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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.
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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.
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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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Contact centres are always under pressure to remain compliant with complex laws and industry standards that frequently change. From data privacy regulations like GDPR to sector-specific rules such as those enforced by Ofcom, FCA, and Ofgem, contact centres must navigate a minefield of compliance requirements.
The increasing number of communication channels that contact centres operate through adds more complexity. Each channel – from traditional phone calls to social media and messaging apps – has its own set of compliance regulations.
One of the most common compliance pitfalls is that agents must read out specific phrases and terms to customers. These mandatory statements are essential for protecting both the customer and the business. Failure to comply can result in severe consequences, including fines and reputational damage.
This is where AI speech analytics steps in, offering a powerful solution to many of these compliance challenges. By using the capabilities of AI, contact centres can gain valuable insights into agent interactions and identify potential compliance risks. In this article, we will explore how AI speech analytics – like our Spokn AI software – can be used to improve regulatory compliance and enhance the overall customer experience.
What is AI Speech Analytics and How Does it Collect Data?
AI speech analytics relies on advanced algorithms, natural language processing and deep learning models to record and analyse phone calls. By converting audio files into text formats, speech analytics systems can extract valuable insights into customer interactions and agent performance.
The data collection process starts with the recording and storage of call transcripts in a secure database. These recordings can then be evaluated post-call, giving contact centre leaders valuable insights into agent interactions with customers against KPIs and more.
These are the key features of call recordings powered by AI speech analytics that enable it to function with advanced accuracy:
Speech-to-text transcripts: Automatically converts spoken language into text, making it easier to review and analyse call content. These transcripts are summarised, providing an easier and quicker way to access and understand historic interactions.
Keyword/topic spotting: Identifies specific words or phrases within the conversation, allowing for targeted analysis of key topics.
Sentiment analysis: Determines the emotional tone of the conversation, identifying areas where customers may be frustrated or dissatisfied. Equally, it also pinpoints positive call interactions and these areas of strength can provide a blueprint for agent training.
Categorise common objections: Sentiment analysis can be used to identify calls that started negatively and ended positively. By pinpointing the most common objections and how they are remedied effectively, call agents can learn how to successfully handle objections.
Call quality metrics: Focuses on metrics that give better insight into call quality, such as talk-to-listen ratio, talk rate, correct call opening, and agent & customer monologues.
With these capabilities, speech analytics provides contact centre managers with the data they need to monitor agent interactions, identify compliance risks and improve overall customer satisfaction scores.
How AI Speech Analytics Data Supports Regulatory Compliance
AI speech analytics can support regulatory compliance across various industries. Contact centres can gain valuable data that help address common challenges and ensure adherence to industry standards.
Let’s take a closer look at some examples of compliance requirements that contact centres must adhere to. And assess how AI -powered speech analytics can support call centres.
GDPR: The Challenge of Data Protection
Data protection is a big concern for most contact centres, governed by both the General Data Protection Regulation (GDPR) and the Data Protection Act (DPA). While GDPR sets the overarching framework for data protection in the EU (and has been incorporated into UK law post-Brexit as the UK GDPR), the DPA sits alongside GDPR and outlines specific provisions tailored to UK legislation.
Both regulations impose strict requirements on businesses to protect personal data, but the DPA also covers areas not explicitly detailed in GDPR.
For example, the DPA stipulates that call centres must tell customers that their calls are being recorded for transparency and fairness in data collection.
Contact centres must also comply with other industry-specific regulations, such as:
Direct debit regulations: Agents must read out specific parts of the script when setting up direct debits to ensure customers are fully informed.
PCI DSS compliance for card information: When handling payment card information, call centres must make sure that sensitive data isn’t recorded. This involves pausing call recordings during the collection of card details to prevent unauthorised access.
Contact centres face significant challenges in complying with these complex and overlapping regulations, including:
Handling Data Subject Access Requests (DSARs) effectively.
Preventing and responding to data breaches.
Obtaining informed consent and ensuring customers are aware of call recordings.
Ensuring sensitive information is handled appropriately during calls.
AI speech analytics helps to address these challenges, helping contact centres to monitor compliance, reduce the likelihood of regulatory fines and strengthen overall data protection practices.
Here’s how AI speech analytics can help overcome these specific challenges:
| The Challenges | How AI Speech Analytics Can Help ||------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|| Challenge 1: Informing Customers About Call Recording (DPA Requirement)Agents must inform customers that their calls are being recorded. Failure to do so can result in non-compliance with the DPA. | Solution: AI speech analytics can log whether agents are informing customers of call recording. Call centres leaders can manually search for specific phrases or keywords in call transcripts. This ensures consistent compliance with the DPA requirement. || Challenge 2: Handling Sensitive Payment Information (PCI DSS Compliance)Call centres must prevent the recording of sensitive card information to comply with PCI DSS. This requires agents to pause recordings during payment processing, which can be prone to human error. | Solution: AI speech analytics can monitor calls to verify that recordings are paused when sensitive information is collected. Call centre leaders responsible for quality assurance will be able to manually check if a recording is or isn’t paused, reducing the risk of non-compliance. || Challenge 3: Reading Mandatory Scripts for Direct Debits and ConsentAgents are required to read out specific scripts when setting up direct debits or obtaining consent, ensuring customers are fully informed. | Solution: AI speech analytics helps to verify if agents have read the necessary parts of the script by identifying specific keywords or phrases. This helps ensure compliance with regulations governing direct debits and informed consent. || Challenge 4: Handling Data Subject Access Requests (DSARs) promptly. Customers can request access to their personal data and ask for deletions or changes to data sharing. Contact centres must facilitate this. However, it’s often a manual process of searching through vast amounts of recorded interactions to locate relevant information. | Solution: By using AI speech analytics, contact centre managers can search through recorded interactions quickly and efficiently to fulfil Data Subject Access Requests. Speech analytics removes the manual process of locating relevant information, and reduces the time it takes to fulfil DSARs. || Challenge 5: Getting Informed Consent from CustomersContact centres must get informed consent from customers before processing their personal data. Individuals must understand the purposes and implications of data collection and it will be used. Call agents must communicate this information clearly and provide consent documentation. | Solution: AI speech analytics provides searchable text files. These call transcripts can be manually reviewed for specific keywords or phrases to verify agents provide correct information and obtain appropriate consent.For example, in the sales industry, informed consent means customers understand the features and benefits of a product before buying. With speech analytics, call centre leaders can manually track keywords or phrases related to the product’s features. This helps call centres leaders to identify calls where the relevant keywords haven’t been used. It gives broader scope to check that agents explain the product clearly and customers understand what they are purchasing. |
Customers can request access to their personal data and ask for deletions or changes to data sharing.
Contact centres must facilitate this. However, it’s often a manual process of searching through vast amounts of recorded interactions to locate relevant information.Solution: By using AI speech analytics, contact centre managers can search through recorded interactions quickly and efficiently to fulfil Data Subject Access Requests.
Speech analytics removes the manual process of locating relevant information, and reduces the time it takes to fulfil DSARs.Challenge 5: Getting Informed Consent from Customers
Contact centres must get informed consent from customers before processing their personal data. Individuals must understand the purposes and implications of data collection and it will be used.
Call agents must communicate this information clearly and provide consent documentation.Solution: AI speech analytics provides searchable text files. These call transcripts can be manually reviewed for specific keywords or phrases to verify agents provide correct information and obtain appropriate consent.
For example, in the sales industry, informed consent means customers understand the features and benefits of a product before buying.
With speech analytics, call centre leaders can manually track keywords or phrases related to the product’s features. This helps call centres leaders to identify calls where the relevant keywords haven’t been used. It gives broader scope to check that agents explain the product clearly and customers understand what they are purchasing.
Consumer Duty Act: Ensuring Fair Treatment and Clear Information
The Consumer Duty Act places a strong emphasis on fair treatment and clear information for consumers. Contact centres must tailor customer interactions to meet individual needs, identify vulnerable customers, minimise human error, and avoid pressurised selling tactics.
So what makes the Consumer Duty Act and FCA guidelines complex to adhere to in a call centre setting?
The ChallengesHow AI Speech Analytics Can HelpChallenge 1: Tailoring Customer Interactions to Address Specific Needs
Every customer has different needs and preferences. But it’s hard for call agents to tailor their approach and give clear and fair information to each customer.
It’s even more difficult to assess whether or not the customer understands what’s been said.As well as taking the overall temperature of the call, sentiment analysislooks at individual phrases, including responses to product explanations. Sentiment analysis shows if customers may feel confused or frustrated.
If customers express negative sentiments or ask clarifying questions, it suggests they aren’t clear about what has been said by the call agent.Challenge 2: Identifying Vulnerable Customers
Identifying vulnerable customers is difficult because the signs of vulnerability are not immediately apparent.
A vulnerable customer covers various circumstances, including disability, financial hardship, language barriers and emotional distress.
This makes it difficult to establish a single criteria for call agents to use to spot vulnerability.Solution: AI speech analytics provides valuable insights into customer behaviour and language patterns to identify vulnerable customers. Sentiment analysis considers the emotional tone of conversations and detects negativity and distress.
As all call transcripts are searchable, any specific referencesto communication difficulties or personal challenges, such as disabilities or financial hardship, can be manually reviewed.
Managers and agents can offer targeted support when additional needs are found.Challenge 3: Minimising Misunderstandings & Human Error
All humans make mistakes – including call agents. Sometimes it’s easy for agents to misunderstand customer questions or provide incorrect information. The challenge is how do call centres catch these incidents and rectify them?Solution: AI speech analytics can be used to analyse recorded transcripts post-call. AI can piece together keywords, tone and pace, to give a better understanding of agent performance.
Call centre leaders can manually compare agent interactions to recommended scripts, highlighting calls where the agent may have given misleading information.
It gives contact centres the opportunity to address mistakes and provide further training.Challenge 4: Encouraging Ethical Selling Techniques
Agents may feel under pressure to close sales and meet their KPIs, which can sometimes lead to the use of overly assertive selling techniques.
This can be difficult to define and monitor.Solution: AI-driven speech analytics can analyse keywords, tone, pace and sentiment to identify instances where sales interactions may not align with best practices. This helps ensure a positive and customer-centred approach to selling.
Consumer Rights Act: Dealing with Customer Complaints
The Consumer Rights Act outlines the fundamental rights of consumers in the UK. As part of this legislation, contact centres must effectively address customer complaints to demonstrate their commitment to consumer satisfaction and compliance with regulatory requirements.
The ChallengesHow AI Speech Analytics Can HelpChallenge 1: Identifying root causes of recurring customer complaints
Agents handle a large volume of calls everyday. So when complaints happen, it’s difficult for contact centre leaders to identify themes of complaints and assess underlying issues. Recurring issues go unnoticed, impacting customer satisfaction scores.Solution:Speech-to-text technology provides searchable call transcripts of customer interactions that are easier to QA. By analysing these call recordings, contact centres can identify common complaint themes, such as recurring issues with products or services. This information can be used to address underlying problems and prevent future complaints.Challenge 2: Addressing Customer Complaints Efficiently
Resolving customer complaints within a reasonable timeframe can be challenging, especially when dealing with complex issues or requiring coordination with multiple departments. Delays in resolution can lead to customer dissatisfaction and potential regulatory action.Solution: Speech analytics analyses call recordings at scale. This makes it easier for contact centres to verify that agents are resolving complaints within a reasonable timeframe, and following appropriate procedures.Challenge 3: Ineffective Complaints Handling Training
Generic training that fails to address the specific needs of agents can lead to ineffective complaint handling. This, in turn,can result in longer resolution times, decreased customer satisfaction, and an increase in escalated complaints. Ineffective complaint handling can also lead to non-compliance with regulations, resulting in fines or penalties.Solution: Analysing agent interactions during the complaints process can help identify training gaps related to complaint handling. This allows contact centres to provide targeted training to equip agents with the skills and knowledge to address customer concerns effectively.Challenge 4: Implementing an Effective Complaint Handling Process
Ensuring that customers are satisfied with the resolution of their complaints is essential for maintaining a positive reputation and avoiding negative publicity. It can be difficult to assess customer satisfaction and identify areas for improvement without the right tools and data.Solution: Speech analytics helps to measure customer satisfaction with the complaint resolution process itself. Customer sentiment during the process identifies areas of dissatisfaction and opportunities for improvement.
Why AI Speech Analytics?
Compared to traditional speech analytics, AI-powered software is much more effective – particularly when it comes to supporting compliance. AI has the ability to recognise nuances in conversation, adapting seamlessly to variances in language and emotion compared to rule based keyword spotting alone.
This means that AI speech analytics is much more accurate when assessing agent performance against intricate regulations. This, combined with the AI software’s capability to process and analyse large volumes of calls makes it offers a time-efficient solution for ensuring compliance with GDPR, DPA, PCI DSS, and other regulations.
Overcoming Common Concerns
Of course, it’s critical that we don’t ignore common concerns that some contact centres have around AI speech analytics.
Questions are often asked around how contact centres can ensure that collected data is handled and stored securely in compliance with regulations. And then there’s the issue of agent resistance, with some call teams expressing concerns over job security and privacy.
While these concerns are all valid conversations to have, they can easily be addressed by putting the right procedures in place:
To ensure data sovereignty and compliance with regulations like those enforced by the FCA, work with providers like MaxContact – as our databases are all located within the UK.
Use encryption, access controls, and regular backups to protect sensitive data and implement robust data security measures.
Take the time to explain how AI speech analytics can improve efficiency, quality and compliance to the wider team.
Offer training to help agents understand how speech analytics can be used to enhance their performance.
Involve agents in the implementation process andseek their input, addressing their concerns and gaining their buy-in.
Reaping the Benefits of AI Speech Analytics
The concerns outlined above are easier to navigate when you work with a trusted provider. MaxContact has worked with numerous contact centres to help them implement our Spokn AI platform – and the results speak for themselves.
Honey Group, a financial services company, was struggling to review all calls for compliance purposes due to limited resources. They worked with MaxContact to implement our AI speech analytics software.
Honey Group has leveraged call transcripts and sentiment analysis to monitor inappropriate mentions of sensitive topics, ensuring compliance with industry regulations. As a result of Spokn AI, Honey Group has drastically improved quality assurance and the way they approach agent training.
AI speech analytics empowers contact centres to stay ahead of complex regulatory compliance. But, by leveraging the power of AI, contact centres can gain valuable insights into agent interactions, identify compliance risks and ensure adherence to industry standards.
From GDPR to the Consumer Duty Act, AI speech analytics offers a robust solution for addressing many compliance challenges.
As regulatory requirements continue to evolve, AI speech analytics can adapt alongside them for effective compliance management.
Ready to take your contact centre compliance to the next level? See how Spokn AI, MaxContact’s industry-leading speech analytics platform, can transform your operations.
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.
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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.
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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.
Blog
5 min read
Make Call Reviews Faster, Fairer, and Evidence Backed.
Introducing AI Call Scoring — now included within Conversation Analytics at no additional cost.
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Here’s a number worth sitting with: 30 minutes.
That's how long it takes to manually score one call. Listen back, fill in the scorecard, write up the notes, log the result. Do that 50 times a month and you've lost on average 4 days per reviewer to scoring alone.
Most QA teams know this. They're not slow or inefficient - the maths just doesn't work. One reviewer for every 25 agents, 30 minutes a call, finite hours in the week. Something must give, and that’s coverage. The industry average sits at around 5% of calls reviewed, which means 95% of what happens on your contact centre floor stays invisible.
Not just to your QA team. To your compliance records. To your coaching programme. To the agents who deserve consistent, fair feedback on every call they handle.
That's the problem AI Call Scoring is built to fix.
Your standards applied to every call you wish to score.
The way it works is straightforward. You define your existing QA standards - built around your business rules, your compliance requirements, your definition of what a good call looks like. AI Call Scoring applies them to your selected calls, scoring each one against your criteria using evidence taken directly from the transcript.
No algorithm deciding what good looks like on your behalf. You set the standard.
What this means for your QA team day to day
One of the things that doesn't get talked about enough in QA is how demoralising inconsistency is. Two reviewers score the same call differently. An agent pushes back. The process loses credibility. And meanwhile, the team is so buried in manual scoring that the actual coaching - the conversations that change behaviour -never happen.
AI Call Scoring brings review time down from around 30 minutes to about 5 minutes per call. Your QA team stop being a scoring machine and start doing what they're good at - calibrating standards, making judgment calls, and coaching agents to improve.
For a reviewer handling 50 calls a month, that's roughly four days back every month. That's a lot of coaching time that wasn't there before.
It's not just for big teams
This is worth saying clearly because it matters: AI Call Scoring isn't just a tool for large operations with dedicated QA departments.
For teams of 50 or more agents, the time savings are significant - around 133 days per year across four QA staff, worth approximately £15k in team time. But beyond the hours, scoring more calls consistently means you start seeing the patterns that a small sample will never show you.
For smaller teams of 10 to 30 agents, it's even more of a shift. Structured QA without dedicated headcount. Team leaders reviewing scored calls in minutes. Compliance coverage that doesn't require a compliance team. And a framework that grows with the business.
When compliance is non-negotiable
For contact centres operating in regulated sectors, the stakes have risen. Consumer Duty, now actively enforced by the FCA, places a direct obligation on organisations to evidence that customers are receiving good outcomes.
When a complaint lands, you can't point to a sample. You need evidence that the specific interaction was handled correctly. AI Call Scoring gives you an auditable record of every scored call, with auto-fail rules that catch compliance breaches - a missed disclosure, an incomplete ID check - regardless of how the rest of the call went. Every call you score is evidenced, auditable and defensible.
Your team stays in control
We want to be clear about this: AI Call Scoring is there to support your QA team, not sideline them. Every scored result can be reviewed, edited, challenged or discarded. Your people stay in the loop. The AI does the volume work - your team does the thinking.
It all lives inside Conversation Analytics — scored calls, common objections, objection handling effectiveness, top performers and saved compliance views, together in one place.
Already on Conversation Analytics? It's yours.
AI Call Scoring is included within Conversation Analytics at no extra cost. If you're already using the suite, it's available to you now.
This is also just the start. Automated QA at Scale is coming later this year - fully automated scoring across campaigns at volume. More on that soon.
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