There’s few things I hate more than fundraising. It combines all of my least favourite things: meetings, pitches, and high pressure follow-ups for data.

When we set out to fundraise last summer, we thought we were well-prepared. We had all of our main metrics ready and memorized, multiple versions of our deck prepared, and we’d already practiced our pitch on a few investors in the previous months with good success.

Having raised multiple rounds of investment for our previous company, Crew, this was the most ready we’d ever been.

Then came the meetings.

With Crew we’d get a few meetings for every handful of intros, but with Unsplash, it seemed everyone wanted to meet. Not only did they want to meet but they also wanted to dive deep into the community to understand the magic disrupting the 800 pound gorillas of Getty, Shutterstock, and Adobe.

First meetings turned to deep dives, and deep dives turned into frequent data requests—day and night.

Most of all, no two investors seemed to be interested in the same slices of data. One investor would want one cohort in one way and another would want it with slightly different attributes over a completely different timeline.

Had we not had Looker, our data tool, things likely wouldn’t have turned out so well.

The past

When raising our first rounds of funding, our data process for investors would be to group all of the questions at once, call up our best engineer, and ask them to manually query the database in SQL.

Problems quickly arose as the burden of investor requests would always fall to the same teammate and inevitably mistakes would happen. With no one being able to check the work, we were never sure if what we were sending was 100% accurate.

Even simple requests would often take hours, if not days, to prepare, given the overhead in communication and our team’s differing timezones relative to San Francisco.

Worse yet, as the queries got more complex, the lack of exploration in the returned data meant that we never seemed to be presenting our business in the best light. Due to data requests taking so long to prepare, the idea of exploring to find a better representation of the data seemed crazy.

How we did it this time

While the team continued growing Unsplash back at home, Steph, Mikael, and I, equipped with all of our company’s data in Looker, traveled weekly between New York and San Francisco to meet with investors.

Our two most used tools quickly became email and Looker, as we’d work to answer investor follow-ups as quickly as possible.

We had a goal of replying to every investor email within a few hours to keep the momentum going.

Neither Steph nor Mikael are particularly technical, and I, despite being an engineer, can barely write SQL (hello ActiveRecord ✌️). Despite this, we were able to answer almost every investor question within our target reply time and without disturbing our team.

Better yet, in meetings, as Mikael would bring up a slide with one of our growth charts, investors would often inquire about a particular segment or cohort. Steph or I would quickly bring up the chart in Looker with the answer to the investor’s question and send it back to Mikael to show on the keynote.