Minimum Viable Product Set
Data Analysis / Product Strategy
A data-driven analysis that reduced a complex catalogue of nearly 6,000 SKUs into a focused selection of 20 essential products — a strategic approach to launching lean, minimising risk and maximising recurring revenue potential.
- Year
- 2024
- Role
- Data analyst
- Source
- Public repository
Technologies
- Python
- Pandas
- Data Analysis
- Jupyter
- Product Strategy
The problem
Launching with 6,000 SKUs means 6,000 ways to tie up capital. The commercial question — which handful of products actually carries the offering — is a data problem disguised as a merchandising one.
The solution
Analyse the catalogue on its own numbers: coverage, overlap and recurring-revenue potential, then narrow to the smallest set that still serves the demand. The output is a defensible shortlist, not an opinion.
Architecture
How it is put together.
Catalogue profiling
The full SKU set is profiled to understand structure, overlap and distribution.
Criteria definition
Selection criteria are defined around coverage and recurring revenue potential.
Reduction
The catalogue is narrowed iteratively against those criteria.
Validation
The resulting set is checked to confirm it still covers the intended demand.
Key features
What it actually does.
- Full profiling of a ~6,000 SKU catalogue
- Explicit, documented selection criteria
- Reduction to a 20-product launch set
- Coverage validation of the final selection
Engineering challenges
The parts that were hard.
Defining "essential"
The analysis is only as good as the criteria. Making them explicit is what turns a judgement call into a repeatable method.
Outcomes
What came out of it.
Qualitative outcomes only — no invented benchmarks or metrics.
A focused 20-product launch set from ~6,000 SKUs
A documented, repeatable selection method
Reduced launch risk and inventory exposure
Next project
Azure Data Engineering Full-Stack Project
A full-stack Azure data project spanning ingestion, processing, storage and analytics.
Let’s build
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