How to use Conversation Analytics to increase B2C outbound sales
Visibility is the biggest obstacle to outbound sales performance. Every team has its top performers, the ones with impressive close rates. The real challenge is understanding what they do differently and scaling that approach across the entire team.
With the high cost of data lists, every call counts. Agents need to hit targets, navigate objections, and keep conversations on track, all while managing high volumes and staying compliant. Without insight into what's actually happening on those calls, leaders are making decisions in the dark.
What role does speech analytics play in B2C outbound sales?
If you’re searching for a tool that can reveal exactly what’s working –and what’s not – during your teams’ outbound sales calls, speech analytics is the answer.
Conversation Analytics software takes things further by providing key insights such as:
Common objections
Objection handling effectiveness
Sentiment shift
Outcome trends across multiple calls
By going beyond basic transcription, leaders get actionable insights that highlight what’s working, and what isn’t, on your sales calls. By analysing calls at scale, leaders can focus on strategy and team development instead of time-consuming manual reviews.
How does speech analytics benefit outbound sales?
Let’s face it. Sales team leaders don’t have time to manually review calls. They need actionable insights, not hours of analysis.
Conversation Analytics provides better visibility into call outcomes, helping leaders pivot strategies when recurring issues arise. With better visibility and clarity, leaders can quickly address training gaps and maximise conversion rates.
For B2C outbound sales teams, this means fewer missed opportunities, better-equipped agents, and a measurable boost in ROI.
Uncover the secrets of top-performing sales agents
Every sales team has its stars – the ones who consistently exceed their targets. The secret to team-wide success? Identifying what these top performers do differently and scaling their approach across the team.
Here’s how our speech analytics platform makes this possible:
Track agent performance: Conversation Analytics measures metrics like objection-handling, customer sentiment, and call outcomes. This allows leaders to pinpoint who excels and why.
Build a blueprint for success: By analysing the behaviours of top agents, you can refine scripts, create targeted training materials, and onboard new hires more effectively.
Provide tools to support success: Insights from multi-call analysis mean you can benchmark performance across campaigns, ensuring every team member is set up to succeed.
Master sales objections with speech analytics
We’ve all been there. A promising sales call suddenly stalls because of a customer objection. Without the right tools, overcoming these moments can feel impossible for call agents.
With the right tools, agents can turn these challenges into opportunities. Conversation Analytics helps teams:
Categorise objections into need, time, trust, and cost.
Knowing which categories arise most often allows leaders to:
Train agents on objection-handling techniques that work best.
Address systemic issues, such as pricing concerns or unclear value propositions.
Refine strategies based on data insights
Adjust scripts, improve product positioning, or reshape training programs based on objection data.
Empower call managers with tools to support agent growth
Give call managers better visibility into how their call agents handle objections during calls. This helps call centre leaders provide specific and personalised feedback to their call teams, encouraging continuous improvement and growth.
Build better teams with data-driven training
Great B2C sales teams don’t happen by accident; they’re built. Speech analytics helps you train more effectively by providing insights that support smarter coaching and development.
Here’s how it helps you build a successful call centre sales team:
Deliver personalised coaching Identify individual agents' strengths and weaknesses, such as handling sensitive calls, articulating product knowledge, or countering objections effectively.
Training programs that work Use real-world examples from successful calls to shape training sessions. Focus on proven objection-handling techniques and ensure every agent has the tools to succeed.
Drive sales growth through insights
Spot trends and adapt by identifying which products or services resonate most with customers.
Gain competitor intelligence by analysing mentions of competitor offers or pricing, enabling leaders to respond with compelling counteroffers.
Adapt to market changes by detecting shifts in customer demand through call data and adjusting messaging or strategies proactively.
Conclusion
Speech analytics is more than just a tool; it’s a transformative solution for B2C outbound sales teams.
Gain post-call insights into agent performance and customer feedback.
Scale best practices across your team to improve outcomes.
Empower agents with actionable data to close more deals.
Streamline training and coaching for long-term success.
Adapt quickly to market and customer changes.
Ready to transform your contact centre’s performance? Book a demo today to see how Conversation Analytics can strengthen your outbound sales strategy.
(() => {
const run = () => {
const rich = document.querySelector('#rich-text');
const toc = document.querySelector('#toc');
if (!rich || !toc) return;
const headings = rich.querySelectorAll('h2');
if (!headings.length) {
toc.style.display = 'none';
return;
}
const slugCounts = Object.create(null);
const slugify = (str) => {
const base = (str || '')
.trim()
.toLowerCase()
.normalize('NFD').replace(/[\u0300-\u036f]/g, '')
.replace(/[^a-z0-9\s-]/g, '')
.replace(/\s+/g, '-')
.replace(/-+/g, '-');
const n = (slugCounts[base] = (slugCounts[base] || 0) + 1);
return base ? (n > 1 ? `${base}-${n}` : base) : `section-${n}`;
};
// Build TOC off-DOM
const frag = document.createDocumentFragment();
for (let i = 0; i < headings.length; i++) {
const h = headings[i];
const text = (h.textContent || '').trim() || `Section ${i + 1}`;
if (!h.id) h.id = slugify(text);
const a = document.createElement('a');
a.href = `#${h.id}`;
a.className = 'content_link is-secondary';
a.dataset.target = h.id;
a.setAttribute('aria-label', text);
const p = document.createElement('p');
p.className = 'text-size-small';
p.textContent = text;
a.appendChild(p);
frag.appendChild(a);
}
// Single DOM update
toc.innerHTML = '';
toc.appendChild(frag);
toc.addEventListener('click', (e) => {
const link = e.target.closest('a.content_link[href^="#"]');
if (!link) return;
e.preventDefault();
const id = link.getAttribute('href').slice(1);
const target = document.getElementById(id);
if (!target) return;
// Only compute layout once
const targetTop = target.getBoundingClientRect().top + window.scrollY;
const finalY = targetTop - 150;
window.scrollTo({ top: finalY, behavior: 'smooth' });
history.replaceState(null, '', `#${id}`);
}, { passive: false });
};
// Webflow-safe “run after everything is ready”
if (window.Webflow && Webflow.push) {
Webflow.push(() => requestAnimationFrame(run));
} else {
document.addEventListener('DOMContentLoaded', () => requestAnimationFrame(run));
}
})();
related articles
you might also like
Our articles and industry insights give you expert perspectives, practical strategies, and the latest trends to help your business connect smarter and perform better.
Blog
December 5, 2025
How to Remove Guesswork from Contact Strategies with Conversation Analytics
Contact centres can be high-pressure environments, with plenty of challenges to tackle; managing customer interactions, staying compliant and helping agents perform at their best, to name a few. That’s where speech analytics comes in.
(() => {
const WORDS_PER_MINUTE = 200;
const MULTIPLIER = 1; // your choice
const estimateMinutes = (el) => {
if (!el) return null;
const text = (el.innerText || el.textContent || "").trim();
if (!text) return 1;
const words = (text.match(/\S+/g) || []).length;
const baseMinutes = Math.max(1, Math.ceil(words / WORDS_PER_MINUTE));
return Math.max(1, Math.ceil(baseMinutes * MULTIPLIER));
};
const findNearestTargetInItem = (itemRoot, rt) => {
if (!itemRoot) return null;
return itemRoot.querySelector('.is-text');
};
const applyWithin = (root) => {
// More forgiving selector: attribute present or equals "true"
root.querySelectorAll('[data-rich-text], [data-rich-text="true"]').forEach((rt) => {
const itemRoot =
rt.closest('[role="listitem"]') ||
rt.closest('.w-dyn-item') ||
rt.parentElement ||
root;
const target = findNearestTargetInItem(itemRoot, rt);
if (!target) return;
const mins = estimateMinutes(rt);
if (mins != null) target.textContent = `${mins} MIN READ`;
});
};
const init = () => {
applyWithin(document);
// Re-apply on dynamic changes (pagination/filters)
const mo = new MutationObserver((mutations) => {
for (const m of mutations) {
for (const n of m.addedNodes) {
if (!(n instanceof Element)) continue;
if (
n.matches('[data-rich-text], [data-rich-text="true"], [role="list"], .w-dyn-items, .w-dyn-item') ||
n.querySelector?.('[data-rich-text], [data-rich-text="true"]')
) {
applyWithin(n);
}
}
}
});
mo.observe(document.body, { childList: true, subtree: true });
};
// Robust bootstrapping
if (window.Webflow && Array.isArray(window.Webflow)) {
window.Webflow.push(init);
} else if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init, { once: true });
} else {
// DOM is already ready; run now
init();
}
})();
By recording and transcribing 100% of customer conversations, analysing sentiment, categorising topics and ranking agent performance, speech analytics helps contact centre leaders uncover actionable insights, make smarter decisions and optimise business operations.In this article, we’ll explore 10 practical ways our speech analytics platform can be used to transform the way contact centres work. From improving compliance checks to empowering agents and enhancing sales strategies, there’s a lot to gain from this powerful technology.
1. Speeding up call quality assessments
In traditional quality assessments, quality assurance (QA) teams are often required to listen to hundreds of call recordings to evaluate, understand agent-customer conversations, and assess the quality of the call. This manual process is time-consuming, especially for call centres dealing with high call volumes.
Speech analytics speeds up the QA process by transcribing speech-to-text, with every call transcribed into a full text version. Thanks to text transcriptions, QA teams can read through calls – which takes 50% less time than listening to them. This is because QA teams can skip directly to specific points in the conversation that need attention.
By searching within transcripts for specific phrases or keywords, it is much easier and quicker to find sections where issues have arisen, saving significant time but without sacrificing detail.
2. Keyword tracking for compliance
Staying compliant is crucial for contact centres, particularly in industries with strict regulations such as sales, collections, or finance. Agents often need to say specific phrases or mandatory statements during calls – for example, those required by the Financial Conduct Authority (FCA) or Ofcom guidelines.
MaxContact’s Spokn AI makes compliance easier to monitor and achieve, as it can be set up to track and filter certain keywords across all call transcripts. Compliance teams can set up the software to flag calls where those important phrases are missing or not used correctly.
This means you can quickly spot and fix potential compliance issues without spending hours manually checking calls. It’s a simple way to reduce risks, save time, and make sure your team is consistently ticking all the right boxes.
3. Keyword tracking for vulnerability detection
Contact centres need to identify and provide special care for vulnerable customers, such as those who express concerns related to financial or emotional hardship. This is where the ability to search for phrases or mandatory statements across transcripts once again supports the process.
Using speech analytics and keyword filtering, it is possible to detect sensitive language within conversations, flagging words and phrases that may indicate vulnerability. By automating this process, speech analytics software helps agents and supervisors identify potentially vulnerable customers quickly, allowing them to respond with empathy, follow specific support protocols, or even escalate the conversation to a specialist.
This targeted approach improves customer care and means vulnerable individuals receive the assistance they need.
4. Optimising sales playbooks
For sales and collections teams, the ability to handle customer objections effectively can have a huge impact on call outcomes. This is where speech analytics steps in to give agents an edge. By analysing call topics, customer objections and sentiment trends, tools like speech analytics can help call centre supervisors to uncover what’s working (and what’s not) in customer interactions.
Senior call centre leaders can use these insights to tweak call centre scripts, refine sales tactics and even create “battlecards” to tackle competitor comparisons head-on. These resources and insights are then shared with call agents.
On top of that, speech analytics can indicate how call agents respond to objections by looking at phrase level sentiment, helping pinpoint successful techniques and areas for improvement. With these insights, teams can continuously optimise their playbooks, leading to smoother calls, better outcomes and more conversions.
5. Improving agent performance through sentiment analysis
Helping agents improve starts with understanding where they might be struggling or excelling. Sentiment analysis does this by breaking conversations into phrases and identifying words of positive or negative sentiment post-call. It draws attention to areas where an agent might be lacking empathy, patience, or clarity – all key factors that impact how customers feel during a call.
When speech analytics software uncovers calls with high customer frustration or negative sentiment, call supervisors can step in with tailored coaching. Whether it’s working on tone, handling sensitive situations, or improving listening skills, this focused feedback helps agents refine their approach. The result? Happier customers, stronger relationships and call agents who feel more confident.
6. Identifying what works well to amplify success
Improvement is important, but so is recognising what’s already working. Speech analytics helps teams to see moments of positive sentiment in calls, showing where call agents have handled objections and sensitive interactions effectively, striking the right chord with customers.
By considering the ‘why’ behind these successful interactions, managers can uncover tactics, language, or approaches that deliver great results time and time again. These insights can be shared across the team to raise everyone’s game, and they’re also an important resource for marketing or sales teams.
Whether it’s refining scripts or crafting new campaigns, using proven techniques ensures greater consistency and impact across your contact centre.
7. Driving operational efficiencies
Efficiency is key in contact centres, but it shouldn’t come at the expense of quality. Speech analytics helps strike the perfect balance, with tools like call summaries, topic detection and sentiment analysis. These features allow call centre supervisors to identify common call topics and recurring customer issues.
Armed with this data, managers can design targeted training and streamline processes to ensure agents are ready to tackle frequent queries right away. This leads to fewer call transfers and more first-call resolutions, cutting down handling times while boosting customer satisfaction. The outcome is a more efficient operation that saves time, resources and costs – all without compromising on quality.
8. Gaining insight into team dynamics
Understanding your team’s performance isn’t just about numbers or individual performance – it’s about seeing the bigger picture. With speech analytics, call centre managers have a clearer view of overall team performance and can break down calls by agent, objection type or campaign. This level of detail helps uncover collective team performance as well as each agent’s individual strengths and areas where they may need extra support.
By tracking objection trends, comparing metrics across agents and teams, and analysing how individuals handle challenging situations, call centre leaders can make coaching much more targeted. Instead of a one-size-fits-all approach, these data-driven insights allow team leaders to tailor their feedback, helping every agent play to their strengths while improving in areas where they struggle. This leads to a more skilled, confident and well-rounded team.
9. Empowering agents to self-improve
Speech analytics isn’t just a tool for managers – it’s also a powerful way to help agents grow and improve. With features like call recaps, sentiment insights, and objection tracking, managers can share clear, objective feedback based on real data from their agents’ interactions.
By providing tailored feedback, managers can help agents spot patterns, identify areas for improvement and refine their skills – whether it’s handling objections more effectively, improving tone, or building stronger rapport with customers.
This personalised coaching creates a sense of accountability and motivates agents to continuously grow.
With clear, actionable feedback, agents can deliver better customer experiences and contribute to the team’s success. Plus, happier, more capable agents are less likely to burn out, which helps reduce turnover and maintain a strong team.
10. Conducting market research to inform growth strategies
Spokn AI is a powerful tool for uncovering what your customers really want. By evaluating recurring topics and sentiment trends across conversations, it helps contact centres build a clear picture of customer needs, preferences and pain points.
With advanced topic detection and transcription capabilities, speech analytics empower businesses to spot emerging trends and patterns that might not be immediately obvious. Whether it’s identifying new product demands, spotting service gaps, or understanding shifts in customer sentiment, these insights are invaluable for shaping future strategies.
This customer-first approach gives contact centres a new-level of visibility into customer information. Often, customer information is recorded in siloed notes (particularly in hybrid working environments) making them inaccessible at a top data level. Speech analytics aggregates and analyses conversations, uncovering actionable insights into sentiment, trends and preferences. This enables businesses to adapt to market changes, create targeted campaigns and design products and services that truly meet customer needs.
Speech analytics is powerful software that makes the way for smarter, more efficient and more impactful operations in your contact centre. By analysing 100% of customer interactions, platforms like Spokn AI offer insights that help tackle the unique challenges of a fast-paced call centre floor, from improving compliance and agent performance to refining sales strategies and uncovering customer needs.
The key to success lies in using these insights to take action: refining processes, tailoring coaching and aligning your strategy with the data. Not only does this lead to better outcomes for your team and business – it also creates a stronger, more positive experience for your customers too.
As customer expectations grow and contact centres become more complex, embracing speech analytics tools can give you the clarity, confidence and competitive edge you need to thrive.
By leaning into the capabilities of speech analytics, you’re not just keeping up; you’re staying ahead.
Blog
5 min read
How to Remain Compliant with AI Speech Analytics
AI-powered speech analytics software helps to drive better performance and outcomes in contact centres. But with regulations tightening, the pressure is on to ensure conversation analytics tools are used compliantly.
(() => {
const WORDS_PER_MINUTE = 200;
const MULTIPLIER = 1; // your choice
const estimateMinutes = (el) => {
if (!el) return null;
const text = (el.innerText || el.textContent || "").trim();
if (!text) return 1;
const words = (text.match(/\S+/g) || []).length;
const baseMinutes = Math.max(1, Math.ceil(words / WORDS_PER_MINUTE));
return Math.max(1, Math.ceil(baseMinutes * MULTIPLIER));
};
const findNearestTargetInItem = (itemRoot, rt) => {
if (!itemRoot) return null;
return itemRoot.querySelector('.is-text');
};
const applyWithin = (root) => {
// More forgiving selector: attribute present or equals "true"
root.querySelectorAll('[data-rich-text], [data-rich-text="true"]').forEach((rt) => {
const itemRoot =
rt.closest('[role="listitem"]') ||
rt.closest('.w-dyn-item') ||
rt.parentElement ||
root;
const target = findNearestTargetInItem(itemRoot, rt);
if (!target) return;
const mins = estimateMinutes(rt);
if (mins != null) target.textContent = `${mins} MIN READ`;
});
};
const init = () => {
applyWithin(document);
// Re-apply on dynamic changes (pagination/filters)
const mo = new MutationObserver((mutations) => {
for (const m of mutations) {
for (const n of m.addedNodes) {
if (!(n instanceof Element)) continue;
if (
n.matches('[data-rich-text], [data-rich-text="true"], [role="list"], .w-dyn-items, .w-dyn-item') ||
n.querySelector?.('[data-rich-text], [data-rich-text="true"]')
) {
applyWithin(n);
}
}
}
});
mo.observe(document.body, { childList: true, subtree: true });
};
// Robust bootstrapping
if (window.Webflow && Array.isArray(window.Webflow)) {
window.Webflow.push(init);
} else if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init, { once: true });
} else {
// DOM is already ready; run now
init();
}
})();
The regulatory pressure on contact centres has never been greater. GDPR and the Data Protection Act set the baseline. FCA Consumer Duty is now in active enforcement, requiring businesses to evidence good customer outcomes and not just document processes. PECR reforms under the Data (Use and Access) Act 2025 have raised maximum fines from £500,000 to £17.5 million. And with the EU AI Act and a UK AI Bill both on the horizon, the direction is clear: more accountability, more evidence requirements and higher stakes for getting it wrong.
Used correctly, AI speech analytics addresses most of these challenges directly. But it also needs to be implemented in a way that is itself compliant, with GDPR, with data protection obligations, and with the trust customers place in organisations that record and analyse their conversations.
Why AI Speech Analytics, Not Just Keyword Spotting
Older quality monitoring tools worked on rules. Flag a call if a specific word appeared. Miss it if the agent used different phrasing, spoke too quickly, or the system didn't recognise the accent.
AI conversation analytics works differently. Rather than matching fixed strings of text, it uses natural language processing to understand context, intent and meaning. It recognises that "I can't afford this right now" and "the payments are getting difficult" are both vulnerability signals, even though neither contains a flagged keyword. It picks up on tone, pace, and sentiment as opposed to individual words.
For compliance purposes the distinction between the two matters. A rule-based system tells you whether a phrase was said. AI tells you whether the conversation was compliant and flags the calls where the right words were used in the wrong way, or where the right words were absent entirely.
How AI Speech Analytics Supports Regulatory Compliance
Staying Compliant with GDPR and the Data Protection Act
Under UK GDPR and the Data Protection Act 2018, contact centres must inform customers that calls are being recorded, handle personal data lawfully, respond to Data Subject Access Requests within statutory timeframes, and ensure sensitive information is never captured inappropriately.
Compliance Challenge
Conversation Analytics Feature
What It Does
Agents must inform customers calls are being recorded on every interaction.
Keyword and phrase tracking
Automatically scans transcripts for required disclosure language and flags calls where it is absent or delivered incorrectly.
Customers can request access to, amendment of, or deletion of their personal data, requiring contact centres to locate relevant interactions quickly.
Transcript search
Every interaction is transcribed and searchable by customer identifier, date range, or keyword, reducing a manual process to minutes.
Agents must pause call recordings during card data collection to prevent sensitive information being captured.
Audit trail and compliance logging
Provides an auditable record of whether pause protocols are being followed and surfaces calls where they may not have been.
AI-driven call scoring, routing, and analytics must be transparent, challengeable, and subject to human override under the DUAA 2025.
Human-in-the-loop architecture
AI augments agents rather than replacing them, satisfying the DUAA's oversight requirements by design.
Meeting FCA Consumer Duty Obligations
Consumer Duty has moved from implementation to active enforcement. The FCA is running four cross-cutting supervisory reviews throughout 2026, and the question regulators are asking has shifted: it's no longer "have you implemented Consumer Duty?" It's "can you demonstrate, with evidence, that your business is consistently delivering good outcomes for customers?"
Most contact centres can't provide that evidence at scale. Conversation Analytics is built to change that.
Compliance Challenge
Conversation Analytics Feature
What It Does
Agents must tailor interactions to individual customer needs. But assessing this consistently at scale is not possible through manual review.
Sentiment analysis
Identifies moments of customer confusion, frustration, or disengagement, flagging interactions where an agent may not have communicated clearly or adapted their approach.
Vulnerability isn't always declared. Signs of financial hardship, emotional distress, health conditions, or communication difficulties can be subtle and easy to miss.
Vulnerability detection
Scans every transcript for language patterns associated with vulnerability and alerts supervisors quickly so agents can adjust, follow support protocols, or escalate.
Agents under target pressure can drift toward assertive selling techniques that conflict with Consumer Duty expectations.
Sales conduct monitoring
Analyses keyword usage, pace, and tone across sales interactions to surface calls where the approach may not meet the required standard.
The FCA expects documented, data-driven proof of good customer outcomes that go beyond process documentation.
AI call scoring and compliance logging
Every scored call is logged with an auditable record, providing evidence of adherence at scale rather than across a manually reviewed sample.
Handling Complaints in Line with the Consumer Rights Act
The Consumer Rights Act requires contact centres to handle complaints effectively by addressing root causes, and demonstrating a genuine commitment to consumer satisfaction. The challenge isn't just what happens on individual calls; it's spotting recurring patterns before they become regulatory problems.
Compliance Challenge
Conversation Analytics Feature
What It Does
High call volumes make it difficult to identify recurring complaint themes before they become regulatory concerns.
Topic detection
Automatically categorises complaint patterns across the full call volume, surfacing recurring issues before they escalate.
Resolving complaints within required timeframes and following appropriate procedures must be demonstrable.
Audit trail and compliance logging
Every interaction is transcribed, searchable, and logged, providing an auditable record of complaint handling that supports verification.
Generic training rarely addresses the specific gaps that lead to poor complaint outcomes.
Agent performance analysis
Identifies specific training gaps from real complaint interactions, enabling targeted coaching rather than blanket training.
How to Monitor and Flag Compliance Issues Automatically
Knowing what compliance requires and having a platform that enforces it consistently are two different things. Here is how compliance monitoring works inside Conversation Analytics day to day.
Keyword and phrase tracking
Configure the platform to track specific language across all transcripts: mandatory disclosure statements, consent language, direct debit scripts, vulnerability indicators. Alerts fire automatically for calls where those phrases are absent, incorrectly used, or appear at the wrong point in the conversation. This runs across 100% of calls and isn’t restricted to a selected sample.
Auto-fail rules
Ensure critical breaches are never obscured by an otherwise acceptable call score. For example, a missed payment recording pause, an absent FCA disclosure or an incomplete ID verification can be configured as automatic failures, meaning every instance is flagged and logged without relying on reviewer judgement.
Real-time alerts
When a vulnerability signal or compliance phrase is detected during a call, supervisors receive an immediate alert. They can join the call, prompt the agent, or prepare for post-call review, allowing them to intervene before the situation escalates rather than discovering it in an audit.
Auditable records
Every flagged call, scored interaction, and compliance alert is logged. When a regulator, internal audit team, or legal function asks for evidence (under Consumer Duty, GDPR, or sector-specific requirements) it exists across every scored call.
Choosing an AI Analytics Platform That Is Compliant by Design
AI speech analytics only supports compliance if the platform handling your data is itself compliant. When evaluating tools, these are the questions to ask and be cautious of providers who cannot answer them clearly.
What to Look For
Why It Matters
MaxContact's Position
Data residency
Processing customer call data outside the UK introduces compliance complexity under UK GDPR.
MaxContact's databases are located within the UK, so data remains subject to UK data protection law throughout its lifecycle.
Encryption and access controls
Data must be protected against unauthorised access, with role-based permissions limiting who can view recordings and transcripts.
Encryption, access controls, and role-based permissions ensure recordings and transcripts are accessible only to authorised users.
Consent and transparency
Customers must be informed their calls will be recorded and analysed. The platform should provide evidence this happened.
Conversation Analytics provides transcript evidence that disclosures were made correctly on every scored call.
Human oversight of AI decisions
The DUAA 2025 requires organisations to maintain a human override mechanism for AI-driven processes.
Human-in-the-loop architecture ensures AI augments rather than replaces human judgement across all platform functions.
How Honey Group Improved Compliance Coverage with Conversation Analytics
Honey Group, a financial services contact centre, was struggling to review calls for compliance at the volume their operation required. Manual review covered only a fraction of interactions, leaving significant compliance exposure unaddressed.
Working with MaxContact, Honey Group implemented Conversation Analytics to monitor calls across their full interaction volume. Using transcript search and sentiment analysis, their team now identifies inappropriate mentions of sensitive topics, verifies that required disclosures are being made consistently, and flags interactions warranting further review without the resource overhead of manual listening. The result has been a significant improvement in QA coverage and a more systematic approach to both compliance monitoring and agent training.
The Regulatory Changes Contact Centres Need to Prepare for Now
The regulatory environment is changing fast:
PECR fines have increased 35-fold under the Data (Use and Access) Act 2025, from £500,000 to £17.5 million or 4% of global turnover
EU AI Act chatbot and AI transparency obligations apply from August 2026, with penalties reaching €35 million or 7% of global turnover for non-compliance
UK AI Bill is expected, adding a further governance layer to AI deployment in contact centres
The organisations that navigate this well are those treating compliance as a strategic investment rather than something to react to. Conversation Analytics is built to move with that environment by providing the audit trails, evidence of outcomes, and human oversight mechanisms that regulators are increasingly demanding.
If you're assessing how AI speech analytics fits into your compliance strategy, the UK Contact Centre Regulatory Guide covers what's changed, what's coming, and what your operation needs to do about it.
Or, if you want to see how Conversation Analytics works in your environment, book a demo and we'll show you.
Blog
5 min read
Your Contact Centre Has Four Problems. AI Is Already Solving Them.
Most contact centre teams are sitting on the same four challenges. Here's what the data says — and what good looks like.
(() => {
const WORDS_PER_MINUTE = 200;
const MULTIPLIER = 1; // your choice
const estimateMinutes = (el) => {
if (!el) return null;
const text = (el.innerText || el.textContent || "").trim();
if (!text) return 1;
const words = (text.match(/\S+/g) || []).length;
const baseMinutes = Math.max(1, Math.ceil(words / WORDS_PER_MINUTE));
return Math.max(1, Math.ceil(baseMinutes * MULTIPLIER));
};
const findNearestTargetInItem = (itemRoot, rt) => {
if (!itemRoot) return null;
return itemRoot.querySelector('.is-text');
};
const applyWithin = (root) => {
// More forgiving selector: attribute present or equals "true"
root.querySelectorAll('[data-rich-text], [data-rich-text="true"]').forEach((rt) => {
const itemRoot =
rt.closest('[role="listitem"]') ||
rt.closest('.w-dyn-item') ||
rt.parentElement ||
root;
const target = findNearestTargetInItem(itemRoot, rt);
if (!target) return;
const mins = estimateMinutes(rt);
if (mins != null) target.textContent = `${mins} MIN READ`;
});
};
const init = () => {
applyWithin(document);
// Re-apply on dynamic changes (pagination/filters)
const mo = new MutationObserver((mutations) => {
for (const m of mutations) {
for (const n of m.addedNodes) {
if (!(n instanceof Element)) continue;
if (
n.matches('[data-rich-text], [data-rich-text="true"], [role="list"], .w-dyn-items, .w-dyn-item') ||
n.querySelector?.('[data-rich-text], [data-rich-text="true"]')
) {
applyWithin(n);
}
}
}
});
mo.observe(document.body, { childList: true, subtree: true });
};
// Robust bootstrapping
if (window.Webflow && Array.isArray(window.Webflow)) {
window.Webflow.push(init);
} else if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init, { once: true });
} else {
// DOM is already ready; run now
init();
}
})();
If you run a contact centre, the chances are you're managing rising call times, inconsistent quality reviews, repeat contacts that erode margin, and a personalisation gap that's hard to close without the right data infrastructure underneath it.
None of these are new problems. But the distance between where most operations are today and what's now achievable is narrowing fast - and the teams pulling ahead aren't waiting for a full platform overhaul to make it happen.
At MaxContact's recent webinar, hosted by Marketing Director Kayleigh Tait and Principal Product Manager Conor Bowler, we worked through four specific challenges that are costing contact centres time and money right now - and showed, live, how AI is solving each one. Here's what we covered.
Challenge 1: Call length is rising, and post-call admin is a big reason why
Average service call duration in the UK is now 422 seconds - seven minutes per call - according to Contact Babel. That's the highest figure recorded in 20 years of data collection, and it's been climbing steadily since 2004. There's no sign it comes down on its own.
A large part of the reason is fragmentation. 96% of agents are still navigating multiple systems on every single call. Only 4% of UK contact centres operate from a single unified desktop. 40% of agents are juggling more than four applications at once - doing real-time system-surfing while simultaneously trying to solve a customer's problem or make a sale.
Then there's wrap time. 18% of every call is post-call admin: writing up notes, updating records, triggering next steps. That's queue time growing while your agents do data entry.
The commercial impact is significant. For a 50-agent contact centre making 50 calls a day, a 50% reduction in wrap time is worth over £175,000 a year - based on MaxContact's own ROI modelling.
What good looks like:
An agent wrap-up summary that generates automatically within seconds of a call ending, built from a live stereo transcript that's already separated the agent's voice from the customer's. The agent reviews it, makes any edits, and submits. No blank page. No three to five minutes of typing between every call.
MaxContact's Agent Wrap-Up Summary feature — currently in alpha testing and moving to beta in mid-June — does exactly this. Prompts are fully configurable via Prompt Studio, so the output format, structure, and language match your operation's context, whether that's a collections agency, a sales team, or a customer service function.
Challenge 2: Repeat contacts are eroding margin and driving churn
42% of UK consumers have already switched provider because of a poor contact centre experience - not because of a product issue, but because of the experience itself. A further 38% have seriously considered it. MaxContact's consumer research, shared at the After Work with MaxContact event, makes clear this isn't an edge-case risk.
First contact resolution is what Contact Babel calls the "miracle metric." It's consistently cited as one of the top two KPIs most influential on customer satisfaction. Every repeat call is a direct hit on that number - and at roughly £5 per service call, a repeat contact doubles your cost before you've factored in agent time and churn risk.
The AI angle here is often misunderstood. 69% of customers rate AI worse than humans for understanding their issue - but the problem usually isn't the AI itself. It's where it's introduced in the customer journey. AI deployed in an emotionally charged or complex situation will struggle. The bigger failure point is the handover: when a customer escalates from an AI interaction to a human agent and has to repeat everything from scratch. That's where trust breaks.
What good looks like:
Context continuity. When a human agent picks up - regardless of whether the previous interaction was with an AI agent, a chatbot, or a colleague - they start with the full picture. Customer history, intent, what happened last time, what was agreed. Not a blank screen.
That requires clean data flowing across your channels and a single interface for agents to work from. It's a foundational requirement, not an aspirational one.
Challenge 3: QA based on a sample of 1–2 calls per week isn't good enough
The average contact centre reviews one to two calls per agent per week. Contact Babel's most recent guide describes this explicitly as "neither fair nor valid as a performance measurement tool." That's not a MaxContact opinion - it's the industry's own assessment of its standard practice.
The consequence is that coaching decisions, script adjustments, and performance reviews are all made on a handful of conversations selected at random. Objection handling failures, compliance drift, and the moments where an agent is genuinely struggling can remain completely invisible until the problem is already embedded.
What good looks like:
100% call coverage. Scorecards built on every conversation, not a sample. AI that makes that achievable without overwhelming your QA team.
MaxContact's AI call scoring — now generally available to all Conversation Analytics customers — reduces QA review time per call from 30 minutes to 5 minutes. That's approximately four days of analyst capacity returned to the team every month. Capacity that can go into actual coaching, script development, and performance improvement.
Scorecards are fully configurable: yes/no questions, rating scales, observation notes, auto-fail criteria. Business context can be set per scorecard so the AI understands your products, processes, and compliance requirements before it starts scoring. Scheduled auto-QA at scale — allowing always-on scoring as calls come in, or one-off compliance campaigns across historical data — is moving to beta on 6 July, with general availability planned for early August.
Challenge 4: Personalisation requires the right building blocks first
76% of consumers say personalised communications influence their brand choice, according to Salesforce's State of the Connected Customer. Personalisation at conversation level isn't a luxury - it's a commercial lever.
But it doesn't start with AI. It starts with having the right infrastructure in place:
Customer history and intent available before the conversation starts
In-call sentiment detection so agents know when someone is frustrated or at risk
Consistent context across channels - what happened on the last call, the last chat, the last AI interaction
Next-best-action guidance that surfaces what your best agents do in key moments, and replicates it across the team in real time
Once those building blocks are in place, personalisation stops being an aspiration. It becomes the logical next step, because you already have everything you need.
The bigger picture: it's not about solving one problem in isolation
The demo Conor ran at the webinar wasn't designed to show five separate features. It was designed to show how they connect.
A single agent interface. An automated wrap-up that feeds clean data into the next interaction. Real-time transcription with stereo accuracy that improves everything built on top of it. AI scoring across 100% of conversations. Context that follows the customer, not the channel.
The teams that are getting this right aren't deploying AI as a standalone fix for one metric. They're building a connected system where each piece makes the next one work better.
That's the direction of travel. And a lot of it is available right now.
Call Centre Quality Monitoring: Why Sampling Isn't Enough
Quality assurance is one of the most compliance-critical functions in any contact centre, and one of the most under-resourced. For most operations, the gap between what QA teams can review and what regulators now expect to see evidenced has never been wider.
(() => {
const WORDS_PER_MINUTE = 200;
const MULTIPLIER = 1; // your choice
const estimateMinutes = (el) => {
if (!el) return null;
const text = (el.innerText || el.textContent || "").trim();
if (!text) return 1;
const words = (text.match(/\S+/g) || []).length;
const baseMinutes = Math.max(1, Math.ceil(words / WORDS_PER_MINUTE));
return Math.max(1, Math.ceil(baseMinutes * MULTIPLIER));
};
const findNearestTargetInItem = (itemRoot, rt) => {
if (!itemRoot) return null;
return itemRoot.querySelector('.is-text');
};
const applyWithin = (root) => {
// More forgiving selector: attribute present or equals "true"
root.querySelectorAll('[data-rich-text], [data-rich-text="true"]').forEach((rt) => {
const itemRoot =
rt.closest('[role="listitem"]') ||
rt.closest('.w-dyn-item') ||
rt.parentElement ||
root;
const target = findNearestTargetInItem(itemRoot, rt);
if (!target) return;
const mins = estimateMinutes(rt);
if (mins != null) target.textContent = `${mins} MIN READ`;
});
};
const init = () => {
applyWithin(document);
// Re-apply on dynamic changes (pagination/filters)
const mo = new MutationObserver((mutations) => {
for (const m of mutations) {
for (const n of m.addedNodes) {
if (!(n instanceof Element)) continue;
if (
n.matches('[data-rich-text], [data-rich-text="true"], [role="list"], .w-dyn-items, .w-dyn-item') ||
n.querySelector?.('[data-rich-text], [data-rich-text="true"]')
) {
applyWithin(n);
}
}
}
});
mo.observe(document.body, { childList: true, subtree: true });
};
// Robust bootstrapping
if (window.Webflow && Array.isArray(window.Webflow)) {
window.Webflow.push(init);
} else if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init, { once: true });
} else {
// DOM is already ready; run now
init();
}
})();
Most contact centres review a small fraction of their calls. A QA analyst picks a handful, scores them, flags what went wrong, and then moves on. It feels like it ticks the box for quality assurance. But for Ofcom-regulated telecoms operations and FCA-regulated financial services firms, it’s not enough, and the consequences of getting it wrong have never been higher.
This article explains why call sampling creates compliance exposure, what always-on monitoring looks like in practice, and what to look for when evaluating your current approach.
What is call quality monitoring?
Call quality monitoring is the process of reviewing agent-customer interactions to assess whether they meet your quality, compliance, and performance standards.
It typically covers:
What was said and how the agent handled the conversation
Whether compliance scripts and protocols were followed
How vulnerable customers were identified and managed
Whether the outcome was appropriate for the customer
How performance compares against your scoring framework
When call quality monitoring is done consistently, it gives you a documented evidence-base across every call type, agent, and campaign. But when it’s done poorly or too infrequently, it leaves gaps that regulators are increasingly likely to find before you do.
How do most contact centres currently monitor calls?
Sampling is the typical approach many call centres take to monitoring calls. A QA reviewer listens to a set number of calls per agent per month, scores them against a framework, and feeds the results back into coaching. It is time-consuming work, so let’s break down the numbers.
Example:
A single reviewer handles 50 calls a month at 30 minutes per call.
This amounts to 25 hours of review time.
And it is still only a fraction of the total call volume reviewed.
The problem here is not the effort; it's the coverage. On average, contact centres manually evaluate 5% of calls per week, meaning many QA operations are leaving the majority of interactions unreviewed. This means:
You don’t know whether your agents are consistently identifying vulnerable customers.
You don’t know whether compliance scripts are being followed on the calls you did not pick.
You are not building an evidence bse, only a small sample.
Manual call sampling statistics
FCA Consumer Duty: you need evidence across every interaction, not a snapshot
For debt collection, insurance, and other FCA-regulated contact centres, the stakes are different but the problem is the same. Consumer Duty requires firms to demonstrate they are delivering good outcomes for retail customers, not just on the calls they reviewed, but consistently and measurably across their entire operation.
The FCA has shifted decisively from implementation to enforcement. Regulators are no longer asking whether you have a quality monitoring process. They are asking whether you can prove, with documented evidence, that your agents are handling vulnerable customers correctly, following compliant scripts, and not causing foreseeable consumer harm. And that’s for every call, not just the ones you checked.
A sampling approach does not produce that evidence. It produces a snapshot.
For more on what the FCA now expects from contact centres in financial services and debt collection, see our Consumer Duty guide.
The problem with call sampling: A Summary
Sampling typically covers around 5% of calls per week, leaving the 95% of interactions unreviewed and unverifiable
Compliance drift happens slowly. By the time sampling catches a behaviour, it is already established and harder to coach out
Poor agent behaviour on outbound calls can go undetected long enough to trigger carrier blocking or an FCA flag
Vulnerable customers may not be identified correctly on calls you never reviewed
Good performance goes unrecognised as you cannot replicate what you cannot see
A sample tells you what happened on the calls you chose to review. It does not tell you what is happening in your operation
From sampling to monitoring: what's actually required
Moving from sampling to consistent call monitoring is not simply a matter of reviewing more calls. It requires the right infrastructure in place, and historically, that infrastructure was either too expensive, too time-consuming, or both.
At a minimum, always-on monitoring requires:
Call recording across all interactions, not just selected campaigns or call types
Transcription that converts voice to text accurately enough to be reviewed and searched at scale
A platform that connects recording, transcription, scoring, and reporting in one place rather than across multiple disconnected tools
Without all three, monitoring at scale either falls back on human reviewers (which is where the 25-hours-per-50-calls problem comes back in) or produces data too inconsistent to be useful as a compliance evidence base.
MaxContact's Conversation Analytics brings all of this together in a single platform. Call recording, real-time transcription, and reporting sit alongside each other. This gives your QA team a single place to monitor, review, and evidence what is happening across every interaction, without stitching together multiple tools or managing separate systems.
The reason most contact centres have defaulted to sampling is not because they did not want better coverage. It is because the operational cost of achieving it manually was prohibitive. A team large enough to review every call would cost more than most mid-market operations can justify. But that has changed.
How Conversation Analytics makes always-on monitoring feasible
Conversation Analytics is the platform that makes consistent, always-on call monitoring operationally viable for mid-market contact centres.
Rather than relying on a QA team to manually select, listen to, and score calls, Conversation Analytics connects call recording, transcription, scoring, and reporting in a single platform – automating quality assurance. Every interaction is captured, transcribed, and made reviewable, giving your QA team complete visibility across all call types, all agents, and all campaigns without the resourcing overhead of manual review at scale.
The cost comparison is significant. Replicating meaningful call coverage with human reviewers alone would cost an estimated £14,000 per month in analyst time for a mid-sized contact centre. Conversation Analytics delivers that coverage at a fraction of the cost, freeing your QA team to focus on coaching, calibration, and the complex calls that genuinely need a human eye.
How AI call monitoring surfaces insights faster
AI is what makes the insights from always-on call monitoring actionable rather than overwhelming.
Without AI, full call coverage creates a different problem; more data than a QA team can meaningfully review and act on. AI-powered call monitoring solves that by doing the heavy lifting on routine scoring, so your team's attention goes where it matters most.
Benefit
What it means for your operation
Structured scorecards answered automatically
Every scorecard question is answered using transcript evidence; no manual listening, no reviewer subjectivity.
Transcript-linked evidence
Every score links back to the exact exchange that informed it, giving you a defensible audit trail.
Faster review cycles
Review time drops from 30 minutes to 5 minutes per call, recovering around 4 days of analyst time every month.
Consistent scoring across your entire operation
The same criteria, applied the same way, across every agent, call type, and campaign every time.
Human oversight built in
Your QA team reviews outputs, calibrates scoring, and focuses on complex calls. AI handles the routine. Governance stays with your team.
The result is not just faster QA. It is a more reliable, more defensible evidence base built on every call, not a sample of them.
What to look for in your call quality monitoring approach
Is your evidence transcript-linked? Generic summaries are not a defensible evidence base. Scoring decisions need to be traceable back to what was actually said.
Is your scoring consistent? If different reviewers score the same call differently, your evidence has a credibility problem. Consistent scoring logic applied across all interactions removes that subjectivity.
Does your QA sit within your analytics platform? If scoring, feedback, and reporting live in separate tools, you create friction and risk. Everything should be in the same place.
Is human oversight built in? Your QA team should be able to review, challenge, and calibrate outputs. Always-on monitoring supports human-led governance, it does not replace it.
Are you scoring the right calls? Configurable triggers and criteria by call type, queue, campaign, or outcome, mean your monitoring effort goes where the compliance risk is highest.
The question is not whether you can afford to monitor every call
It is whether you can afford not to.
Ofcom and the FCA have both made clear that evidence of compliance needs to be consistent, documented, and demonstrable. A sampling process may satisfy an internal audit. It is unlikely to satisfy a regulator asking for proof of good outcomes across your entire customer base.
Always-on call quality monitoring closes that gap. It gives your QA team better data, gives your compliance function defensible evidence, and gives your operation a consistent view of what is actually happening on the phones across all calls, rather than just the ones you happened to pick.
Download the UK Contact Centre Regulatory Guide 2025–2027 to see how the FCA and Ofcom compliance obligations facing your sector map to your call monitoring approach and what good evidenced practice looks like in both.
Download
5 min read
What UK Customers Really Want from Contact Centres in 2026
We've just published our Voice of the UK Consumer 2026 report — and the picture it paints for contact centre leaders is both urgent and actionable.
(() => {
const WORDS_PER_MINUTE = 200;
const MULTIPLIER = 1; // your choice
const estimateMinutes = (el) => {
if (!el) return null;
const text = (el.innerText || el.textContent || "").trim();
if (!text) return 1;
const words = (text.match(/\S+/g) || []).length;
const baseMinutes = Math.max(1, Math.ceil(words / WORDS_PER_MINUTE));
return Math.max(1, Math.ceil(baseMinutes * MULTIPLIER));
};
const findNearestTargetInItem = (itemRoot, rt) => {
if (!itemRoot) return null;
return itemRoot.querySelector('.is-text');
};
const applyWithin = (root) => {
// More forgiving selector: attribute present or equals "true"
root.querySelectorAll('[data-rich-text], [data-rich-text="true"]').forEach((rt) => {
const itemRoot =
rt.closest('[role="listitem"]') ||
rt.closest('.w-dyn-item') ||
rt.parentElement ||
root;
const target = findNearestTargetInItem(itemRoot, rt);
if (!target) return;
const mins = estimateMinutes(rt);
if (mins != null) target.textContent = `${mins} MIN READ`;
});
};
const init = () => {
applyWithin(document);
// Re-apply on dynamic changes (pagination/filters)
const mo = new MutationObserver((mutations) => {
for (const m of mutations) {
for (const n of m.addedNodes) {
if (!(n instanceof Element)) continue;
if (
n.matches('[data-rich-text], [data-rich-text="true"], [role="list"], .w-dyn-items, .w-dyn-item') ||
n.querySelector?.('[data-rich-text], [data-rich-text="true"]')
) {
applyWithin(n);
}
}
}
});
mo.observe(document.body, { childList: true, subtree: true });
};
// Robust bootstrapping
if (window.Webflow && Array.isArray(window.Webflow)) {
window.Webflow.push(init);
} else if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', init, { once: true });
} else {
// DOM is already ready; run now
init();
}
})();
We surveyed 1,000 UK adults who had interacted with a contact centre in the last 18 months. The findings reveal something that goes beyond wait times, channel preferences, and AI adoption. This year, the biggest barrier to customer contact isn't the interaction itself - it's getting consumers to engage in the first place.
The Numbers Don't Lie: Trust Is Now an Operational Problem
Before we get to what consumers want, we have to address what's getting in the way. Our research reveals a structural trust deficit playing out before a single agent picks up the phone.
69% of UK consumers always or often screen calls from unknown numbers
46% have ignored a message from a legitimate company because they assumed it was a scam
Only 22% strongly agree they can tell when unexpected company contact is genuine
77% of those who ignored a legitimate call experienced a real consequence such as a missed appointment, an unresolved problem, a missed payment deadline
This is the Trust Gap: the growing distance between a company's confidence in its own outbound contact and what consumers actually believe when they see an unknown number. It affects every sector with outbound ambitions and it can't be fixed by dialling more.
Call Avoidance Varies Dramatically by Sector
Not all sectors face the same screening wall. Our data shows stark differences in call avoidance rates, and the gap between best and worst performing sectors is significant.
Loans, credit and debt management companies are the most avoided, with 37% of consumers saying they'd be least likely to answer a call from this sector. Insurance follows at 25%, with telecoms, technology, and retail/e-commerce close behind at 22–23%. Banks and building societies fare better at 16% avoidance, and notably, they also hold the highest sector trust score at 96%.
The lesson? Trust and answer rates move together. Sectors that have invested in consumer trust over time are reaping the operational benefits in their outbound performance. Those that haven't are paying the price at the identification gate.
"This data should make every contact centre leader pause," says Ben Booth, CEO of MaxContact. "Consumers broadly trust the sectors they deal with, but that trust doesn't translate into picking up the phone. If consumers can't tell the difference between a legitimate call and a scam, outbound strategies will struggle to deliver."
What Actually Makes Consumers Pick Up
The good news is that the Trust Gap is closable. When we asked consumers what would make them more likely to answer, two things stood out clearly:
82% say they would be more likely to answer if caller ID clearly identified the company name
80.5% say a pre-call text or email would make them more likely to pick up
These aren't aspirational preferences - they're operational levers. The problem isn't the dialler. It's the identification gate. Legitimate contact centres aren't losing the persuasion game. They're often not getting on the pitch.
Contact centres should prioritise:
Branded caller ID and carrier number reputation management - so consumers can recognise your call before they decide whether to answer
Pre-call communication - give consumers a reason to expect your call, especially in high-avoidance sectors
Treating contact frequency as a trust variable -too-frequent contact doesn't just frustrate consumers; in regulated sectors, it carries compliance risk
AI Is Here — But Transparency Is Non-Negotiable
UK consumers have been interacting with AI in contact centres for some time. The problem is, many didn't know it.
87% of consumers believe they've interacted with AI or automation in a recent company contact. Of those, 22% were sure or fairly sure they'd been talking to AI — but weren't aware of it at the time. That's more than one in five consumers who discovered, after the fact, that part of their experience was automated.
Nearly 9 in 10 (88%) consumers say it's important for companies to clearly disclose when AI is being used. Half say it's very important.
"The reputational risk of undisclosed AI is real and avoidable," says Ben Booth. "Consumers aren't opposed to AI - they're opposed to being kept in the dark about it. Deploying AI without disclosure doesn't just frustrate customers; it reinforces the same uncertainty that's causing them to screen your calls."
AI Adoption: Where It Works and Where It Doesn't
Consumer opinion on AI is nuanced. Where AI genuinely adds value, consumers are broadly willing to accept it:
Answering FAQs: 36%
Routing to the right department: 35%
Account updates and billing information: 26%
But the picture reverses sharply when it comes to high-stakes interactions. Over half (54%) say they don't want AI involved in emergency situations. Significant numbers also object to AI involvement in complex account problems (50%), financial discussions (49%) and when negotiating terms (46%).
Crucially, 71% of consumers say they'd be comfortable with AI helping resolve an issue faster — as long as a human agent was available throughout. The acceptance of AI is conditional on a clear, accessible escalation path.
Humans Still Matter Where It Counts
Despite the growth of AI and automation, consumers are clear about when they need a person:
Emergency situations - 41% want a human agent
Complex account queries - 33%
Financial discussions — 29%
Explaining a sensitive or personal matter - 26%
Making a complaint - 23%
These aren't edge cases. An AI that handles a billing query well creates modest goodwill. An AI that mishandles a bereavement disclosure or an emergency can permanently damage a customer relationship.
When things go wrong and complaints happen, consumers care most about: a clear explanation of the outcome (39%), being kept updated throughout (37%), appropriate compensation when the company is at fault (33%), and only having to explain the issue once (31%).
What Builds and Breaks Consumer Trust
Our research shows consistent patterns in what drives contact experience, positively and negatively.
What puts consumers off before they even try:
Long wait times: 36%
Being transferred multiple times: 34%
Difficulty reaching a human: 29%
Having to repeat themselves: 28%
What good looks like:
Quick resolution -36%
Easy access to a human when needed - 35%
Knowledgeable agents - 34%
Clear communication throughout - 32%
On channel trust, email remains the most trusted channel for company contact (51%), followed by phone calls (30%) and letters (27%). For outbound communications that don't need an immediate response, email is still the most credible messenger.
Five Focus Areas for Contact Centre Leaders in 2026
Based on our findings, these are the areas that will have the most impact:
Fix the identification gate: Deploy branded caller ID, carrier number reputation management and pre-call communication. The recoverable opportunity isn't every screened call; it's the willing contacts who are filtering themselves out because they fear scams.
Make AI disclosure the default: Clearly disclose AI use at the start of every AI-assisted interaction. In regulated sectors, it's a compliance requirement. Everywhere else, the reputational risk is reason enough.
Protect the human escalation path : Across every question about AI in this study, the most-cited condition for consumer acceptance was the same: a human must be available and clearly signposted. Design the escalation as carefully as you design the AI.
Treat 'only explain once' as an infrastructure target: CRM integration, context-passing between channels, and warm handoffs are the operational response to the number one complaint driver.
Audit complaint journeys against what consumers actually need : A clear outcome explanation, ongoing updates, appropriate compensation, not having to repeat themselves, and a human presence. These five things determine whether a resolved complaint becomes a trust-builder or a churn trigger.
Want the full picture? The Voice of the UK Consumer 2026 report includes sector-by-sector breakdowns across utilities, telecoms, finance/debt, and insurance — with data on vulnerability handling, AI comfort, complaint experience, and regulatory risk. Download the full report here.
.png)
.png)
