Explore fresh, expert perspectives on customer engagement and innovation

As we approach the end of 2024, it’s time to look back at our predictions from last year and reflect on how the contact centre industry has evolved. While some trends played out as expected, others took unexpected turns, and new challenges emerged that shaped the industry’s direction.

AI: From Hype to Reality

We predicted that 2024 would be the year AI moved from the “excitement phase” to the “deployment phase.” This proved largely accurate, though perhaps not in the way many expected. The rush to implement AI solutions in early 2024 revealed important lessons about the technology’s current capabilities and limitations.

The reality check came quickly: while AI showed promise, its productivity improvements landed closer to 25% rather than the marketed 70-80%. Auto-summarisation emerged as the unexpected hero, delivering the most tangible value among AI applications. This taught us an important lesson: sometimes the most valuable AI solutions are the ones that enhance existing processes rather than completely revolutionising them.

Security and Compliance: More Critical Than Ever

Our prediction about security and compliance becoming front and centre proved remarkably accurate. The year saw several significant security incidents that highlighted the vulnerability of customer data, including Transport for London’s widespread system disruption and Ticketmaster’s massive data breach affecting over half a billion customers. These high-profile cases served as sobering reminders of the critical importance of robust security measures.

The SaaS-driven nature of modern contact centres amplified these concerns, as a single breach can now impact entire client networks simultaneously. This catalysed a fundamental shift in how the industry approaches data protection, particularly around AI models and their implementation. The full implementation of Consumer Duty in July 2024 added another layer of complexity, requiring financial services contact centres to demonstrate how they’re promoting fair customer outcomes and increased transparency in every interaction.

The conversation evolved beyond basic compliance checkboxes to encompass deeper ethical considerations about data usage, ownership, and customer profiling. This regulatory evolution, combined with heightened security awareness, has prompted many organisations to reassess their data practices, especially as AI technologies become more deeply embedded in customer service operations.

Hybrid Working: Still a Work in Progress

While we predicted that 2024 would be the year contact centres refined their hybrid working models, the reality showed that this journey is far from complete. With over 60% of contact centres now incorporating home working, the industry continues to grapple with challenges around maintaining company culture, effective onboarding, and managing attrition rates.

Sustainability and CSR: Economic Realities Bite

Our prediction that Environmental, Social and Governance (ESG) and Corporate Social Responsibility (CSR) would take centre stage in 2024 proved to be one of our more challenging forecasts. While we anticipated growing pressure on contact centres to demonstrate their commitment to sustainability and social responsibility, economic headwinds forced many organisations to reprioritise their initiatives.

The tough macroeconomic climate saw sustainability taking a back seat for some contact centres to immediate operational concerns, mirroring broader trends across industries. This shift was evident in the scaling back of net-zero commitments and the reprioritisation of resources toward cost management and operational efficiency.

However, this doesn’t mean sustainability has lost its importance. Rather, organisations have had to become more pragmatic in their approach. Hybrid working, initially championed as a way to reduce carbon footprints, has become more valued for its operational benefits and cost savings. This demonstrates how environmental initiatives can align with business necessities when properly implemented.

Looking back, 2024 taught us that while sustainability remains crucial for long-term success, its implementation needs to be balanced against immediate business survival needs. The challenge going forward will be finding ways to maintain environmental and social commitments while navigating economic pressures.

Customer Experience vs Cost Efficiency: A Delicate Balance

Perhaps our most accurate prediction was about the challenge of reducing costs while improving performance and customer experience. This became the defining challenge of 2024, as inflation and economic pressures forced difficult decisions across the industry.

Throughout the year, we’ve also seen a trend where customer experience initiatives have taken a back seat to cost reduction strategies. The industry’s pivot towards digital deflection, while economically motivated, has sometimes come at the cost of customer satisfaction. This tension between efficiency and experience will likely continue to shape industry decisions going forward into 2025.

Looking Forward

As we end 2024, the contact centre industry stands at a crossroads. The promise of AI remains strong, but with more realistic expectations about its capabilities. The challenge of balancing cost efficiencies with customer experience has never been more acute, and the industry continues to adapt to new working models.

The year has taught us that successful innovation isn’t just about implementing new technology – it’s about understanding our limitations, focusing on tangible value, and maintaining sight of what matters most: delivering quality service to customers while supporting our workforce.

The contact centre industry proved resilient and adaptable in 2024, even if the path forward wasn’t always clear. As we look to 2025, this ability to adapt while maintaining core service values will be more important than ever.

Key Learnings from 2024:

• AI implementation requires focused, realistic goals rather than broad transformations.
• Security and compliance must be built into every new initiative from the ground up.
• Hybrid working isn’t just about technology – it’s about culture and connection.
• The balance between cost efficiency and customer experience requires constant attention.
• Industry evolution must consider both technological and human factors.
• Sustainability initiatives need to demonstrate clear business value alongside environmental benefits.
• Economic pressures can reshape priorities, but long-term ESG commitments shouldn’t be abandoned.

As we move into 2025, these lessons will be crucial in shaping the next phase of contact centre evolution. The industry may face continued challenges, but it has shown it has the resilience and creativity to meet them head-on.

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The regulatory pressure on contact centres has never been greater. GDPR and the Data Protection Act set the baseline. FCA Consumer Duty is now in active enforcement, requiring businesses to evidence good customer outcomes and not just document processes. PECR reforms under the Data (Use and Access) Act 2025 have raised maximum fines from £500,000 to £17.5 million. And with the EU AI Act and a UK AI Bill both on the horizon, the direction is clear: more accountability, more evidence requirements and higher stakes for getting it wrong.

Used correctly, AI speech analytics addresses most of these challenges directly. But it also needs to be implemented in a way that is itself compliant, with GDPR, with data protection obligations, and with the trust customers place in organisations that record and analyse their conversations.

Why AI Speech Analytics, Not Just Keyword Spotting

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Older quality monitoring tools worked on rules. Flag a call if a specific word appeared. Miss it if the agent used different phrasing, spoke too quickly, or the system didn't recognise the accent.

AI conversation analytics works differently. Rather than matching fixed strings of text, it uses natural language processing to understand context, intent and meaning. It recognises that "I can't afford this right now" and "the payments are getting difficult" are both vulnerability signals, even though neither contains a flagged keyword. It picks up on tone, pace, and sentiment as opposed to individual words.

For compliance purposes the distinction between the two matters. A rule-based system tells you whether a phrase was said. AI tells you whether the conversation was compliant and flags the calls where the right words were used in the wrong way, or where the right words were absent entirely.

How AI Speech Analytics Supports Regulatory Compliance

Staying Compliant with GDPR and the Data Protection Act

Under UK GDPR and the Data Protection Act 2018, contact centres must inform customers that calls are being recorded, handle personal data lawfully, respond to Data Subject Access Requests within statutory timeframes, and ensure sensitive information is never captured inappropriately.

Meeting FCA Consumer Duty Obligations

Consumer Duty has moved from implementation to active enforcement. The FCA is running four cross-cutting supervisory reviews throughout 2026, and the question regulators are asking has shifted: it's no longer "have you implemented Consumer Duty?" It's "can you demonstrate, with evidence, that your business is consistently delivering good outcomes for customers?"

Most contact centres can't provide that evidence at scale. Conversation Analytics is built to change that.

For a full breakdown of what Consumer Duty means for your operation, read our guide for contact centre teams in financial services.

Handling Complaints in Line with the Consumer Rights Act

The Consumer Rights Act requires contact centres to handle complaints effectively by addressing root causes, and demonstrating a genuine commitment to consumer satisfaction. The challenge isn't just what happens on individual calls; it's spotting recurring patterns before they become regulatory problems.

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How to Monitor and Flag Compliance Issues Automatically

Knowing what compliance requires and having a platform that enforces it consistently are two different things. Here is how compliance monitoring works inside Conversation Analytics day to day.

‍Keyword and phrase tracking

Configure the platform to track specific language across all transcripts: mandatory disclosure statements, consent language, direct debit scripts, vulnerability indicators. Alerts fire automatically for calls where those phrases are absent, incorrectly used, or appear at the wrong point in the conversation. This runs across 100% of calls and isn’t restricted to a selected sample.

‍Auto-fail rules

Ensure critical breaches are never obscured by an otherwise acceptable call score. For example, a missed payment recording pause, an absent FCA disclosure or an incomplete ID verification can be configured as automatic failures, meaning every instance is flagged and logged without relying on reviewer judgement.

‍Real-time alerts

When a vulnerability signal or compliance phrase is detected during a call, supervisors receive an immediate alert. They can join the call, prompt the agent, or prepare for post-call review, allowing them to intervene before the situation escalates rather than discovering it in an audit.

‍Auditable records

Every flagged call, scored interaction, and compliance alert is logged. When a regulator, internal audit team, or legal function asks for evidence (under Consumer Duty, GDPR, or sector-specific requirements) it exists across every scored call.

Choosing an AI Analytics Platform That Is Compliant by Design

AI speech analytics only supports compliance if the platform handling your data is itself compliant. When evaluating tools, these are the questions to ask and be cautious of providers who cannot answer them clearly.

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For full details on how MaxContact handles data security and compliance, download our security and compliance brochure.

How Honey Group Improved Compliance Coverage with Conversation Analytics

Honey Group, a financial services contact centre, was struggling to review calls for compliance at the volume their operation required. Manual review covered only a fraction of interactions, leaving significant compliance exposure unaddressed.

Working with MaxContact, Honey Group implemented Conversation Analytics to monitor calls across their full interaction volume. Using transcript search and sentiment analysis, their team now identifies inappropriate mentions of sensitive topics, verifies that required disclosures are being made consistently, and flags interactions warranting further review without the resource overhead of manual listening. The result has been a significant improvement in QA coverage and a more systematic approach to both compliance monitoring and agent training.

The Regulatory Changes Contact Centres Need to Prepare for Now

The regulatory environment is changing fast:

• PECR fines have increased 35-fold under the Data (Use and Access) Act 2025, from £500,000 to £17.5 million or 4% of global turnover
• EU AI Act chatbot and AI transparency obligations apply from August 2026, with penalties reaching €35 million or 7% of global turnover for non-compliance
• UK AI Bill is expected, adding a further governance layer to AI deployment in contact centres

The organisations that navigate this well are those treating compliance as a strategic investment rather than something to react to. Conversation Analytics is built to move with that environment by providing the audit trails, evidence of outcomes, and human oversight mechanisms that regulators are increasingly demanding.

‍If you're assessing how AI speech analytics fits into your compliance strategy, the UK Contact Centre Regulatory Guide covers what's changed, what's coming, and what your operation needs to do about it.

Or, if you want to see how Conversation Analytics works in your environment, book a demo and we'll show you.

Picture this: It's Monday morning, and your quality assurance manager sits down with a stack of randomly selected call recordings from last week. They'll spend hours listening, scoring, and writing feedback notes. By Friday, they might have reviewed 50 calls from the thousands that took place.

Meanwhile, compliance risks hide in unmonitored interactions, top-performing agents go unrecognised, and struggling team members miss opportunities for targeted improvement. The traditional approach to quality assurance isn't just inefficient—it's leaving your contact centre exposed and underperforming.

But what if you could monitor every single customer interaction automatically? What if quality assurance became a real-time, comprehensive process that drives continuous improvement rather than sporadic checking?

This transformation is exactly what AI-powered quality assurance delivers—and it's revolutionising how smart contact centres ensure compliance, develop agents, and deliver exceptional customer experiences.

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The Hidden Costs of Traditional QA

Manual quality assurance creates a cascade of problems that impact every aspect of contact centre operations:

Limited Coverage: Reviewing only a tiny percentage of interactions means most quality issues, compliance risks, and coaching opportunities remain invisible.

Subjective Bias: Human reviewers bring their own perspectives and inconsistencies, leading to unfair evaluations and missed insights.

Reactive Response: By the time manual reviews identify problems, customers have already experienced poor service, and compliance breaches may have occurred.

Resource Drain: QA managers spend countless hours listening to recordings instead of developing strategies and coaching agents.

Missed Opportunities: Without comprehensive analysis, contact centres can't identify what makes their best agents successful or replicate those behaviours across teams.

The result? Compliance risks, disengaged agents, and missed opportunities to transform performance.

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How AI-Powered QA Changes Everything

Modern AI-driven quality assurance systems flip this model entirely. Instead of sampling a fraction of interactions, they analyse every single customer conversation across all channels—calls, emails, web chat, and SMS.

Here's how the technology transforms QA processes:

100% Coverage: Every interaction receives quality analysis, eliminating blind spots and ensuring comprehensive oversight.

Real-Time Insights: Issues are identified immediately, enabling proactive intervention rather than reactive damage control.

Objective Analysis: AI removes human bias, providing consistent, data-driven evaluations based on defined criteria.

Multi-Channel Monitoring: Quality assurance extends beyond phone calls to encompass your entire customer engagement ecosystem.

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Four Pillars of Intelligent Quality Assurance

1. Comprehensive Monitoring That Misses Nothing

AI-powered QA software monitors conversations across every channel where customers interact with your business. Whether it's a phone call about a billing query, an email complaint, or a web chat support request, every interaction receives the same thorough quality analysis.

This comprehensive approach reveals patterns and insights that single-channel monitoring misses, providing a complete picture of customer experience quality.

2. Compliance Made Simple and Bulletproof

Compliance isn't optional, and random sampling isn't sufficient. AI QA tools track, review, and evidence compliance across 100% of interactions, helping contact centres meet industry regulations consistently.

Instead of hoping your sample catches compliance issues, you gain confidence that every interaction adheres to required standards. This proactive approach protects your reputation, avoids costly fines, and gives your team the confidence to operate compliantly.

3. Insightful Coaching That Drives Real Improvement

AI-powered analysis reveals the 'why' behind customer objections, agent performance, and interaction outcomes. Instead of generic feedback, managers can provide specific, evidence-based coaching that addresses real performance gaps.

Identify what techniques your top performers use, understand common objection patterns, and create tailored training programmes that drive measurable improvement across your team.

4. Real-Time Performance Intelligence

Advanced QA systems provide instant insights into key performance metrics, balancing customer satisfaction with operational efficiency. Managers can spot trends as they develop and adjust strategies before small issues become major problems.

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The Technology That Powers Transformation

Modern QA systems integrate multiple AI technologies to deliver comprehensive insights:

Speech-to-Text Analytics: Convert every conversation into searchable transcripts, enabling keyword searches for compliance terms, quality indicators, and trending topics.

Sentiment Analysis: Understand customer emotions throughout interactions, identifying positive experiences worth replicating and negative patterns that need addressing.

Success Intelligence: Analyse what makes top-performing agents successful and provide actionable recommendations for improving overall team performance.

Advanced Reporting: Real-time dashboards and analytics provide the data needed to make informed decisions about training, processes, and resource allocation.

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Real-World Results: The HoneyLegal Success Story

The transformation potential is clear in real customer results. Karl from HoneyLegal explains: "AI will absolutely revolutionise the way we approach sales training and people's individual performance."

HoneyLegal transformed their QA process by gaining deep insights into call performance, compliance, and agent success. This helped them streamline quality control, enhance coaching, and scale performance improvements across their team—moving from reactive management to proactive optimisation.

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The Business Case for AI-Powered QA

The benefits of implementing intelligent quality assurance extend throughout contact centre operations:

Risk Reduction: Comprehensive compliance monitoring protects against regulatory penalties and reputational damage.

Cost Efficiency: Automated analysis eliminates manual review time, freeing QA managers to focus on strategic improvement initiatives.

Performance Enhancement: Data-driven coaching delivers measurable improvements in agent effectiveness and customer satisfaction.

Scalable Operations: AI-powered systems grow with your business without proportional increases in QA resources.

Competitive Advantage: Understanding exactly what creates exceptional customer experiences enables you to consistently deliver superior service.

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Moving Beyond Tick-Box QA

Traditional quality assurance often becomes a compliance exercise that provides limited value to agents or operations. AI-powered QA transforms this dynamic entirely.

Instead of random checks that may miss critical issues, you gain comprehensive insight into every customer interaction. Instead of subjective evaluations, you receive objective, data-driven analysis. Instead of reactive problem-solving, you enable proactive performance improvement.

Most importantly, QA becomes a strategic tool for business growth rather than a necessary cost centre. When you can identify exactly what creates successful customer interactions and replicate those behaviours across your team, quality assurance becomes a competitive advantage.

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The Future of Contact Centre Excellence

The contact centres that succeed in today's competitive environment are those that learn from every customer interaction. They don't just handle calls—they extract intelligence that drives continuous improvement.

AI-powered quality assurance makes this comprehensive learning possible. By analysing every conversation, it reveals patterns, trends, and opportunities that traditional sampling methods miss entirely.

When you can see what works, understand what doesn't, and provide targeted coaching based on real performance data, your contact centre transforms from reactive operations to proactive excellence.

The conversations are happening. The data is there. The question is: are you using it to drive the performance and compliance your business needs?

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Ready to transform your quality assurance from random sampling to comprehensive intelligence? Discover how AI-powered QA software can improve compliance, enhance agent performance, and drive better customer outcomes across every interaction.

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Many BPO leaders are under growing pressure to deliver consistent performance, all whilst operating in an environment that’s increasingly competitive and cost-sensitive.

One of the biggest challenges is not the lack of data, but knowing how to interpret it. BPOs often track dozens of metrics, yet they still struggle to understand whether performance is strong, weak, or simply average for the market. Without a clear point of comparison, it’s impossible to know where to focus improvement efforts, or how to demonstrate value to clients.

In this article, we explore how accurate benchmarking helps outsourced contact centres to:

• Assess performance more accurately.
• Identify meaningful improvement opportunities.
• Use data to drive better outcomes across sales, service and debt collection operations.

What is Benchmarking?

Benchmarking is the process of comparing your contact centre’s performance against industry standards or peer groups to understand how well you’re really performing.

Rather than viewing KPIs in isolation, benchmarking provides context. It shows where performance is competitive, where gaps exist, and which metrics matter most for your operating model.

There are two primary approaches:

• Competitive benchmarking: Comparing performance against similar BPOs or outsourced contact centres
• Process benchmarking: Comparing workflows against recognised best practice, sometimes drawn from other industries (used less frequently in contact centres)

The Benchmarking Challenge for BPOs

In practice, benchmarking is not always straightforward. BPOs often face inconsistent data, varying KPI definitions between clients, and limited visibility of reliable industry standards. Combined with the pace and pressure of contact centre operations, this makes it difficult to establish benchmarks that are both accurate and actionable.

Despite this, benchmarking remains essential for BPO performance and long-term competitiveness.

Done well, benchmarking allows BPOs to:

• Understand whether KPIs reflect strength or emerging risk
• Use objective data to support operational and commercial decisions
• Identify best practices and apply them consistently across teams
• Spot early warning signs, such as rising agent workload or churn, before they impact service delivery

Overcoming benchmarking challenges starts with a clear strategy: defining objectives, selecting relevant KPIs and measuring performance consistently over time. Our complete guide to call centre reporting metrics explains which KPIs matter most and how they should be read together, rather than in isolation.

What are the Top KPIs Contact Centres Prioritise in 2026?

MaxContact’s 2025/26 UK Contact Centre KPI Benchmarking Insights Report reveals a shift in how contact centres prioritise performance metrics. Rather than focusing on a single efficiency measure, decision makers are increasingly balancing customer experience, responsiveness and commercial outcomes.

Based on responses from 300 UK contact centre leaders, the three most focused on KPIs are:

• Customer Satisfaction (CSAT) – prioritised by 48% of respondents
• Speed of Answer – cited by 35% as a critical performance metric
• Service Level Achievement – selected by 34% of contact centres

Close behind these sit commercially focused metrics such as conversion rate (33%), first call resolution (33%), and revenue per contact (32%), reflecting the ongoing pressure to balance service quality with financial performance.

We explore why a blended approach to call centre metrics is the best way to measure call centre efficiency.

Of course, the metrics that contact centres report on, is also dependent on the industry they operate in.

Key KPIs for Sales and Debt Resolution BPOs

Understanding which KPIs matter most is critical for BPOs operating in sales and debt resolution. While both rely on outbound performance, the metrics that drive success (and the way they should be interpreted) differ significantly between the two.

The latest benchmark data shows that high-performing BPOs don’t track more metrics than their peers. They focus on the right ones and use them together to guide decisions, not just report outcomes.

Sales-focused BPOs

For sales-driven BPOs, performance is ultimately measured by revenue. But revenue outcomes are shaped by a combination of efficiency, lead quality and agent effectiveness.

The 2025/26 Benchmark Report shows that while sales volumes have softened slightly year-on-year, revenue performance is holding up, suggesting agents are working harder and conversations are becoming more complex.

Key KPIs for sales BPOs include:

• Conversion rate: Measures the percentage of contacts that result in a sale. Benchmark data shows a mean conversion rate of 16%, with nearly 30% of teams achieving rates between 20-29%. Improving conversion is less about increasing call volume and more about better lead prioritisation, agent coaching and script effectiveness.
• First-call close rate: Indicates how often a sale is achieved on the first interaction. The benchmark mean sits at 25%, down slightly year-on-year, reflecting a tougher sales environment. Falling first-call close rates can point to lead-quality issues or gaps in agent confidence and product knowledge.
• Average revenue per call: The mean revenue per call now sits at just under £230, although this figure is heavily skewed by top performers. Over 45% of sales teams generate less than £59 per call, highlighting a significant performance gap between average and high-performing BPOs.
• Calls to success ratio: Tracks how many calls are needed to secure a sale. A rising ratio often signals inefficiencies in targeting, messaging or dialling strategy, issues that cannot be solved by increasing activity alone.

High-performing sales teams use these metrics together to understand why performance varies between campaigns, agents or lead sources, not simply whether targets were met. We explore how sales teams follow data effectively in our article Is your outbound sales team truly data-driven?

Debt Resolution BPOs

Debt resolution BPOs face a different challenge: recovering outstanding balances while navigating increasingly complex and sensitive customer conversations.

Benchmark data suggests debt collection teams are operating in a more difficult economic environment, with performance under pressure despite consistent effort.

Key KPIs for debt resolution BPOs include:

• Right Party Contact (RPC): Measures how effectively agents are reaching the correct individual. The current benchmark mean is 27%, making RPC one of the most important early indicators of list quality and call timing effectiveness.
• Promise to Pay (PTP) rate: Indicates the percentage of contacts that result in a commitment to pay. The benchmark mean sits at 28%, broadly in line with last year, suggesting agents are maintaining performance despite tougher circumstances.
• First Call Resolution (FCR): Measures whether a payment or promise to pay is achieved on the first interaction. The benchmark mean has fallen to 37%, down five percentage points year-on-year. A meaningful decline that reflects more complex debtor situations rather than declining agent capability.
• Percentage of debt collected: A high-level indicator of overall effectiveness. The benchmark mean has dropped to 28%, down from 32% last year, reinforcing the need for smarter call strategies, better timing and more personalised conversations.

For debt resolution BPOs, these KPIs must be interpreted in context. Falling FCR or recovery rates may signal broader economic pressure rather than operational failure. But without benchmarking, that distinction is impossible to make.

The Role of Technology for Benchmarking Success

Benchmarking only becomes valuable when insight leads to action. This is where technology plays a critical role.

The 2025/26 Benchmark Report shows that 66% of contact centres are already using or piloting AI, with 60% planning further investment in AI and automation in 2026. This reflects a clear shift away from retrospective reporting and towards real-time performance control.

Modern contact centre platforms enable BPOs to:

• Optimise agent performance: Use real-time dashboards, coaching tools and conversation analytics to identify what high performers do differently and replicate it at scale.
• Improve customer outcomes: Balance efficiency with experience by monitoring metrics such as conversion, FCR and CSAT together rather than in isolation.
• Drive operational efficiency: Adjust dialling strategies, lead prioritisation and resource allocation based on live performance data, not end-of-day reports.

In a highly competitive outsourcing market, technology is no longer a differentiator on its own. The advantage lies in how effectively BPOs use data to benchmark performance, guide decisions and demonstrate value to clients.

Not Sure How You Measure Up?

The 2025/26 UK Contact Centre KPI Benchmarking Insights Report provides in-depth analysis of industry performance, with actionable insight for sales and debt resolution BPOs.

Download the report to compare your KPIs against UK benchmarks, identify performance gaps and understand what high-performing outsourced contact centres do differently.

Download your copy today

Ben Booth, CEO of MaxContact has been recognised as one of The LDC Top 50 Most Ambitious Business Leaders for 2024, in partnership with The Times.

Created by trusted investment partner LDC – part of Lloyds Banking Group, The Top 50 celebrates entrepreneurs demonstrating remarkable ambition, and is now in its seventh year.

This year the programme received more than 700 nominations showcasing the exceptional individuals building successful and growing businesses right across the UK.

The business leaders featured in The LDC Top 50 for 2024 are making a real impact by creating jobs, promoting social equality, championing sustainability, expanding internationally and integrating purpose into their business practices. They operate from 39 towns and cities across the UK and span every sector of the economy, collectively they employ 5,146 people and generate revenues of more than £1.1bn.

MaxContact is the best cloud contact centre platform for delivering conversation outcomes and customer insights to generate more revenue – compliantly. After ten years working for a contact centre solution reseller, CEO Ben Booth was frustrated with providers that overpromised and underdelivered on features, support and resilience. Determined to enhance the agent and customer experience, MaxContact was formed. Over 7 years later, the business has become one of the fastest growing contact centre technology specialists in the UK.

Ben Booth, CEO of MaxContact said: “Being recognised as one of the UK’s Top 50 Most Ambitious Business Leaders is a testament to the incredible work the team at MaxContact has accomplished. We started this journey with a vision to transform the contact centre industry, focusing on delivering exceptional customer experiences and driving revenue growth for contact centres, globally. This accolade reinforces our commitment to innovation and excellence in the field. As we continue to grow and evolve, we remain dedicated to our mission of enhancing both agent and customer experiences in the contact centre space.”

John Garner, Managing Partner at LDC, added: “It’s been seven years since we launched The LDC Top 50 and in that time we’ve had the privilege of meeting some truly remarkable people. Our business leaders for 2024 show relentless drive and determination in their growth ambitions, and I’d like to congratulate them on everything they’ve achieved so far. This is certainly not the end of their success and we can’t wait to see what the future holds.”

Read more about The LDC Top 50 Most Ambitious Business Leaders for 2024 here: https://bit.ly/3Y3rfxY

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If you work in a contact centre that operates in the debt resolution industry, you’ll know that achieving a high right-party contact (RPC) rate is key to success. After all, how can you collect payments efficiently if you’re not speaking with the right recipient in the first place?

Despite this, our recent benchmark report shows that many contact centres struggle to achieve optimal RPC rates. In fact, 23% of contact centres have a RPC rate below 20%, while the industry average sits at just 26%.

So, what can contact centre managers do to improve their RPC rates and, ultimately, increase the percentage of debt collected?

In this blog post, we’ll explore effective strategies that can help your call team connect with the right people consistently.

Challenges to Achieving High Right-Party Contact Rates

Contact centres in the debt collection industry need to carefully consider and address several common challenges to improve right-party contact rates (RPCs).

Challenge 1: Poor Data Quality & Incorrect Information

Poor data quality is a common culprit of low right-party contact rates (RPCs). When contact information is incorrect, incomplete, or outdated, it becomes difficult to reach the intended recipient, leading to wasted time and resources.

Here are some examples of data quality oversights and their consequences:

Oversight: Inaccurate or incomplete information due to data entry errors, system integration problems, or customer-provided data.

Consequence: Difficulty reaching the intended recipient, wasted time and resources and reduced customer satisfaction.

Oversight: Duplicate records for the same customer, once again caused by data entry errors or inconsistent data sources.

Consequence: Confusion and inefficiency in call routing, resulting in unnecessary attempts to contact the same person.

Oversight: Outdated contact information due to changes in addresses, phone numbers, or email addresses.

Consequence: Failed attempts to reach customers, decreased efficiency and potential lost revenue.

Challenge 2: Inefficient Call Routing

Call routing is an invaluable tool- but only when used correctly. Inefficient call routing is another culprit of low RPCs. Here are some examples of the oversights contact centres often make with call routing and the impact on KPIs.

Oversight: Call routing rules are not implemented properly or updated.

Consequence: Calls are directed to the wrong agents or departments. Unnecessary transfers and delays lead to frustrated customers and negatively impact agent efficiency.

Oversight: Outdated call routing systems are not updated in real-time with information about agent availability, call volumes and customer preferences.

Consequence: If a system is unaware of a sudden increase in call volume or an agent’s unavailability, it will continue to route calls to that agent, resulting in inefficient routing and longer wait times.  

Oversight: Agents aren’t trained to identify and route calls correctly.

Consequence: Untrained agents may misinterpret caller information, direct calls to the wrong departments, or transfer calls to agents ill-equipped to handle the customer’s query.

The Impact of Low Right-Party Contact Rates

Low right-party contact rates lead to increased costs, reduced customer satisfaction and potential compliance issues.

Let’s explore each one in more detail to understand the impact.

Increased Costs

• Inefficient call routing leads to agents spending time on unproductive calls, wasting resources and delaying debt recovery.
• Dealing with frustrated customers and repetitive tasks contributes to high agent turnover rates, increasing hiring and training costs.

Reduced Customer Satisfaction

• Difficulty reaching debtors can lead to missed payments and lower debt collected percentages.
• Negative customer experiences can tarnish a debt resolution company’s reputation, making it harder to collect debts in the future.
• Frustrated debtors may take legal action against debt resolution companies, leading to increased legal costs and potential reputational damage.

Potential Compliance Issues

• The Financial Conduct Authority (FCA) has guidelines to treat customers fairly and reasonably. If contact centres fail to reach the right party, it can be seen as a breach of this requirement, leading to regulatory action, including fines or penalties.

Strategies for Improving Right-Party Contact Rates

How to Manage Data Accuracy and Quality

So we know the accuracy and quality of contact data impact right-party contact rates, but what strategies can call centres action to improve data?  

StrategyOverviewStrategy 1: Implement a Data Cleansing and Validation ProcessIt’s important to regularly review and clean contact data to remove typos, inconsistencies and outdated information. Use data validation tools to ensure data is formatted correctly and adheres to specific standards.Strategy 2: Enrich Contact DataSupplement existing data with additional information, such as demographic details, preferences and recent interactions. This can help you tailor your outreach efforts and increase the likelihood of reaching the right person at the right time.Strategy 3: Integrate with CRM SystemsConnect your contact centre software with your CRM system to access comprehensive customer information and ensure data consistency. This can help you avoid duplicate records and provide agents with a complete view of each customer’s interactions.Strategy 4: Leverage Third-Party Data SourcesSupplement your existing data with information from third-party providers to improve targeting and accuracy. Consider using demographic data, credit bureau information, or social media data to enhance your understanding of customers.Strategy 5: Establish Data Governance PoliciesDevelop clear policies and procedures for data management and quality. This includes regular data audits, data cleansing, and data retention guidelines. To ensure this is carried out effectively, all employees must be aware of and adhere to these policies.Strategy 6: Combine Accurate Data with Automated DiallingAutomated dialler systems can reduce human error and improve call efficiency. However, accurate data is essential for these auto-diallers to function effectively and minimise wasted calls.Strategy 7: Monitor Data Quality MetricsUse analytics tools like MaxContact’s reporting capabilities to track RPC rates, data accuracy and other relevant metrics. This will help you identify areas for improvement and make data-driven decisions to enhance your contact centre’s performance.

How to Optimise Call Timing

Call timing plays a key part in improving right-party contact rates and debt recovery. Here are some effective strategies to enhance call timing.

StrategyOverviewStrategy 1: Introduce Predictive DiallingPredictive dialling can automatically dial numbers based on predicted availability and considers factors such as past call history and customer behaviour. This reduces the time agents spend on unanswered calls and improves overall efficiency.Strategy 2: Analyse Historical DataUse MaxContact’s analytics features to examine historical data and identify optimal calling times for debtor segments. Consider factors such as the day of the week, time of day and specific customer preferences when scheduling calls.Strategy 3: Implement Skills-Based Call RoutingSkills-based call routing directs calls to agents with the appropriate skills and expertise to handle specific debt collection scenarios, such as dealing with difficult debtors or negotiating payment plans. Customers are connected with the best-qualified agents, improving their overall experience and increasing the likelihood of successful debt recovery.Strategy 4: Use Intelligent Retry StrategiesImplement a system of intelligent retries to increase the chance of reaching debtors when calls go unanswered. Vary the time between call attempts based on factors such as the debtor’s history, the urgency of the debt and previous attempts. Consider omnichannel communication, such as email or text messages, to reach debtors who may not answer phone calls.

How to Boost Call Agent Skills in Debt Collection

Agent training and development can improve right-party contact rates and enhance overall performance. Here are some ways you can deliver impactful training:

StrategyOverviewStrategy 1: Give Comprehensive TrainingTrain agents on customer data, contact strategies and communication skills, all tailored to debt collection. Utilise features such as call scripting and training on objection handling to equip agents with the tools they need to interact with customers effectively.Strategy 2: Analyse Call PerformanceUse speech analytics to analyse call transcripts and identify areas for improvement in agent performance. This can help you pinpoint areas where agents may need additional training or coaching.Strategy 3: Prioritise Regular Feedback and CoachingOffer regular feedback and coaching to agents based on their performance analysis. Address identified areas for improvement with tailored coaching, helping agents develop their skills and increase their confidence (and boost your retention rates).Strategy 4: Track Additional KPIsMonitor additional KPIs such as average handle time (AHT), average call rate (ACR), and customer satisfaction (CSAT) alongside RPC to gain a more comprehensive view of agent performance. These metrics can help you identify areas for improvement and measure the impact of your training and development efforts.

By implementing these strategies to improve data quality, call routing and call agent skills, debt resolution contact centres can significantly improve their right-party contact rates and achieve better outcomes.

Incorporating advanced outbound contact centre software is essential for achieving these goals. Features such as auto-diallers, speech analytics and reporting analytics can provide valuable insights and automation capabilities and help measure performance.

By investing in the right technology and implementing effective strategies, you can overcome the challenges highlighted in our benchmark report and achieve higher RPC rates. Increase revenue, improve customer satisfaction and reduce operational costs, ultimately boosting the overall performance of debt resolution.

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