EDIT: Image now visisble!
From Anders Sandberg:
Another piece examining predictive performance, this time in the pharmaceutical industry. How well can industry experts predict sales?
You guessed it, not very well. Not even when data really accumulated.
Large pharma has less bias than small companies, but the variance still overshadows everything.
First, most consensus forecasts were wrong, often substantially. And although consensus forecasts improved over time as more information became available, accuracy remained an issue even several years post-launch. More than 60% of the consensus forecasts in our data set were either over or under by more than 40% of the actual peak revenues (Fig. a). Although the overall median of the data set was within 4%, the distribution is wide for both under- and overestimated forecasts. Furthermore, a significant number of consensus forecasts were overly optimistic by more than 160% of the actual peak revenues of the product.
The unanswered question in this analysis is what companies and investors ought to be doing to forecast better. We do not offer a complete answer here, but we have thoughts based on our analysis.
Beware the wisdom of the crowd. The 'consensus' consists of well-compensated, focused professionals who have many years of experience, and we have shown that the consensus is often wrong. There should be no comfort in having one's own forecast being close to the consensus, particularly when millions or billions of dollars are on the line in an investment decision or acquisition situation.
Broaden the aperture on what the future could look like, and rapidly adapt to new information. Much of the divergence between a forecast and what actually happens is due to the emergence of a scenario that no one foresaw: a new competitor, unfavourable clinical data or a more restrictive regulatory environment. Companies need to fight their own inertia and the tendency to make only incremental shifts in forecasting and resourcing.
Try to improve. It appears that some companies and analysts may be better at forecasting than others (see Supplementary information S1 (box)). We suspect there is no magic bullet to improving the accuracy of forecasts, but the first step is conducting a self-assessment and recognizing that there may be a capability issue that needs to be addressed.