Assessing advertising profitability is relevant for marketing decision making. Marketing Mix Modelling (MMM) is a kind of modelling that applies accumulated lag effects (adstock) and non-linear transformation (saturation) for the estimation of how spending on advertising (adspend) impacts sales. These models have become a very relevant statistical issue within marketing advertising assessment. Robyn by Meta® and Meridian by Google® are two open-source tools widely used by practitioners. However, they have weaknesses and still possibility to be improved. On this work we propose a modelling solution that gathers the Weibull flexibility from Robyn with the theoretically backgrounded joint estimate via MCMC from Meridian. For that we created a simulator of MMM data and implemented our solution estimation through STAN and R. The results are acceptable but far from perfect. We have retrieved main behaviors, but some of them present great uncertainty. Nevertheless, some windows of certainty arise, which allow to marketing insights and data-driven decisions for strategic advertising. This reflects how difficult is the advertising assessment task and encourages us to guide our efforts towards improving Marketing Mix Modelling more.