How Justin Mulvaney at Spacious Saves Time With Panoply

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. We have a Postgres database backend, we connect through Segment (a data aggregation vendor), and we pull in several data sources. We connect our Shopify store. We also connect 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 in place when I arrived. But I’m told that before Panoply was implemented, 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 rely 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 export lists and upload them into Panoply - and in 10 seconds later have all this data aggregated with everything else.

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 pull data from Shopify and Segment and have it live within an hour of the transaction or event occurring. Now that I have all this data, I can very quickly create new tables, and with SQL - build reports and surface key business metrics in Mode Analytics (our viz tool) almost immediately.

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 have limited resources and we can’t afford to hire 5 people to build out a data infrastructure team. With Panoply, as a single data analyst, I can very quickly stand up a whole data warehouse and serve all the data needs of the organization - particularly because Panoply has native connectors to services like Stripe, Shopify, and Segment - all services that startups typically use. This means I don’t have to write custom code to integrate data sources. Instead, I can point Panoply at these third party tools and within an hour, have all the data flowing into our warehouse.

Also with the CSV upload feature, even if Panoply doesn’t have a native connector, I have an easy workaround. I can easily be opportunistic and experimental with data. Because of all this, I can spend more time on analysis and insights, and less time on data engineering.

Finally, Panoply gives me the ability and confidence to onboard team members to the data work - they can easily pull data and create their own reports, which takes the burden off me - the single analyst.

What do you wish Panoply could do?

I wish Panoply had built in BI tools or data visualization, but Mode Analytics really fills that gap for us.

What are you currently tracking/analyzing?

I’m currently tracking CAC, revenue per space, revenue per customer, average revenue per customer per visit - also tracking operational costs associated with different locations, space quality metrics, and demand forecasting. We’re also trying to identify what makes a successful space and why certain locations fail.

I’d like to start leveraging machine learning to predict which spaces will be successful and to build a churn model to predict which customers will stop coming back. I’d also like to build a more robust pricing model to maximize revenue.

How do you go about ensuring data quality?

We maintain a very strict process. I’ve collaborated with our ops team and engineering team to build a 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.

Originally published on the Panoply blog.