Can compliance teams defend small sampling sizes when 100% monitoring is now possible?

By Ben Booth, CEO, MaxContact

Compliance and quality assurance teams have always had to rely on sampling to monitor customer interactions because manually reviewing every single conversation is unrealistic and impractical. With thousands of inbound and outbound conversations every week across calls, emails, web chats, and social media messages, sampling became a practical way for teams with limited capacity to monitor customer outcomes. 

But with some industry observers suggesting that sample sizes were as low as just 2-5% of monitored calls, there has been a significant risk of a blind spot emerging in the remaining 98% of interactions that were not monitored. As regulatory expectations tighten, a new question is coming through for compliance managers and operational directors. 

If technology has made it possible to monitor 100% of customer interactions, is it still justifiable to make decisions and base evidence of fair customer treatment on small sample sizes?

Sampling became the industry standard because it was achievable

For customer interactions to be fairly reviewed and monitored, there needed to be a way to make it manageable. As the number of customer touchpoints has grown, it’s become harder to maintain clear visibility because that data often sits across different systems and platforms rather than in a centralised place.

Sampling a small number of customer conversations made it physically possible for teams to review feedback scorecards, manually listen to recorded calls, monitor conversations, and respond to frustrations. By choosing to review 2-5% of all customer interactions, it was believed that businesses could gain a useful snapshot of what was happening and provide evidence that customers were routinely treated fairly. 

But with those small sample sizes, there was a strong possibility of missing opportunities to identify when a customer may have been treated unfairly or when there were clear indicators that a customer was vulnerable and needed additional help and support. This means that there is a growing gap between what businesses believe is actually happening and what evidence they can show regulators to prove it. 

Fair customer outcomes need to have clear evidence behind them

The increasing regulatory scrutiny that continues to take effect has underscored the growing need for clear evidence of fair customer treatment. Those historic small sample sizes may have shown clear intent and logical policies and procedures for fair outcomes, but they may not have provided enough data to constitute solid proof. 

Quality assurance teams are now being asked to provide proof that all customers are being treated fairly, that signs of vulnerability are quickly identified and acted upon, that the right processes are followed, and that customers genuinely understand what products or services they are buying. That proof can’t be provided if the majority of customer interactions are not monitored. 

Regulators have moved away from requiring good intentions and now expect to see evidence of good outcomes.

But if those conversations are not monitored, it places additional pressure on businesses to find documentation to protect themselves if they’re subject to a complaint, review, or audit. Without this in place, they risk financial or reputational damage. 

Why the 98% blind spot has become a significant problem

Adding to the challenges is the increasing fragmentation of customer data. Teams are not just managing growing volumes of customer interactions; they are also trying to build a complete picture of the customer journey across multiple channels. 

This makes it difficult for teams to scale up their quality assurance processes if they rely on manual checks or have limited resources. 

Customer interactions take place across multiple channels, and individuals expect an omnichannel experience where conversations can continue seamlessly, whether the discussion began on social media, moved to email, or ended up in a phone call. Technology can now create accurate transcripts of customer conversations, identify signs of vulnerability through conversational intelligence, and track behavioural changes. 

However, that data often sits across different systems and spreadsheets, adding to the complications teams face. 

When fragmented data is combined with small sample sizes, it poses a significant compliance risk. If teams are reviewing only a limited proportion of conversations, they do not have full visibility into where potential issues should have been identified. 

For businesses monitoring less than 5% of interactions, there is a strong chance that the selected interactions are fully compliant with processes and policies. However, there is a greater risk that the non-compliant conversations are the interactions that have not been reviewed.

This is important when looking for signs of vulnerability or distress. 

Customers are not always willing to express their concerns or admit when they need additional support. They may reveal their frustrations or worries through subtle signals that could easily be missed, especially if they occur at different points in their customer journey. The customer might make a passing comment about affordability, reveal that their personal circumstances have changed or show hesitation when asked whether they truly understand what they’ve purchased.

 If 98% of interactions are not monitored and data is spread across multiple platforms, there is a strong risk that those signals will be missed entirely. That blind spot will rapidly become a major customer outcomes issue because these subtle indicators need to be identified early so individuals can be routed to a different pathway that provides additional support or appropriate treatment. 

Technology has made it easier to scale QA processes.

Advances in customer engagement technology mean that businesses can now better overcome the practical limitations of small sample sizes. A mix of conversational intelligence, automated transcriptions, workflow automation, and quality assurance protocols can now be seamlessly integrated into customer engagement platforms, so every conversation is tracked appropriately. 

This makes it easier to bring customer interaction data together, providing a complete picture of what is happening for individual customers across different channels and touchpoints. These enhanced monitoring capabilities will automatically detect subtle signs of hesitation or vulnerability, while automation can easily route customers to different pathways for better support. Small sample sizes can scale to full oversight without additional workload, giving compliance teams confidence to demonstrate consistent and fair customer treatment. 

The technology reduces the need to track issues manually. Instead, operational teams can reallocate their time to identifying exceptions, preventing new risks, and improving customer outcomes and satisfaction scores. 

For small businesses that have had to adhere to the same regulatory restraints as their larger competitors, this shift to technical support could be transformative. It allows them to scale up their quality assurance processes and have full oversight of customer conversations and interactions without adding to their workloads. The technology has made 100% monitoring achievable.

Why comprehensive monitoring creates a clearer view of customer outcomes

With such comprehensive monitoring now accessible to businesses of all sizes across all sectors, it’s time to ask whether quality assurance is fit for purpose if it relies solely on small samples to inform strategy. 

Compliance monitoring needs to move away from checking individual interactions to ensuring a complete understanding of customer outcomes for everyone. 

That’s where customer engagement platforms become far more than call centre technology; they become the central destination for quality assurance, compliance monitoring, customer intelligence, and evidence gathering. By connecting all customer interactions to compliance processes, it becomes easier to see in real time where vulnerabilities or risks are emerging and where additional support may be needed. It solves the problems of disconnected systems and gives businesses an easier way to scale up their QA processes while also providing visibility that customers are consistently treated fairly. 

Technology has removed the practical limitations for small sample sizes. With comprehensive monitoring now accessible to businesses of all sizes and regulators now demanding evidence of fair outcomes and vulnerability management, compliance teams need to ask whether sampling is the best way to prove that every customer is being treated fairly. 

Author Bio:

Ben is the CEO and co-founder of MaxContact. As CEO, Ben sets the strategic direction for the business, leads the senior leadership team and champions MaxContact’s distinctive culture of distributed leadership and transparency. He oversees all aspects of the company’s growth, from product innovation and AI technology advancement to team development and market positioning.