Specialty
Python has changed what's possible in digital marketing. Work that used to eat up hours in spreadsheets (pulling data together, cross-referencing campaigns, building reports) now runs automatically in minutes. For a marketer, knowing Python doesn't mean becoming a developer. It means having a real competitive edge.
Digital marketing produces a staggering amount of data: impressions, clicks, leads, conversions, cost by channel, user behavior. The problem is that this data lives in different places: Google Ads, Meta Ads, GA4, Search Console, your CRM, and a pile of spreadsheets.
Python is the ideal language for connecting those sources, turning raw data into useful insights, and automating repetitive work. Libraries like Pandas, Requests, Selenium, and Matplotlib make up a powerful toolkit for anyone who works with performance data.
In practice: instead of manually exporting reports from five platforms every week, a Python script does it for you, merges everything into a single DataFrame, and feeds a Power BI dashboard with zero human involvement.
A pipeline that connects to Google Ads, Meta Ads, and TikTok Ads through their APIs, pulls performance data every day, transforms it, and loads it into a database, BigQuery, or Google Sheets for Power BI or Looker Studio. No more manual exports.
Scripts that build automated PDF or Excel reports from multiple data sources and email them to clients every Monday at 8 a.m. sharp, before anyone has finished their first cup of coffee.
Google Ads API, Meta Marketing API, GA4 Data API, Search Console API: all of them have official Python SDKs. With them, you can create, pause, and adjust campaigns programmatically without ever opening the platform interfaces.
Processing lists of tens of thousands of keywords, classifying them by intent, removing duplicates, finding long-tail opportunities, and generating ad groups automatically. Work that would take days in a spreadsheet gets done in minutes.
Automated monitoring of competitors' pricing, ads, and organic rankings. With BeautifulSoup and Selenium, you can collect market data systematically and spot opportunities fast.
Scikit-learn classification models that learn from historical conversion data and score new leads, automatically surfacing the contacts most likely to close so your sales team knows exactly who to call first.
Pulling GA4 data through the API for custom analysis that goes beyond what the native interface offers: custom funnels, user cohorts, multi-touch attribution, and retention analysis.
Automated analysis of search terms reports to find and group irrelevant queries, producing structured negative keyword lists by campaign. Less wasted spend, better traffic quality.
The core libraries I use in marketing and data projects:
Tabular data manipulation and analysis. Think of it as Excel for Python, only far more powerful and fully automatable.
Working with REST APIs. Used to integrate any platform that has an API: Google, Meta, HubSpot, Salesforce, and more.
The official SDK for creating, editing, and pulling Google Ads campaign data programmatically.
Meta's official SDK for the Marketing API: campaigns, ad sets, creatives, and insights.
Pulling GA4 data with Python for custom analysis and integration with other systems.
Machine learning for marketing: lead scoring, customer segmentation, and churn prediction.
Data visualization for exploratory analysis and report-ready charts.
Database integration for storing and querying marketing data in a structured way.
One of the most significant projects was building a complete ETL pipeline that consolidated data from Google Ads, Meta Ads, TikTok Ads, Google Search Console, and GA4 into Power BI dashboards for multiple clients at once.
The pipeline ran on a daily schedule, pulled data from each platform through its API, normalized the metrics into a common schema, stored everything in a database, and refreshed the dashboards automatically. That completely eliminated manual report consolidation and gave every client real-time visibility into their media performance.
The bottom line: hours of manual work saved every week, data that's always up to date, and optimization decisions based on consolidated information instead of siloed platform reports.