This is a precis of a research paper entitled ‘The Decomposition of Promotional Response: An Empirical Generalisation’ by David R. Bell, Jeongwen Chiang and V. Padmanabhan, published by Marketing Science.

This very interesting paper based on research conducted in 1999, studies 173 brands in 13 categories in the USA over a 52 week period, based on the household expenditure of 250 families and 3 stores. The overall conclusion is that the percentage of promotional lift attributable to brand switching is an average of 75%, which is somewhat below previous studies. In 1988 Gupta measured this at 84%, and A.C.Nielsen more recently (precise date unknown, but published in 1996) at 80%.

Of even more interest is the category by category variance. Categories were selected to encompass both those that are known to expand consumption, as well as those that are ‘storable’ or ‘necessities’.The balance of the lift not attributable to brand switching was also analysed into the two components of category expansion (genuine additional incremental volume) and accelerated purchase (reduction in expected inter-purchase interval). A determination was also made of the relative impact of brand factors, category factors and consumer factors to identify which is the most significant. All in all, they are able to explain 70% of the actual promotional response. One of the significant findings is that category effects are more significant than brand effects, and consumer effects (i.e. demographics) are quite minimal.

Download the full Composition of incremental lift Case Study PDF

Promax PX fully supports buying rules and patterns. It is also possible to load similar products as part of a multi-causal model to measure cannibalization.

The Promax PX sales Gantt report & Gantt view clearly shows promotional clashes.

Promax PX allows the user to define customers for which they need to avoid clashing promotions. For example, define Customer 1 & 2 as clash customers for Customer 3, the user will be notified of a clash when they save or confirm a promotion.

Shown above is an example of a Promax analytics report configured for a Promax customer that leverage’s the wealth of information that can be stored in the Promax system. These reports can be generated at any level of the product and customer hierarchy. In this instance the client chose to report the information via the Promax analytics OLAP cube and Microsoft Excel pivot tables. You will note in this report there are an extensive breakdown of discount categories and costs that have been included in this client’s consideration of the Promax system. This report can be developed for any product/customer combination. It should be noted however that this is not a standard reports from the Promax system but one that was prepared through the configuration of the Promax analytics OLAP cube. These configurations service can be provided by Promax consultants.