In particular, it can be used in order to assess how much different marketing campaigns have contributed to the change in web search volumes, product sales, brand popularity and other relevant indicators.
Difference-in-differences models[1] and interrupted time series designs[2] are alternatives to this approach.
"In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls.
[1] A possible drawback of the model can be its relatively complicated mathematical underpinning and difficult implementation as a computer program.
However, the programming language R has ready-to-use packages for calculating the BSTS model,[3][4] which do not require strong mathematical background from a researcher.