Net Conversion uses predictive signals and external factors to create a custom reporting tool for a theme park client.
With three years of historical data paired with multiple external factors, we wanted to create a tool to generate extremely accurate revenue forecasts beyond using simple year over year performance.
To accurately predict online sales at the individual DMA level.
Because our model forecasts the next six weeks and our projections typically change less than 0.1%-0.5% each week, we allow our client time to plan for soft periods. We’re also able to use the model as a media planning tool to reallocate marketing budget, as it lets us identify markets that show need for upcoming periods.
Additionally, beyond a more accurate revenue forecast, we’re able to compare the accuracy of previous forecasts against current state to continually monitor and improve the model’s success.
We created a daily automated forecast model relying on Python and automated data feeds from Adobe Analytics, Data Studio, Big Query, and our own internal software Conversionomics. Using factors such as public holidays, school holidays, actual sales for historic arrival date, cumulative revenue, seasonality, recent trends, and prior growth, we are able to predict revenue up to six weeks in advance.