Background
The client, one of the top-tier Australian supermarket chains with a widespread network of stores across the country, had been traditionally reliant on Census data for strategic decision-making and forecasting. However, the evolving dynamics of economic conditions and shifts in the demographic profile of Australia rendered this method increasingly insufficient.
Problem Statement
The supermarket chain needed a more accurate and real-time forecasting methodology that could adequately account for economic changes, such as interest rate fluctuations, and the impacts of mortgage and rental stress on their customer base. This would empower the company to make strategic decisions on store locations, marketing initiatives, and inventory based on the financial health and habits of their customers.
Approach
We suggested a novel approach that involved the use of comprehensive property data to build an agent-based simulation. The goal was to infer the age, income, and mortgage repayments for every household in the country, thereby creating a granular and up-to-date profile of the supermarket's potential customers.
The data was procured from sources including real estate platforms, mortgage brokers, financial institutions, and was supplemented with additional data points such as consumer expenditure collected by the supermarket chain. This extensive dataset was processed, anonymized, and categorised, with households classified into unique demographic and economic groups.
Using this processed data, we developed an agent-based model (ABM). In this simulation, each 'agent' represented a household with inferred demographic and economic characteristics. This allowed us to simulate micro-level economic changes, showing how individual households might respond to shifts in economic conditions, such as increased interest rates, and modelling the subsequent mortgage and rental stress.
Results
The ABM provided the supermarket chain with a robust, dynamic forecasting tool. By simulating increases in interest rates, the team could identify areas most prone to mortgage and rental stress and forecast the probable impact on customer buying behaviours.
For example, the ABM revealed that a 1% rise in interest rates would precipitate significant mortgage stress in certain Melbourne and Sydney suburbs. Armed with this insight, the supermarket could prepare for potential decreases in consumer spending in those areas and adjust their operations proactively.
Moreover, the data was instrumental in pinpointing potential growth opportunities. The agent-based model showed that some regions displayed economic resilience, suggesting areas where the supermarket chain could confidently expand.
Conclusion
Incorporating property data into an agent-based simulation provided the supermarket with a potent, dynamic tool for strategic decision-making. It delivered insights into the economic health of Australian households at an unprecedented level of granularity and precision, enabling the retailer to anticipate changes and adapt swiftly.
This case exemplifies how pioneering data approaches can empower businesses to transition from outdated, static data sources to more dynamic, predictive forecasting methods. The adoption of such methodologies can significantly enhance a business's capacity to respond to economic changes, ensuring their strategies remain future-proof.
