Large Australian Supermarket

A large Australian supermarket group wanted to refine its digital infrastructure and use of available store, sales and product data to increase revenue and ultimately profitability.

This included utilising data analytics to optimise product mix, inform customer marketing, understand customer demand based on geography and channel, understand foot traffic flow to its stores, and improving the accuracy of product sales forecasting.

Our work

Context

Customer and operational data combined provides access to a rich and valuable source of new insights on ways to optimise operations, including purchasing decisions, management of supply and distribution channels, and understanding of customer decisioning.

In retail operations, the ability to accurately anticipate the volume and characteristics of customers to a store, and have the right product available when and where they need it, is a source of new competitive advantage and operational efficiency.

Project

Maltem is a trusted partner across multiple projects supporting the retailer in enhancing the accuracy and effectiveness of its data analytics.   Recent examples include:

  • Development and deployment of a new forecasting engine, using custom machine learning algorithms to more accurately predict sales demand and inform supply.
  • Development of a new dashboard to provide near real-time estimates of expected customer foot traffic to a particular store, to support management of in-store capacity limits and staffing levels during the Covid-19 pandemic.
  • Development of a new segmentation model to inform marketing decisioning, utilising available sales data to segment customer groups into high, medium and low categories based on their predicted potential to purchase a product line. The new model improved purchase prediction accuracy by more than 10%.

Related references