There is an intrinsic data driven culture at mobile-only bank Monzo. Neal Lathia, machine learning lead, believes there are a few reasons for this. The first is that it is a very young tech company that was founded just four years ago.
The second is that there is a lot of support and encouragement from the senior leadership to look to data to answer questions. Lathia even mentioned that the CEO is an avid user of data exploration platform Looker.
And finally, almost everything that has been built can be quantified in some way. With such a vast amount of data on which to run experiments, tests are carried out all the time.
Lathia gave the example of ‘golden tickets’. These are invitations that an account holder can extend to a friend or acquaintance so they too can join Monzo. The Monzo employees experimented with this by seeing if changing the placement of the golden ticket button would encourage more users to share it.
“All of these questions have led to a lot of experiments being run and personally that is very satisfying, as opposed to people debating which is best,” he said.
Monzo has a staff count of approximately 700 people, of which 20 are in the data team. The data team is split into three roles: product analysts, domain analysts and the machine learning team.
Lathia heads up the latter. He explained that the product analysts will join teams of engineers, designers and product managers, and support all of their data needs. They might do this by helping to test new features on customers and seeing what the uptake is.
The domain analysts deal with the analytics that supports the customer support function. There, data is used to help the customer support staff look at how well they are working, but also get feedback from the customers. “We might say, ‘oh look. A lot of customers are asking us about x. We could put that into the app and make it super easy for them to do that themselves.’
The machine learning team has the biggest mix of data and engineering. “We build parts of the app or parts of our tooling that use machine learning to help achieve some outcomes,” said Lathia.
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