More specifically, the company boasts being the largest share registration provider in the UK, with about 60% of major public companies using it - so big name brands like BT, Tesco, Lloyds and other companies outsource a lot of their customer admin and contact to it, including for employee share and pension plans.
It manages discretionary share plan administration, as well as more high-level corporate governance functions to clients running their annual general meetings or even mergers and acquisitions, like proxy solicitation and contemporary corporate governance advisory services; the company claims about £90 billion worth of transactions go through its ledgers every year.
An easier way to think about this: Equiniti is an outsourced customer contact specialist - so obviously delivering a positive customer experience has always been important. The internal leader charged with using technology to do that, Gareth Wallis, its Customer Insight Manager:
We've obviously always had a Customer Experience Centre where we deal with people directly over the phone, but Customer Insight is relatively new here. We've introduced this function as we never really had enough granularity to really understand what the customer experience really feels and looks like - so our job is very much to really dig in to the data, and get our arms around as much data as possible about the customer journey from as many varieties of sources as possible - whether it be from our CRM tools, whether it be from Speech Analytics, whether it be from customer surveys, and gather that all together to really understand what that customer experience look like, what are our pain points, what are our successes, and then try and improve the business based on that data collection thereafter.
But there's a challenge - one that's common to many firms across multiple sectors:
We don't know what we don't know: we make assumptions every single day about what the behaviour is like in our call centre, but when we start digging into the detail behind it, there's always things that you don't expect - things that you're surprised by. Every time we go looking at something and we think, 'Oh, there might be a problem over here,’ it’s always a bit of a shock about how many calls we get on an issue, or how long those calls are getting, or what the process really looks like.
Wallis is refreshingly candid with us that before introducing speech analytics into the mix, there were some definite Donald Rumsfeld-style ‘unknown unknowns’ - issues that the company suspected were there for customers on certain pathways, but surprisingly, it turned out there were other broken processes his team weren’t really aware of:
We were very aware that there were failures within the business; we are very open about the fact that we don't do everything right, perfectly first time. So we wanted to understand, 'Where are we going wrong? What are those areas that we are unsure about, where those failures occur?' - essentially, 'How do we start to know what we don't know at the moment?’
Walls and his team started with their best resource for doing this - its speech bank. To help meet compliance targets, financial services platers like Equiniti and basically the entirety of the City record all the calls that happen on their trading floors. If there’s ever a problem, regulators or auditors, or even internal governance teams, can in principle work their way through all this to see where a suspicious or faulty interaction occurred.
The problem: this was a very crude way of working:
We've always had a call recorder in the system, we’ve always been able to sit and listen to calls and go through them. But it's very much needle in a hay stack… you sit with your headphones on and you listen to call after call after call. You might go through five hundred before you find the one thing that you're looking for. We wanted to try and pinpoint those calls more easily.
Hence a move, starting in 2016, to embed more intelligence into this process, using specialist Speech Analytics software from Nexidia implemented by Business Systems (UK), which Walls now pitches as a tool that's part and parcel of everyday life in the company:
We use it in a variety of different ways. So we use it to monitor our agent compliance - are they doing the things we expect them to do on those calls? We use it to solve problems, and quickly identify a particular call type very readily.
A 25% saving of valuable human agent time
Wallis uses a recent example of a report that can pop up out of the system that 30% of calls in a given week relate to a change of address. That enables the ream to ask why so many, and why now, and why calls are coming in in-bound to the contact centre when they might be better health with on the website:
Having this deeper, data-based level of insight into what we’re doing really helps us pinpoint areas we can improve. One of the first successes, for example, was our share-dealing brokerage; customers phone up and they can deal their shares over the phone. And it wasn’t until we started using Speech Analytics that we found we were asking an awful lot of questions that the customers didn't really know the answer to - things that the customer wasn't able to give an answer to, and we weren't getting to that final point of resolution with the customer able to sell their shares. We were putting lots of barriers in their way.
A classic example of an unknown unknown, then - the company just hadn’t realised this was a process that was causing inefficiency to both it and its users:
So we started looking at this using analytics, identifying the key flash points and the volume of calls like this we hadn’t known about. That delivered a quantifiable cost, agent time being spent on the phone that we could avoid if we tightened up our business process, so we started looking at rewriting those scripts and removing the bits that were causing customers confusion or pain. Year on year we ended up making about a £30,000 saving, just on agent time alone - but even better, what had been a lengthy customer call that didn’t always work is 25% shorter, and now does.
Summing up, for Wallis it all comes back to working with real data to help real customers:
We may have what we think is a perfect process; agents may run through it seamlessly and it works fine, but actually doesn’t work for the customer, making them go through a period of pain, jump through hoops in our system. We also might have a perfect customer process as well, but it falls down for our business, and we've got lots of risk and things in that.
Balancing the two is always a bit of a juggling act - but analytics gives us an ability to identify both areas - risk and customer pain - and try and then balance the two and create new processes that work for both sides.