I spent years running B2B sales and marketing operations — pulling data from five different platforms, building reports in spreadsheets, and presenting numbers to leadership without being sure which campaigns were actually driving revenue.
The moment that changed everything: A Monday morning where I realized the "weekly report" I'd been manually assembling for three hours every Sunday night could be automated with 40 lines of Python. That script ran in 90 seconds. I never assembled that report manually again.
That was the inflection point. I started seeing every marketing bottleneck as a data problem — not a people problem. Attribution gaps, slow reporting, campaign performance questions that took days to answer. Python didn't make me an engineer. It made me a marketer who could actually see what was happening.
What I do now
- Automated marketing reporting Turn weekly manual reports into self-updating dashboards that refresh while you sleep.
- Campaign analytics & attribution Connect spend data to revenue outcomes so you know which channels actually convert.
- Marketing mix modeling (starter kits) Build simple models that show how budget shifts between channels affect outcomes.
- Customer segmentation & RFM analysis Identify your highest-value customers and retention risks without expensive tools.
Why this combination works
Most Python developers don't understand marketing pressure. Most marketers don't speak Python. I sit in the middle — I know what "end of quarter reporting" means, and I can write the script that delivers it in minutes instead of days.
Based in Indonesia, working with teams globally. Background in B2B sales, digital marketing, and marketing operations. Technical stack: Python, pandas, DuckDB, Streamlit, and whatever gets the insight across fastest.
Stuck with manual reports or unclear campaign data?
I work with marketing teams who are tired of spreadsheet gymnastics. If you're spending more time pulling data than acting on it — let's talk.
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