In the first of our Omni-channel Series we looked at why you should adopt an omni-channel customer experience. Here we go into a bit more details and look at 5 top use cases for omni-channel analytics that will be top priority for most CX leaders:
Improving NPS
You are likely to have a target to increase NPS or some similar metric for satisfaction and potentially customer loyalty within your business – this is a common use case for us and we have helped many clients increase satisfaction by 20% or more by helping to find drivers and root causes of customer dissatisfaction and setting an action plan for improvement – in many cases this is linked to a poor channel experience so getting this right can pay dividends.
For many clients we focus on understanding the drivers of dissatisfaction typically driven from channel shift and increased effort from customers. Understanding those insights leads to a pretty significant improvement in MPs. We would typically see between 10 and 20 point improvement by acting on those drivers of dissatisfaction.
Knowing the amount of channel failure
It’s really important to understand the amount of failure that you’re handling to help inform your channel strategy. Using analytics you can do this by:
Isolating contact where the customer mentioned that they tried to complete something in a different channel – frighteningly we have seen this number to be nearly half of the context we analyse;
Identifying the processes behind the root cause drivers of failure. Prioritise a set of actions that the organisation should take to help reduce that channel failure.
By carrying out these two actions you could easily achieve between a 5 and 7% reduction in headcount which could equate to around 150 FTE. A huge business benefit from the insights and if there’s a very high failure rate, there’s much more to go after too.
Understanding the types of demand you are handling
To understand the different types of demands being handled within a contact centre, you first determine whether a transaction is a value to the customer or an irritant to the customer.
And then do the same from your organization’s view i.e. is the transaction a value to you or not.
You can then plot these on a matrix. In this example (figure 1), the organisation was handling more than half (54%) of transactions that were of value to the customer but an irritant to the organisation. So these are prime for what we call automated drive self-service and that creates a massive opportunity for operational efficiency by maintaining a great customer experience, but freeing up that expensive resource in the contact centre.
Previously only 3% of the cases agents promoted self service to the customer but once they understood the value irritants and coached agents to promote self service, the number rose to over 60% in just 6 weeks.
Best of all, there was a massive impact in the contact volume with a 30% reduction in the contact centre
More accurate classification
When agents are manually classifying all of the data after every call, a lot of these classified labels are not actually very helpful. We call them bucket categories, because they are literally another category, which can be up to about 20%.
And there are also pseudo bucket categories, which is when two smart agents are doing what they think is right, but actually they’re classifying things differently. The class becomes confused and becomes unactionable. The organisations ability to develop their customer experience is limited because the analytics is just not granular or accurate enough.
By automating the classification or labelling, these bucket categories are removed and you start to see things that you may have missed from an early warning point of view that you wouldn’t have otherwise spotted. We call it a back cast.
And the nice thing is, when you’re automating all of that human activity, you’re taking away sometimes 10% of the work from those human agents as well as providing better insight.
Whatever methods that you, or your analytics partner choose, always look at how the customer is feeling in the beginning and then look at what the agents do during the interaction to make them feel happier. To achieve this it can be very powerful to look at the topics or emotional intent within the conversation. With the right analytics this can be done automatically and precisely – there’s no guessing what the customers are telling you or how they’re telling you. As Bill Gates said: “If you want to learn about the business, you’ll learn the most from your unhappiest customers.”
Now we’re not suggesting that you deliberately make your customers unhappy in order to retrieve information. But it is ironic that organisations tend to send follow up surveys people get very fatigued about, just to understand how a conversation went.
A lot of the time the customer is clearly telling you how they are feeling or what their intent is – you just need the right analytics to pick these sentiments up and the need for the survey is removed.
For example, if a customer is actually telling you they’ve switched channel you can look at the meta data of the topics to get a much more accurate and actionable qualitative view about that issue. Or conversely, if they have come over to a different channel you might not have picked it up in your initial FCR analysis.
Being able to isolate those comments where your customers are really telling you these things is really powerful. Yes, it’s a motive. Yes, no one likes to hit bad news. But it’s all there. And if you let it speak to you, you can follow these pathways, see which are the big ones and tackle things in the priority order to be able to drive the quickest improvements.
There’s always a lot of low hanging fruit when you can effectively and accurately identify multiple intents across an omni-channel experience. Because when you know what you didn’t know before, then you can go after it quickly.