Order frequency reveals recurring weekly shopping windows.
Customer analytics / Recommendation framing
Instacart Behaviour & Item Recommendation
Shopping data contains more than a list of products. Order timing, basket composition and reorder behaviour reveal routines that can make recommendations more useful and less generic.
- Role
- Data Analyst
- Tools
- Python, Pandas, exploratory visualization
- Focus
- Order patterns and repeat behaviour
- Output
- Recommendation opportunities
The problem
Understand when, what and why customers reorder.
The project explored Instacart order histories to understand recurring shopping patterns and identify signals that could support item recommendations. Rather than treating every basket as independent, the analysis considered customer routines, time between orders and products frequently bought again.
The goal was not to claim a production recommender from exploratory work, but to define the evidence and product logic needed to build one responsibly.
Behavioural signals
Recommendations become stronger when context is included.
Time between orders provides a replenishment signal.
Products bought together create cross-sell candidates.
Method
Move from exploration to recommendation logic.
- Profile ordering behaviourExplored order volume by day and hour to identify the strongest shopping periods.
- Measure repeat behaviourCompared reorder rates and the interval between purchases to distinguish routine items.
- Study basket relationshipsLooked for products and categories that repeatedly appeared together.
- Frame recommendation momentsSeparated replenishment, complementary-item and next-basket opportunities.
Product layer
Different signals support different recommendations.
| Signal | Recommendation use | Risk to monitor |
|---|---|---|
| High reorder rate | Remind a customer about likely replenishment items. | Avoid recommending an item too soon. |
| Frequent basket pair | Suggest a complementary product in context. | Popularity can hide individual preference. |
| Regular order timing | Choose a more useful recommendation window. | Routines can change over time. |
Outcome
An analytical foundation for a more relevant product experience.
The work translated exploratory patterns into three clear recommendation modes: replenish, complement and prepare the next basket. A production version would validate each mode through offline ranking metrics and controlled customer experiments.
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