Advertising adstock is a term used to measure the memory effect of advertising carried over from start of advertising. For example, if a company advertises at a certain level in week 1, week 2 will have a portion of week 1 level. Week 3, in turn, will have a portion of week 2 level. In other words, adstock is a percentage term that measures the decaying effect of advertising throughout the weeks.
The term that comes up often in response models where we try to measure the effect of advertising on sales or on purchase intent. The models are usually regression based but are often published under names like Marketing Mix Models (MMM), Marketing Mix Optimization (MMO), Network-Effects and Hierarchical models.
The theory behind adstock is that marketing exposures build awareness in consumers’ minds. That awareness doesn’t disappear right after the consumers see the ad but rather remains in their memory. Memory decays over the weeks and hence the decay portion of adstock.
The formula for advertising adstock is At = Xt + adstock rate * At-1.
The files below show a simple implementation of advertising adstock transformation:
• Excel Adstock Transformation
• SAS Adstock Transformation
• R Adstock Transformation
• Python Adstock Transformation – coming soon