Independent portfolio case study
Case study / Conversion Analytics
E-commerce Funnel Analysis
Business question
Where do users drop before purchase?
- View users
- 3,022,130
- View-to-cart rate
- 11.14%
- Cart-to-purchase rate
- 58.35%
Dataset disclosureExternal event-level e-commerce data used for analytical demonstration. The full dataset is excluded from the repository because of its size; a sample, aggregated outputs and methodology documentation are provided. This is not a client engagement.
Executive decision brief
The decision, before the documentation.
A concise chain from the commercial question to the recommended action.
- Question
Where do users drop before purchase?
- Observed signal
View-to-cart bottleneck: 11.14%.
- View-to-cart rate
- 11.14%
- Total conversion rate
- 6.50%
- Interpretation
Only 11.14% of viewers add to cart, while 58.35% of cart users complete a purchase. The primary conversion bottleneck is therefore product view → add-to-cart, not checkout.
- Recommended decision
A/B test product page CTAs and add-to-cart visibility
Primary evidence
Inspect the analytical exhibit.
The dashboard is presented as reviewable evidence, with the full analytical context preserved.
Funnel Analysis / primary analytical output
Funnel dashboard — conversion & drop-off by stage
External event-level e-commerce data used for analytical demonstration. The full dataset is excluded from the repository because of its size; a sample, aggregated outputs and methodology documentation are provided. This is not a client engagement.Analysis and diagnosis
From signal to commercial meaning.
This independent case study examines where users abandon an e-commerce purchase journey and quantifies conversion drop-offs at each funnel stage.
Strict user funnel
Cart-to-purchase rate: 58.35%- 01Funnel entry
View users
3,022,130 - 02View-to-cart rate: 11.14%
Cart users
336,718 - 03Total conversion rate: 6.50%
Purchase users
196,474
Only 11.14% of viewers add to cart, while 58.35% of cart users complete a purchase. The primary conversion bottleneck is therefore product view → add-to-cart, not checkout.
Finding / interpretation
Only 11.14% of viewers add to cart, while 58.35% of cart users complete a purchase. The primary conversion bottleneck is therefore product view → add-to-cart, not checkout.
Decision layer
Recommended business action
Recommendations follow the evidence in this independent case study; no tested uplift is implied.- 01
A/B test product page CTAs and add-to-cart visibility
- 02
Analyze product categories with highest view-to-cart drop-off
- 03
Implement retargeting for high-intent viewers who did not add to cart
- 04
Track view-to-cart rate as a core conversion KPI alongside purchase rate
Method and quality
How the conclusion was built.
The technical record stays inspectable without displacing the business question.
Methodology
- 01
Defined funnel stages: view → cart → purchase
- 02
Used SQL CTEs to calculate unique users, conversion rates and drop-offs
- 03
Analyzed 3,022,130 view users with DuckDB for performant aggregation
- 04
Built a Tableau funnel dashboard for stakeholder reporting
- 05
Isolated the view-to-cart step as the primary friction point
Quality controls
- 01
Strict time-ordered user-level funnel logic in SQL
- 02
Data quality checks and Tableau-ready exports
- 03
Tableau workbook and dashboard screenshot
- 04
Sample data and documented methodology
Tools in service of the question
- SQL / DuckDB
- Tableau
- Python
Transparency record
What this work does—and does not—claim.
Dataset origin, ownership and material limitations remain part of the main narrative.
- 01Project type
- Independent portfolio case study
- 02Ownership
- Individual end-to-end project
- 03Dataset origin and boundary
- External event-level e-commerce data used for analytical demonstration. The full dataset is excluded from the repository because of its size; a sample, aggregated outputs and methodology documentation are provided. This is not a client engagement.
Evidence handoff
Inspect the work.
Open the underlying repository, methodology and analytical artifacts.
- 01Strict funnel SQL(opens in a new tab)
Time-ordered user-level logic for first view, first cart after view and first purchase after cart.
- 02Data quality checks(opens in a new tab)
SQL checks covering row counts, event types, null identifiers, date range and funnel-ready records.
- 03Methodology(opens in a new tab)
Documented funnel definitions, sequencing rules, formulas and segment-analysis principles.
- 04Sample event data(opens in a new tab)
Reviewable CSV sample showing the event schema used by the executable demonstration pipeline.
- 05Tableau workbook(opens in a new tab)
The Tableau workbook connected to the project’s generated analytical outputs.
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