Skip to content
MH
All projects
Data EngineeringAI/ML

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
Minimum Viable Product Set — concept diagram

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.

01

Catalogue profiling

The full SKU set is profiled to understand structure, overlap and distribution.

02

Criteria definition

Selection criteria are defined around coverage and recurring revenue potential.

03

Reduction

The catalogue is narrowed iteratively against those criteria.

04

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.

01

A focused 20-product launch set from ~6,000 SKUs

02

A documented, repeatable selection method

03

Reduced launch risk and inventory exposure

Azure Data Engineering Full-Stack Project — Data Engineering / Cloud

Next project

Azure Data Engineering Full-Stack Project

A full-stack Azure data project spanning ingestion, processing, storage and analytics.

Let’s build

Want something like this for your business?

Tell me the problem and I will come back with an architecture, a scope and a timeline.