSAT): Evaluates satisfaction with specific interactions.
Customer Lifetime Value (CLV): Estimates long-term revenue potential from a customer.
Revenue Growth: Tracks growth attributed to customer experience initiatives.
Customer Retention Rate (CRR): Measures the ability to retain customers over time.
Return on Investment (ROI): Calculates profitability from specific CX investments.

Proactive and Predictive Insights

Traditional NPS feedback often reflects past interactions, which may lose relevance over time. Real-time insights and predictive analytics allow businesses to shift from reactive to proactive strategies. Effective leadership requires leveraging real-time service data and AI to understand the customer journey and sentiment upfront. This data-driven approach moves beyond instinctual responses to dynamic customer sentiments, ensuring satisfaction before and after interactions.

Implementing Effective Customer Feedback Strategies

To utilize these metrics effectively, companies must define clear metrics, data sources, scorecards, and KPIs aligned with strategic goals. This ensures a comprehensive evaluation of customer experience efforts, fostering continuous improvement. Companies like Tencent and Nestlé exemplify the integration of diverse metrics to drive customer-centric strategies and enhance relationships.

Building a Customer-Centric Culture

Creating a 360-degree feedback system involves:

Multi-Channel Feedback Collection: Capturing diverse perspectives across touchpoints.
Segmentation and Personalization: Tailoring feedback mechanisms to different customer segments.
Continuous Feedback Loop: Establishing real-time responses to customer issues.
CRM Integration: Correlating feedback with customer profiles for deeper insights.
Advanced Analytics and AI: Analyzing large volumes of feedback data.
Cross-Functional Collaboration: Ensuring feedback insights drive strategic decisions.
Clear Metrics and KPIs: Aligning metrics with business objectives.
Cultural and Regional Sensitivity: Making feedback methods relevant across diverse bases.
Customer-Centric Culture: Valuing and acting on feedback at all levels.

Conclusion

While NPS remains valuable, its limitations are evident. The rise of advanced analytics and real-time feedback offers a transformative opportunity to move beyond NPS. By embracing a diverse range of metrics and technologies like AI, businesses can gain a nuanced understanding of customer sentiment. This holistic approach enables proactive decision-making, anticipates customer needs, and delivers personalized experiences. Integrating qualitative insights with quantitative metrics provides a deeper exploration of customer motivations and behaviours. Evolving NPS into a broader feedback strategy is essential for thriving in dynamic markets. By leveraging advanced technologies and diverse metrics, organizations can unlock deeper insights, foster meaningful relationships, and lead in customer experience excellence.

Is generative AI is making customer experience worse?

By Tom Lewis, Senior Vice President of Consulting, TTEC Digital

When ChatGPT burst onto the scene, it was so widely adopted – and so quickly, by so many – that consumers soon started to expect generative AI-level responses from customer service bots. As generative AI suddenly infiltrated so many aspects of daily life, consumers assumed brands would be using it to deliver relevant information and answers at a moment’s notice.

But there’s a major disconnect: customers are expecting generative AI-level responses from customer service chatbots, yet they’re often presented with very narrow-scoped bots that don’t know anything about them. The result? Bots often fail to deliver a satisfactory experience and customers have to be transferred to live associates.

The expectation gap

While chatbots and interactive voice response (IVR) technologies have advanced significantly in the past decade, they are still often chosen by brands not because they deliver a more seamless customer experience but because they are a “cheaper cost channel,” as highlighted in the book The Effortless Experience.

Chatbots are still useful since they often manage to bypass the need for human interaction, a scenario many consumers seek to avoid. But since many of these automated systems are so bad, many consumers just take the stance that they will circumvent the technology and go straight to the human. They believe that they will have to repeat everything they just communicated in the automated system anyway, so why go through the effort?

Many consumers are asking, “Why, if my kid can converse with ChatGPT on an iPad, can’t company chatbots handle basic prompts like ‘What is my balance?'” The typical chatbot experience really highlights the deficiencies with most of the current technology. And recent advances in generative AI just make traditional bots’ limitations more evident.

Traditional chatbots being used by many contact centers are falling short of customers’ expectations in the modern AI age. That’s why I feel generative AI is causing worse customer experiences with a technology that hasn’t changed, simply because of that expectation gap.

Bridging the gap

A major investment focus for private equity and venture capital firms of late has been around AI, specifically generative AI. Many of these firms are also looking at companies that focus on the customer experience SaaS (software as a service) industry, which creates a unique opportunity for investors and businesses.

But some companies remain hesitant. Many brands are still experimenting with this technology in customer service departments because they are concerned it will “hallucinate” or otherwise provide inaccurate answers. This happened recently to an Air Canada customer who was granted a refund via a bot and then told “no” by a human at the company.

For sophisticated voice and text bots to evolve, the next step will be the complete integration of generative AI. As software companies roll out these capabilities and brands experiment and gain comfort with the answers they give customers, consumers will see more and more of these technologies.

How they embrace generative AI will be a true differentiator for brands. Customers will choose those that make interactions effortless.
As they evolve, not only will these bots be able to handle a wider range of inquiries, but they’ll also be able to relate to consumers specifically based on their relationships with the brand. They won’t offer options that are irrelevant to customers, and they will tailor their responses and recommendations. This “mass personalization” will further reduce friction, differentiate brands, and endear consumers to those brands.

The current state may not be that pretty, but the near future looks bright. Brands should be experimenting now with this technology and pressing to roll it out quickly to grow customer loyalty and stand out from competitors.

A version of this article originally ran in Forbes.

A Comprehensive Analysis of AI’s Impact on the Employee Experience

By Ricardo Saltz Gulko

This article is published through a partnership with the European CX Organisation (ECXO). Read the original here.

As we have explored, AI is fundamentally transforming the employee experience, touching every aspect from recruitment and onboarding to learning, development, and day-to-day engagement. This transformation, driven by advanced data analytics, machine learning, and predictive technologies, is ushering in a new era of workplace efficiency and personalization. However, the path forward is not without its challenges. To fully harness the potential of AI, organizations must navigate a complex landscape of ethical, privacy, and change management considerations. This comprehensive conclusion examines these facets in greater depth and outlines a strategic approach to leveraging AI for enhanced employee experiences.

The Multi-Dimensional Impact of AI

AI’s impact on the employee experience is multi-faceted, providing significant benefits while also posing substantial challenges. By examining these dimensions closely, we can better understand how to maximize the advantages while mitigating the risks.

Enhancing Recruitment and Onboarding

AI’s role in recruitment and onboarding is transformative. Automated resume screening, AI-powered interviews, and predictive analytics streamline the hiring process, making it faster and more efficient. This not only improves the candidate experience but also ensures that organizations attract the best talent. However, the reliance on AI also necessitates stringent measures to avoid biases and ensure fairness, as AI systems can inadvertently perpetuate existing biases in hiring practices.

Personalized Learning and Development

AI-driven personalized learning paths and continuous feedback mechanisms are revolutionizing employee development. By tailoring learning experiences to individual needs and career aspirations, AI fosters a culture of continuous improvement and skill acquisition. This adaptability is crucial in an era where the pace of technological change demands ongoing learning. Yet, the success of these initiatives depends on the quality of data and the sophistication of AI algorithms. Organizations must invest in robust data infrastructure and continuously refine their AI models to ensure accuracy and relevance.

Employee Engagement and Well-being

AI’s ability to monitor employee sentiment and personalize well-being programs is a game-changer for maintaining high levels of engagement and satisfaction. Sentiment analysis tools that gauge employee morale through various communication channels enable HR to proactively address issues, thereby preventing potential burnout and disengagement. Personalized well-being programs tailored to individual preferences further enhance this engagement. However, organizations must ensure that these AI tools are used ethically, respecting employee privacy and obtaining explicit consent for data usage.

Performance Management

Image source DALL·E

AI enhances performance management by providing real-time insights and predictive analytics, allowing for timely interventions and support. This leads to more accurate performance evaluations and targeted development plans. The predictive capabilities of AI can identify trends and potential issues before they become significant problems, thereby enhancing overall productivity. Nevertheless, the implementation of AI in performance management requires transparency and fairness to ensure that employees trust the system and feel valued.

Collaboration and Communication

AI-powered communication tools facilitate better collaboration, especially in remote and hybrid work environments. Smart assistants and enhanced collaboration platforms streamline workflows and ensure that employees have the necessary information and resources at their fingertips. This not only boosts productivity but also fosters a more cohesive and collaborative work culture. The challenge lies in ensuring that these tools are user-friendly and seamlessly integrated into existing workflows to minimize disruption.

Navigating Challenges and Ethical Considerations

While the benefits of AI are substantial, the challenges and ethical considerations cannot be overlooked. Data privacy, ethical AI use, and effective change management are critical to the successful implementation of AI technologies.

Data Privacy and Security

The increasing use of AI necessitates stringent data privacy and security measures. Organizations must implement robust data governance frameworks to manage data access, usage, and protection. Ensuring transparency and obtaining employee consent for data usage are essential steps in building trust and compliance with regulations.

Ethical AI Use

The ethical use of AI is paramount. Organizations must regularly audit AI systems for biases and take corrective actions as needed. Maintaining clear documentation of AI decision-making processes and establishing mechanisms for accountability are critical to ensuring that AI decisions are fair and transparent.

Change Management

Integrating AI into the workplace requires careful change management. Organizations must provide comprehensive training programs to help employees understand and effectively use AI tools. Open communication about the benefits and challenges of AI adoption is essential to address employee concerns and foster a positive attitude toward technological change.

The Strategic Path Forward

To fully leverage the potential of AI, organizations must adopt a strategic approach that integrates AI responsibly and effectively into their operations. This involves several key steps:

Invest in Robust Data Infrastructure: Ensure that data used by AI systems is accurate, comprehensive, and up-to-date. Invest in data management technologies and practices that support the effective use of AI.
Develop Ethical AI Practices: Establish clear guidelines and practices for the ethical use of AI. Regularly audit AI systems for biases and ensure transparency in AI decision-making processes.
Foster a Culture of Continuous Learning: Encourage a culture of continuous learning and development. Provide employees with access to AI-driven learning tools and resources that support their career growth and development.
Engage Employees in the AI Journey: Involve employees in the AI adoption process. Communicate the benefits and challenges of AI, address concerns, and provide training and support to help employees adapt to new technologies.
Monitor and Evaluate AI Impact: Continuously monitor the impact of AI on the employee experience. Use feedback and performance data to refine AI systems and practices, ensuring they continue to meet organizational and employee needs.

Read the complete article for a strategic path forward and to learn how Samsung, Unilever, Siemens and IBM are using AI to enhance their employee experience.

Emerging technologies meet familiar challenges at CCW conference

During the dot com boom, I got my first real job at internetnews.com, reporting on new internet startups and how legacy brands tried to keep up with the new online world. There, I was introduced to trade shows by attending Internet World, a sprawling event where the enthusiasm and excitement was palpable for how the internet was changing the world in real time.

This year, I got a similar feeling at the recent Customer Contact Week conference, the popular annual gathering of contact center players in Las Vegas hosted by Customer Management Practice. The show — its largest to date — featured two expo halls full of AI and other tech vendors touting their digital wares. Meanwhile, nearly 5,000 attendees included many contact center practitioners looking to stay on top of the changing industry.

The yin and yang of people and technology was on full display. Conversations volleyed between AI and data analysis to mental health and employee burnout. Last year’s fear and skepticism about AI on the show floor was replaced with an urgency to understand which tools are best suited for a variety of contact center challenges.

The one question on everyone’s mind, as it was at Internet World decades ago: “How do I integrate technology to innovate without damaging my brand, customer, or employee experience?”

Nicole Kyle, managing director of CMP Research, said it’s a question of managing digital dexterity for brands and customers. Digital experiences have been normalized by consumers across all industries, and expectations are high for AI and other digital tools to support internal and external operations. Companies are still hesitant to take big steps, however.

“Inertia is in the industry generally,” Kyle said, but data shows companies can’t afford to wait. Only 7% of consumers feel experiences have gotten much better over the past year, while 55% say they have gotten worse, according to CCW Digital’s 2024 June Market Study.

Like in the internet’s nascent days, there’s no one proven path. “When everyone [in the vendor space] does everything, it’s hard to know where to start,” said James Bednar, head of product at TTEC, during a thought leadership session. Adaptability and flexibility are critical to the CX industry’s evolution, especially as the theory of AI outshines its current practical impact, he added.

“Experiences may not be human to human, but they have to feel human to human.”
Nick Cerise, Chief Marketing Officer, TTEC

For example, Kyle presented data from the new report, “2024-2025 Customer Contact Benchmarking Report,” which found that a majority of consumers are theoretically willing to use chatbots for self-service, but the current reality doesn’t match the vision. Only 17% are confident they can successfully address a service issue with a chatbot.

Balance humanity with technology

Low confidence levels in AI come from uncertainty when humans are not present in the interaction. Discussions at the show focused on balancing technology with humans and knowing when to lean into AI and when to lead with a human touch. Technology alone is not the answer.

“We don’t view automation as being bad,” said Bednar. “A lot matters in the quality of the implementation, with time and effort to get the most out of [the technology].” For customers, he said, use automation and AI tools to reduce friction and escalate to humans quicker with context.

This aligns with new CMP research that cited “creating a frictionless customer experience” as the top priority among global customer contact and CX leaders.

Frictionless doesn’t mean completely automated or completely human. Find the balance that works for unique customer journeys, said TTEC Chief Marketing Officer Nick Cerise.

“Experiences may not be human to human, but they have to feel human to human,” he said.

Leadership lessons from Las Vegas

Besides technology conversations, attendees were hungry for insight on how to engage employees and lead when employees don’t share the same space. Kyle and her colleagues shared research showing that only 11% of contact center employees work on site, compared to 76% before COVID. As a result, engaging and retraining associates is a top leadership priority.

“Even if people don’t say it, they yearn for connection,” added Mark Killick, senior vice president of experience operations for logistics company Shipt, in a conversation with Kyle.

Contact center employees are understandably uneasy about the industry’s uncertain future. They are dealing with more complex calls, unhappy customers, and adopting new tech. Mental health awareness and burnout are challenges that shouldn’t be ignored.

“Even if people don’t say it, they yearn for connection.”
Mark Killick, senior vice president of experience operations, Shipt

Today’s leaders must create deeper, more authentic, and supportive relationships with employees to manage AI’s disruption without jeopardizing their engagement, Kyle said during a leadership workshop. She shared seven competencies for effective CX leaders:

Stability
Autonomy
Emotional intelligence
360-degree coaching
Mindfulness
Leadership
Functional expertise

In a snap poll, most attendees said they felt most confident in their functional expertise, and least confident in their autonomy to trust themselves and their teams to produce good work without micro-managing. That’s backed by CMP research data — only 6% of contact centers grant associates freedom to go off-script and provide “above and beyond” care.

“Leaders need to create space for employees, to help them to see where AI and new technology changes work for the better,” Kyle said.

My overall takeaway from the show was that the CX industry is vibrant and energized about the shift taking place. CX leaders are beginning to show some digital dexterity and are taking steps to balance humans and technology with AI implementations to improve associate work and gain deeper customer insights.

There’s a lot to be optimistic about for the future, even if it’s not yet fully defined, according to Amber Scott, vice president of CX at Serta Simmons Bedding.

“I think there will be space in CX for anyone who wants to be in CX, it will just look different.”

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