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A national restaurant chain was exploring new locations for expansion and had identified a potential site in a regional city.
As part of their business case, they engaged Marketview to provide a data driven estimate of revenue that could be expected once the site was operational, including a forecast for next 5 years.
Marketview’s data science team used historical spend data to quantify spend at similar venues in the area, and the residential origin of the cardholder to create a catchment. Spend originating from outside of the catchment was designated as passing trade and a benchmark for its value was determined.
Similar stores on the company’s network were also analysed and these trading patterns were incorporated into a model, along with aggregated competitor data and passing trade projections based on traffic flow data for the site.
The modelling benefitted from similar projects conducted previously. This inherent continually improving methodology produced a range of forecasts which the client found to be credible and achievable, and aligned with their own calculations.
The new store was commissioned and since opening, has been trading broadly in line but slightly above the forecasts that Marketview’s model predicted. The feedback loop the actual new store data provides is invaluable in refining the methodology for similar projects in the future, and for incorporating techniques such as machine learning to maximise the effectiveness of the process.