Jumpshot has a large datastream of web browser activities of an international panel. To produce accurate estimates of aggregate metrics, we need to calibrate from our data, a fraction of the actual web traffic, to its true value for any particular country, platform, domain and day. For precise calibration, correction of bias of our panelists is inevitable task that traditionally requires knowledge about both panelists and the whole population, which is not available in our case. Popular approach is to utilize sophisticated learning methods that produce phenomenological models with high accuracy but low interpretability. Contrary to that, our story shows that simpler but mathematically sound microscopical model can outperform such models and provide more clarity at the same time.