First call resolution. Net promoter score. Customer effort score. Customer satisfaction. From FCR to CSAT, there’s an alphabet soup of contact centre data at your fingertips. And while you can extract valuable insight from all those metrics, it’s easy to fall victim to:
- Information overload
- A frustrating inability to get the answers you want
- Difficulty translating insights into improved service
These difficulties come when – underneath your dashboards – your contact centre data is actually disorganised.
In this article, we look at three signs your contact centre data is disorganised – and how to fix the underlying issues.
1. You can’t see integrated data across the customer journey
The first sign is that your data doesn’t give you a holistic view of the customer journey. Can you track people across touchpoints? For example, if they start with digital self-service channels, move to the AI-powered IVR system and end up speaking to an agent, do you get data that shows the flow? Or is each interaction logged separately?
Similarly, can you see where drop-off points are? Or missed opportunities to cross- and up-sell?
In this scenario, customer experience is negatively impacted because it’s hard to proactively address customer needs and resolution times are longer.
How to fix it:
- Implement a journey orchestration tool – that gives you a bird’s eye view of every interaction across every online and offline channel (without requiring changes to underlying systems and databases)
- Use one AI chatbot that you can implement across all digital and voice self-service channels – so you get integrated data (and can then deliver a more coordinated response)
2. It’s hard to track and monitor customer preferences – and to provide personalisation
We’re not talking about basic preferences like preferred contact methods. We’re talking about the advanced stuff that turns the dial when it comes to conversions, revenue and satisfaction.
Is it cost-effective to deliver hyper-personalised offers? Can you provide a tailored Next Best Action for customers based on a complete view of their preferences and history?
If these capabilities are just out of reach, then there are some quick fixes.
How to fix it:
- Map customer journeys – to see where automation can add the greatest value (more on that here)
- Introduce customer journey automation – starting with the ideal journey and scaling out
- Link your agent solution to the automation platform – so agents can have access to full contact transcripts, including preferences and cross-channel history
- Plug in a Next Best Action solution – so you can automate hyper-personalisation
3. There’s growing pressure on contact centre costs
Inadequate data management practices affect profitability. Abandonment rates, diminishing loyalty and missed sales opportunities meaning less revenue.
And if agents can’t access the right information at the right time, you see the impact across critical operational areas like productivity, staffing requirements and training outcomes. After all, if contact and resolution times are longer, you can end up needing more people on shift.
How to fix it:
- Have a reliable way to capture customer intent – so you’re directing people in the most efficient way
- Use one AI chatbot across all digital and voice self-service channels – retaining that history will improve resolution times
- Use an AI-powered, multi-channel agent communication solution – to ensure agents can access and update all relevant customer data (such as call outcomes, contact history, and consent preferences) in real-time, from one place
There are no two ways about it – you need access to holistic, accurate and real-time customer data
None of these recommended fixes require changing underlying systems or data storage methods. Rather, they involve more efficient ways of bringing all that data together so you can do more with it – and easily.
Fundamentally, you can address these 3 contact centre data management headaches when you have the ability to map, visualise, automate and optimise interactions in real-time to meet customers’ unique needs. You can then make evidence-based improvements that increase customer retention, operational efficiency and profitability.