An annual deliverable to predict website traffic and set performance targets
Data Engineering, Data Science, Data Analysis
Python, R, Adobe Analytics
- Critical Mass
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
- Improved primary visit RMSE by (122,717 vs 128,367)% and r-squared by (0.72 vs 0.66)
- Improved secondary visit type 1 RMSE (128,524 vs 117,020)% and r-squared by (0.68)
- Improved secondary visit type 2 RMSE (314,578 vs 63,599)% and r-squared by (0.70)
- How to apply a multilinear regression in R
- Check for model accuracy
- Check for model stability