Meet Justin Mulvaney, he’s the data analyst at Spacious, a company based in New York City. We recently sat down with Justin to talk about his background, how he serves the data needs of a fast-growing startup, and how a cloud data warehouse has made his day-to-day easier.
Tell us about Justin:
I’m our company’s only Data Analyst, but I like to say I’m a mighty team of one. 😃 I’ve been at Spacious for about a year, and before that, I did data analytics at a SaaS startup in Nashville. I decided to move to NYC and I’ve enjoyed my work at Spacious ever since.
Awesome! Fill us in about Spacious
At Spacious, we call ourselves a “future of work” company. We’ve developed a network of working spaces. Our business involves making available real-estate such as restaurants (that are otherwise vacant during the day) available for working during regular business hours.
What’s your day-to-day like at Spacious?
Our various teams generate a ton of data because we’re kind of complex in how we operate. I support all our teams from a data perspective, including our marketing, supply-side, operations, executive level needs and more. We have customer engagement data and customer revenue data. We have a wide swath of data that you can go in-depth with if you want to.
Our data comes from many sources including Stripe, Intercom, Google Analytics, Facebook ads and our own MongoDB instance where we track user engagement. I spend most of my time working with our customer engagement data and our subscription data from Stripe. We also have accounting data sources as well.
Our main analyses focus on our customer side and on our supply/space side. For customers, the key ratio is customer acquisition cost (CAC) vs LTV (lifetime value of a customer). Our business depends on keeping this ratio in check because it tells us our profit generated per member versus the cost we incurred in acquiring that customer.
With reference to the customer ratio above, we ask questions such as:
- How is revenue affected by users who frequent a space over and over again?
- How much revenue is generated when users check-in to a space?
Also, at Spacious, we’re evaluating our supply side spaces and asking questions such as:
- How much revenue are specific spaces/locations generating for us?
- How much does it cost to operate a space?
- How can we learn from existing spaces to replicate success throughout a city?
What was your working life like before you had Panoply in-house?
Luckily for me, Panoply was already set up before I joined Spacious. Our CTO, early on in Spacious’ existence, wanted a rock-solid data pipeline to power insights in the business. Before we had Panoply, we had data all across the organization such as Facebook data, Google Analytics, Intercom et al - and the issue was they were all in the little separate silos. It was incredibly frustrating to run any type of analysis.
They were manually exporting data and looking at everything individually in spreadsheets. In a nutshell, everything was siloed and no dataset could talk to or be compared to one another. I walked in, in a two-week project, I could run queries and immediately help make decisions that bettered our business, from the get-go - because I have Panoply to reply upon.
When evaluating a new platform, is Panoply compatibility a decision factor?
Absolutely, if we’re going to be working with a platform that generates a large quantity of data, it must work with Panoply, or Segment, which is another data vendor we use.
Alternatively, if it’s something that generates a small amount of data, I LOVE the fact that Panoply has a CSV upload into a Redshift warehouse. This capability means we can experiment with platforms that might be custom or one-off projects. I can exports lists and upload them into Panoply - and in 10 seconds later have all this data
We did the same thing with some survey data - I used SurveyMonkey, massaged the data just a little bit and uploaded into Panoply. It was magic!
I love being able to experiment with these lightweight data sources without having to build a pipeline. As an analyst, I enjoy that I can collect and manage my data without even having to talk to engineering. It’s a godsend.
Which team’s data needs do you support within Spacious?
I’m the only data person in Spacious, so I support marketing, finance, operations, the C-Suite, customer success and product team. If there’s someone who needs to use data within our small company, I’ll collaborate with them.
What’s a big win you’ve had that Panoply has enabled?
We have a fairly complex way of allocating revenue to different partners across our network and before Panoply, we had many many hours of data exports, manual file checking by every team within Spacious. Now, with Panoply, each person uploads their data into Panoply easily, and with a little SQL, I can generate our revenue numbers in a matter of minutes, where it used to be hours.
I don’t have a dollar amount savings in mind - but every team was affected by this rigorous accounting effort - it literally sucked a week of our CEOs time and head of operations. It literally took up 25% of our executives time. As a data analyst, I was able to prescribe a much easier way with Panoply at the center.
What’s another favorite Panoply feature?
I love how when I run a query more than once, it just gets faster and faster thanks to Panoply’s optimization and ‘special sauce’.
Why is Panoply particularly valuable to a startup?
Well, startups or any small business doesn’t have the time or cash resources to spend on a data engineer or data team. We love having a solution that means we can get our data all in one place without having to involve engineering. Also, it lets me move with the speed I need to, which is key to my success in this job.
How is it working with Stripe data?
In a word - it’s really great. I really enjoy Stripe’s data. They do a good job of organizing data to be actionable for me. Also, it’s great to have a *real* source of truth on revenue - and that source is error free.
What do you use for Data Visualization?
We use Mode Analytics - and we’re really happy with it.