How to Stay Compliant Using AI Speech Analytics in Your Contact Centre

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.

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.

For a full breakdown of what Consumer Duty means for your operation, read our guide for contact centre teams in financial services.

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.

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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.

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For full details on how MaxContact handles data security and compliance, download our security and compliance brochure.

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.

Our articles and industry insights give you expert perspectives, practical strategies, and the latest trends to help your business connect smarter and perform better.

How to Remain Compliant with AI Speech Analytics

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September 29, 2025

How to use speech analytics to increase B2C outbound sales

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10 Speech Analytics Use Cases for Contact Centres

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5 min read

10 Speech Analytics Use Cases for Contact Centres

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.

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.

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Speech Analytics: Turning Conversations into Insights

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5 min read

Speech Analytics: Turning Conversations into Insights

Conversation analytics isn’t a new thing. In fact, it’s been around for nearly two decades. But thanks to developments in AI and natural language processing (NLP), speech analytics is growing in popularity and is a powerful tool for contact centres. But why is speech analytics needed?

What is speech analytics?

Call centre speech analytics (or conversation analytics) uses technology to analyse recorded phone conversations between call centre agents and customers. AI powered speech analysis helps to identify trends, patterns and areas for improvement in customer service, agent performance and overall contact centre operations.‍

Conversation analytics isn’t a new thing. In fact, it’s been around for nearly two decades. But thanks to developments in AI and natural language processing (NLP), speech analytics is growing in popularity and is a powerful tool for contact centres. But why is speech analytics needed?

Why do call centres need speech analytics?

75% of customers will spend more to buy from a company that offers a good customer experience (CX). On the other hand, nearly half of consumers would ditch a brand for a competitor due to poor CX.

In other words, good CX is a huge win for your business. Customers who are happy with the experience you provide will spend more with your business, forgive your occasional mistakes and recommend your products and services to others.

The problem is that it’s not always clear if your customers rate their experience as highly as you hope they do.

Many customers stay silent about their issues, leading businesses to miss crucial feedback. It’s not malice on the customers’ part, but discomfort or wanting to avoid hassle. This silence hurts business and can result in low customer satisfaction (CSAT), higher customer churn rates and reduced sales revenue.

Businesses can’t fix what they don’t know. So, how do we encourage open communication for happier customers and thriving businesses?

How does speech analytics software work?

Speech analytics empowers contact centres by automatically analysing call recordings to understand both agent performance and customer sentiment. It identifies keywords and phrases which reveal customer satisfaction or frustration, even if they didn’t explicitly say it. Looking beyond spoken statements, speech analytics software analyses voice characteristics, like intonation and pitch, picking up on emotions such as happiness, anger or confusion.

To measure agent performance, call centre speech analytics tracks metrics such as time on hold and silence periods, giving insights into both agent efficiency and customer satisfaction.

How is speech analytics used to improve contact centre operations?

The ability to analyse every customer interaction is a powerful tool but how is speech analytics used and what are the benefits?

Real-time speech analytics software can boost customer satisfaction by 20% and reduce churn

Proactive Issue Identification: Speech analytics can be used to understand call drivers including emerging problems. Real-time call analysis can alert agents to any new issues and encourage them to address the issue and implement corrective measures to nip the problem in the bud.
Real-time Sentiment Analysis: Call agents are able gauge customer emotions more accurately throughout the call, allowing them to ease any frustrations and personalise interactions, which all lead to happier customers.
Targeted Upselling Opportunities: Eradicate the need for relying on your agent’s memory of what’s been said on the call. Real-time analysis can review calls and push alerts to agents to discuss new products or services, driving revenue by 10%.

Insights from ai speech analytics can be used to improve agent performance and drive engagement

Personalised Coaching: Coaching your call centre agents is easy with post-call analysis. Review performance and develop targeted training and development plans based on individual strengths and weaknesses.
Compliance & Quality Assurance: AI-led speech analytics can automate compliance checks across all calls. Real-time analysis (alongside post-call analysis) also prompt agents to read the relevant scripts while the call is taking place. This leads to increased agent compliance and reduces the risk of fines associated with non-compliance.
Performance Benchmarks and Recognition: Use insights from speech analytics to identify and reward high-performing agents and showcase their successes to motivate and inspire the entire team.

Automated call analysis can be used to enhance quality management and call handling efficiency

Quality management: Be more confident in the validity of your quality scores and agent assessments with speech analytics software that automates analysis of all calls rather than relying on a small sample of contact centre interactions.
Reduce average handling time (AHT): Customers want quick and efficient resolutions over the phone. Speech analytics software can give you powerful insights that help you design better scripts to reduce AHT and cut costs.

One solution to the problem of reticent customers is conversation analytics software. You’ve probably come across hype around speech analytics before – the technology has been around for nearly two decades. The difference today is that it actually works.

The industry certainly seems to think so too. One recent study estimated that the market for speech analytics will grow at a CAGR of 22.14% over the next five years. And we believe its popularity is entirely justified.

How does speech analytics software work?

Why choose AI-led conversation analytics from MaxContact?

Our speech analytics software will help you to make better business decisions, and understand the ‘why’ behind 100% of your contact centre interactions.

We’ve kept it simple

High-level data is displayed on an intuitive dashboard and further information can be accessed from a central control panel. Simple to set up and easy to understand, MaxContact ai speech analytics provides sophisticated insights at the click of a button.

Powerful filtering at your fingertips

Our advanced solution pre filters nearly 70% of critical and problematic conversations that need further attention. This lets contact centre managers focus on priority issues before it’s too late. Thanks to faster response times, timely interventions and streamlined operations, managers can enhance performance and improve customer satisfaction by coaching agents in real-time.

Easy to integrate

And if all this sounds expensive and complex, it really isn’t. We offer a fully integrated platform designed to work seamlessly with our feature-rich, cloud-based contact centre software.

Conversation analytics from MaxContact gives you the information you need to deliver market-leading CX, even if your customers are not always as candid about your service as you’d like them to be.

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Book a customised demo to learn how our AI-led conversation analytics software can understand customer sentiment, improve call quality, and reduce churn in your contact centre.

Leveraging conversation analytics for contact centre improvement

Download our Build a Business Case for Conversational Analytics framework.

Using AI Speech Analytics for Quality Assurance (QA) in Contact Centres

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5 min read

Using AI Speech Analytics for Quality Assurance (QA) in Contact Centres

If you work in a contact centre, you know quality assurance (QA) can often feel like a box-ticking exercise. Yet, QA is the backbone of exceptional customer service, agent development and operational efficiency. The challenge? Many QA processes are time-consuming, resource-intensive and limited in the insights they provide.

What if your QA process could do more? Imagine evaluating every customer interaction, uncovering what drives success, and delivering precise, actionable feedback – all without additional effort. This is where AI speech analytics steps in, offering transformative solutions that redefine how QA drives results.

At MaxContact, we’re taking this a step further with Spokn AI Success Intelligence. Designed for revenue-driven teams, this next-generation platform delivers actionable insights to optimise performance, ensure compliance and boost customer experiences.

The challenges with traditional QA in contact centres

Do your current QA methods give you the insights you need to truly drive agent performance?

QA is essential, especially when you consider that 93% of customers are likely to make repeat purchases from companies offering excellent customer service (HubSpot). However, traditional QA methods often struggle to meet the demands of modern contact centres. Here’s why:

Resource constraints: Reviewing even a small sample of calls manually is time-intensive and costly. Due to limited resources, many contact centres evaluate only a fraction of their total interactions, leaving critical insights untapped.
Subjectivity and bias: Human evaluators often bring unconscious biases into assessments, leading to inconsistent results and insights that may not accurately reflect agent performance or customer sentiment.
Shallow insights: Traditional QA often focuses on generic metrics like call handling time or first call resolution. While useful, these metrics don’t capture the complexities of customer interactions or agent effectiveness.
Missed opportunities: Manual QA rarely identifies deeper trends, such as recurring objections, common customer frustrations, or effective objection-handling techniques. These insights could shape training, scripting and processes to drive better results.

For sales and collections teams under pressure to deliver more with fewer resources, these limitations highlight the need for a smarter, more scalable approach.

How does AI speech analytics transform QA?

AI-powered speech analytics addresses the challenges of traditional quality assurance by automating call analysis and uncovering insights that traditional methods miss. Tools like Spokn AI transcribe 100% of calls from speech to searchable text files, providing a more comprehensive view of performance.

Key benefits of AI speech analytics

Comprehensive call coverage: By evaluating every interaction, AI eliminates sampling errors and ensures that no valuable insight is missed.
Objective assessments: AI removes human bias, delivering consistent evaluations based on data rather than opinion.
Nuanced insights: Sentiment analysis, phrase identification, and trend tracking aid a deeper understanding of customer-agent interactions.
Faster feedback loops: With automated insights, managers can provide near-real-time feedback to agents, reducing time between evaluation and improvement.

By automating the labour-intensive aspects of QA, contact centres can focus on driving results instead of sifting through data.

Set clear QA objectives with AI speech analytics

To make the most of AI, it’s essential to align your QA process with clear objectives. Here’s how to get started:

Step 1: Identify QA challenges

Focus on areas where your current QA process is falling short. For example:

Are call agents struggling to follow scripts?
Is customer satisfaction lagging?

Understanding these pain points will guide how to strengthen the output of QA processes with AI speech analytics.

Step 2: Define success metrics

Traditional metrics like average call handling time or first call resolution are helpful but don’t tell the whole story. AI speech analytics allows you to measure more nuanced factors, such as:

Sentiment shifts during calls.
Frequency and type of customer objections.
Agent adherence to compliance scripts.

The real power of AI lies in turning data into action. Use insights to refine training programs, improve scripts, and optimise processes, ensuring continuous improvement across the board.

Practical applications of AI speech analytics

Now we’ve defined why using AI speech analytics to aid QA is better than traditional methods, let’s explore how tools like Spokn AI can be used to address specific QA challenges and improve outcomes.

1. Identifying training needs

AI can quickly identify where agents struggle – whether it’s handling objections, following scripts, or navigating sensitive customer issues. With a clearer view of these challenges, managers can:

Develop targeted training programs that address specific skill gaps.
Track progress over time to ensure improvements are sustained.

2. Enhancing compliance

Non-compliance can lead to fines and reputational damage. Speech analytics platforms, like Spokn AI, makes compliance checks faster and more reliable by helping QA teams to uncover calls where agents:

Fail to disclose required information.
Use unapproved language or phrases.

Without the use of AI speech analytics, this part of the process relies on quality assurance teams listening to calls, which is not only time consuming but focuses on a very small sample of calls.

The capability to search or track text transcripts for specific keywords related to compliance, speeds up the process and makes it much more scalable and robust, protecting your organisation from unnecessary risks.

3. Providing tailored feedback

Generic feedback rarely drives meaningful improvement. AI powered speech analytics software allows managers to deliver precise, data-driven feedback tailored to each agent. For instance:

Highlight areas of strength, such as effective objection handling or empathy.
Address weaknesses with actionable suggestions, like improving script adherence.

This targeted approach empowers agents to improve rapidly while creating a culture of continuous learning.

4. Recognising top performers

Spokn AI identifies challenges, yes. But it also highlights what’s working well. By analysing successful calls, contact centres can learn what top-performing agents do differently and replicate their techniques across teams.

Rewarding and recognising these agents boosts morale, also reduces turnover, and sets a standard of excellence for others to follow.

Ready to welcome AI speech analytics? Build your business case.

Can we take quality assurance even further with speech analytics?

Quality assurance is undeniably a crucial part of contact centre operations, with 83% of contact centres recognising it as a critical process, according to our benchmark report. But even with the help of speech analytics, QA is often limited to providing only surface-level insights.

For revenue-driven teams in particular – such as those in sales or collections – standard QA outputs often fall short. These teams need more than just metrics; they need precise, targeted and rapid feedback to drive results.This is where advanced solutions like Spokn AI Success Intelligence come in, elevating QA from a process of evaluation to a driver of performance and improvement.

Introducing Spokn AI Success Intelligence

Designed for revenue-driven teams, our upcoming Spokn AI Success Intelligence feature provides deeper insights into what drives performance, enabling more targeted coaching and process optimisation.

By delivering actionable insights into the “why” behind performance metrics, it empowers teams to go beyond compliance and drive real business growth.

So, what’s the difference?

To understand what Spokn AI Success Intelligence is, you need to know how it moves beyond traditional speech analytics to add a deeper level of insight into sales and collections processes:

Objection handling analysis

Categorises common objections, correlates them with outcomes, and provides actionable insights to refine scripts and coaching strategies.

Call DNA mapping

Identifies the key elements of successful calls—such as sentiment shifts and effective phrasing—allowing teams to replicate these across the organisation.

Enhanced coaching tools

Provides tailored insights that empower managers to deliver precise, impactful feedback, accelerating agent development.

Scalable automation

Analyses 100% of calls without additional effort, freeing QA teams to focus on implementing insights and driving improvements.

Transform QA into a driver of growth

MaxContact has over 20 years of experience in the sales and collections space, working closely with leading AI providers to develop best-in-class solutions.

Deliver tailored coaching that drives measurable improvement.
Optimise processes and scripts to maximise performance.
Enhance customer experiences by addressing systemic issues proactively.

With Spokn AI Success Intelligence, we’re helping contact centres unlock the full potential of their teams and processes.Ready to see it in action? Request a demo today.

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Blog

5 min read

After-call work isn’t an efficiency problem- it’s a trust problem.

After-call work isn't just a time drain - it's a trust problem. Discover how inconsistent CRM records erode customer loyalty, and how AI-generated call summaries close the loop.

Ask a contact centre leader about after-call work and they'll usually frame it as a time problem. Wrap time is too long. Agents aren't “going available” quickly enough. AHT is inflating. The fix, in most conversations, is operational: better templates, tighter ACW targets, more monitoring.

That framing is not wrong, but it is incomplete. After-call work is not just a time problem. It’s a quality problem, one which has a direct customer-facing cost that most operations are not measuring.

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What actually happens when the call ends

The call ends. The agent is under pressure to “go ready” and be available for the next call in the queue. They have notes to write, a CRM record to update, a disposition to log. Often with multiple systems to update. They have approximately two minutes to do all of that before the queue moves. So, they write what they can. A sentence, maybe two. A shorthand that makes sense to them right now but will mean nothing to the agent who picks up next week's call. Sometimes nothing at all, and a disposition code carries the entire context of a complex interaction. Now multiply that across your team. Ten agents handling the same call type will leave ten different records. Some thorough, some minimal. Some missing the most important detail entirely - what was promised, what was escalated, what the customer was told to expect next. This is the quality problem, and it compounds quietly.

The customer pays for it twice

The first cost is visible: longer calls, higher AHT, agents unavailable for longer than they should be. This is what gets measured. The second cost is less visible but more damaging. The customer calls back. A different agent picks up. They open the record - and it tells them almost nothing useful. So, they ask the customer to explain themselves again. That moment - the repetition, the sense that the company was not paying attention - is where trust erodes. It’s not dramatic. It does not show up immediately in CSAT. But it accumulates, and eventually it becomes the reason a customer switches.

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Our Voice of the UK Consumer 2026 research found that 42% of UK consumers have already switched provider due to poor contact centre experience. The word ‘already’ matters. These are not consumers who are at risk of switching – they’ve already left. The post-call gap is not just an internal inefficiency. It's a retention risk dressed up as an admin problem.

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Why training cannot fix this

The instinct, when notes are inconsistent, is to retrain. Set clearer standards. Remind agents what a good record looks like. Monitor more closely. This rarely works. Not because agents do not want to do it well, but because the system is not set up to support consistency at pace. An agent writing notes under queue pressure, with no template and no structure, will produce exactly what the conditions allow. Varying quality, varying detail, varying usefulness. The problem is not discipline or intent. It is that the task is being done manually in the least forgiving conditions possible.

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What changes when AI writes the notes

Agent Wrap Up Summary generates a structured call record automatically the moment the call ends; drawing on the conversation to produce a consistent summary of what was discussed, what was agreed, and what happens next. Every call. Every agent. Every time.

Consistency is the point. Not just the time saving, though that is real: wrap time typically accounts for 15–20% of an agent's working day, and a 50% reduction returns meaningful capacity to productive contact time. For a 50-agent team, that translates to an illustrative annual saving of £175,000: based on 50 agents, 50 calls per day, a 50% reduction in wrap time, and an average fully loaded agent cost of £25,000 per year.

The more significant change is downstream. When every call produces a reliable, structured record, that record becomes the foundation for what the next agent sees before their call begins. Customer History in Contact Hub surfaces that context automatically - so the agent who picks up next week is starting the call informed.

This is how personalisation at scale works. Not by asking agents to memorise histories or search through fragmented notes. By generating a complete record on every call, so context accumulates and becomes genuinely useful over time.

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The record is where the loop closes

Agent Wrap Up Summary is the start of a feedback loop, not the end of one. The structured data it generates - consistent, covering 100% of calls - feeds everything downstream.

Conversation Analytics can analyse that data at scale, identifying coaching opportunities, surfacing compliance drift, and enabling AI Call Scoring that cuts QA review time from 30 minutes to approximately 5 minutes per call. Real Time Agent QA (available in Beta Q4 2026), uses it to guide agents in the moment, surfacing compliance prompts, flagging sentiment shifts, and steering conversations towards the outcomes that best records show actually work.

Better calls produce better records. Better records enable better coaching. Better coaching produces better calls. The loop only works when it is closed. And it closes after the call ends.

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Start with the audit

You do not need a platform overhaul to find out where you stand. Pull a sample of CRM records from last week. Read them. Ask a simple question: if the next agent had only this record to go on, what would they know? The answer will tell you more about the state of your post-call process than any metric can.

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Want to see how Agent Wrap Up Summary works in practice? Download The Assisted Agent - our practical guide to AI-enabled agent assistance across the full call lifecycle. Or if you'd rather see it live: book a demo with the MaxContact team.

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.

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.

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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.

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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.

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Challenge 1: Call length is rising, and post-call admin is a big reason why

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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.

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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.

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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.

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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.

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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.

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Challenge 2: Repeat contacts are eroding margin and driving churn

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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.

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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.

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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.

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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.

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Challenge 3: QA based on a sample of 1–2 calls per week isn't good enough

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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.

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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.

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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.

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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.

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Challenge 4: Personalisation requires the right building blocks first

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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.

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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

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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.

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The bigger picture: it's not about solving one problem in isolation

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The demo Conor ran at the webinar wasn't designed to show five separate features. It was designed to show how they connect.

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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.

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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.

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That's the direction of travel. And a lot of it is available right now.

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Watch the full webinar and explore the resources

▶ Watch the full replay on YouTube → https://youtu.be/C2ED-KwbKns

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📄 Download the Contact Babel UK Contact Centre Decision-Makers' Guide → https://www.contactbabel.com/

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📅 Book a demo or speak to your account manager → https://www.maxcontact.com/book-a-demo

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Blog

5 min read

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.

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.

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.

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How to Stay Compliant Using AI Speech Analytics in Your Contact Centre

Why AI Speech Analytics, Not Just Keyword Spotting

How AI Speech Analytics Supports Regulatory Compliance

Staying Compliant with GDPR and the Data Protection Act

Meeting FCA Consumer Duty Obligations

Handling Complaints in Line with the Consumer Rights Act

How to Monitor and Flag Compliance Issues Automatically

Choosing an AI Analytics Platform That Is Compliant by Design

How Honey Group Improved Compliance Coverage with Conversation Analytics

The Regulatory Changes Contact Centres Need to Prepare for Now

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After-call work isn’t an efficiency problem- it’s a trust problem.

What actually happens when the call ends

The customer pays for it twice

Why training cannot fix this

What changes when AI writes the notes

The record is where the loop closes

Start with the audit

Your Contact Centre Has Four Problems. AI Is Already Solving Them.

Challenge 1: Call length is rising, and post-call admin is a big reason why

What good looks like:

Challenge 2: Repeat contacts are eroding margin and driving churn

What good looks like:

Challenge 3: QA based on a sample of 1–2 calls per week isn't good enough

What good looks like:

Challenge 4: Personalisation requires the right building blocks first

Watch the full webinar and explore the resources

Call Centre Quality Monitoring: Why Sampling Isn't Enough

What is call quality monitoring?

How do most contact centres currently monitor calls?

FCA Consumer Duty: you need evidence across every interaction, not a snapshot

The problem with call sampling: A Summary

From sampling to monitoring: what's actually required

How Conversation Analytics makes always-on monitoring feasible

How AI call monitoring surfaces insights faster

What to look for in your call quality monitoring approach

The question is not whether you can afford to monitor every call