1. Data
From one organization to another, there will be a great spectrum in the capabilities of using data. But how you secure that data is something that is going to influence the relationships that you have with customers. Privacy data has now become part of the user experience, and when we are designing conversations, through both machines and people, we need to take a careful look at the usability of that privacy. So make sure you have a very positive view of the customer controlling their own data, rather than a defensive view of “we want your data”, whether the customer wants to give it or not.
(How you use data) is becoming really quite important in the impression that you create at the start of the interactions, and it requires a lot of thought
2. Usability
One of the most important things to remember when you are designing an app, IVR or anything else, is that there is no substitute for testing. You have to test because there are just so many surprises in how people will try to use your systems. Unfortunately, the resources to do testing are often in short supply, so when you start to look at optimization as a subject, you need people and a budget to create the time to do lots and lots of testing. It’s a fundamental in getting any kind of user experience right. And most contact centres don’t have an optimization team optimizing conversations and developing the IVR, self-service and other vital systems. While some of the very large companies might, the vast majority don’t recognize the value in a daily or even hourly check of self-service applications and using that to optimize interactions. There is great value in optimization. For example, if you have 1,000 advisors, by optimizing and having better conversations or lowering demand for conversations, you might cut the workload of many advisors by having just one optimizer. “At any one time, there should be a distinct bias towards investing in optimizing in order to not have the contacts, rather than having loads of contacts and only optimizing how you handle them,” Peter concludes.3. AI Jargon
AI is a phrase that’s being dropped into the contact centre conversation by many consultants and vendors, but a lot of that is lip service. We need to first recognize the distinction between AI and machine learning. A good place to start with this is to consider: what does good look like? If you’re asking a machine to do a job, you need to be able to say whether the machine did the job well or not. When put into that perspective, the difference between machine learning and AI is that with AI, the machine can tell you whether or not it has done a good job. Whereas, with machine learning, a person needs to check the results and see. But why is this distinction important in terms of creating great customer relationships through exceptional customer service? Peter says: “Machines can learn very quickly; they can handle large amounts of data these days. But if you haven’t got enough resources to be teaching the machine what to do and checking that it is achieving the right result, your development programme is at a wholly different pace. True AI is not what most people are buying today.” So, if it’s machine learning, you need to be manually checking quality – you might be putting a system in front of your customers that negatively influences their experience unless you invest the skilled resources to go with it. For example, this is a mistake that many organizations have made when rushing to install a chatbot…In Summary
There are six key reasons why great customer relationships can be lost. These are:- Customers don’t want one
- Staff don’t want one
- You’re stopping one
- Data collection is messy
- Process optimization failures
- AI misunderstandings
