MaxContact Wins at the 2025 Megabuyte Emerging Stars Awards
We’re proud to announce that MaxContact has been named the Best Performing Company in Customer Relationship Management at the 2025 Megabuyte Emerging Stars Awards!
A Significant Achievement
This recognition places us among an elite group of 50 Emerging Stars, selected from an initial pool of over 6,000 companies. With only 825 meeting the strict eligibility criteria in a challenging economic climate, this award reflects our team’s commitment to innovation and excellence.
“Despite experiencing some softness in sectors like energy and longer sales cycles in recent years, MaxContact has maintained c. 30% revenue growth, ahead of most other vendors in the contact centre software market. A key proponent of this is its conversational AI product strategy, with AI-enabled products featuring in 80%+ of deals.”
Moving Forward
This award validates our strategic direction and focus on delivering innovative solutions that address real customer needs. Our investment in conversational AI technology, Spokn AI, continues to drive our success and differentiate us in the market.
We remain committed to supporting our 100+ UK customers across BPO, communications, financial services, utilities, and retail sectors with solutions that enhance their customer engagement capabilities.
We’d like to thank our dedicated team, partners and valued customers for their ongoing support and confidence in MaxContact.
About Megabuyte
Megabuyte supports UK scale-up and mid-market software & ICT Services companies to develop robust growth strategies, understand their competitive landscape and customer sentiment, benchmark their financial performance and valuation, and identify and track M&A targets. They provide proprietary insights and data through their subscription research service, offer packaged consulting services, and give access to their network of some 500 tech sector CEOs through events and their expert network.
About the Emerging Stars Awards
The Emerging Stars awards are part of the Megabuyte100 award series which collectively celebrate the 100 best-performing technology companies in the UK. The awards identify the UK’s best-performing technology scale-ups as defined by Megabuyte’s proprietary Scorecard methodology, complemented by expert qualitative insights from their analysts. Evaluation criteria encompass factors such as size, growth, and margins, highlighting the top 50 best-performing scale-up companies in the UK.
Learn More
For further information about the 2025 Megabuyte Emerging Stars Awards and to see the complete list of winners, click here to access the full Megabuyte report.
Here’s to continued success and innovation throughout 2025!
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Are we at the beginning of the end, or the end of the beginning? As far as Covid-19 is concerned, nobody seems entirely sure. The vaccination rollout promises an eventual release from lockdown, but scientists remain cagey about when everyday life might properly resume.
In the meantime, many of your sales and customer service staff are getting used to working from home. They may be there for a while yet, at least until a combination of the vaccine rollout and better summer weather allows for a cautious return to the office.
Even then, not every business will be forcing staff back into work full time. Social distancing rules are likely to remain in place for the foreseeable future, limiting the number of employees in the same space at the same time.
There’s also growing speculation that many companies will never return to pre-Covid work practices, and that some form of flexible working will become standard practice. A recent Call Centre Helper poll showed that just 7% of contact centres planned to return to the office as normal2, and ONS reported a 47% increase in demand for home working amongst call centre staff1. The data’s there to see, hybrid working, where employees spend some of the week in the office and some working from home, will become far more widespread once the pandemic ends.
The hybrid model of work
There are certainly benefits to it. According to research from Slack, most knowledge workers want a hybrid remote-office model in future. Hardly any want to return to the office full time. Those results are mirrored across sectors and industries, inferring that companies who want to attract the best talent may have to offer hybrid working as a benefit.
Even among businesses with large sales and customer service teams, the benefits of hybrid working may outweigh inevitable misgivings. Many businesses believe they can cut post-pandemic costs by reducing office estates and moving to smaller premises that only have to accommodate a proportion of a firm’s total workforce at any one time.
Put it all together, and it means remote working probably isn’t going anywhere, even after Covid. And even businesses that remain determined to return en masse to the office eventually have no real idea of when that time might be.
So, we’re trying to balance all of this, with changing consumer contact habits – increased adoption of new channels and differing contact patterns – as well as a globally reported increase in contact centre demand, with support firm ZenDesk seeing a steady 16 per cent increase in support contact requests above pre-pandemic levels3.
Productivity challenges
So instead of holding on to pre-pandemic processes, businesses might be better asking how they can adapt more effectively to the ‘normal’ of today while also preparing for whatever tomorrow might throw at them.
Or to put it simply, how do you equip your remote staff with the tools they need to work as productively away from the office as they can in it? How do you train and mentor your teams remotely? How do you continue to delight customers and drive sales when most of your team is working from home? When your workforce returns to a socially distanced office, or splits its time between the office and home, how do you maintain a consistent customer experience?
Businesses have been asking similar questions since last March, of course. But many now realise that the sticking-plaster solutions hastily implemented then are no longer enough, especially when it comes to customer contact. Communications platforms need to give employees the tools they need today, while future-proofing businesses for whatever next month, next year or even the next decade might bring.
Is cloud-first the answer?
It’s for that reason that businesses of all kinds are turning to cloud-based solutions. When your contact centre solution lives in the cloud, your agents can access it from anywhere, on any device, onboarding new starters and completing system training remotely is easier. And, with solutions like MaxContact, new features and functionality are consistently introduced as market trends and business needs change.
But there’s more to a future-proof solution than simply the convenience of cloud. It has to be highly reliable, flexible and secure, while giving you the data you need to measure performance and implement change.
MaxContact is cloud-native for that reason. You get the assurances with the combination of a fully equipped contact centre solution that fulfils all these requirements combined with a resilient uptime guarantee of 99.999% so dropouts and downtime won’t affect sales and customer service. Whilst real-time dashboards and custom reporting give managers full transparency over their teams’ efficiency, wherever they happen to be located.
reduction in training, the issue of training a workforce in the contact centre fast and easily remotely has been of major benefit
This valuable data can eventually be used to inform decision making around post-pandemic working models, giving a real insight into the benefits and challenges of remote working models based on your specific circumstances for every campaign and every call centre agent. This data becomes invaluable in a world were presenteeism in some form has been part of most of our everyday lives for the past 12 months.
Security concerns
Working from home has meant that the companies networks – that were once a secure perimeter – have expanded to every employee home and the plethora of devices they use. There’s now an increasing need to deploy communications solutions with network and data centre security baked in. But we go further. Granular permissions mean you can limit the data your home working teams have access to and remove restrictions at the click of a mouse on those days when they are working from the office.
In fact, MaxContact gives you that kind of agility throughout your contact centre operation. You can add and remove users in minutes, and equip new sites in just a couple of days. MaxContact lets you calculate your staffing requirements using statistical analysis so that, wherever your workforce is based, you always have the right staff with the right skills in place.
In other words, powerful contact centre solutions like MaxContact don’t just solve the temporary challenges of remote working. They equip your business for an uncertain future.
Use our powerful cloud platform to create an agile, secure and streamlined contact centre that not only adapts to the unpredictable challenges of the post-pandemic world.
Most outbound sales teams would describe themselves as “data-driven”. They track activity, review performance reports and measure success against targets.
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But reporting on results isn’t the same as being data-driven. In outbound sales, data only creates value when it is used to actively influence decisions; ideally, while activity is still happening rather than when it is reviewed days later.
A genuinely data-driven outbound sales team will use live performance data to shape how calls are placed, which leads are prioritised, how agents are routed and where coaching is applied. Data, technology and execution work together as a single system.
In this article, we explore what “data-driven” should really mean for outbound sales teams operating in a contact centre environment. We look at the outbound sales metrics that matter most, how technology turns those metrics into real-time decisions, and share the latest data from our Benchmark Report to help you determine whether your performance is average or genuinely competitive.
Use data to decide which leads deserve agent time
Outbound sales teams should focus on maximising productive talk time as the foundation. But the next question becomes, who should agents be spending that time speaking to?
The average first-call close rate across outbound sales teams is 25%, with 31% of teams achieving rates between 20% and 29%. This shows that conversion performance is driven less by how many calls are made and more by how effectively effort is focused.
Understanding which metrics genuinely influence outcomes is critical here. Our complete guide to call centre reporting metrics breaks down the KPIs that matter most, and how they should be interpreted in context rather than in isolation.
Sales teams should concentrate on prospects that are most likely to convert. Which means the first-in, first-out approach to lead prioritisation is an ineffective strategy.
This is where intelligent lead prioritisation tools powered by AI have a huge operational impact. By pulling data from multiple sources, such as recent engagement, historical call outcomes, conversion performance, and potential deal value, intelligent lead prioritisation ranks leads dynamically. As prospect data signals change, prioritisation updates are applied automatically, which means agents consistently spend their available talk time on the opportunities most likely to deliver results.
Use data to match the right agent to the right lead
Data-insights need not stop at determining high-value and high-intent leads. It can also influence who handles them.
While the mean average revenue per call across outbound sales teams is just under £230, over 45% of teams generate less than £59 per call. This gap highlights how widely outcomes can vary depending on agent capability.
When data is used to create value, agent assignment isn’t random or purely availability-based. Instead, performance data is used to match leads with the agents most likely to convert them. For example:
Higher-value or more complex opportunities can be routed to experienced agents with deeper product knowledge or a proven track record of closing similar deals.
Price-sensitive or early-stage leads may be better suited to agents who perform strongly at qualification and objection handling.
Sector-specific prospects can be matched with agents who have previous success in that industry or campaign type.
Skill-based routing makes this possible by using historical performance data such as conversion rates by product, deal size, objection type, or lead source. As new performance signals are captured, routing rules can be refined so decisions improve continuously.
Use real-time performance data to intervene early
Outbound sales performance can change quickly. So, relying on end-of-day or weekly reports limits how effectively teams can respond. Retrospective reporting removes the opportunity to correct issues such as poor lead targeting or gaps in agent performance.
Access to real-time performance data gives sales managers the visibility they need to intervene without burning through contact. Live dashboards show early signals, such as declining connect rates, falling conversion performance, or uneven agent productivity.
Instead of waiting for performance reviews, managers can guide execution as it happens. This might involve reallocating resources, adjusting call scripts, changing lead allocation, or providing targeted coaching.
Contact centres that use real-time insight to guide daily decision-making are better positioned to protect conversion rates and maximise the impact of agent time.
For outsourced or multi-client environments, this ability to intervene early is particularly important. Our article on how BPOs can meet their KPIs explores the additional performance and reporting challenges faced by outsourced contact centres.
Use Conversation Analytics to understand why performance varies
Surface-level metrics such as contact rate, conversion rate and first-call close rate explain what is happening in outbound sales. But the why behind performance differentiation is dependent on agents, campaigns, or lead types, and teams need insight from the conversation itself.
Our Conversation Analytics analyses 100% of outbound calls, transforming unstructured call audio into actionable insight that would be impossible to capture through manual review or random sampling.
With the ability to analyse conversations at scale, sales leaders can review and identify the underlying drivers of performance. This insight helps explain why certain agents convert more effectively, why objections stall progress, or why specific lead types underperform despite similar call volumes.
In practice, Conversation Analytics supports data-driven outbound sales teams by enabling:
More targeted coaching: Identify the techniques used in successful calls and pinpoint where individual agents need support
Better script and messaging optimisation: Surface patterns in high-performing conversations and common objections
Improved quality and compliance oversight: Analyse every call rather than small samples
Earlier identification of emerging issues: Spot shifts in sentiment, objections, or competitor mentions
If you’re looking for a broader view of how these metrics work together, our guide on how to measure call centre efficiencyexplores how performance indicators combine to drive overall effectiveness.)
Key benefits for outbound sales teams include:
Enhanced Agent Training: Identify successful techniques and areas for improvement, allowing for targeted training programmes.
Customer Sentiment Analysis: Detect changes in tone and emotion, helping agents adapt their approach in real-time.
Quality Assurance at Scale: Analyse every call, ensuring comprehensive QA and quick identification of compliance issues.
Identifying Sales Opportunities: Recognise patterns in successful calls to refine sales scripts and strategies.
Competitor Intelligence: Flag mentions of competitors, providing valuable market insights.
Trend Identification: Quickly spot emerging trends in customer behaviour or common objections.
By implementing speech analytics, outbound sales teams can gain data-driven insights that lead to more effective strategies, improved customer experiences, and better business outcomes. Use these insights to identify common objections, spot successful sales techniques, and provide targeted coaching to your team. A recent study by Forrester found that companies using AI-driven speech analytics saw a 10% increase in customer satisfaction scores and a 15% improvement in first-call resolution rates.
With speech analytics, you’re not just collecting more data – you’re gaining the ability to understand and act on the nuances of every customer interaction, transforming your outbound sales operation into a truly data-driven powerhouse.
Sustaining data-driven outbound sales performance
When combined with performance data, conversation analytics closes the loop between insight and action. Conversation analytics doesn’t sit alongside metrics. It explains them and enables more confident decisions and continuous improvement.
Using more tools or tracking additional metrics doesn’t automatically make an outbound sales team data-driven. Data only becomes valuable when it actively guides decisions across the contact strategy, from how calls are dialled, and leads are prioritised, to how agents are routed, coached and optimised.
In genuinely data-driven teams, agents and managers understand what key metrics mean, how they influence outcomes and when intervention is needed. Performance reviews focus on interpreting trends and agreeing on clear next actions, rather than simply reporting on results after the fact.
The most effective outbound sales teams connect data. By linking real-time performance insight with intelligent technology and informed decision-making, they improve results while activity is still in progress, not once opportunities have already passed.
If you want to understand how your outbound sales performance compares to other UK contact centres, benchmarking is the most effective next step.
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If you run a contact centre, the chances are you're managing rising call times, inconsistent quality reviews, repeat contacts that erode margin, and a personalisation gap that's hard to close without the right data infrastructure underneath it.
None of these are new problems. But the distance between where most operations are today and what's now achievable is narrowing fast - and the teams pulling ahead aren't waiting for a full platform overhaul to make it happen.
At MaxContact's recent webinar, hosted by Marketing Director Kayleigh Tait and Principal Product Manager Conor Bowler, we worked through four specific challenges that are costing contact centres time and money right now - and showed, live, how AI is solving each one. Here's what we covered.
Challenge 1: Call length is rising, and post-call admin is a big reason why
Average service call duration in the UK is now 422 seconds - seven minutes per call - according to Contact Babel. That's the highest figure recorded in 20 years of data collection, and it's been climbing steadily since 2004. There's no sign it comes down on its own.
A large part of the reason is fragmentation. 96% of agents are still navigating multiple systems on every single call. Only 4% of UK contact centres operate from a single unified desktop. 40% of agents are juggling more than four applications at once - doing real-time system-surfing while simultaneously trying to solve a customer's problem or make a sale.
Then there's wrap time. 18% of every call is post-call admin: writing up notes, updating records, triggering next steps. That's queue time growing while your agents do data entry.
The commercial impact is significant. For a 50-agent contact centre making 50 calls a day, a 50% reduction in wrap time is worth over £175,000 a year - based on MaxContact's own ROI modelling.
What good looks like:
An agent wrap-up summary that generates automatically within seconds of a call ending, built from a live stereo transcript that's already separated the agent's voice from the customer's. The agent reviews it, makes any edits, and submits. No blank page. No three to five minutes of typing between every call.
MaxContact's Agent Wrap-Up Summary feature — currently in alpha testing and moving to beta in mid-June — does exactly this. Prompts are fully configurable via Prompt Studio, so the output format, structure, and language match your operation's context, whether that's a collections agency, a sales team, or a customer service function.
Challenge 2: Repeat contacts are eroding margin and driving churn
42% of UK consumers have already switched provider because of a poor contact centre experience - not because of a product issue, but because of the experience itself. A further 38% have seriously considered it. MaxContact's consumer research, shared at the After Work with MaxContact event, makes clear this isn't an edge-case risk.
First contact resolution is what Contact Babel calls the "miracle metric." It's consistently cited as one of the top two KPIs most influential on customer satisfaction. Every repeat call is a direct hit on that number - and at roughly £5 per service call, a repeat contact doubles your cost before you've factored in agent time and churn risk.
The AI angle here is often misunderstood. 69% of customers rate AI worse than humans for understanding their issue - but the problem usually isn't the AI itself. It's where it's introduced in the customer journey. AI deployed in an emotionally charged or complex situation will struggle. The bigger failure point is the handover: when a customer escalates from an AI interaction to a human agent and has to repeat everything from scratch. That's where trust breaks.
What good looks like:
Context continuity. When a human agent picks up - regardless of whether the previous interaction was with an AI agent, a chatbot, or a colleague - they start with the full picture. Customer history, intent, what happened last time, what was agreed. Not a blank screen.
That requires clean data flowing across your channels and a single interface for agents to work from. It's a foundational requirement, not an aspirational one.
Challenge 3: QA based on a sample of 1–2 calls per week isn't good enough
The average contact centre reviews one to two calls per agent per week. Contact Babel's most recent guide describes this explicitly as "neither fair nor valid as a performance measurement tool." That's not a MaxContact opinion - it's the industry's own assessment of its standard practice.
The consequence is that coaching decisions, script adjustments, and performance reviews are all made on a handful of conversations selected at random. Objection handling failures, compliance drift, and the moments where an agent is genuinely struggling can remain completely invisible until the problem is already embedded.
What good looks like:
100% call coverage. Scorecards built on every conversation, not a sample. AI that makes that achievable without overwhelming your QA team.
MaxContact's AI call scoring — now generally available to all Conversation Analytics customers — reduces QA review time per call from 30 minutes to 5 minutes. That's approximately four days of analyst capacity returned to the team every month. Capacity that can go into actual coaching, script development, and performance improvement.
Scorecards are fully configurable: yes/no questions, rating scales, observation notes, auto-fail criteria. Business context can be set per scorecard so the AI understands your products, processes, and compliance requirements before it starts scoring. Scheduled auto-QA at scale — allowing always-on scoring as calls come in, or one-off compliance campaigns across historical data — is moving to beta on 6 July, with general availability planned for early August.
Challenge 4: Personalisation requires the right building blocks first
76% of consumers say personalised communications influence their brand choice, according to Salesforce's State of the Connected Customer. Personalisation at conversation level isn't a luxury - it's a commercial lever.
But it doesn't start with AI. It starts with having the right infrastructure in place:
Customer history and intent available before the conversation starts
In-call sentiment detection so agents know when someone is frustrated or at risk
Consistent context across channels - what happened on the last call, the last chat, the last AI interaction
Next-best-action guidance that surfaces what your best agents do in key moments, and replicates it across the team in real time
Once those building blocks are in place, personalisation stops being an aspiration. It becomes the logical next step, because you already have everything you need.
The bigger picture: it's not about solving one problem in isolation
The demo Conor ran at the webinar wasn't designed to show five separate features. It was designed to show how they connect.
A single agent interface. An automated wrap-up that feeds clean data into the next interaction. Real-time transcription with stereo accuracy that improves everything built on top of it. AI scoring across 100% of conversations. Context that follows the customer, not the channel.
The teams that are getting this right aren't deploying AI as a standalone fix for one metric. They're building a connected system where each piece makes the next one work better.
That's the direction of travel. And a lot of it is available right now.
Call Centre Quality Monitoring: Why Sampling Isn't Enough
Quality assurance is one of the most compliance-critical functions in any contact centre, and one of the most under-resourced. For most operations, the gap between what QA teams can review and what regulators now expect to see evidenced has never been wider.
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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.
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