Annual Performance Forecasting

Annual Performance Forecasting

An annual deliverable to predict website traffic and set performance targets for the upcoming year using a multilinear regression model on historical performance.

Annual Performance Forecasting
Calgary, AB
2 Months
Project Type
Supervised Machine Learning - Multilinear Regression


Forecasting website performance is a reoccurring deliverable we provide to clients. Therefore, understanding the history behind what was tested, what worked and what didn't was crucial. Each year we analyze what would be a reasonable scope to improve the data science model used.

In FY19, the following was used:

  • Multilinear regression model used for primary website traffic
  • FY18 rates for secondary visit types were applied on predicted visit traffic

For the FY20 models, we needed to consider the limitations on context in the data as well as forecasting primary and secondary KPIs that were missed in the FY19 model.


  • Mutlilinear regression model continued to be used for primary website traffic. This model approach was also applied to secondary visit types.
  • For paid media budgets we considered Inflation rate
  • Correlation and p-value tested conducted for model accuracy
  • Normality, linearity and homoscedascity were evaluated for model stability

FY2020 IUSA Forecasting Model Presentation
FY2020 IUSA Forecasting Model Presentation


  • Improved primary visit RMSE by -10% and r-squared by +6%
  • Improved secondary visit type 1 RMSE  -10% and establishing an r-square of 0.68 as this was the first year building a multilinear regression model for this KPI.
  • Improved secondary visit type 2 RMSE -10% and establishing an r-square of 0.70 as this was the first year building a multilinear regression model for this KPI.

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