Explore fresh, expert perspectives on customer engagement and innovation

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.

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.

‍

Watch the full webinar and explore the resources

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

‍

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

‍

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

‍

Most contact centres review a small fraction of their calls. A QA analyst picks a handful, scores them, flags what went wrong, and then moves on. It feels like it ticks the box for quality assurance. But for Ofcom-regulated telecoms operations and FCA-regulated financial services firms, it’s not enough, and the consequences of getting it wrong have never been higher.

This article explains why call sampling creates compliance exposure, what always-on monitoring looks like in practice, and what to look for when evaluating your current approach.

What is call quality monitoring?

Call quality monitoring is the process of reviewing agent-customer interactions to assess whether they meet your quality, compliance, and performance standards.

It typically covers:

• What was said and how the agent handled the conversation
• Whether compliance scripts and protocols were followed
• How vulnerable customers were identified and managed
• Whether the outcome was appropriate for the customer
• How performance compares against your scoring framework

When call quality monitoring is done consistently, it gives you a documented evidence-base across every call type, agent, and campaign. But when it’s done poorly or too infrequently, it leaves gaps that regulators are increasingly likely to find before you do.

How do most contact centres currently monitor calls?

Sampling is the typical approach many call centres take to monitoring calls. A QA reviewer listens to a set number of calls per agent per month, scores them against a framework, and feeds the results back into coaching. It is time-consuming work, so let’s break down the numbers.

Example:

• A single reviewer handles 50 calls a month at 30 minutes per call.
• This amounts to 25 hours of review time.
• And it is still only a fraction of the total call volume reviewed.

The problem here is not the effort; it's the coverage. On average, contact centres manually evaluate 5% of calls per week, meaning many QA operations are leaving the majority of interactions unreviewed. This means:

• You don’t know whether your agents are consistently identifying vulnerable customers.
• You don’t know whether compliance scripts are being followed on the calls you did not pick.
• You are not building an evidence bse, only a small sample.

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

For debt collection, insurance, and other FCA-regulated contact centres, the stakes are different but the problem is the same. Consumer Duty requires firms to demonstrate they are delivering good outcomes for retail customers, not just on the calls they reviewed, but consistently and measurably across their entire operation.

The FCA has shifted decisively from implementation to enforcement. Regulators are no longer asking whether you have a quality monitoring process. They are asking whether you can prove, with documented evidence, that your agents are handling vulnerable customers correctly, following compliant scripts, and not causing foreseeable consumer harm. And that’s for every call, not just the ones you checked.

A sampling approach does not produce that evidence. It produces a snapshot.

For more on what the FCA now expects from contact centres in financial services and debt collection, see our Consumer Duty guide.

The problem with call sampling: A Summary

• Sampling typically covers around 5% of calls per week, leaving the 95% of interactions unreviewed and unverifiable
• Compliance drift happens slowly. By the time sampling catches a behaviour, it is already established and harder to coach out
• Poor agent behaviour on outbound calls can go undetected long enough to trigger carrier blocking or an FCA flag
• Vulnerable customers may not be identified correctly on calls you never reviewed
• Good performance goes unrecognised as you cannot replicate what you cannot see
• A sample tells you what happened on the calls you chose to review. It does not tell you what is happening in your operation

From sampling to monitoring: what's actually required

Moving from sampling to consistent call monitoring is not simply a matter of reviewing more calls. It requires the right infrastructure in place, and historically, that infrastructure was either too expensive, too time-consuming, or both.

At a minimum, always-on monitoring requires:

• Call recording across all interactions, not just selected campaigns or call types
• Transcription that converts voice to text accurately enough to be reviewed and searched at scale
• A platform that connects recording, transcription, scoring, and reporting in one place rather than across multiple disconnected tools

Without all three, monitoring at scale either falls back on human reviewers (which is where the 25-hours-per-50-calls problem comes back in) or produces data too inconsistent to be useful as a compliance evidence base.

MaxContact's Conversation Analytics brings all of this together in a single platform. Call recording, real-time transcription, and reporting sit alongside each other. This gives your QA team a single place to monitor, review, and evidence what is happening across every interaction, without stitching together multiple tools or managing separate systems.

The reason most contact centres have defaulted to sampling is not because they did not want better coverage. It is because the operational cost of achieving it manually was prohibitive. A team large enough to review every call would cost more than most mid-market operations can justify. But that has changed.

How Conversation Analytics makes always-on monitoring feasible

Conversation Analytics is the platform that makes consistent, always-on call monitoring operationally viable for mid-market contact centres.

Rather than relying on a QA team to manually select, listen to, and score calls, Conversation Analytics connects call recording, transcription, scoring, and reporting in a single platform – automating quality assurance. Every interaction is captured, transcribed, and made reviewable, giving your QA team complete visibility across all call types, all agents, and all campaigns without the resourcing overhead of manual review at scale.

The cost comparison is significant. Replicating meaningful call coverage with human reviewers alone would cost an estimated £14,000 per month in analyst time for a mid-sized contact centre. Conversation Analytics delivers that coverage at a fraction of the cost, freeing your QA team to focus on coaching, calibration, and the complex calls that genuinely need a human eye.

How AI call monitoring surfaces insights faster

AI is what makes the insights from always-on call monitoring actionable rather than overwhelming.

Without AI, full call coverage creates a different problem; more data than a QA team can meaningfully review and act on. AI-powered call monitoring solves that by doing the heavy lifting on routine scoring, so your team's attention goes where it matters most.

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.

‍

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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.

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.

If you're not yet using Conversation Analytics and want to see what AI Call Scoring could do for your team, come and talk to us. Book a demo and we’ll show you what it looks like in your environment.

High-volume cold calling based on limited data is no longer a cost-effective outbound strategy and in the B2C world can be non-compliant.

With both sales and debt collection, the public has grown wary of unsolicited calls and generic conversations. But that doesn’t mean outbound dialling is over as a revenue engine.

It means it has to be smarter, multi-channel and data-led. Based on real-time information and a refined dialling strategy. Often personalised in tone, timing and approach.

So, if you’re reselling UCaaS today, there’s a strong chance your customers’ outbound results are being held back by the platform they’re on. When revenues stagnate, contact rates fall or conversions get harder to close, the problem usually isn’t effort - it’s strategy, data and tooling. This blog sets out what a high-performing outbound operation looks like, so you can have that conversation with confidence.

The outbound metrics your customers should be measuring

The foundation of any smart outbound strategy is good information. Help your customers understand that without the right data, they can’t tell whether calls are reaching the right people, whether agents are performing, or whether their scripts are working. Measurement isn’t a nice-to-have - it’s where improvement starts.

Outbound KPIs to share with your customers:

•      Connect rates: are calls being connected to a real person?

•      Contact rate: how often are agents reaching the right decision-maker?

•      Data penetration rate: is their data being used effectively - are they making the most of high-value leads?

•      Conversion rate: the percentage of contacts that result in a positive outcome

•      Calls to success rate: the number of calls needed per successful result

Take conversion rate as a case in point. It tells your customers two things at once: the quality of their contact data, and the effectiveness of their team.

Better data means a higher likelihood of reaching the right person. Skills-based routing - matching the right agent to the right call- increases that further. And stronger training, combined with more refined scripts, means more of those conversations end the way they should.

Qualitative insight matters as much as the numbers

Quantitative KPIs don’t tell the full story.Improving contact rates will generate more conversations - but without the right skills in place to handle them, conversion rates won’t follow.

Encourage your customers to combine the numbers with qualitative insight: what objections are coming up most, what their customers are saying about competitors, and where conversations are breaking down.Helping them bring both lenses together is one of the most valuable things you can do as a partner - and it’s a conversation most resellers never have.

Industry-specific KPIs worth knowing

The metrics above apply broadly, but it’s worth helping your customers zone in on numbers that are specific to their sector.

In debt collection, promise to pay (PTP - the percentage of calls resulting in a commitment to pay) and percentage of debt collected are key indicators. In sales, first-call close rates and average revenue per call say a lot about campaign effectiveness.

MaxContact’s KPI Benchmark Report gives a detailed breakdown of what good looks like across sectors. It’s a useful resource to share with customers who want to know how their numbers stack up.

Benchmarking: what good looks like

Once your customers know what to measure, the next step is helping them understand what the numbers mean.

MaxContact’s own research found that the largest proportion of respondents - 34%, across both sales and debt collection - reported conversion rates of between 10% and 19%. Cold outbound sales calls typically convert at 1–3%; warmer, more targeted calls can reach as high as25%.

Broad benchmark ranges for common outbound KPIs:

•      Average handling time: 4–12 minutes

•      Contact rate(cold calls): 5–15%

•      First call resolution: 10–40%

These are broad ranges and will vary significantly by sector and product complexity. The more important thing for your customers is to track their own numbers consistently over time - and to understand what’s driving movement in either direction.

What your customers can do to improve outbound performance

Once your customers are tracking the right metrics,the focus shifts to moving them. Here are the levers most likely to make a meaningful difference - and the conversations worth having:

•      Team training and coaching - conversation analytics can surface objection patterns, benchmark individual and campaign performance, and show exactly where coaching will have the biggest effect.

•      Smarter dialling strategy - when are their contacts most likely to answer? Are they prioritising by lead value? Are they using the right dialler mode for the campaign? These are practical questions you can help them think through.

•      Omnichannel engagement - how does combining SMS, email and calls affect contact and conversion rates? Could AI agents handle routine calls while human agents focus on more complex or sensitive interactions?

The performance advantage you can offer your customers

Helping your customers understand and act on their outbound performance data is a powerful way to open the door to a bigger conversation. Standard UCaaS platforms can’t offer the range of insight and capability that a specialist customer engagement solution like MaxContact provides - and once customers seethe gap, the case for change makes itself.

Think conversation analytics, AI chatbots, workforce management, intelligent outbound dialling and sophisticated contact strategies - capabilities that standard UCaaS systems simply can’t match, and that enterprise-grade platforms price out of reach for most teams.

MaxContact delivers measurable results - from 200–300% increases in contact rates to doubling sales teams’ conversion rates. Benchmark Insights Report.

That’s because its intelligent, intuitive platform lets teams build smarter outbound strategies and tailor them for every campaign.

Talk to the MaxContact partner team about adding a specialist customer engagement solution to your portfolio. Book a call

‍