Forecasting promotions is notoriously difficult. It involves data challenges, process challenges and optimization challenges. As promotions are present everywhere in the retail sector, they have been a long-term concern for Lokad.
However, while nearly every single retailer has its share of promotions, and while nearly every forecasting vendor claims to provide full support for handling promotions, the reality is that nearly all forecasting solutions out there are far from being satisfying in this regard. Worse still, our experience indicates that most of such solutions actually achieve poorer results , as far as forecasting accuracy is concerned, than if they were to use the naive approach which consists of simply ignoring promotions altogether.
What make promotions so challenging is that the degree of uncertainty that is routinely observed when working with promotions. From the classic forecasting perspective, which only considers the mean or median future demand, this extra uncertainty is very damaging to the forecasting process . In fact, the numerical outputs of such forecasting solutions are so unreliable that they do not provide any reasonable options for using their figures for optimizing the supply chain.
Yet, at Lokad, over the years, we have become quite good at dealing with uncertain futures. In particular, with our 4th generation probabilistic forecasting engine, we now have the technology that is completely geared towards the precise quantification of very uncertain situations. The probabilistic viewpoint does not make the uncertainty go away, however, instead of dismissing the case entirely, it provides a precise quantitative analysis of the extent of this uncertainty.
Our probabilistic forecasting engine has recently been upgraded to be able to natively support promotions. When promotional data is provided to Lokad, we expect both past and future promotions to be flagged as such. Past promotions are used to assess the quantitative uplift, as well as to correctly factor in the demand distortions introduced by the promotions themselves. Future promotions are used to anticipate the demand uplift and adjust the forecasts accordingly.
Unlike most classic forecasting solutions, our forecasting engine does not expect the historical data to be “cleaned” of the promotional spikes in any way. Indeed, no one will ever know for sure what would have happened if a promotion had not taken place.
Finally, regardless of the amount of machine learning and advanced statistical efforts that Lokad is capable of delivering in order to forecast promotions, careful data preparation remains as critical as ever. End-to-end promotion forecasts are fully supported as part of our inventory optimization as a service package.