For retail network planning analysts, evaluating potential store locations is a complex puzzle. They must balance current operations against future opportunities, protect existing stores from cannibalisation, and build compelling business cases for major capital investments.
At the heart of this analysis lies a critical equation:
Future Sales = Average Spend per Household × Number of Future Households
While loyalty programs and transaction data provide reliable household spend figures, forecasting the number of future households (the population multiplier) requires specialist demographic forecasting that most organisations can't maintain in-house. This is where .id's National Forecasting Program becomes invaluable.
Trusting the population multiplier
Network planning analysts combine multiple data sources to evaluate locations:
- Transaction data showing spend per household
- Drive-time analysis of catchment areas
- Housing density thresholds
- Competitor locations
- Population forecasts from .id's National Forecasting Program
While all these factors matter, the household forecast acts as a multiplier in the analysis.
Think of it this way: if your household count is off by 10%, your entire revenue projection shifts by that same magnitude - even if you've perfectly calculated average spend, drive times, and competitor impacts. This is why analysts and their stakeholders need confidence in how the forecast was developed - it's the number that amplifies everything else in the model.
From analysis to action
A typical workflow shows how analysts turn these insights into action:
- The property team identifies a potential location
- Analysts overlay transaction data with population forecasts from .id
- They calculate future households within drive-time radiuses
- Sales forecasts are generated using the population multiplier
- Results inform negotiations with developers and property teams
- Finance teams use the analysis for investment decisions
Solving the technical challenges
Maintaining demographic forecasts in-house presents several challenges:
- Managing spatial data across different jurisdictions
- Tracking policy changes affecting development
- Interpreting constant changes in demographic trends
- Ensuring consistency nationwide
By using .id's National Forecasting Program, analysts can:
- Access consistent SA1-level data (and smaller in growth areas) across Australia
- Generate forecasts for custom catchments that neatly fit drive-time catchments
- Receive regular updates from .id's specialist demographers to help them understand the impact of new information and changing conditions
- Focus on analysis rather than data maintenance
Building stakeholder confidence
The forecasts support multiple stakeholders:
Network Planning Teams:
- Evaluate new store viability
- Protect existing store performance
- Optimise network coverage
Property Teams:
- Negotiate with developers
- Time store openings with development
- Plan parking and infrastructure
Finance Teams:
- Assess investment returns
- Validate business cases
- Manage risk
Strategic planning applications
The data supports decisions across multiple timeframes:
Short-term (1-2 years):
- Immediate property opportunities
- Store renovations
- Capacity planning
Medium-term (2-5 years):
- Network optimisation
- Market penetration strategies
- Competition response
Long-term (5-20 years):
- Growth corridor planning
- Format evolution
- Network capacity
The value of an independent assessment
In our conversations with network planners, they tell us they value having access to a single, credible forecast that represents the most likely future scenario. In particular;
- Our close collaboration with local government provides credibility; they appreciate the consideration of local planners who have an intimate knowledge of local planning controls and development activity
- Property teams value having a trusted external source when negotiating with developers, who often present more optimistic timelines
- When presenting multi-million dollar investment cases to stakeholders, planners appreciate being able to point to forecasts that are widely used across hundreds of organisations, rather than relying solely on internal projections
Conclusion
For retail network planning analysts, reliable population forecasts from .id's National Forecasting Program are the foundation of confident investment decisions. By eliminating the need to maintain complex demographic datasets in-house, analysts can focus on their core strength: turning data into actionable insights that drive network growth and optimisation.
The combination of granular geography, nationwide consistency, and regular updates enables analysts to rapidly evaluate opportunities and produce trusted sales forecasts. This not only saves significant time but also provides the credibility needed to support multi-million dollar investment decisions.
