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The Role Forward

Joel Blachman on Getting Off the Ad Hoc Analysis Treadmill

In this episode of The Role Forward, Joel Blachman, an operations and finance lead at Amper Technologies, gets into the importance of automation tools while doing ad hoc analysis and getting more proactive with analysis so you aren't constantly behind requests for numbers.

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Episode Summary

Ad hoc analysis is often used when there is a need to investigate a particular issue or answer a specific question, and it can be a powerful tool for gaining insights into data quickly and efficiently. 

But ad hoc analysis can be time-consuming and challenging without automation, especially if you are dealing with large datasets. 

In this episode of The Role Forward, Joel Blachman, an operations and finance lead at Amper Technologies, gets into the importance of automation tools while doing ad hoc analysis and answering ad hoc requests. Joel and our host Joe Michalowski discuss the ad hoc analysis treadmill, the challenges of responding to last-minute requests, and how Amper Technologies raised their Series A.

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Featured Guest

Joel Blachman

Operations and Finance Lead, Amper Technologies

Joel Blachman has been working as an operations and finance lead at Amper Technologies for four years. While studying industrial engineering at Northwestern University, Joel was part of their startup incubator, The Garage, and worked at different manufacturing technology startups. During a research project, Joel came across Amper Technologies, founded out of The Garage in 2016. He started interning there one summer, stuck around throughout school, and upon graduation, joined full-time in a finance and operations role.

Key Themes from the Episode
  • The challenges of investor due diligence isn't the specific metrics they're looking for. SaaS metrics like ARR, retention, burn multiple, LTV:CAC, etc. are always key. You get bogged down in constant ad hoc analysis because investors want different granular views of those metrics.
  • Startups have to make rapid decisions all the time and need instant access to data, but being able to answer those ad hoc requests quickly is tough if you don't have any automation in place or analysis tools beyond Excel.
  • Financial and accounting departments are intertwined, so it's essential they have a close relationship. But these departments also need to know how to collaborate with other departments, especially sales. When departments align on the same goals and find a common language, workflows run smoother and problems are solved quickly.

Episode Highlights from Joel Blachman

23:51— Challenges Responding to Last-Minute Requests

“One aspect is certainly just being able to manage that at a very rapid pace. Obviously, around month-end close, my time is spent on accounting, so being able to switch out of that and still make sure I have the numbers updated on time when they’re needed. That’s certainly one challenge, but an even bigger challenge is being able to do that analysis. So, for example, our CEO might just ask something simple, like, ‘What is our average deal size over the last six months?’ Maybe we are trying out a new pilot program that we launched; we want to see how that was working. I can generally provide that answer pretty quickly, but that’s not enough. I have to understand why I am being asked this question. So, in the case of what is our average deal size, if we’re looking at marketing budgets and what we should plan for over there, I want to go one level further.”

30:53 — Automate Data Collection

“In preparing all those materials during the Series A, process was a huge lift to get the initial data room put together in the beginning, but every month — the process continued to drag on rolling forward, countless spreadsheets, all of the financial models and the SaaS metrics, and all of that data — it was taking so much time every month, and even every week. If we had a big deal close, we were trying to get that data as close to real-time as possible, and there’s just no way to do that while ensuring you have accurate data. So, that was definitely the breaking point for us, and I knew that we had to turn to technology to be able to automate some of the data collection.”

32:47 — Lessons Learned from Raising a Series A

“There are two major changes I would make going through that process again. The first would be all of the data needs to be structured in such a way that you can get as granular as possible. Some investors wanted to look at data monthly or quarterly, but if you construct a report quarterly and you don’t actually have, for example, a point-in-time metric — like ARR at the end of a quarter — as soon as that investor says, ‘Oh, okay, that’s cool, now can you show it to me monthly?’ If you don’t already have it ready to go, you have to either go reconstruct that from another spreadsheet or sometimes go through all the source systems, contracts, et cetera. So getting as granular as possible with the data allows you to answer all of those requests much faster. The second change, definitely tech comes into this, but making that data self-serve is so important. “

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