Optimisation of Product Placement for a major Aussie Supermarket - Helium

OVERVIEW

A leading Australian supermarket engaged Omnia to design a scalable, data-driven process for optimising fruit and vegetable product placement across their national store fleet. Inconsistent layouts, stock loss and fluctuating availability were costing the business — and the customer experience.

Client Story

Strategy and Priorities:

Build a single, data-led approach to product placement — replacing manual, store-by-store decision-making with a consistent, scalable model.

Modernize the retail banking experience to retain younger demographics and increase daily app engagement.

Digital Priorities:

Apply applied data science and category strategy to generate a placement model that store teams could actually use.

Shift from a purely transactional utility to a proactive financial advisory tool.

The Challenge:

Fragmented, manually managed layouts across 1,000+ stores driving stock loss, inconsistent customer experience and operational inefficiency.

Our Collaboration:

Omnia embedded across Category, Operations and Strategy — iterating rapidly from concept to prototype to national rollout over two years.

We partnered with the bank’s digital and data science teams to design and validate an AI-driven homepage that anticipates user needs and provides tailored financial guidance.

CONTEXT

The Challenge

Store teams were manually arranging sections on a weekly basis — producing inconsistent layouts, stock loss and fluctuating availability across a fleet of more than 1,000 locations. Without a shared model or data-driven approach, there was no scalable way to improve performance at the category level, or to replicate what was working in the best-performing stores.

OUR EXPERTISE

Omnia partnered with the client to design a product placement and space model built on category data and applied data science. We worked directly with Category, Operations and Strategy stakeholders — moving quickly from concept through prototyping to a nationally deployable solution.

How we helped

The Solution

Conceptual design

Multi-source pipeline development

Reviewed available data on manual placement patterns and business performance to establish a baseline — and define what a better model would need to solve.

Built robust data ingestion pipelines connecting cloud platforms, CRM databases and legacy batch systems — standardising ETL/ELT processes across the organisation.

Prototyping

Cloud data warehouse

Worked in a fast-moving, cross-functional way with Category, Operations and Strategy teams to rapidly iterate the model using applied data science.

Designed and deployed a scalable AWS and Snowflake architecture capable of processing billions of annual transactions — with optimised storage and compute separation for cost efficiency.

Small-scale testing

Data model harmonisation

Rolled out to a pilot store sample. Iterated based on real-world results — improving the model and building confidence before broader deployment.

Developed a high-fidelity mobile prototype featuring predictive ML spending insights, Gen-AI chat support, and dynamic UI modules.

National roll-out

Secure data sharing

Following successful testing, delivered a broader national business case and supported rollout to the full store fleet across FY25.

Validated the AI-driven features through extensive qualitative user testing, ensuring the algorithms felt helpful rather than intrusive.

IMPACT

A national standard for product placement.

2 years

From concept to national deployment

1,000+

Stores reached in FY25 rollout

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