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.

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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."

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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

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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.

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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%).

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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.

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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.

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

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AI doesn’t fix contact centres. It scales them. If your journeys are joined up, automation can reduce the pressure your team is facing. However, if they’re fragmented, AI amplifies the friction - faster transfers, repetition and customer effort. That’s why the most useful question contact centre leaders can ask themselves isn’t “What can AI do?” - it’s, "Are we actually ready for it?".

Whether you’re running sales and retention in telecoms, payment collections with vulnerability considerations in finance, customer support in utilities, or managing multiple client programmes in a BPO, the readiness question is the same - do we have the foundations to automate without increasing customer effort or operational risk?

“Always-on” support is an operating model, not a staffing one. It's built to remove avoidable demand, protecting your team's time for high-judgement conversations, and making escalation safe when risk or complexity arises. ‍

Always-On Service Starts with Resolution, Not Headcount.

Consumers are increasingly expecting help at any time of day, across voice and digital channels. But increasing headcount to meet 24/7 customer support expectations isn’t sustainable for most contact centres operating on tight margins.

An always-on contact centre doesn’t mean agents working around the clock. It means using AI and automation to absorb predictable demand across inbound and outbound – from service updates and appointment changes to sales follow-ups and renewals, to payment reminders and self-serve arrangements - without needing an agent for every interaction.

The trap many leaders fall into is assuming that automation alone creates always-on. It doesn’t. Always-on is the result of clear journeys, consistent rules, and controlled escalation.

The Real Readiness Problem: Avoidable Demand

Most contact centres don’t struggle because customers contact them. They struggle because customers are contacting them more than once.

A lot of volume is created by operational gaps:

• Unresolved issues driving repeat contact
• Too many transfers caused by poor routing
• Long handle times driven by missing context
• Channels operating as seperate service silos

This is the stuff that quietly drains performance. It also explains why some AI programmes stall: they automate interactions on top of broken flows, then wonder why customer effort doesn’t fall, and agent workload doesn’t change.

If you want a pragmatic AI strategy, start by identifying where the operation is generating demand it shouldn’t have to handle.

A Practical Readiness Lens: Demand, Continuity, Control

To make readiness tangible, use this simple lens. If any one of these is weak, automation outcomes will be capped - or worse, you’ll scale the wrong things.

1) Demand: Do You Know What Should Be Automated?

AI delivers value when it absorbs predictable, repeatable demand - the structured interactions that don’t require human judgement. If you can’t clearly separate predictable from complex demand, you’ll either automate the wrong things and frustrate your customers or keep too much with agents and miss the efficiency gains.

A pragmatic starting point is mapping the top drivers and asking: which ones are genuinely structured, and which are only “simple” because we’re not seeing the full context?

2) Continuity: Does Context Move with The Customer?

Customers think in outcomes, not channels. Readiness means your operation can maintain continuity when a conversation starts in chat and moves to voice, or when an outbound reminder triggers an inbound response, or when a customer returns with a follow-up and expects you to remember what happened last time.

If context doesn’t travel, automation becomes a reset button, and resets are where handle time, repeat contact, and frustration grow.

3) Control: Can You Escalate Safely and Measure Outcomes?

Automation should never be a dead end. When complexity rises, or when there’s vulnerability, a complaint, payment risk, or compliance exposure, you need controlled escalation to a human agent with the full context carried across.

If you can’t define escalation rules and success measures beyond containment” you’re not ready to scale. You’re ready to pilot.

Where AI Fits When You’re Ready: Layers, Not Channels

A common mistake is deploying AI as separate tools by channel - a chatbot here, an AI agent there - and expecting it to add up to an always on operation. It simply adds more mini contact centres to the one you already have.

A more practical approach is to treat AI as layers across the operating model:

• Decision layer (AI Agents): Interprets intent, resolves structured interactions, and prevents outbound activity from automatically creating inbound pressure through unmanaged follow-up

• Asynchronous layer (chatbots and messaging): Allows customers to complete routine tasks without joining a queue, while keeping journeys connected across voice and digital
 
• Visibility Layer (Conversation Analytics): Shows where demand originates, where conversations stall, and what drives repeat contact so you can improve routing, coaching, and automation design based on evidence rather than instinct

When these layers support end-to-end workflows, AI stops being a bolt-on and becomes a genuine performance lever.

A Quick Readiness Check: The Questions Most Teams Skip

If you’re planning AI-enabled automation this quarter, these questions are worth answering before you commit time and budget:

• What proportion of our demand is truly predictable and repeatable?
• Where do customers repeat themselves, get transferred, or drop out?
• What's creating repeat contact and how will we remove it?
• What are our escalation triggers for risk, vulnerability or complexity and do we trust them?
• How will we measure success beyond containment - effort, quality, outcomes, stability?
• Do inbound and outbound journeys reinforce each other, or create extra pressure?
 

If those answers aren’t clear yet, that’s not a blocker, it's your roadmap.

Pressure-Test Your Readiness With The Scorecard

If you want a structured way to benchmark readiness across the foundations that matter - demand, continuity, escalation, and operational fit - our scorecard is designed for exactly that. Use it to create alignment internally, prioritise improvements, and shape an automation roadmap that holds up under real-world volume, not just pilot conditions. Download the Always-On Contact Centre Readiness Scorecard here.