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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Quality assurance in a call centre is the process of monitoring, evaluating, and improving agent interactions to ensure consistent customer experience and performance standards. All contact centres have a QA process. But most struggle to drive change from the data it provides.
For years, manual QA was the only option, and for many contact centres it still is. Supervisors sample a handful of calls, score them against a checklist and then file the results. Roughly 5% of interactions get reviewed on average. And then any feedback is given to agents days later (if at all).
It was never a great system. But as interaction volumes rise, agent workloads increase, and 42% of customers say they'll switch providers after a single poor experience, the cost of that insight-to-action gap is getting harder to absorb.
This guide covers how to make the shift from using QA as a monitoring exercise to using it as a driver of performance with AI-powered QA software.
You're monitoring quality. But are you actually improving it?
Knowing how to improve quality assurance in a call centre starts with an honest question: is your QA process actually producing change, or just producing data?
Traditional QA has a lag problem built in. A call happens and a supervisor reviews it days later. Feedback reaches the agent at a point where they've had dozens of conversations since the one that’s been reviewed. The connection between the behaviour and the coaching is weak, and the window for meaningful learning has already closed.
There's also the sampling issue. Manual QA typically covers around 5% of interactions.
“Leaders want answers, but those answers sit behind small QA samples, anecdotal feedback, and performance dashboards that only tell part of the story.” Connor Bowler, Principal Product Manager at MaxContact
The result is stark: manual QA gives you a story about some of your calls while an AI-powered platform gives you the truth about all of them.
Stop treating QA as an audit. Start treating it as a coaching tool.
Improving quality assurance starts with how you think about the QA function. It’s not an audit, but rather a coaching engine.
Approach QA with an audit mindset, and you’ll get reports. Approach QA with a coaching mindset, and you’ll get improvement. Contact centres that use QA to drive real behaviour change tend to do three things differently:
What They Do
Why It Works
Close the feedback loop fast
Feedback delivered within 24 hours lands harder. Agents have context, they remember the call, and the learning is concrete rather than abstract.
Make QA data visible to agents, not managers only
When agents can see their own scores and track their own trends, QA becomes something they're engaged with rather than something that's done to them. That ownership is where improvement starts.
Coach patterns, not just incidents
A single low-scoring call is an incident. Five with the same failure point is a pattern. Coaching patterns is where QA data creates lasting change.
As AI handles monitoring and scoring at scale, QA teams move away from manual call reviews and closer to coaching, analysis, and performance design; a more valuable role, and a more sustainable one.
It's a shift some organisations are already making.
The ICX Use Case
ICX, a customer engagement provider for brands including Nissan, Suzuki, and Stellantis, replaced manual call reviews with MaxContact's Conversation Analytics platform. Quality assessors moved from repeated audio replays to transcript-based reviews, with AI-powered search surfacing compliance issues, objection patterns, and coaching opportunities across every interaction. Training is now built directly from sentiment and objection data, feeding into one-to-ones and agent development.
Call centre quality assurance metrics: What they're actually telling you
Once you've made the shift from audit to coaching mindset, the next question is: what is your QA data actually telling you?
The most common scorecard measures script adherence, handling time, first call resolution, CSAT, and compliance markers. All of these are valid, but they can mislead if you're drawing conclusions from a 5% sample. The same metrics applied across 100% of interactions tell a very different story.
A few principles that make QA data more actionable:
1. Work out whether you've got a data problem or a coaching problem.
An agent who consistently mis-dispositions calls might not need coaching, they might need better data or a clearer process. An agent whose sentiment scores drop in the last hour of every shift has a different problem entirely. QA data is most valuable when it helps you tell the difference.
2. Don't look at scores in isolation. Connect them to outcomes instead.
A call that scores well on process but ends in a complaint tells you the agent followed the script and still got it wrong. Map your QA scores against CSAT, NPS, or complaint rates to find out which quality indicators actually predict good outcomes and which ones are just measuring process-following.
3. Track how quickly agents improve after coaching.
The rate of improvement following a coaching session is more useful than the score itself. If coaching isn't producing measurable change within a defined window, perhaps it’s the approach that needs to change, not just the agent's behaviour.
4. Use sentiment data to find what scores can't show you.
Scores tell you what happened procedurally. Sentiment analysis tells you how the customer felt at the start of the call, at the point of objection, and at sign-off. The gap between a high compliance score and a negative end sentiment is often where the most valuable coaching insight sits.
MaxContact's Auto QA applies customisable scorecards consistently across every selected interaction, including auto-fail criteria for non-negotiable standards, and surfaces sentiment alongside compliance scoring in a single view. QA managers spend less time manually reviewing calls and more time acting on what the data reveals.
Auto QA Score Card
The difference between feedback that lands and feedback that doesn't
Call centre quality monitoring best practices all point to the same conclusion: data doesn't change behaviour. Coaching does.
Specific beats general, every time. "You need to listen more actively" isn't actionable. "On this call at 2:34, the customer mentioned they'd been waiting three weeks and you moved on without acknowledging it. Here's what it sounds like when it's handled well" is constructive, actionable feedback. .
Frequency matters more than depth. Regular short coaching sessions (ten minutes a week focused on one call or one skill) tend to produce better outcomes than monthly deep-dives. Behaviour change is cumulative.
Self-review builds ownership. Agents who listen back to their own calls and score themselves before a coaching session arrive with more self-awareness and more investment in the gaps. The manager is coaching, not judging.
Use your best calls as teaching tools. Sharing anonymised examples of top-performing interactions gives the whole team a concrete standard to aim for. Not a number. A behaviour.
Data from MaxContact's Conversation Analytics platform drawn from over 700,000 objections across a six-month period, shows agents successfully overcome just 39% of objections, while 61% remain unresolved. The most challenging category is need objections ("not interested", "no immediate need"), which represent 46% of all objections but carry the lowest conversion rate. That's a pattern, and it needs a pattern-level response.
For BPOs like ICX, managing quality assurance across multiple client accounts at scale, running separate manual processes for each campaign simply isn't viable. "Anything that helps us connect to more of the conversations, especially given the volume we handle, is incredibly valuable. The team does a fantastic job, but no one can review everything manually. With Conversation Analytics, we can proactively support our agents and maintain complete oversight, so we never miss a critical moment or insight," said Sarah Franks, Call Centre Manager at ICX.
The hidden reason your QA findings never make it to the coaching conversation
There's a practical barrier between QA insight and coaching action that doesn't get talked about enough: post-call admin.
After every interaction, agents log outcomes, complete call notes, update CRM records, and prepare for the next contact. In high-volume environments, where over 52% of contact centre leaders report agent workloads have increased year-on-year, that wrap-up time absorbs the space that could go into engaging with coaching materials or reviewing their own performance data.
When agents are constantly catching up on admin, QA becomes something that happens to them in scheduled sessions, not something they engage with actively. The feedback loop gets longer. The shift from "QA as audit" to "QA as improvement" stalls.
Agent Wrap-Up Summary changes this directly. By automatically capturing call summaries, key outcomes, sentiment, topics, and follow-up actions at the point of wrap, it creates a consistent record for every interaction without adding work for the agent. That frees up the time and headspace to engage with performance data in real time.
What better QA actually means for your bottom line
For contact centre leaders asking how to improve quality scores in their call centre, the answer comes down to this: quality improvement has to show up in numbers the business cares about.
For BPOs, that means client retention. Clients expect their customers to be handled to a defined standard; when quality slips, renewals are at risk. With agent attrition running at 31% across the industry, AI-powered QA becomes even more valuable. New agents can be held to the same standard from day one, without relying on institutional knowledge that walks out the door.
For financial services and insurance contact centres, the case is just as direct. Agents who handle complaints well, identify vulnerability accurately, and resolve queries first time produce better CSAT, fewer escalations, and stronger retention. With first call resolution rates dropping from 43% to 37% year-on-year, the contact centres that reverse that trend through better coaching protect both customer relationships and commercial performance.
Either way, QA only delivers value if it produces measurable change, closing the loop between monitoring, coaching, and results, consistently and at pace.
How to make all of this work when you're dealing with real call volumes
The barrier has always been operational: the volume of calls, the limits of manual review, and the admin overhead that eats into coaching time.
The contact centres that improve quality consistently aren't doing something fundamentally different. They've just stopped treating QA as something that happens after the call and started building it into how the operation runs; faster feedback, visible data, coaching that's based on patterns rather than incidents.
At the volumes most contact centres are dealing with, that only works if the infrastructure supports it. AI-powered QA removes the manual overhead that makes it impractical, covering every interaction, surfacing what matters, and giving agents and managers the time to actually act on what the data shows.
If your QA process is still generating reports instead of results, it's time to change how it works. See how MaxContact's Auto QA and Agent Wrap-Up Summary turn insight into action.
Blog
5 min read
MaxContact named one of the UK's most thriving companies to work for
We're proud to announce that MaxContact has been named a winner of the Culture 100 Awards 2026, recognising us as one of the top 100 growing companies in the UK with a genuinely people-first working environment.
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The Culture 100 Awards, run by Maya, evaluate thousands of companies across more than 22 industry sectors. What makes this recognition different is how it's determined: not by self-reported data, but by anonymous sentiment surveys and open-ended responses from employees across participating organisations. Companies are assessed on verified employee benchmarks - the kind designed to uncover how people actually feel about where they work, not just how a business wants to present itself. For us, that's exactly what makes it meaningful.
As a team of around 70 people, we've grown steadily as demand for cloud-based contact centre and engagement technology has increased, and we're thrilled to have been selected for our commitment to building an environment that holds our people as a genuine competitve advantage.
Hannah Holmes, our Head of People, put it well: "We've been working hard to build an environment where expectations are high, accountability is clear, and people feel genuinely supported. This recognition tells us that work is landing in the right way."
CEO Ben Booth sees it as central to how we run the business: "Building a high-performing culture isn't a side project for us. We believe that getting our people strategy right is what enables us to serve our customers well and grow sustainably."
Being listed among the UK's most thriving places to work is something the whole team has earned, and it reflects the kind of company we're committed to being as we continue to grow.
Want to be part of it?
We're hiring. If you're looking for a place where the culture is real, not just a slide in an onboarding deck, take a look at our open roles.
Blog
5 min read
After-call work isn’t an efficiency problem- it’s a trust problem.
After-call work isn't just a time drain - it's a trust problem. Discover how inconsistent CRM records erode customer loyalty, and how AI-generated call summaries close the loop.
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Ask a contact centre leader about after-call work and they'll usually frame it as a time problem. Wrap time is too long. Agents aren't “going available” quickly enough. AHT is inflating. The fix, in most conversations, is operational: better templates, tighter ACW targets, more monitoring.
That framing is not wrong, but it is incomplete. After-call work is not just a time problem. It’s a quality problem, one which has a direct customer-facing cost that most operations are not measuring.
What actually happens when the call ends
The call ends. The agent is under pressure to “go ready” and be available for the next call in the queue. They have notes to write, a CRM record to update, a disposition to log. Often with multiple systems to update. They have approximately two minutes to do all of that before the queue moves. So, they write what they can. A sentence, maybe two. A shorthand that makes sense to them right now but will mean nothing to the agent who picks up next week's call. Sometimes nothing at all, and a disposition code carries the entire context of a complex interaction. Now multiply that across your team. Ten agents handling the same call type will leave ten different records. Some thorough, some minimal. Some missing the most important detail entirely - what was promised, what was escalated, what the customer was told to expect next. This is the quality problem, and it compounds quietly.
The customer pays for it twice
The first cost is visible: longer calls, higher AHT, agents unavailable for longer than they should be. This is what gets measured. The second cost is less visible but more damaging. The customer calls back. A different agent picks up. They open the record - and it tells them almost nothing useful. So, they ask the customer to explain themselves again. That moment - the repetition, the sense that the company was not paying attention - is where trust erodes. It’s not dramatic. It does not show up immediately in CSAT. But it accumulates, and eventually it becomes the reason a customer switches.
Our Voice of the UK Consumer 2026 research found that 42% of UK consumers have already switched provider due to poor contact centre experience. The word ‘already’ matters. These are not consumers who are at risk of switching – they’ve already left. The post-call gap is not just an internal inefficiency. It's a retention risk dressed up as an admin problem.
Why training cannot fix this
The instinct, when notes are inconsistent, is to retrain. Set clearer standards. Remind agents what a good record looks like. Monitor more closely. This rarely works. Not because agents do not want to do it well, but because the system is not set up to support consistency at pace. An agent writing notes under queue pressure, with no template and no structure, will produce exactly what the conditions allow. Varying quality, varying detail, varying usefulness. The problem is not discipline or intent. It is that the task is being done manually in the least forgiving conditions possible.
What changes when AI writes the notes
Agent Wrap Up Summary generates a structured call record automatically the moment the call ends; drawing on the conversation to produce a consistent summary of what was discussed, what was agreed, and what happens next. Every call. Every agent. Every time.
Consistency is the point. Not just the time saving, though that is real: wrap time typically accounts for 15–20% of an agent's working day, and a 50% reduction returns meaningful capacity to productive contact time. For a 50-agent team, that translates to an illustrative annual saving of £175,000: based on 50 agents, 50 calls per day, a 50% reduction in wrap time, and an average fully loaded agent cost of £25,000 per year.
The more significant change is downstream. When every call produces a reliable, structured record, that record becomes the foundation for what the next agent sees before their call begins. Customer History in Contact Hub surfaces that context automatically - so the agent who picks up next week is starting the call informed.
This is how personalisation at scale works. Not by asking agents to memorise histories or search through fragmented notes. By generating a complete record on every call, so context accumulates and becomes genuinely useful over time.
The record is where the loop closes
Agent Wrap Up Summary is the start of a feedback loop, not the end of one. The structured data it generates - consistent, covering 100% of calls - feeds everything downstream.
Conversation Analytics can analyse that data at scale, identifying coaching opportunities, surfacing compliance drift, and enabling AI Call Scoring that cuts QA review time from 30 minutes to approximately 5 minutes per call. Real Time Agent QA (available in Beta Q4 2026), uses it to guide agents in the moment, surfacing compliance prompts, flagging sentiment shifts, and steering conversations towards the outcomes that best records show actually work.
Better calls produce better records. Better records enable better coaching. Better coaching produces better calls. The loop only works when it is closed. And it closes after the call ends.
Start with the audit
You do not need a platform overhaul to find out where you stand. Pull a sample of CRM records from last week. Read them. Ask a simple question: if the next agent had only this record to go on, what would they know? The answer will tell you more about the state of your post-call process than any metric can.
Want to see how Agent Wrap Up Summary works in practice? Download The Assisted Agent - our practical guide to AI-enabled agent assistance across the full call lifecycle. Or if you'd rather see it live: book a demo with the MaxContact team.
