5 Tricks and Tips for Working with Python in Revenue Operations
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Python Tales (and Tricks)
I remember the exact moment I realized spreadsheets were killing me.
I was working as an investment banker at the time. It was 2 AM. I was staring at an Excel model that was crashing every time I tried to change a single assumption. The file size was bloated. The circular references were a nightmare. I was manually copying and pasting data from three different CSV exports into a “master” tab. I was tired. I was frustrated. And I knew there had to be a better way.
That moment was the catalyst for my transition into data engineering and eventually into the world of Revenue Operations.
Most people in RevOps live in spreadsheets. They are wizards with VLOOKUP and INDEX MATCH. They know how to build pivot tables in their sleep. But there is a ceiling to what you can do with a spreadsheet. Spreadsheets are fragile. They are hard to audit. They do not scale. When you are dealing with a CRM with hundreds of thousands of records or trying to join marketing intent data with sales activity logs, Excel simply breaks.
This is where Python changes the game.
Python is not just for software developers. It is the ultimate tool for the modern Revenue Operations leader. It is the bridge between being a “back-office admin” and a “strategic systems architect.” When you learn Python you stop doing the work yourself and you start building robots to do the work for you. You gain leverage.
In my time as a data engineer I learned that data is messy. In my time as an investor I learned that messy data costs companies millions of dollars in lost revenue. RevOps sits right in the middle of this problem.
If you are ready to leave the spreadsheet behind and build a true revenue engine here are my top five tricks and tips for using Python in Revenue Operations.
The Philosophy: Code as Infrastructure
Before we get to the specific tactics you need to understand the philosophy.
In the old world you solve a problem by opening a spreadsheet and manipulating data manually. You download a report from Salesforce. You download a report from HubSpot. You put them in two tabs. You match them up. You fix the formatting. You make a chart. You email it to the VP of Sales.
Next week the VP of Sales asks for the same report. What do you do? You do it all over again.
This is “manual operations.” It scales linearly with your time. If the company grows 10x you need 10x more people to do this work.
In the Python world you write a script once.
You write code that connects to the Salesforce API. You write code that connects to the HubSpot API. You write logic to clean and merge the data. You write code to generate the chart and email it.
Next week when the VP of Sales asks for the report you press a button.
Better yet you schedule the script to run every Monday morning automatically. You are sleeping while your code provides value. This is “systems thinking.”
Now let’s look at the specific ways you can apply this.