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0
The Transformation Issue | Idealog.co.nz
048
F I N D T H E M O N E Y
I
n a Callaghan Innovation report released this year
called Growing The Pie, CEO Victoria Crone said 10 to
15 years ago, New Zealand founders aspired to have
an exit strategy with their business that would give
them a great lifestyle – the boat, the beach and the
bach – through selling their company for $10 to $15 million.
However, she says there has since been a cultural shift
for founders to think beyond just having a New Zealand exit
and look at what they can achieve on the global stage.
“But, what we have seen with the likes of Stephen
Tindall, Rod Drury, and Peter Beck is that we can solve
the world’s problems from New Zealand,” Crone said.
“And that we can create businesses that can fundamentally
change the world.”
Another contender to add to this list is Predict HQ
founder Campbell Brown. He has had the US market in his
sights since day one, uprooting his family to San Francisco
18 months after founding the company in Auckland to open
its American arm.
Now, it’s one of New Zealand’s fasting growing and
biggest data-as-a-service (DaaS) companies and has picked
up a slew of awards in the process.
Brown says it’s a shame that Kiwi founders err more on
the conservative side than the aspirational, as his theory is if
you start a business thinking about an exit, you’re not going
to build a great business.
“You’re constantly thinking, ‘how can I shape this so it
looks attractive for someone to acquire?’” he says. “The key
thing is, it’s not rocket science – although it is with [Rocket
Lab founder] Peter Beck – is build a fucking kickass business.
Build something that is generating revenue. Build something
that has a great culture and customers rave about. Build
something where you’re turning people into rock stars inside
your business.”
The vehicle for that vision is Predict HQ. The first thing
to understand about the company is it’s doing something
that is very ambitious: it’s helping companies understand
and predict the 'why' behind any form of demand.
A lot of this is drawn from mapping future real-world
events through data, so companies can understand
when demand will increase for their products. In a
world of on-demand services like Uber and Airbnb, this
is a significant coup.
Put simply, it’s helping companies tell the future. In
technical terms, Predict HQ aggregates and verifies more
than two billion data points from 20 million events across
more than 30,000 cities worldwide into a single Application
Programming Interface (API).
So, just how hard is it to predict the future, anyway?
Predict HQ chief data officer Dr Xuxu Wang says it’s not a
simple task, but when you have the right tools and data, it’s
achievable and powerful.
“Predicting the future is a journey, starting with
understanding the historical data, learning the patterns
hidden in there and then building the prediction engine,”
Wang says.
“Some people look at PredictHQ and think it’s just an
event aggregator, but this is not true. Aggregating data from
hundreds of sources is challenging, but the really complex
work begins with the cleansing, verifying and deduping of
20 million events that are constantly evolving.
“Scheduled events are events that are known to take
place in advance and they have a predictable impact on
business demand. Therefore, by knowing upcoming events,
months even years in advance, businesses can predict future
demand. This allows businesses to make informed decisions
about revenue management, supply optimisation, pricing
and other factors ahead of their competition."
While this process is immensely complex, Brown and
his team’s approach is not. In a world of analytics and data,
they’ve brought a fun, humorous approach to business.
Staff have been known to don eye-catching suits covered in
Flamingos or Star Wars patterns to business conferences
around the world to get noticed by potential clients.
“I like to lead by example," Brown says. "If I’m asking
salespeople to wear a crazy suit because it helps them to
break the ice, then I’m going to do it, too. You walk into a
room and they go, ‘You absolute fucking muppet – but I’m
going to come talk to you’."
He is planning on framing his now infamous R2D2
suit that has since been discontinued and putting it in
the Auckland office alongside a small hammer, with the
inscription ‘break in case of a sales emergency’.
One of the initial investors in the business, Rampersand
co-founder and partner Jim Cassidy, got word of Predict HQ
in an unusual way.
“It’s a genuinely very funny story, as everything with
Campbell tends to be,” Cassidy says.
When Brown was still based in Auckland, he happened to
be taking the recycling out at the same time as his neighbour
Paul Naphtahli, another partner at Rampersand. Brown
mentioned his idea for Predict HQ and Naphtahli told him to
get in touch with Cassidy, who ran a VC fund in Melbourne
that invested in early-stage businesses. Rampersand ended
up leading Predict HQ’s seed funding. Lesson learnt? Seize
the opportunity to pitch, even when taking out the rubbish.
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Predict HQ was founded in 2014 off the back of learnings
Brown gleaned in forecasting and marketing at GrabOne and
global travel group Online Republic. He had identified a major
gap in forecasting and marketing – knowing exactly why a
demand spike was happening – and found that more
often than not, it was due to events.
“I had a theory that if we could understand real-world
events, we’d be able to better predict demand,” Brown says.
“We were sitting in our meetings going, ‘Shit, G20 Summit
in Canberra, if we had known that we could’ve yielded more
out of car rentals or priced it better’, or we could’ve put on
more inventory we could’ve sold. It was always a retrospective
feeling, so I thought there had to be a better way.”
This theory was tested through a booking widget
at Online Republic that explained to customers to why
availability was high and pricing was low. By telling them the
events behind the demand – ‘car rental prices are higher than
usual in New York because Beyonce is playing a concert and
the New York Knicks are on’ – sales conversions improved by
35 percent. This was all the proof Brown needed.
lily
(lily)
#1