Our partnership with InDebted is an example of AI working hand-in-hand with humans, a combination we will see more of in the future. Since their AI Agent joined the team, InDebted’s contact centre productivity grew by 30% and resolution rate by 12%.

Creating space for humans to focus their time and efforts where it’s most needed is one of the greatest values AI can bring to contact centres.

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InDebted is a fintech startup revolutionising debt collection by helping customers boost their financial fitness. Their product uses empathetic digital messaging and offers self-serve options which makes it easy for customers to resolve their accounts. Since its launch in 2016, InDebted has helped over 250,000 customers with a 98% customer satisfaction.

When COVID-19 hit, InDebted saw a spike in the need for debt repayment support. When reaching out to customers, the team realised they were spending most of their time guiding customers on tasks that were fully enabled through self-serve. InDebted was looking for a way to continue supporting customers to self-serve their enquiries and payments while creating the space for their call centre team to help with the trickier enquiries. They wanted a solution that was smarter than an Interactive Voice Response (IVR), could provide great customer experience and deliver on their call deflection goals.

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In Australia, 21,000 people die every year from smoking-related diseases. Quitline Victoria is looking to improve this statistic.

Quitline offers trained counsellors to help and support those who want to quit smoking. Studies show Quitline dramatically increases a person’s chances of stopping smoking. Quitline is a phone-based service and so engagement is an incredibly important metric. The more people that interact with Quitline, the more impact counsellors can have. That’s why Quitline was intrigued by our AI Agents ability to call people at scale with conversational AI.

Completion rates and re-engagement with the Quitline programme have increased since using AI Agent. With AI Agents, Quitline sees answer rates of 62%, completion rates of 18% and re-engagement rates of 10%. Here is the story of how Quitline is helping people quit smoking with us.

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South Western Sydney Local Health District (SWSLHD) is a major healthcare provider in New South Wales, Australia. It’s responsible for the health services of over one million people, operating seven hospitals and a number of other health facilities. The district is known for its diverse population and its commitment to providing high-quality, patient-centered care. SWSLHD plays a critical role in the community, addressing a wide range of health needs from primary care to complex surgical procedures.

SWSLHD’s adoption of an automated rostering system is a prime example of how technology can optimise human-centric operations. By streamlining staff management, the health district saw a significant reduction in administrative time and a substantial decrease in reliance on costly agency staff.

This shift allowed SWSLHD to reallocate resources and empower its clinical staff, ensuring they could focus their valuable time and expertise on what matters most: delivering high-quality patient care.

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What does a plumbing apprenticeship, ten years on a building site, and a career in debt collection have in common?

Absolutely nothing.

But what I learned in all those jobs is that the people closest to the work, the ones actually doing it, always have the best ideas.

When I first started in a contact centre, I was terrible. After a month of following the script and struggling, my manager asked if I wanted to sit next to someone else. In all my wisdom, I decided to sit next to the highest-collecting agent in the room.

His name was Vinny.

The Agent Who Changed Everything

We were working in debt collection. Everyone around us followed the script - voices raised, pushing for payments. Vinny sat there with a dusty Rubik's cube and a stained coffee mug, looking like an old hippy(which, by his own admission, he was).

Everyone else was loud and intense. Vinny was calm. He just talked to people.

And he was the top performer on the floor.

After a few days, I asked him what his secret was.

He smiled: "If you want to collect honey, you don't kick the hive."

That line stuck with me. Vinny showed me something important- sometimes the quietest people, the ones who do things differently, are the real innovators. He hadn't been told to change his approach. He just noticed what worked and trusted his instinct.

But here's the sad part - Vinny never told his manager.

"Because it's not in the script. I'd probably get marked down on QA."

He thought doing what worked meant breaking the rules. And as I sat in the chaos of the contact centre, I realised something:

If innovation feels like rebellion, we've built the wrong culture.

Your Agents Are Already Innovating

Frontline people are natural innovators. They live the process every day - they see what works, what doesn't, and they care about making it better.

But they don't do it because they're thinking about efficiency or the bottom line. They do it because they're human. They want to make their jobs simpler, smoother, less stressful.

Agents find smarter ways through clunky systems and shave seconds off repetitive tasks - not out of laziness, but instinct. It's the most natural form of innovation there is.

That's actually what Lean thinking is all about. It's not a corporate framework - it's common sense, done consistently. It's asking everyday: "What's getting in the way, and how can we make it easier?"

Our agents are already doing that. They just don't call it Lean.

The challenge for leaders is to recognise that behaviour, support it, and give it a name - to turn natural innovation into intentional improvement.

But too often, agents don't speak up. Rigid scripts, metrics obsession, or fear of "breaking process" make them feel like their ideas don't count.

So if we want innovation to thrive, our job as leaders isn't to create it - it's to unblock it.

When Doing the Right Thing Looks Like Breaking the Rules

A few years later, when I became a manager, I inherited Katie. She cared deeply about customers but was struggling with collections. Every one-to-one she'd say, "I'm doing it the way they tell us to, but itdoesn't feel right to push people like that."

So I sat in on her calls.

She wasn't talking about taking payments. She was talking about helping people get out of debt. "Let's figure out a plan that works for you." "What's getting in the way right now?"

On paper, she was off-script. She wasn't hitting the "ask for payment" markers. But her customers trusted her. They opened up. And slowly, her results climbed.

One day she said, "I know it's not what they want, but I feel like I'm actually helping people this way."

Sometimes doing the right thing looks like breaking the rules. I backed her. She became my best collector.

The Power of Perception

If you owed £400 to British Gas and your mortgage advisor told you to pay it, you'd thank them for protecting your credit score.

But if I, a debt collector, called about that same £400, it would feel completely different - even though it's the same advice.

Katie understood that. She changed the conversation from "Can we take a payment?" to "Let's help you get out of debt."

That tiny shift - from transaction to transformation -changed everything. Quiet, human innovation.

And here's the thing: this was before Consumer Duty. Before Treating Customers Fairly. Before the FCA. Katie was ahead of the industry curve.

It's Not People That Stop Innovation. It's Process.

Most of the time, our systems make innovation difficult.

QA, KPIs, scripts, compliance - all built with good intentions. But somewhere along the line, the systems started running the people instead of the other way around.

• ‍QA should measure the quality of the outcome, not just the accuracy of the process. Too often it's about catching mistakes instead of coaching improvement.
• KPIs - we measure speed, wrap time, promises to pay, then wonder why empathy gets rushed. If you measure speed, you'll get speed. If you measure empathy, you'll get empathy. If you measure both - you'll get balance.‍
• Scripts protect consistency, but they shouldn't control humanity. The best conversations are guided, not governed.‍
• Culture is the biggest killer. Not process - fear. Agents stay quiet because they've seen others shot down. Silence in a contact centre isn't peace - it's potential going unheard.

These systems aren't bad. They were just built for consistency, not creativity. Our challenge is to rebuild them for both.

The Ripple Effect of Small Ideas

When people feel safe to share and experiment, you see the ripple effect.

I've seen it first-hand:

An agent suggests a note template - saves 30 seconds per call, three hours a day across the team.

Another swaps "you need to" for "what we can do together is" - complaint rates drop.

A team starts a Friday "what worked this week?" huddle - positivity skyrockets.

Tiny things. Massive impact.

When one person's idea is implemented, everyone starts looking for their own. That's how culture changes - not with slogans, but with ripples.

Start With One Question

When I think back to those days sitting next to Vinny, I realise he probably had no idea how much he changed my outlook.

At the time, I thought it was about keeping calm on the phones. But now I see it was about leadership, culture, and trust. You don't get great performance by pushing harder - you get it by creating the conditions for people to do their best work.

My mission has always been to change the world - not the whole world, at least not at first - but the world of debt collection. Because for too long, our industry has carried a negative perception. But what we really do is help people move forward.

And for me, that started with Vinny. That one quote. He was the stone that started the ripple - the ripple I intend to turn into a wave.

So when you go back to your teams, try this: ask questions.

"What's one small thing we could fix this week?"

"What's that one hack that is an absolute must?"

"How would you improve this process?"

Listen to the answers. Act on them.

Because once people see their ideas become real, that's when the ripples start. And that's when cultures change.

If you want to collect honey, don't kick the hive.

Trust your people. Listen to your frontline. And give them permission to make things better - one small idea, one ripple, one wave at a time.

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Jamie Corbett is an Operations Leader at Advantis Credit. He spoke at Afterwork with MaxContact, a contact centre community event, where he shared his journey from the frontline to leadership and his mission to change the perception of debt collection through agent-focused innovation.

Want to unlock innovation in your contact centre? Discover how MaxContact's AI-powered platform gives your agents the tools and insights to do their best work. Learn more

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As we close out 2025, it's time to reflect on the predictions we made at the start of the year and examine how the contact centre industry actually evolved. While some trends played out largely as anticipated, others took different paths, and the year brought valuable lessons about the pace of technological adoption and the realities of AI implementation.

Let's look back at what we predicted for 2025 and how it measured up against reality.

AI Enters the Value Creation Phase: Prediction Validated

We predicted that 2025 would be the year AI moved from deployment to proving its worth, with organisations becoming more pragmatic about ROI and accepting realistic efficiency gains of around 25% rather than the marketed 70-80%. This proved to be one of our most accurate predictions.

The shift happened – and not just among buyers. AI vendors themselves fundamentally changed their positioning throughout the year, moving away from revolutionary promises toward demonstrable value delivery. The industry experienced a collective reality check, with both clients and vendors acknowledging that AI's current capabilities, while valuable, require focused implementation and realistic expectations.

The buyer sophistication we anticipated materialised as predicted. Organisations approached AI investments with the same caution they learned from the early SaaS era, demanding proof of value before committing resources. This pragmatic approach has actually accelerated successful implementations, as companies focused on achievable wins rather than transformational moonshots.

The adoption patterns we're seeing reflect this pragmatic approach. Our recent benchmark data shows that chatbots (57%), virtual or AI agents (56%), and fraud detection (46%) lead AI adoption – a mix of customer-facing and operational applications that demonstrates organisations are deploying AI across diverse use cases rather than betting everything on a single transformational solution.

This diversified approach demonstrates that organisations have learned a crucial lesson: AI value comes from multiple focused applications working together, not from a single revolutionary solution. Rather than seeking the one AI tool to transform everything, successful contact centres are building an ecosystem of AI capabilities, each solving specific problems and delivering measurable returns.

Looking to 2026, this value-focused approach will intensify. The stakes have never been higher for demonstrating real return on AI investment.

Agent Role Evolution: Still a Work in Progress

Our prediction about enhanced focus on emotional intelligence and complex problem-solving, driven by agents juggling 5-10 applications, proved partially accurate – but the evolution happened more slowly than anticipated.

The fundamental problem we identified, cognitive load from application switching, still exists. When agents need to hunt for information across multiple systems, that friction hasn't been solved at scale. However, there's a crucial development: vendors are now actively prioritising this challenge for the next 12-18 months in a way they weren't before.

The reality is that other AI use cases – digital deflection and efficiency improvements – showed faster, more measurable results and therefore attracted more immediate attention. Agent-focused solutions, which require more complex integrations and change management, naturally took longer to implement.

What's changed is the industry's recognition that solving the agent experience is the next frontier. The easy wins have been captured; now the focus is shifting to the harder problem of reducing cognitive load and empowering agents to focus on high-value interactions. The agent role evolution we predicted is happening – it's just unfolding across a longer timeline than a single year.

Personalisation Meets Privacy: Not Yet

This was one of our predictions that didn't materialise as expected. We highlighted that while 76% of consumers say personalised communications are a key factor in considering a brand, 80% are concerned about how their data is being used – a tension we believed would define how contact centres approached personalisation in 2025. In reality, this particular dynamic didn't become the defining issue we thought it would.

Personalisation absolutely grew, but not in the data-driven, privacy-challenging ways we envisioned. Instead, we saw incremental improvements that enhanced customer experience without crossing privacy boundaries. Conversational IVR systems that recognise customers and speak naturally. Auto-summarisation that gives agents context about previous interactions. Proactive outreach based on known customer journeys.

These are all forms of "respectful personalisation" – but the anticipated tension with privacy regulations didn't materialise because those regulations themselves haven't fully arrived yet. The comprehensive AI-specific data protection frameworks we expected are still pending, creating a situation that's both liberating and potentially dangerous.

The privacy conversation is coming – regulation always lags behind technology. But 2025 taught us that personalisation can advance through improvements in how we interact with customers, not just through deeper data mining. The human touch in personalisation is key, making interactions feel more conversational and contextual.

Economic Pressures Drive Innovation: Absolutely Accurate

This prediction proved devastatingly accurate. The economic pressures we highlighted – minimum wage increases to £12.21 per hour and National Insurance changes – drove exactly the responses we anticipated, and then some.

The most significant development was the dramatic acceleration of offshoring. Organisations didn't just explore offshore and nearshore options; many made wholesale moves, relocating entire operations because even with 20-30% AI-driven productivity improvements, the cost savings of offshore operations (often halving expenses) proved more immediately impactful.

This created an interesting dynamic: AI and offshoring aren't competing strategies, they're complementary ones. Organisations are doing both. The combination delivers the cost reductions that economic pressures demand, while AI provides the efficiency gains needed to maintain service quality in distributed operations.

For UK-based contact centres, this created an urgent imperative: AI implementation is no longer optional for competing at scale. The economics are stark – without AI-driven efficiency improvements, domestic operations struggle to justify their cost premium against offshore alternatives.

The year also validated our prediction about investment focusing on technology with clear cost benefits. Organisations moved past experimentation to demand concrete ROI demonstrations before committing to new platforms. First-contact resolution gained renewed focus as a direct cost-reduction strategy, and workforce management sophistication increased as organisations sought to maximise resource efficiency.

Economic pressure didn't just drive innovation – it fundamentally reshaped operational strategies across the industry.

Hybrid Working 2.0: Matured and Settled

Our prediction about hybrid working evolving beyond basic remote capabilities proved largely accurate. With over 60% of contact centres incorporating home working [Source: MaxContact 2024 KPI Benchmarking Report], 2025 was the year hybrid models matured from experimentation to established practice.

The persistent challenges we identified – training, culture, team cohesion, and the 10% higher attrition in remote teams – didn't disappear, but organisations developed more sophisticated approaches to managing them. The industry moved from asking "does hybrid work?" to "how do we make hybrid work better?"

That said, the journey isn't complete. Hybrid working remains an ongoing optimisation challenge rather than a solved problem. Organisations continue refining their approaches to onboarding, knowledge management, and cultural cohesion in distributed environments. The difference is that these are now recognised as manageable challenges within an accepted working model, rather than existential questions about hybrid working's viability.

What 2025 demonstrated is that hybrid working in contact centres has settled into a mature, sustainable model. It's not perfect, and it requires ongoing attention, but it's no longer experimental. Organisations know what works, what doesn't, and what trade-offs they're making.

Data-Driven Decision Making: Awareness Achieved, Implementation Pending

We predicted 2025 would see contact centres move beyond scratching the surface toward sophisticated analytics and better cross-channel insights. What actually happened was more nuanced: awareness arrived, but implementation is still catching up.

The year's defining lesson about data came from AI implementations: data quality and integration determine success or failure. Organisations attempting AI projects quickly discovered that siloed, fragmented data blocks effective AI deployment. This painful lesson elevated data strategy from a nice-to-have to a fundamental requirement.

As a result, conversations about new technology implementations now start with data. Where is it? How timely is it? How well integrated across systems? This represents genuine progress – the industry now understands that data foundations must come before AI applications.

However, understanding the problem and solving it are different challenges. Data consolidation and integration remain complex, expensive projects that don't happen overnight. Many organisations spent 2025 realising the depth of their data challenges rather than solving them.

Interestingly, we also learned that AI tools themselves are creating more data. Speech analytics transcribing 100% of calls, conversation analytics tracking interaction quality, AI agents generating workflow data – the volume of available information is exploding. The new challenge isn't just integrating existing data sources, but making the tsunami of new data accessible and actionable for frontline decision-makers.

The data-driven future we predicted is coming, but 2025 was the year of recognition rather than transformation. The real work lies ahead in 2026.

Regulatory Compliance and Security: The Waiting Game

Our prediction about new AI-specific regulations joining existing frameworks like Consumer Duty didn't materialise in 2025 – though the reality is more nuanced than a simple absence of regulation.

While dedicated AI legislation hasn't arrived, existing regulatory frameworks continue to apply robustly to AI implementations. The FCA's technology-agnostic, outcomes-focused approach means that contact centres using AI remain fully accountable under Consumer Duty requirements – including obligations to act in good faith, avoid foreseeable harm, and deliver good outcomes for customers. This principles-based approach has proven flexible enough to address AI-related risks without requiring entirely new regulatory structures.

What we're seeing is that leading organisations aren't waiting for AI-specific regulations to establish best practices. Forward-thinking contact centres and vendors are proactively embedding responsible AI principles into their implementations – focusing on data protection, algorithmic transparency, fairness, and customer consent. These organisations recognise that existing regulatory requirements around consumer protection, operational resilience, and data governance already provide a comprehensive framework for responsible AI deployment.

This proactive approach positions organisations well regardless of future regulatory developments. By building AI systems that align with existing regulatory principles and industry best practices, they're creating implementations that are inherently compliance-ready. When AI-specific guidance does arrive – and regulators continue to monitor the space closely – organisations that have already embedded responsible practices will adapt seamlessly rather than facing disruptive retrofitting.

Security incidents throughout the year kept data protection at board-level attention, reinforcing the importance of robust governance around AI implementations. The industry's focus on operational resilience, secure outsourcing arrangements, and clear accountability structures demonstrates mature risk management even in the absence of AI-specific mandates.

The regulatory landscape for 2026 remains one to watch closely. While comprehensive AI-specific frameworks may still be developing, the application of existing regulations to AI use cases continues to evolve through regulatory guidance and industry practice. Organisations taking a principles-based, outcomes-focused approach to AI implementation – prioritising customer outcomes, transparency, and accountability – are positioning themselves as industry leaders in responsible innovation.

Looking Back: What We Learned

Perhaps the most important insight from 2025 is that AI is delivering real value, but in focused applications rather than wholesale revolution. Hybrid working has matured into standard practice, but the human challenges persist. Economic pressures accelerated strategic shifts that might have taken years in different circumstances.

The year validated our core message from the start of 2025: success comes from realistic expectations, focused implementations, and keeping sight of what matters – delivering excellent customer service in sustainable ways for both businesses and employees.

What surprised us least was how little surprised us. As an industry voice advocating for realistic AI expectations while others promised transformation, we saw our predictions largely validated. The technology evolution we anticipated happened; it just happened at the measured pace we expected rather than the revolutionary speed others marketed.

2025 Trends in Brief: What Actually Happened

• AI Value Creation: Vendors and clients shifted focus to ROI and measurable value delivery. Chatbots (57%), virtual/AI agents (56%), and fraud detection (46%) lead AI adoption, with organisations taking a diversified approach rather than betting on single transformational solutions.
• Agent Role Evolution: The cognitive load problem persists, but vendors are now prioritising agent experience as the next frontier after capturing easier AI wins in digital deflection.
• Personalisation vs Privacy: Personalisation grew through conversational IVR, auto-summarisation, and proactive outreach, but the anticipated privacy tensions didn't materialise as AI-specific regulations remain pending.
• Economic Pressures: Offshoring accelerated dramatically as the combination of minimum wage increases and National Insurance changes made offshore operations (often halving costs) more impactful than AI's 20-30% efficiency gains alone.
• Hybrid Working: Matured from experimentation to established practice, with over 60% of contact centres incorporating home working and developing sophisticated approaches to managing persistent challenges.
• Data-Driven Operations: Awareness arrived as AI implementations proved that data quality determines success or failure, but many organisations spent the year realising the depth of their data challenges rather than solving them.
• Regulatory Landscape: AI-specific regulations didn't materialise, but existing frameworks like Consumer Duty continue to apply robustly. Leading organisations are proactively embedding responsible AI principles aligned with existing regulatory requirements.

As we move into 2026, these lessons will guide the next phase of contact centre evolution. The industry has learned to balance innovation with pragmatism, efficiency with experience, and automation with human expertise. The challenges ahead are significant, but 2025 proved the sector's ability to adapt thoughtfully rather than reactively – and that may be the most important trend of all.

Contact centres are under pressure. Rising costs, increased competition, and shifting customer expectations mean teams are being asked to do more with less. The challenge? Making decisions based on incomplete data or small samples that don't represent the full picture.

In our recent webinar, we explored how conversation analytics helps contact centres move beyond guesswork and make data-driven decisions that improve performance, reduce costs, and deliver better customer experiences.

Four Forces Reshaping Contact Strategies

Contact centres face a perfect storm of challenges:

Rising costs and increasing competition
The barrier to entry has lowered across most sectors, meaning competition can move with agility and quickly challenge established players. Every interaction is getting more expensive, whilst high attrition rates mean teams are working harder just to stand still.

Stagnating effectiveness
Sales conversions and first call resolutions are trending downwards for many businesses. Conversations are becoming more complex and harder to resolve on the first attempt.

Growing commercial risk of poor CX
Customers switch providers faster when service falls short. There's no loyalty in those first few minutes of an interaction. Many organisations struggle to route customers accurately, creating inconsistency and avoidable friction for both consumers and agents.

Shifting consumer behaviour
AI call screening, digital buying journeys, and social search are making people harder to reach and changing where and how they want to engage with organisations.

t's not just one challenge – it's the combination of these forces that means traditional contact strategies need to evolve.

What the Data Tells Us

Our 2024 Contact Centre Benchmark Report surveyed 300 contact centre leaders across the UK. The findings are clear: contact centre teams are being asked to do more with the same level of resource, or less.

Key statistics:

• 52% of contact centres report increased agent workloads this year – a 10-point rise since last year‍
• Average agent churn rate sits at 31% – a costly cycle of recruitment and retraining
• Agents are handling more conversations with more complexity and pressure than before

This level of attrition creates both financial costs and operational challenges, impacting team performance and customer experience.

The Sampling Problem

Many contact centres still rely on sampling to understand what's happening in their conversations. The traditional approach might involve listening to 2-3 calls per agent per month – a tiny fraction of overall activity.

When you're handling thousands or tens of thousands of conversations, sampling simply doesn't give you the full picture. You might miss critical trends, coaching opportunities, or compliance issues that only become visible when you analyse conversations at scale.

How Conversation Analytics Works

MaxContact's conversation analytics platform uses AI to analyse 100% of your conversations, not just a sample. Here's what that makes possible:

AI-powered call summaries
Every conversation is automatically summarised, capturing key points, outcomes, and next steps. This saves hours of manual note-taking and makes it easy to understand what happened on any call at a glance.

Sentiment analysis
Track customer and agent sentiment throughout conversations. Identify where interactions go well and where frustration builds, helping you understand the emotional journey of your customers.

Objection tracking
Automatically identify common objections across all conversations. See which objections come up most frequently, how often they're successfully handled, and spot patterns that point to process improvements or product issues.

Custom saved views
Create filtered views that surface the conversations that matter most to your team. Whether you're looking for calls with specific outcomes, objections, sentiment patterns, or compliance markers, saved views let you quickly find what you need without manually searching through thousands of recordings.

AI assistant prompts
Ask questions of your conversation data in natural language. For example, "Show me calls where customers mentioned pricing concerns" or "Find conversations where agents successfully overcame objections." The AI assistant helps you explore your data and uncover insights without needing technical skills.

Real-World Use Cases

Coaching and development
Identify specific coaching opportunities by finding conversations where agents struggle with particular objections or where sentiment deteriorates. Move from generic training to targeted coaching based on actual performance data.

Process improvements
When you see patterns across hundreds of conversations – repeated objections, common confusion points, or friction in specific processes – you have clear evidence to drive process changes and improvements.

Compliance monitoring
Analyse 100% of calls for compliance markers, not just a small sample. Identify potential issues quickly and address them before they become serious problems.

Understanding what drives success
Compare conversations that result in positive outcomes with those that don't. What do successful agents do differently? What patterns emerge in conversations that lead to sales, resolved issues, or satisfied customers?

From Reactive to Proactive

The shift from sampling to comprehensive analysis changes how contact centres operate. Instead of reacting to issues after they've escalated or basing decisions on limited data, conversation analytics gives you:

• Complete visibility into what's happening across all conversations
• Early warning signals when trends start to emerge
• Evidence-based decisions supported by comprehensive data
• Measurable improvements that you can track over time
  

Getting Started with Conversation Analytics

Implementation includes working with MaxContact's product team to define success criteria and create custom views that align with your specific goals. Many organisations start with core use cases – coaching, compliance, objection handling – and then expand as they see the value and discover new applications for the platform.

The platform includes templates to get started quickly, but the real power comes from tailoring the analysis to your specific needs and challenges.

The Bottom Line

Contact centres can't afford to make decisions based on guesswork or small samples. When you're handling thousands of conversations, you need to understand what's happening at scale.

Conversation analytics removes the guesswork, giving you the insights you need to improve coaching, enhance processes, ensure compliance, and ultimately deliver better outcomes for both your team and your customers.

Want to see how conversation analytics could work for your contact centre? Get in touch with our team to arrange a demonstration.

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