Outbound still pays - your customers just need a smarter approach
High-volume cold calling is losing ground. Here's what a high-performing, data-led outbound strategy looks like - and how to sell it to your customers.
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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September 29, 2025
5 Must-Have Dialler Features You Need In Your Outbound Call Centre
Whether you’re looking to supercharge sales, streamline debt collection, or elevate customer service, the right outbound dialler can redefine how you connect and communicate. So, continue reading to learn more about automated diallers and discover the potential they hold for your contact centre’s success
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Put simply, outbound dialling is the process of making calls to customers or contacts, typically for sales or marketing purposes.
While outbound dialling can be performed manually on a mobile or business phone, this is not practical when dealing with a high volume of calls. A range of additional features can enhance outbound calling in a contact centre setting through the use of an automated dialler.
What does an outbound dialler do?
An outbound dialler is generally a cloud or software solution that automatically dials phone numbers and makes calls on behalf of your sales, collection or customer service teams. As such, it’s an essential ingredient in any organisation where you need to make outbound calls to clients and prospects throughout the day.
What are the different types of call centre diallers?
The main options are a manual dialler and an auto dialler.
Manual Dialler
A manual dialler is like a traditional phone. A call agent manually dials numbers from a call list, one after another.
Auto Dialler
As the name suggests, an auto dialler automates much of the dialling process. It digitally dials numbers, and can also dial multiple numbers at once, passing answered calls to available agents.
Manual vs Auto Dialler
Why would you choose one over another? Most call centres now opt for auto dialling, because it significantly boosts productivity. Agents spend more time talking to customers and less time dialling unresponsive numbers.
Manual dialling can still be useful, but only for campaigns involving a small number of high value customers who demand a more personal approach.
The different outbound dialler modes
If you’re using an auto-dialler, there are likely three dialler modes that you’ll frequently use, depending on the type of outbound calling you are doing. These are predictive diallers, progressive diallers and preview diallers.
What is it? When most people think of outbound dialling software, they tend to think of predictive dialling. Predictive dialling places calls based on the software’s predictions of agent availability. It dials multiple numbers simultaneously, so that when agents finish one call they can be instantly connected to the next.
What are the benefits? The best predictive diallers minimise abandoned calls (and the amount of time customers spend on hold) and maximise the time your agents spend having conversations. When should I use it? Predictive dialling is the standard for straightforward, high volume sales campaigns (like commodity sales) or debt collection activity.
What is it? Progressive diallers are predictive diallers that slow the pace down by only dialling a number when an agent is available to take the call. Dialling is instant and automatic, so the system still allows for a relatively high number of calls.
What are the benefits? Progressive dialling eliminates the risk of customers abandoning calls or waiting a frustratingly long time before being connected to an agent. Because an agent is always available, the customers you have painstakingly nurtured over a period of time feel valued and importan
When should I use it? It is often used in campaigns that target current customers. It’s a low risk option that can improve customer experience and effectively help agents upsell additional products and services.
What is it? A preview dialler takes the pace down another notch. When an agent indicates availability, information about the next call is sent to the agent for preview.
After a set amount of time – say, one minute – the number is automatically dialled. This delay lets the agent prepare for the call, using information typically taken from the company CRM system – which are often integrated into the dialler.
What are the benefits? Agents can have more in-depth, focused conversations, based on a customer’s real experiences and challenges. It can improve customer experience and increase the number of positive outcomes.
When should I use it? Preview diallers are particularly helpful when the reason for the call is complex or sensitive. For example, following up with web leads or dealing with customer complaint calls.
Outbound diallers can be integrated into many industries. Any company with an outbound contact centre who are cold calling or making high volume phone calls can benefit from outbound dialler software.
Power up your sales teams
Sales campaigns are often high volume and low touch. Predictive dialling is the gold standard for straightforward, high volume outbound campaigns (like commodity sales). It can quickly and efficiently work through large datasets, making sure leads are contacted while they’re still warm.
The best predictive diallers minimise abandoned calls (and the amount of time customers spend on hold) and maximise the time your agents spend having conversations. They can be set to play messages if they meet an answerphone, and will recycle numbers (placing unanswered calls back into the call queue) in a way that ensures your customers or leads are contacted, but never pestered.
The right outbound dialler can make selling straightforward by helping to connect your sales people to the right customers at the right time. Combined with the contact centre-specific features mentioned earlier, it can offer powerful tools for contacting customers, winning business and exceeding customer expectations.
Increase debt collection rates
Your credit and debt resolution teams can use effective targeting to reach priority customers at times that suit them. Maximise collection rates using advanced data segmentation and encourage self-serve with automated communications. Automate payments with self-serve options providing customers choice and improving satisfaction.
Preview diallers are particularly helpful when the reason for the call is complex or sensitive. For example, debt collection calls are more likely to end positively if agents have the time to gather all the information they need beforehand.
Elevate your customer service teams
Customer service teams often use progressive dialling to target current customers with after sales information or courtesy communications. It’s a low risk option that can improve customer experience, help nurture loyalty and effectively help agents upsell additional products and services. Because an agent is always available to have a conversation, the customers you have painstakingly nurtured over a period of time feel valued.
5 must-have outbound dialler features
Answer Machine Detection (AMD)
Answer Machine Detection (AMD) lets your auto-dialler software identify answering machines before connecting calls to agents. This means agents only spend time on live conversations, saving them valuable time and boosting productivity.
AMD is particularly helpful for high-volume sales campaigns where every minute counts. MaxContact’s AMD boasts a 90% success rate in detecting answering machines, freeing up agents to focus on reaching real people.
Speech analytics
Forget manually reviewing call recordings! Speech analytics uses AI to analyse every conversation, automatically identifying customer sentiment, call quality, and agent performance. This lets you:
Spot frustrated or vulnerable customers who need extra care.
Ensure agents follow compliance guidelines.
Understand what customers are saying about your products and competitors.
Speech analytics gives you valuable insights from all calls, not just a select few. It saves time and helps you improve the overall performance of your contact centre.
A secure payment manager
A secure payment IVR gives customers the payment options they want, while giving teams the time they need to deal with more complex or sensitive cases.
Payment automation helps you speed up debt collection and improve cash flow. When you give customers more convenient ways to pay, they’re more likely to stick to payment schedules.
MaxContact’s payment IVR is fully PCI compliant, protecting customer information at all times. We offer both assisted payments, in which staff safely guide customers through the payment process, and automated payments, which are fully self-serve and available 24/7.
Analytics and reporting
You can only improve contact centre performance when you can measure it. When you’ve done that, you need to present the data in a way that is easy to understand and act on. That’s where analytics and reporting come in.
MaxContact’s pre-configured reporting gives you complete visibility around productivity, issue resolution rates, revenue and customer satisfaction, to name just a few. You can set targets for campaigns, channels, teams and agents and track performance over time.
All teams – sales, service and debt resolution – benefit from better information. Pre-configured reports give you the data you need in the quickest and most hassle-free way.
Easy integration
A powerful dialler is even better when it works hand in hand with your existing systems. Imagine a sales agent having instant access to customer history, preferred contact methods, and past feedback – all within the dialler interface (thanks to CRM integration).
This allows for personalised conversations that address specific needs, leading to happier customers and improved outcomes. Easy integration applies to after-sales and debt resolution teams too. By connecting your dialler with other systems, you can put all relevant information at agents’ fingertips, reducing hold times and boosting overall efficiency.
The benefits of auto-dialler software you can’t ignore
Improve contact centre metrics like AHT
Average Handling Time (AHT) is a calculation based on the time agents spend talking to a customer, the amount of time callers are on hold and the time taken on follow up tasks, divided by the number of calls handled. The lower your AHT, the better. It means you can handle more calls, improve efficiency and reduce costs. A good dialler can improve AHT and a host of other contact centre metrics, by allowing agents to handle more calls, more efficiently.
Excel at sales and debt collection
Whether it’s sales or debt collection, the best results happen when good agents talk to customers. Whether it’s a high volume, low touch sales campaign, or more sensitive debt resolution calls, the right dialler means your agents spend more time in conversation with customers, and less time processing unanswered calls or connecting to answering machines.
Keep your contact centre compliant
A powerful predictive dialling algorithm speeds up and slows down depending on the conditions in your contact centre. If fewer agents are available, the dialling slows down, helping to ensure you stay within compliant boundaries for abandoned and dropped calls. Or you can switch to progressive or preview modes for more personal contacts. The dialler can also ensure that the frequency of calls to a contact never exceeds official limits.
Seamlessly integrate with your CMS
A dialler that integrates with your CMS system is a huge advantage. It means that the systems feed information to each other, so your agents always have the details of previous contacts at their fingertips. That reduces the risk of customers becoming annoyed by having to repeat information they’ve already previously given. It can also provide insights into customer satisfaction rates, preferred times and methods of communication and so on.
These companies boosted performance with auto diallers
We worked with these companies to replace ageing systems with modern cloud-based diallers – and the results are impressive.
Compare My Insurance
Compare My Insurance is one of the largest independent insurance and protection specialists in the UK. But dialler downtime, data issues and missed opportunities were hampering the business.
MaxContact’s dialler solution integrated seamlessly with the company back office systems. It has significantly increased contact rates while providing complete transparency around performance and progress.
APJ Solicitors
APJ Solicitors, a leading financial mis-selling specialist, needed to increase call volumes and boost efficiency, but its basic VOIP phone system was no longer up to the task.
MaxContact’s solution increased call volumes by 110% in the first year, and improved average agent call efficiency by 36%. Productivity has risen five fold over the company’s previous solution.
Improve your call centre performance with MaxContact
MaxContact offers the most sophisticated outbound dialler currently available. This continually improving cloud-based dialling solution gives you the flexibility to run your contact centre your way, letting you choose the right blend of productivity and compliance for your business needs. With over a 1,000 unique features, MaxContact’s outbound dialler helps meet your contact centre challenges in new and powerful ways.
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Running an Effective Outbound Call Centre
Running an effective outbound call centre can boost sales, help retain customers and ensure timely payment collection. They are a vital cog in the day-to-day running of many different types of businesses, especially those that rely heavily on sales, lead generation or market research.
Unfortunately, many outbound contact centres run into the same challenges time and time again, from low call connect rates to poor performance and difficulties measuring or handling data.
Whether you run an in-house or outsourced contact centre, solving some of these issues can boost your bottom line.
An outbound call centre is a common business operation that handles outgoing phone calls to current or prospective customers, whether it be for sales, telemarketing, surveys and research, appointment setting, or customer notifications and payments.
Inbound vs Outbound Call Centre
As the name suggests, the main difference between an inbound and an outbound call centre is the type of call. An inbound call centre deals primarily with incoming phone calls, supporting new and existing customers with queries, complaints, bookings, or sales. Meanwhile, an outbound contact centre focuses on outgoing calls, where agents are proactively reaching out to prospects or clients.
Many contact centres operate on both an inbound and outbound basis, depending on the needs of the business.
Since outbound call centre operations face a unique set of challenges, we will focus primarily on those for the remainder of this article. Nevertheless, contact centres with diverse and complex requirements can also benefit from the CCaaS solutions mentioned below.
What are the Challenges of an Outbound Call Centre?
Whether it be cold calling, debt collection, or simply ringing customers to remind them of their upcoming appointments, actually getting through to the desired recipient is one of the main challenges of an outbound call centre.
Meanwhile, data management and compliance pose a different, although equally important problem. Let’s look at some of the common challenges of outbound call centres in more detail, and see how they can be solved with effective CCaaS solutions.
Challenge #1: Low Call Connect Rates
A low call connect rate has the capacity to defeat the entire object of an outbound call centre. If you can’t get through to your recipients, you’re unlikely to see a return on the investment of the operation, whether you’re trying to make more sales, raise funds or collect vital market data.
Problems
Incorrect or outdated contact information: With the wrong information, not only do agents fail to reach the recipient, but also waste valuable time on erroneous calls.
Uninterested recipients: We’re all guilty of hanging up quickly or even ignoring unsolicited calls, but large volumes of uninterested recipients can seriously impact results.
Busy signals and voicemails: Reaching live individuals can be challenging depending on the time of day and individual communication preferences.
Solutions
Predictive Dialling and AMD
Predictive dialling is a powerful tool to boost the number of successful connections, using advanced algorithms to optimise call attempts. The technology works by analysing historical data to predict when agents are more likely to reach the desired recipient.
Meanwhile, AMD (answering machine detection) screens and bypasses calls that go to voicemail to reduce idle time, with software provided by MaxContact seamlessly integrated with your existing infrastructure.
Contact Prioritisation
CCaaS software can also identify the most important calls and put them at the top of an agent’s list, using custom data fetching on your leads. This helps you connect more quality calls with customers or prospects who are less likely to hang up.
Outbound Skills-Based Routing
This clever functionality supercharges your campaign results by intuitively matching customers with their ideal agents.
Outbound skills-based routing is a relatively new technology that works across progressive, predictive and preview campaigns to connect customers with agents who have the relevant skills to handle the call. Live-call data is used to assign skill ratings to agents.
Challenge #2: Inefficient Agent Performance
While performance is on some level, determined by the individual agent, there are still plenty of things you can do to increase efficiency and motivation.
Problems
High call volume and workload: When there is a high call volume to keep up with, agents can experience stress and burnout, leading to a significant drop in performance.
Lack of proper training: Some call centres lack the resources to provide personalised agent training, meaning they don’t have the necessary skills and knowledge to handle a variety of call types and objections effectively.
Inadequate scripting and resources: Poorly designed scripts or a lack of training and support resources are guaranteed to reduce productivity and ultimately, results.
Solutions
Automated Scripting and CRM Integration
An excellent feature of MaxContact CCaaS is the built-in scripting tools, which help agents handle calls efficiently and consistently with confidence in their script.
Meanwhile, integration with your existing CRM software helps agents easily personalise calls, with all the relevant information at their fingertips.
Call Recording and Data Insights
Using a CCaaS platform to record calls helps supervisors easily identify areas for improvement and provide tailored training to agents, boosting their skills and effectiveness.
Data Dashboards and Performance Management
Most modern CCaaS platforms feature data and performance dashboards which can encourage a more engaging work environment and introduce a dose of healthy competition. They use gamification elements to boost motivation and track progress.
Challenge #3: Difficulty Measuring Success
Collecting information about what works and what doesn’t is one of the only ways to tailor your approach to your unique customer base and operations, improving success exponentially. But collecting and organising that information can be a headache waiting to happen, and is often deprioritised as a result.
Problems
Multiple success metrics: Many campaigns have more than one goal, and monitoring conversion rates, appointment bookings, or survey responses simultaneously can be challenging.
Attribution challenges: It can be difficult to attribute specific outcomes to outbound calls as a range of other marketing efforts and customer touchpoints can also play a role.
Lack of data-driven insights: Without comprehensive, readily available data, it’s difficult to make decisions to optimise call centre performance.
Solutions
Automated Analytics
Using CCaaS software that has automated analytics built-in is a simple way to optimise your campaign strategies. MaxContact provides custom solutions with the capacity to collect and present automated data insights from a range of performance metrics such as call volume, agent utilisation, and call duration.
Multi-Channel Reporting
Taking it one step further, CCaaS can also facilitate data analysis across a range of different communication channels when integrated with your existing software. This provides a holistic view of customer interactions across all touchpoints, helping you gain a broader view of campaign performance.
Challenge #4: Compliance and Security
Breaches of compliance or security can be incredibly costly to an outbound call centre operation. But staying on top of ever-changing regulations can be just as pricey and time-consuming, without one streamlined solution.
Problems
Complying with Do Not Call (DNC) lists: Dialling someone on the DNC list can result in hefty fines and penalties if you’re not careful.
Taking payments quickly and securely: If your outbound call centre deals with payments, calls can be vulnerable to security breaches, damaging your reputation and leading to a significant loss of revenue.
Solutions
Built-in Regulatory Compliance
Having a CCaaS platform with built-in regulatory features is one way to avoid the compliance nightmare altogether.
MaxContact software features innovative DNC list scrubbing to automatically remove all names and contact information that appears on the registry from your database, reducing the likelihood of a fine. It also keeps your system up-to-date with call recording compliance and allows you to locate, edit or remove someone’s information on one simple page.
Secure Payment Handling
Take payments quickly and securely with PCI-DSS (Payment Card Industry Data Security Standard). The MaxContact CCaaS system allows agents to handle payments without viewing or hearing the specific card information of the customer. Instead, they can securely input their details using their keypad.
Data Encryption
Implementing robust security features into your CCaaS system is the best way to proactively protect against a potential breach. Depending on your operational needs, you can add a range of special features such as data encryption, access controls, and regular audits to safeguard sensitive customer information.
Outbound call centre outsourcing is incredibly common, but BPOs often face more challenges than those that operate in-house. These include:
Data Security and Privacy: BPOs that deal with sensitive customer data may face heightened concerns about privacy and compliance. This means they often require even more robust security measures and special technology to stay compliant with data protection regulations.
Geographical limitations: Outbound call centre outsourcing puts distance between the business and its end customer, which sometimes makes it difficult to maintain performance and service quality. Auto quality assurance (QA) can assist in providing continued support to an outsourced team and connect the dots between the client and their customers.
Running an Effective Outbound Call Centre: Final Thoughts
While not without its challenges, running an effective call centre can be a lot easier with the right tools, technology and knowledge on your side.
Get in touch today to start running your outbound contact centre more efficiently, or download our guide for more information.
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5 min read
Is Your Outbound Sales Team Truly Data-Driven?
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.
How do consumers actually feel about AI in customer engagement?
As more UK businesses cut contact centre jobs in favour of AI, we asked over 1,000 UK consumers where they actually want automation - and where they still expect a human. The answers aren't as simple as "customers are moving online."
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A major UK utility provider announced this week that it's cutting 1,300 jobs, leaning harder into AI and digital service. It won't be the last business to make this call, and it's not the first either. Across most industries right now, there's a version of the same bet being placed: that customers are ready to swap people for bots.
Our sales team has had some version of this conversation with almost every customer and prospect this week. The question underneath it is always the same - should we be doing this too?
"It's the question everyone's asking right now," says Richard Langham, VP of Sales at MaxContact. "Listening to your customers is exactly the right instinct. The mistake is assuming what worked for one business, in one industry, applies everywhere else. Consumer behaviour isn't uniform, and neither are the moments that matter to people. Before following someone else's playbook, it's worth checking what your own customers think - not what another sector's did."
We put that question to the public directly. Our Voice of the UK Consumer 2026 report surveyed over 1,000 UK consumers on exactly this: where do you want AI, and where do you want a human? The answers are more specific, and more useful, than "customers are moving online."
Consumers draw a hard line on where AI belongs
We asked people which situations they'd want to keep AI out of entirely. They were clear. Over half (54%) don't want AI anywhere near an emergency. Half say the same for complex account problems. Financial discussions (49%) and negotiating terms (46%) aren't far behind.
Flip the question and ask where a human matters most, and you get the same answer from the other direction: emergencies top the list at 41%, then complex account queries (33%), financial discussions (29%), and explaining something personal or sensitive (26%).
These situations are more common than they might sound. A missed bill. A bereavement. A boiler packing in over winter. Someone explaining a difficult situation to a company for the first time. Contact centres deal with moments like these every single day - and our data says people want a person on the other end when it happens.
AI has its place
There's real appetite for automation where it's genuinely useful: answering FAQs, routing calls, pulling up account updates. People are happy to let a bot handle the boring stuff.
There's even one situation where AI wins outright: talking to a lender about financial difficulty. More people choose AI here than a human agent. But when you dig into why, it's not enthusiasm for the technology - it's privacy. People find it easier to admit they're struggling to a screen than to a person. That's not proof consumers prefer bots. It's proof they want to avoid judgement.
Listen to your own customers, not someone else's headline
"A telecoms customer, an insurance customer and an energy customer don't necessarily feel the same way about AI," Langham says. "What one industry can get away with, another can't. What worked for one company's customer base might land completely differently with yours."
Here's what to get right before making any move on the back of someone else's numbers:
Check your own data before borrowing someone else's conclusion - a drop in call volume tells you what stopped happening, not why, and not whether it's safe to read as "customers don't want people anymore."
Keep the human path open exactly where our data says it counts - emergencies, complex issues, money problems, anything personal. These are the situations where AI is least welcome, and where getting it wrong does the most damage to trust.
Tell people when they're talking to AI - 88% of consumers say this matters, half calling it very important. If AI is handling more of your first-line contact, being upfront about it isn't optional. It's what keeps trust intact.
Moving contact online doesn't close the trust gap on its own. Keeping a human reachable for the moments people care about most does, and knowing which moments those are for your customers - not someone else's - is the bit worth getting right.
If you're weighing up a similar decision, don't do it on assumptions borrowed from a headline. Get the full picture - including sector-by-sector breakdowns for utilities, telecoms, finance/debt and insurance - in our Voice of the UK Consumer 2026 report.Download here.
Blog
5 min read
What Good Contact Centre Performance Looks Like… and Why Most Fall Short
Most contact centres are measuring the right things. But often, they're drawing the wrong conclusions from the data.
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Tracking KPIs quarter-on-quarter tells you whether you're improving. It doesn't tell you whether the number you're improving towards is actually good. And it doesn't tell you whether your measurement is giving you an accurate picture of your operation in the first place.
That last point is where the real performance gap comes from.
Two thirds of UK contact centres are now using or piloting AI, with 99% of those operations feeling confident in its impact. Yet across the metrics that matter most, performance has barely shifted year-on-year. FCR is flat or declining. Conversion rates are down. AHT is unchanged. Agent workloads are up 10%. The sector has invested heavily in technology but the performance gap remains as wide as ever.
The reason isn't that AI doesn't work. It's that most organisations use it in response to visible symptoms such as reducing call volume or speeding up response times, which leaves poor performance problems unaddressed at the root cause.
To understand where that gap sits, MaxContact surveyed 300 UK contact centre decision makers across sales, customer service and debt collection for our 2025-26 KPI Benchmarking Insights Report. It's the second year we've run the research, so we can track how performance is shifting over time.
The measurement problem nobody's talking about
Before we get to the performance benchmarks, let's draw your attention to the issue that runs through all of them.
Most contact centres measure performance through post-call surveys, manual QA sampling and periodic reporting. Post-call surveys capture a small sample of interactions, skewed toward customers at the extremes of satisfaction. Manual QA in organisations we speak to reaches max 5% of calls. Periodic reporting tells you what happened last month.
At that sample rate, you can identify which metric is underperforming but you can't reliably identify why. And if you can't identify why, you can't fix it. You're just guessing, which is what most performance optimisations actually are, dressed up as data-driven decision making.
This matters because every benchmark in this piece is only useful if your measurement is telling you the truth. A CSAT score built on 10% of calls, an FCR rate from manually sampled interactions, a conversion rate that looks reasonable against a sector average you've never questioned. These numbers can give you false confidence about performance you haven't really achieved.
If you want to review how your operation is capturing performance data, our complete guide to call centre reporting metrics covers the full picture. Keep that in mind as we work through each benchmark.
CSAT: the conditions matter more than the score
48% of contact centre leaders rank CSAT as a top 5 KPI
CSAT is the metric most contact centre leaders care about most. It's also the one where the gap between stated priority and operational reality is widest.
There's no single sector mean for CSAT in our data. Satisfaction scores vary too much by sector, use case and measurement method to produce a meaningful average. That variability is the first signal that something's wrong with how most operations approach it.
The more useful question isn't "what's a good CSAT score." It's "what conditions in your operation are producing the score you have." And on that measure, the benchmark data reveals a problem most operations aren't looking at directly.
Agent workloads increased at 53% of contact centres last year, a 10% rise on the year before. Annual agent turnover sits at 31.2% on average, with some operations reporting churn of 41-50%. These metrics are the direct mechanism through which CSAT deteriorates. Stretched agents handle calls differently, and high turnover means less experienced people managing more complex conversations. Neither produces the kind of consistent interactions that drive strong satisfaction scores.
Only 10% of contact centres in our survey reported annual churn below 20%. Those operations aren't just performing better on a staffing metric. They're building the kind of service consistency that CSAT is actually measuring. For most, high turnover is a structural drag on service quality that no post-call survey will fix. The survey measures the symptom. The cause goes unaddressed.
What good looks like also depends on your use case. In outbound sales, CSAT is partly a lead quality and script problem. Customers who feel mismatched to a product will score you poorly regardless of how the agent handled the call. In inbound customer service, resolution and wait time are the key drivers. The levers are different, so the benchmarks should be treated differently too.
The contact centres with the strongest CSAT scores aren't waiting for survey results to tell them something's wrong. They're using Conversation Analytics to understand sentiment across 100% of calls, spotting where calls are going wrong before it shows up in a survey score and before the customer has already decided not to come back.
Speed of answer: you're prioritising a metric you're not hitting
Sector mean: 17.4 seconds. High performer target: under 10 seconds
Speed of answer is the second most prioritised KPI in our survey, with 35% of respondents ranking it in their top five. The benchmark data reveals something uncomfortable about it.
Across all contact centres, the mean is 17.4 seconds. The high performer target is 10 seconds. Nearly 40% of respondents are already hitting between 2 and 10 seconds. That's the bracket that defines good. Therefore, most contact centres are actively prioritising a metric they're not hitting, against a standard they may not have explicitly set.
The difference between 17 seconds and 10 seconds may not sound critical. But for a customer who has already decided to pick up the phone, who has exhausted self-service and committed to the friction of a call, every additional second in a queue adds to existing frustration. Our data shows mean call abandonment sits at 4.1%. Operations answering in under 10 seconds are less exposed to that abandonment, and the customers they do lose are disproportionately the ones closest to walking away entirely.
Speed of answer is primarily an inbound metric. For outbound sales and debt collection, the equivalent is connect rate, the percentage of dialled numbers that reach a person. The sector mean is 42.8%, with high performers achieving 60-79%. If your operation is outbound-led, that's the speed benchmark driving your performance.
For inbound teams, closing the gap between 17 and 10 seconds comes down to workforce management and call deflection. Smarter IVR design, AI-assisted resolution of routine queries, and capacity planning that reflects actual demand patterns. Our report shows 38% of respondents are already using predictive analytics for capacity planning. Those operations hold speed of answer during demand spikes without the cost of permanent overstaffing.
Service level achievement: a baseline, not a definition of good
34% of contact centre leaders rank service level achievement as a top 5 KPI
Service level achievement sits third in our KPI rankings. It's also the metric where the definition of good is most taken for granted.
The standard most operations work to is the 80/20 rule: 80% of calls answered within 20 seconds. It's been around long enough to feel like a law of physics rather than a performance target. Most contact centres treat hitting it as the definition of good when it isn't. It's the definition of acceptable.
The operations genuinely performing well on service level aren't managing to 80/20. They're using it as a floor. The 39% of respondents answering calls within 2-10 seconds haven't set a different target. They've built operations that outperform the standard because their workforce management, demand forecasting and deflection are sophisticated enough to absorb volume spikes without queue times creeping up.
The biggest threat to service level is workload. Agent workloads increased at 53% of contact centres last year. When demand rises faster than capacity, service level is the first metric to suffer and the one customers feel most directly. You can have strong FCR and solid CSAT and still lose customers at the queue stage because they simply couldn't get through.
For outbound operations, service level works differently. It's less about queue management and more about campaign pacing, maintaining consistent contact rates without burning through call lists or hitting compliance thresholds. Good performance in any channel means holding your numbers when conditions get difficult, not just when demand is manageable.
The 38% of respondents using predictive analytics for capacity planning are better placed here than those still forecasting from historical averages. Demand patterns have shifted, and operations working from last year's data are likely to be caught short. Service level is where that shows up first.
80/20 is where you start. Consistently beating it is what good actually looks like.
Most contact centres trying to improve conversion rate start in the wrong place. They review scripts. They add coaching. They push agents to make more calls. The data suggests none of those things address the primary problem.
The sector mean conversion rate is 16%. Average daily calls per agent have fallen from 65.5 to 59 year-on-year. First-call close rates have dropped from 28% to 25%. That looks like a performance decline. But consumer confidence has fallen sharply over the same period. Agents aren't getting worse. They're working harder against a more resistant environment. Pushing them to make more calls doesn't close the gap. It increases attrition.
The operations holding conversion rates above 20% are doing it by being more precise about when they call, who they call, and which dialling mode they use for which campaign type. Most operations run one dialling mode across all campaign types and wonder why conversion is flat.
To show how wide that gap can be: across 7.8 million calls on the MaxContact platform, our customers achieve a 38.8% success rate against a sector mean of 16%. That difference doesn't come from better agents. It comes from smarter contact strategies. Predictive dialling for high-volume campaigns, preview dialling for high-value or sensitive conversations, and call timing built around when a specific customer is likely to answer.
For debt collection, the benchmarks shift but the underlying point holds. Promise to pay rate sits at a sector mean of 28%, and good is above 30%. Right party contact averages 27%. FCR in debt collection has dropped five percentage points to 37%, reflecting economic pressure on consumers rather than operational failure. But 15% of debt collection operations are still hitting collection rates of 40-49% in those same conditions. Reaching the right person at the right moment with an agent trained for a sensitive financial conversation produces a fundamentally different result than high-volume dialling and hoping for the best, and the operations hitting those numbers know it.
If your conversion rate is below the sector mean, the most useful question isn't "how do we improve our scripts." It's "are we contacting the right people, at the right time, in the right way" and do we have the data to answer that honestly.
FCR and AHT: the metric relationship that determines everything else
Inbound FCR mean: 40.9%. High performer bracket: 50-79%. Mean AHT: 7.8 minutes
FCR and AHT are the two metrics most likely to be misread in isolation and most likely to be managed against each other in ways that make both worse.
The pattern is familiar. Leadership sees AHT creeping up and pushes agents to handle calls faster. AHT comes down. FCR comes down with it, because customers whose issues weren't properly resolved call back. Cost per contact rises. CSAT drops. The operation has optimised for a number and degraded its actual performance in the process.
The metric that looked like it was improving was masking a problem getting worse. This is why FCR and AHT need to be read together, always.
The inbound FCR mean is 40.9%. High performers, the top 27% of respondents, achieve 50-79%. That's the bracket to aim for. Mean inbound AHT is 7.8 minutes, with over a third of respondents reporting 10-15 minutes. That higher figure isn't a problem in isolation. A 12-minute call that resolves the issue completely is a better outcome for the customer, the agent and the cost base than a 5-minute call that generates a callback.
Good isn't a low AHT. It's a high FCR with an AHT that reflects the complexity of what your operation actually handles.
The use case split matters here. In inbound customer service, FCR above 50% is the high performer threshold. The operations hitting it aren't rushing calls. They're doing it through better agent knowledge, faster access to customer history, and calls that get to the point without losing the quality of the resolution. In debt collection, FCR has dropped five percentage points year-on-year to 37%, reflecting economic pressure on consumers. The 15% of debt collection operations achieving 40-49% FCR in those conditions are differentiating on contact precision and conversation quality, not volume. For outbound sales, the paired benchmarks that matter are calls to success ratio, with a mean of 15.6 calls per sale, and handle time, where 4-5 minutes is the sweet spot.
The performance gap on FCR is also where the measurement problem bites hardest. Most operations run manual QA on 2-5% of calls. At that sample rate, you can identify that FCR is low but you can't reliably identify why, which means you can't fix it systematically. Is it a knowledge gap? A script failure? A specific objection type agents aren't equipped to handle? A process breakdown adding unnecessary handle time without improving resolution? You can't answer those questions from 2-5% of your call data.
If AHT is the metric you're most focused on right now, our guide to reducing average handle time without sacrificing customer experience covers the specific tactics for closing that gap without pushing FCR in the wrong direction.
Why the performance gap isn't closing
Two-thirds of UK contact centres are using or piloting AI, and sentiment is overwhelmingly positive. So why haven't the numbers moved?
The answer is in where the investment is going. Chatbots and virtual agents are the most common AI applications in our survey, at 57% and 56% respectively. Both are built to stop calls reaching agents in the first place. That's a legitimate goal. But deflection doesn't tell you anything about the conversations that do get through. It can't explain why your FCR is sitting at 40% when high performers are hitting 60%. It won't surface the objection that keeps killing conversions at the same point in the call. It has no view on where resolution breaks down, or why.
The contact centres with the strongest metrics aren't necessarily running more AI tools. They're running them in a different place. Conversation Analytics applied across 100% of calls gives you a fundamentally different picture than manual QA on 2-5%. You can see patterns. You can identify what high-performing agents are doing differently. You can stop guessing at why a metric is underperforming and start knowing. Most contact centres already have the technology to do this. The question is whether it's being pointed at the right thing.
Where does your contact centre performance sit?
These benchmarks aren't a verdict. An FCR of 38% or a conversion rate below the sector mean doesn't mean your operation is in trouble, it means you now have a clearer sense of where the gap is and roughly how wide it is. Most operations never get that far.
The contact centres pulling ahead aren't doing anything the average operation couldn't. Agent stability is managed deliberately rather than accepted as an inevitability. Dialling strategy is matched to campaign type. FCR and AHT are read together. And critically, performance decisions are based on what's happening across the full call volume, not the fraction that manual QA and post-call surveys ever reach.
That last point is what makes underperformance so persistent. If your measurement is only capturing part of what's happening, you're optimising against an incomplete picture. The benchmarks in this report are only useful if the data behind your own metrics is sound.
For a broader look at efficiency measurement across your operation, our guide to measuring call centre efficiency is worth a read.
Want to go deeper on the data? The full 2025-26 UK Contact Centre KPI Benchmarking Insights Report covers all 300 respondents, segmented by use case, with year-on-year comparisons across every metric in this piece.
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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.
