How ZipMoney has scaled

ASX-listed fintech ZipMoney reported1100 per cent growth in customers in its first half

ZipMoney, an ASX-listed fintech that offers point-of-sale credit and digital payment services, is growing: In the company's first half results it reported revenue of $6.7 million — up 722 per cent on the prior comparable period — and 120,000 customers — up 1100 per cent on 1H16.

The business has been able to meet the challenge of growth by keeping its team focused on Zip’s key differentiators in the market while integrating with a variety of scalable cloud services, according to its chief technology officer, Mike Greer.

ZipMoney allows an individual to make a purchase and pay for it later (its key service offers a three-month interest-free period and charges for setting up and maintaining an account with a non-zero balance at month’s end).

“It allows our customers to shop seamlessly and responsibly online and in-store,” Greer said. ZipMoney integrates into “all the major ecommerce platforms” as well as point-of-sales terminals — “anywhere we can make Zip available — anywhere our customers want to be able to purchase,” the CTO said.

The company was founded towards the end of 2013, with an angel investment round funding ZipMoney until its listing on the ASX in 2015. In August 2017, Westpac announced a $40 million equity investment in the company.

For the first 12 months or so of Zip, the company had half a dozen employees, Greer said.

“We were doing things on a shoestring at that time,” the CTO said. “Now we’re looking at probably just over a hundred full-time employees, and then we also have a bunch of part-time employees that do customer service and things like that.”

Close to a third of the full-time employees are part of Zip’s product and tech division.

As the company has scaled, Zip’s team has remained focused on its core IP, integrating with a range of services to plug the gaps in its operations.

At the heart of the business is the proprietary platform built by Zip’s developers.

“We built all our customer experiences, the real-time credit decisioning which differentiates us in market, all our machine learning algorithms, customer workflow management – it’s all been built bespoke,” Greer said.

“Then we leverage a lot of services that solve a particular problem”, the CTO explained.

The company employs Amazon’s public cloud services, Salesforce and Zendesk, as well as Twilio for two-factor authentication and SMS delivery.

Twilio provides a communications channel, while how and when the business communicates with its customers resides within the Zip platform, Greer said.

“Our core IP is very much around how we utilise [those services],” the CTO said. “We don’t want to build a platform for connecting through to the telcos for delivery of SMS messages. Likewise, we don’t want to be having teams of network engineers nurturing hardware in server rooms.

“We’ve tried to use those services that offer us a high level of redundancy [and] solve a single problem for us, and then that allows us to focus on delivering value to our customers by solving their problems and giving them seamless, frictionless experiences with Zip.”

In the case Twilio, the service has been employed from the beginning: It was used to send Zip’s first SMS in December 2013 to the company’s first customer — Naomi, who bought a $500 bicycle from Zip’s first merchant Chappelli Cycles.

“There’s always alternatives for a lot of these services, but from an engineering perspective we want reliability, we want scalability and we want ease of use,’ Greer said.

“I think Twilio’s really made themselves a major player internationally by offering really simple and frictionless APIs for developers to integrate [with] and get up and running really quickly.”

The real-time decisioning engine at the heart of ZipMoney draws on input from a range of sources.

“There’s things that we have to gather from a [legal] standpoint —verifying income, expenses, all those kinds of things,” the CTO explained.

“But really what you want to be doing is you want to be asking for as little information as you need and then supplementing it with additional data sources that are available — whether it be data coming in from the big credit bureaus, banking data; there can also be social data analytics and things like that.”

“What we try to do is tap into as many data sources as we can to really give us a better profile of a customer,” he said. “When we have all these additional data points then we can really crunch that data to approve [customers] in real time. It also gives us a better confidence in the actual decisions compared with some of the players in market.”

The decisioning engine and its underlying model are constantly being refined.

“A/B testing is ingrained in our business everywhere,” Greer said. The business takes a “champion/challenger approach where we are always analysing and crunching our existing data to tune our models, and then we have competition models brought in to see if they will perform better.”

As you would expect from a data-heavy business, Greer is interested in applying machine learning approaches across Zip wherever it makes sense.

“Our decisioning engine was probably the first place we looked at, but now we’re looking at things like NLP [natural language processing] for analysing and categorising all the communications that come in from our customers,” Greer said. “Really what we want to do is take those insights and apply them into actual resolutions for our customers.”

The company is investigating the potential of chatbots, including through Facebook, the CTO added.

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