How do you measure the accuracy and reliability of your ad inventory forecasts?

3 How to measure ad inventory forecasting accuracy?

Ad inventory forecasting accuracy is the degree to which the forecasted ad impressions match the actual ad impressions delivered. There are different ways to measure ad inventory forecasting accuracy, but one of the most common methods is to use the mean absolute percentage error (MAPE). MAPE is calculated by dividing the absolute difference between the forecasted and actual ad impressions by the actual ad impressions, and then averaging the results across all time periods and ad units. MAPE can be expressed as a percentage, and the lower the MAPE, the higher the accuracy.

4 How to measure ad inventory forecasting reliability?

Ad inventory forecasting reliability is the degree to which the forecasted ad impressions are consistent and stable over time. Reliability is important because it reflects the confidence and trust that publishers and advertisers have in the forecasting system. One way to measure ad inventory forecasting reliability is to use the standard deviation of the percentage error (SDPE). SDPE is calculated by taking the standard deviation of the percentage difference between the forecasted and actual ad impressions across all time periods and ad units. SDPE can also be expressed as a percentage, and the lower the SDPE, the higher the reliability.

5 How to improve ad inventory forecasting performance?

Ad inventory forecasting performance can be improved by utilizing various techniques and best practices. Historical data and trends should be used to inform the forecasting model, which should be regularly updated to account for seasonality, events, and changes in user behavior. Multiple sources of data and metrics can validate and refine the forecasts, such as traffic analytics, ad server reports, third-party data, and industry benchmarks. Additionally, different scenarios and sensitivity analysis should be used to test the impact of various factors and assumptions on the forecasts. Lastly, feedback and communication should be employed to monitor and adjust the forecasts, as well as share them with relevant stakeholders like sales teams, clients, and partners.

6 What are some of the challenges and limitations of ad inventory forecasting?

Ad inventory forecasting is not an exact science and faces various difficulties and restrictions. These include the quality and availability of data used to feed the forecasting model, which can be incomplete, inaccurate, outdated, or inconsistent. Additionally, the future is unpredictable, with user behavior, market conditions, competition, technology, and regulations all having the potential to cause fluctuations in forecasts. Furthermore, the complexity of the online advertising ecosystem with its multiple players, platforms, formats, and metrics makes it difficult to forecast ad inventory across different dimensions and levels of granularity. This complexity also makes scaling the forecasting system to handle large volumes of data and requests a challenge.