Human experts are unable to take into account all the variables involved and also cannot provide an analytic prediction of campaign behavior and trends.
A recent survey by Aberdeen Group showed that 78 percent of companies used Microsoft Excel spreadsheets as their primary trade promotion forecasting technology tool.
The limitations of spreadsheets for trade promotion planning and forecasting include lack of visibility, ineffectiveness and difficulty in tracking deductions.
[7] TPF is complicated by the fact that campaigns are described by both quantitative (such as price and discount) and qualitative (such as display space and support by sales representatives) variables.
One researcher validated the ability of multivariate regression models to forecast the impact on sales of a product of many variables including price, discount, visual merchandizing, etc.
Some companies have begun using machine learning methods to utilize the massive volumes of unstructured and structured data they already hold to better understand these connections and causality.
Starting from a collection of promotional characteristics, IML is able to identify and present in intelligible form existing correlations between relevant attributes and uplift.
This approach is designed to automatically select the most suitable uplift model in order to describe the future impact of a planned promotion.
[12] Groupe Danone used machine learning technology for trade promotion forecasting of a range of fresh products characterized by dynamic demand and short shelf life.