Chip Category Analysis
This project analyses customer purchasing behaviour within the chip category to support data-driven retail category strategy and decision-making.
To identify key customer segments, understand what drives spending (volume vs price), and translate insights into clear strategic recommendations for category review.
-
Category: Packaged savoury snack products (chips)
-
Customers: Segmented by
- Life Stage
- Premium Affinity (Budget, Mainstream, Premium)
-
Focus Areas:
- Sales contribution by segment
- Purchase volume vs price sensitivity
- Pack size and brand preference
The analysis follows a structured analytics workflow:
-
Data Preparation
- Cleaned and formatted transaction and customer datasets
- Filtered to chip-only products
- Removed extreme outliers to reflect realistic behaviour
-
Feature Engineering
- Extracted pack size and brand from product descriptions
- Merged transactions with customer segmentation data
-
Metrics & Analysis
- Total sales and customer counts
- Average units per customer
- Average price per unit
- Segment-level spend drivers
-
Visualisation & Insight
- Comparative charts across customer segments
- Identification of volume-driven vs price-driven behaviour
- Customer segment performance analysis
- Spend driver breakdown (volume vs price)
- Visualisations supporting strategic recommendations
- A reproducible R Markdown report suitable for stakeholder review
- R
- R Markdown
- tidyverse
- Open the project in RStudio
- Ensure the working directory is set to the project root
- Knit
chip_analysis.Rmdto generate the report (HTML or PDF)
The analysis highlights:
- Which customer segments contribute the most value
- Whether spend is driven by higher prices or higher volumes
- How category strategy can be tailored by segment to improve both sales and margin
This project demonstrates the application of data analytics to retail commercial strategy, combining technical analysis with business interpretation.
Sinta Ahwalisa