Do you need customer purchase data from the last year but don’t have it structured or accessible?
I’ve built a custom data extraction tool that solves exactly this problem.

I am a data extraction specialist with hands-on experience in Python, SQL, Excel, and Power BI. I have developed a proprietary tool that can automatically extract customer-level purchasing data for the past 12 months from various sources – including websites, internal databases, PDF reports, or CSV exports.

🔧 What my tool does:
✅ Extracts customer names, IDs, purchase dates, product categories, quantities, unit prices, and total spend

✅ Filters data specifically for the last calendar year or trailing 12 months

✅ Handles messy, inconsistent, or incomplete source data

✅ Outputs clean, structured files in Excel (XLSX), CSV, or JSON

✅ Ready for analysis in Power BI, SQL databases, or Python

📊 What you get (depending on your needs):
Full customer purchase history (last year)

Cleaned and deduplicated dataset

Basic summary statistics (total revenue, top customers, monthly trends)

Optional: Interactive dashboard in Power BI showing purchase patterns, repeat customers, seasonality, etc.

💼 Example use cases:
E‑commerce stores wanting to analyze last year’s customer buying behavior

Retailers migrating to a new CRM or ERP

Marketers preparing customer segmentation for campaigns

Financial analysts needing historical transaction data

🚀 Why choose me?
Proven tool already built – no lengthy R&D or trial/error

Fast turnaround (typically 1–3 days depending on data volume)

Clean, documented data ready for your business insights

I also offer follow‑up analysis: EDA, statistical summaries, and Power BI dashboards

📌 To get started:
Please share:

Source of the customer purchase data (website URL, database access, file format, etc.)

Approximate number of records or customers

Any specific columns or metrics you need

I’ll provide a fixed quote and sample output format before starting.

Let me extract your last year’s customer purchase data – fast, accurately, and affordably.