The conversation around AI and customer engagement has so far centred on one key question: what can we automate?
Increasingly, the answer is: “Quite a lot.”
Automated workflows, both with and without AI, are helping boost efficiency, speed up response times and improve both employee and customer satisfaction levels. But once automation capabilities are established, a different question becomes vitally important:
How do you know if your automation is actually working?
The gap between automation and understanding
Measuring activity isn’t the same as understanding outcomes. Automation metrics alone don’t tell you why a customer got in touch or whether they achieved their goal. And in some instances, they can even be misleading.
On the face of it, a high automation rate indicates an AI journey is performing well. But what if customers are coming back because they didn’t get what they needed the first time? Similarly, a low escalation rate is ostensibly positive, but what if it means that customers are abandoning the journey?
The metrics that are easiest to measure aren’t typically the ones that tell you whether the experience is working. To close this gap between automation and understanding, organisations need to zoom out beyond individual interactions and take a big-picture view of the customer journey.
How context and perspective change the picture
Looking at an interaction in isolation rarely tells the whole story.
A customer getting in touch for the third time will inevitably have a different experience to one making first contact. Equally, an escalated AI conversation is likely to be more frustrating for the customer if they’ve already tried (and failed) on other channels.
The challenge for organisations is that this context is often spread across different systems. Bring it together and the picture starts to change. When you bring together customer journey data from across all touchpoints, you have a more full understanding of what’s driving demand, where automation is falling short and where the customer experience could be improved.
Conversational analytics helps identify important patterns
That’s not to say that dashboards no longer have a place in CX analysis. Predefined reports provide a consistent view of performance and allow you to track key metrics over time. But a dashboard can only answer the questions it was designed to answer.
Customer journeys change, new issues emerge and unexpected patterns appear. When that happens, teams need to look deeper:
- Which journeys generate the most repeat contact?
- Why are these conversations being escalated?
- Has this change affected customer behaviour?
Historically, answering those questions might have meant requesting data, creating a new report or waiting for an analyst. Conversational analytics offers a different approach.
Users can ask questions of their data in natural language, exploring answers as they go with one enquiry leading naturally to another. This enables teams to investigate a trend, drill into a particular journey and identify where action is needed. As a result, useful insight becomes more accessible – and you can actually optimise based on what’s happening in real time.
And that’s where the next part of the story begins.
Coming next: From insight to optimisation and intelligent outcomes
In Part 2 of this series, we’ll explore what happens when analytics connect with orchestration – from investigating individual AI conversations to identifying opportunities for improvement and turning insight into action.
And for more insight on conversational analytics, read our new whitepaper.
