Path To Profitability

I emailed Dave over the weekend with a query about the path to profitability.

I was interested to note that when Buddy first listed Dave provided guidance that we would not be profitable until CY2019. Moreover, despite the loss of Zentri/Noveda he has consistently reiterated this timeline in recent updates. This suggested to me that despite recent perceived setbacks, Buddy remains on track with the core business plan and market strategy, as evidenced by early achievement of device/data metrics.

Delving a little deeper I wanted to try and better understand the importance of the devices connected/data generated metrics, from there the intrinsic value of the aggegrated and anonymized data to the broader market, especially but not limited to Parse, and what that means for the path to profitability.

Dave's reply is instructive, and helps better understand where we are and where we are going.

Hi Writer:

You’ve caught me on a flight to Taiwan, and I’ve found a pocket of connectivity,
so let me get some answers back to you here. ☺.

Yes, the CY19 guidance was based on the original business plan absent any
acceleration from acquired businesses or assets. The reason I’ve maintained that
guidance is that from 50,000ft the plan hasn’t changed. Our execution has – and
delivering end-to-end solutions in the smart cities space wasn’t specifically the
plan in 2015, but picking a vertical and focusing in on that always was (whatever
that vertical would be). Also remember that our sales teams have been pre-selling
into smart cities building monitoring & verification (M&V) since 3Q last year
expecting to be selling in Noveda product by now. Walking away from the Noveda
transaction just meant that we had opened up a sales channel that we now have the
opportunity to fill with our own product.

Also note that selling into municipalities and governments here is quite different
from say, military or larger government contracts which can take a long time. We’re
talking about much-reduced lead times and greatly increased velocity. I had one
meeting with a potential municipality customer that went from first meeting to verbal
agreement on a paid trial in a few weeks, and if successful would progress to an
at-scale deployment in a few months thereafter. We’re not talking about 9-12mo
sales cycles here.

The plan always has been, and remains, to sell processing and management of the data
generated by connected things. In the case of the smart cities solution, it just
happens that we’re packaging that up into a vertical product – bundling in
hardware and a consumption mechanism (such as dashboards) to come up with what is
effectively a boxed product (not quite, but you get my point). That isn’t to say
that we aren’t bidding on, or in advanced negotiations with customers seeking to
build on the core platform, it’s just that our 2017 focus is the solution – we
know we can sell that more easily.

I think you’ll find that we’ll look back at 2017 as the year that revenues went
beyond inbound service-based (ie: subscriptions, cost of sending the data in) and
became larger due to product sales and outbound service based (ie: selling the data
back out of the system). Remember that you can’t sell the data out if you haven’t
gotten it in first, and nobody buys it out without getting it in, stopping there and
taking a breather, and then figuring out what they want from it. The reason I advised
that revenues would remain light prior to CY19 is because I knew that until customers
had developed significant hunger for their data, we’d primarily be storing and
managing it – not doing a lot of processing (where our margins are best).

You’ve probably heard me talk about the three stages of selling data out of the
platform – how it progresses from being an exchange to a marketplace? If not, the
short version is this: 1) sell the data back to the companies that push it into us,
2) sell the data back to related entities (partners, agencies, etc…) and 3) sell
the data as a marketplace – anonymized and aggregated – to anyone for whom it has
value. #3 is where the big money is, and you can’t do that without volume.

What’s an example of that? Take hedge funds. For years they employed high school
kids and interns to sit outside of places like McDonalds to count people going in/out
in order to try and understand whether their business was growing/declining and then
trade accordingly on market prior to quarterly earnings – they figured out that if
they could develop “alpha” data, they could use that to inform trades. Well, they
don’t need to employ kids anymore, they can buy the data. Some estimates have there
being 10,000-15,000 funds around the world buying totally anonymous data for the
purposes of market research. There are now dedicated brokers of data (it’s called
“alternative data” – Google it, there’s fascinating reading to be found)
specifically for the finance industry.

So what does this have to do with us? Well, it turns out that the data set that Buddy
is building has a lot of very relevant data for the finance industry. Smart
streetlights connected to us can detect pedestrian foot traffic, which can be
extrapolated to ascertain changes in retail/commercial property values or relative
success of retail locations. Mobile application data can be used to derive quarter on
quarter changes in people volumes through airports – providing indicators on
relative performance of airlines. In fact, think of any change in people flow or
attendance or traffic through a given location, and what that might mean for those
locations and then again for those who invest in or against those locations. And
that’s just finance – there’s a huge number of other scenarios, but this is one
that is real, is current and for which we’re building an asset that is already
attracting attention.

Here’s the best part – the privacy implications are very few indeed. These data
buyers are not advertisers, they’re not looking to send you coupons to buy a Big
Mac because you walked past a McDonald’s. They actively don’t want any PII
(personally identifiable information) because they’re only interested in the trends
that can be extrapolated to their investment decision making. It’s fascinating
stuff, and requires no sharing of any identity or social demographic data.

So anyhow – that’s just one example. And it’s a real example – so when I talk
about Parse and its impact on our revenue base, I don’t think about the
subscription fees – gosh, there’s plenty of modeling we’ve done where making
Parse on Buddy free makes a lot of sense (we haven’t done that yet though). As a
significant driver of traffic, correlating that data with data we get from
streetlights and buildings and pedestrian counters and park systems, etc… is hugely
valuable.

Here’s the kicker – when valuing that data, you know what the first two questions
I get asked by buyers are? 1) How much daily traffic are you processing, and 2) how
many unique devices are you seeing? Those two metrics are the difference between a
$10,000/mo data feed and a $1,000/hr data feed (per customer). THAT is why those
metrics are the ones we care about and set in place way back in June 2015 when we
were preparing to list.

So anyhow – I hope that provides some more insight. I’m going to be talking more
about this in the February half-yearly cover letter, because I’m learning that I
can’t reiterate and underscore our business model/plan often enough. I know a lot
of investors (or even traders) are focused on the quarter by quarter results –
which I understand, but which is not aligned with the vision we outlined from the
get-go and which we’ve been following since day one.

The Noveda and even Zentri acquisitions were attempts to accelerate our traction
while also increasing revenues by bringing in long sales cycle customers where
they’d each done the legwork of winning those customers without us having to invest
the time (in some cases years) to do the same. Not closing those deals only meant
that we remained on the original timeline and business plan. What was curious is that
the market did not reward the stock with meaningful appreciation when we announced
those deals, but penalized us heavily when those deals didn’t materialize. In other
words, investors were not rewarded with the potential revenue they would have
brought, but were penalized with losing the potential revenue they would have
brought. Oh well, nobody said the market was a perfect machine…

BTW – you don’t get very far in my job if you can’t take criticism so don’t
ever be afraid to send me candid thoughts or input. I always respond to every
investor mail I get – perhaps not immediately, but ultimately (if I haven’t
responded, I didn’t get it or it got lost – don’t be afraid to nudge me). We
have an IR team we work with, but I really prefer to answer these emails myself
wherever possible.

Cheers, Dave.