Trying to derive insight from your contact centre interactions is nothing new: ever since agents have sat together in a room to make and take calls, people have been looking for ways to understand and optimise their customer conversations. It’s why businesses like Liquid Voice exist.
But in the hunt for those nuggets of wisdom that promise to transform your customer experience, or behind the haystack of ever-growing datasets at your disposal, I sometimes wonder if some fundamentals are being missed completely, or if certain metrics might look very different if other contextual data was factored in.
It’s a common challenge that many contact centres have – whether they know it or not – and how they reach this point is perfectly understandable. The technology stack of most contact centres we know is one that has often taken shape over many years, and evolved in response to changes in customer behaviours or broader technological / operational changes. So naturally, we find data in multiple pockets across the organisation, often telling different parts of the same story.
There is a well-known Buddhist parable about a group of blind men who encounter an elephant, each feeling it in different places:
The first person, whose hand landed on the trunk, said, “This being is like a thick snake”. For another one whose hand reached its ear, it seemed like a kind of fan. Another person, whose hand was upon its leg, said, the elephant is a pillar-like a tree trunk. The blind man who placed his hand upon its side said the elephant, “is a wall”. Another who felt its tail, described it as a rope. The last felt its tusk, stating the elephant is that which is hard, smooth and like a spear.
Ultimately the group comes to blows over their individual perceptions of the truth, revealing the moral of the story, that the truth needs a complete understanding of the context.
What on earth does that have to do with call recording? Actually, quite a lot: by concentrating attention on a single set of data – such as a single channel phone channel – or over a short time period, any contact centre data runs the risk of being fundamentally flawed. It’s easy to draw conclusions based on narrow datasets, So it can be appealing to take the outcomes of these at face value. Furthermore, it can often be near impossible to uncover the broader ‘truth’ where platforms are unconnected: It can look like your compliance training is having the desired effect on your Contact Centre lines, only for behaviours to be different on back-office lines.
This is the kind of challenge that our team at liquid voice thrive on – it’s what we come to work for. We know that having an incomplete picture of events is often a risky position to be in – drawing the wrong conclusions or having gaps in your knowledge can easily result in poor decision making, poor customer service, and regulatory risk.
Our tools and our team work to alleviate those risks first by bringing all of your datasets – phone, text or video-based, current and legacy, together into a single, secure and compliant repository, and then provide a single, synchronous source of truth across all customer interactions. That means contact managers can truly understand the full picture, tracking customer journeys across channels and systems, understanding the broader context of customer and agent behaviours and quickly put in place processes that champion a job well done, and eradicate areas of poor performance.
In terms of picturing the Impact that such insight can have on your business, Let me give you an example from the world of emergency services, a sector in which we have several clients: In this context, call handling and operations teams can revisit the timelines of major incidents and piece together a complete picture of events; across any initial 999 call, through responders’ radios and body-worn / vehicle cameras, and even third party CCTV and other media. You can probably appreciate the importance in getting an a truly complete picture of events in this kind of scenario where such data can be used both to ensure appropriate responses to future incidents, but also to establish evidence that may be used in court of law.
So to sum up on this slightly rambling post: we should all try to be less like the confident blind men of the parable, and be looking at the areas of incompleteness in our datasets. Filling in those gaps can often give a very different view of ‘the truth’