By Tim RogersJan 20133 min read
Here at GoCardless, we try to make all our decisions in a data-driven way. Staying customer-centric whilst we do so means making sense of all the interactions we have with our customers.
On the Customer Support team, we spend more time than anyone else speaking to customers. We generate a lot of rich but unstructured data: our email, calls and other interactions. Recently, we’ve been working on new ways to analyse it to improve our support and drive product development.
How things were
When I joined GoCardless we were providing great support but our tracking was ad-hoc. We were manually recording some interactions, but the data was patchy and the process was time-consuming.
Worse still, our support data wasn’t linked with the rest of our data, meaning we weren’t using what we did record. It was buried.
We decided to begin our overhaul of support-tracking with our phone channel, and I set about building a new, metrics-driven system.
We had a good foundation to build on - all external calls come in on a Twilio number and are forwarded to phones in our office. To get good quality tracking I built a system called “Nodephone” to sit in the middle. Built in Node.js with the Express framework and Socket.io, it’s on the other end of Twilio, responding with TwiML, but it also communicates with GoCardless and a web interface.
Any incoming call is logged and looked up in our merchant records. We then display the call on a simple web interface, where the support agent can see the caller’s name and click straight through to their records. At the end of the call, they can add descriptive tags and notes.
Now when customers call we know who they are and can greet them personally! If we don’t recognise their number and we find out who they are on the call, we can save it from the web client for future calls.
All the data entered, alongside the duration of the call, is saved on the merchant’s account for future reference.
Data, data, data
All that data we’re collecting has already proved incredibly useful since we can analyse it to find the trends. For example, we want to know between what hours we should provide support, so I graphed the number of calls in different hours of the day over a typical month:
Clearly the vast majority of people call between 9am and 6pm, so we decided to set our office hours for then. We also use this kind of data to inform recruitment for the customer support team.
We can also see why people are calling - that is, whether they’ve paid via GoCardless (customers), collected money (merchants) or are interested in taking GoCardless payments (prospects) from the tags:
As a start-up that prides itself on reducing friction to signup we were amazed to see so many prospects trying to call us. What’s more, they were finding our number from parts of our site designed for our current users. From the data above, we decided to put our number front and centre on our merchant signup pages - check it out here.
The tags we logged on calls from merchants also showed that there was a lack of helpful information on the site to answer common questions, so we’re revamping our FAQs with a whole range of new content.
We’ve built a powerful new automated metrics system for logging our phone calls.
Next we’re targeting our other support channels. We’ll be using Desk.com’s API to analyse our email support, providing the ability to do all sorts of calculations which aren’t possible with the software’s own analytics.
We're also going to build an internal dashboard so anyone can see the headline stats for support in a couple of clicks.
Over time, we want to make all our internal analytics as powerful as we can. If you find problems like this interesting, we’re hiring and would love to hear from you.