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Overview
Goal: Segment Instacart customers and surface behaviors that improve retention, campaign targeting, and lifetime value.
Role: Data Analyst
Timeline: 2 weeks
Tools: Python, Excel, Tableau
Process
Research
- Investigated order timing, spend patterns, and department preferences.
- Compared loyal, new, and regular cohorts to quantify value drivers.
Design / Implementation
- Cleaned and merged large Instacart datasets; resolved missing values and duplicates.
- Engineered features (loyalty flags, spend tiers) and aggregated KPIs per segment.
- Built dashboards to visualize behavioral patterns and customer profiles.
Testing / Results
- Spending spikes during early mornings (4–5 a.m.) and evenings (8–10 p.m.); weekends showed higher pricing.
- Top departments: Produce, Dairy & Eggs, and Snacks.
- Regular customers form the largest cohort, while loyal customers drive a disproportionate share of revenue.
Outcome
Recommended timing-based promotions, loyalty upgrades for regular customers, and category-focused campaigns to lift retention and LTV.