Beyond the Script: How Contact Centre Data Becomes Business Gold
Contact centre data is one of the most underutilised assets within modern organisations, despite offering a direct window into customer behaviour, sentiment and unmet need. Every interaction, whether a complaint, query or cancellation, contains rich, unfiltered insight into why customers make decisions. Yet in many businesses, this data remains siloed, inconsistently captured, or reduced to surface-level metrics like call duration, abandonment rates or resolution time, metrics that show what is happening, but rarely explain why it is happening or where operational improvements should be prioritised.
Using AI-assisted analysis and expert interpretation to transform this data into structured operational insight is a vital step in scaling automation successfully. By surfacing not only what customers are saying but identifying where journeys are best suited for process redesign, self-service or automation to deliver measurable operational improvements.
The result is a contact centre that becomes not just a service function, but a strategic source of insight for improving customer journeys, reducing cost to serve, and guiding AI investment with confidence as Jack Godfrey, Global VP Sales at Connect explores.
Contact Centre Evolution
Contact centres have been quick to grasp the opportunities offered by AI and automation – in theory. From early chatbot deployments, there has been a rapid push towards AI-driven orchestration and automation, with the goal of freeing up human expertise to deal with the most complex interactions. And yet, far too many contact centres have failed to achieve the desired goals. Of course, automation and AI can help to reduce abandonment rates and improve resolution time – but only if organisations understand the underpinning causes of abandonment and slow resolution. Simply layering another technology over the top of existing flawed processes and friction filled customer journeys will never meet expectations.
The current lack of clarity and understanding regarding how to automate effectively and successfully is due, in part, to the focus on technology before understanding what is required and where it is needed. The temptation to explore the native AI now embedded in every single application is only adding to the confusion. When a contact centre is using Customer Relationship Management (CRM), Contact Centre as a Service (CCaaS), Workforce Engagement Management and Customer Service Management (CSM) systems, deploying disconnected AI capabilities across each platform often creates additional operational complexity rather than measurable value. Furthermore, as these models are rarely optimised for the highly specific nature of contact centre interactions – typically short, question and answer responses – or the vertical market specific requirements, they lack the accuracy required in any customer facing situation.
Yet companies already own the resources required to provide clear insight into innovation strategies. Contact centres generate vast volumes of interaction data at scale. Information within these diverse operational systems. Customer interactions via voice, text, chat and email. This is an extraordinary resource that can shed light on every aspect of the contact centre experience – if it is correctly analysed and reviewed.
Data-Driven Insight
Using AI-assisted analysis and expert interpretation, this data reveals the underlying drivers of demand, friction and operational inefficiency. Consolidating these diverse operational data sources into a single, structured view reveals so much more than surface-level abandonment metrics. It highlights the root causes of repeat customer contacts. It identifies where customer journeys are breaking down and exposes gaps between brand promise and actual experience. And it also identifies repeatable, high-volume interactions suitable for automation.
Using this rich contact centre data to ensure automation initiatives are evidence-based and optimally aligned with actual operational needs completely changes the focus. It might reveal, for example, that AI automation is not the priority: if the customer journey is flawed and can be changed by simple process redesign, there will be an immediate improvement in contact centre outcomes.
Indeed, this detailed data led insight is key to understanding how and where AI can best be deployed. Identification and Verification (ID&V) is a prime example. According to recent research, 74% of inbound calls to a contact centre require ID&V to progress to the next step. And 91% of those calls are still handled by a human agent. So, while the actual customer request to access their record or check test results could be handled by AI, the need for a human to undertake the initial ID&V process removes an opportunity for automation: a ‘human to bot’ hand off would break up the journey and create unnecessary friction for the customer. Automating ID&V removes one of the biggest operational barriers to scaling self-service and AI within the contact centre.
Unlocking Incremental Value
Understanding this extraordinary data asset and what it reveals about the business is the key to unlocking value – whether the organisation chooses to prioritise AI immediately or first address process and journey inefficiencies. And if AI is the route forward, it is important to consider which AI to use. Is it trained on contact centre specific interactions? Does it recognise the language used routinely within this vertical market? Areas such as healthcare and finance, for example, benefit hugely from small language models (SLM) that have been trained to recognise vertical market keywords allowing customers to be seamlessly routed to the correct AI or human agent.
SLMs can also be tuned using the behaviours and interaction patterns of high-performing contact centre agents, mimicking their behaviours to further optimise outcomes. Building on this, micro models can be deployed for highly deterministic, task-specific activities, such as ID&V which demands account numbers and vehicle registrations. Training a micro model to understand alphanumerics is key to achieving the high levels of accuracy required. Plus, Micro models also require significantly less compute than frontier-scale LLMs, which is also a significant consideration for any business case.
Leveraging SLMs is also an important part of the continuous improvement process, improving relevance and accuracy to incrementally deliver more tailored insights from the data. SLMs also provide the context required for agentic AI to move beyond automating speech to automating actions. Combined with an automated ID&V model, agentic AI can manage the repeat actions – the test results and appointment creation – that do not require human expertise.
Conclusion
The power of AI and automation to transform contact centre performance and customer experience is compelling. It provides tangible opportunities to meet clear business goals, from reducing staff attrition to cutting costs and improving customer engagement. A successful strategy, however, demands understanding of the current problems, bottlenecks and friction points.
The existing contact centre data resource is the most valuable asset available. Using AI-assisted analysis and expert interpretation to create an optimisation strategy, investment can be focused and measured. Organisations have the insight to prioritise high-impact changes, whether through process redesign, digital self-service or targeted automation. The result is a contact centre transformed from a cost centre into a strategic engine for customer experience and operational efficiency.
