Customer Purchase Trends Analysis

I need a data-savvy mind to turn raw customer transaction records into clear, actionable insights on how, when, and why people buy from us. The goal is simple: uncover purchase-trend patterns so I can sharpen product planning, inventory, and targeted promotions.

You will receive a clean CSV export containing order history, customer IDs, dates, SKUs, prices, and basic demographics. Feel free to pull the data into Python (Pandas, NumPy), R, SQL, or even Power BI/Excel—whatever lets you slice cohorts, spot seasonality, highlight high-value segments, and surface cross-sell opportunities. If in the process you see signals around website interaction or product preferences, mention them; my priority, though, is rigorous purchase-trend analysis.

Deliverables
• A brief methodology summary (tools, key steps, any assumptions)
• Visual dashboards and/or charts that illustrate core trends—repeat purchase cadence, average order value shifts, top-selling categories over time, emerging SKU clusters, and churn indicators
• A concise written report translating the visuals into plain-language takeaways and next-step recommendations
• Source files or notebooks so I can reproduce the work later

Acceptance criteria: findings must be reproducible from the dataset provided, visuals should be interpretable at a glance, and recommendations need to tie directly back to the quantitative evidence you surface.

If this sounds like the kind of puzzle you enjoy cracking, let’s dive into the data.