As a participant in the Innovation Track of Meta APAC Robyn Hackathon 2022, we aim to propose some valuable improvements in the use of Robyn, an open-sourced Marketing Mix Modeling (MMM) R-package developed by the Meta Marketing Science team. It is worth noting that our proposal comes mostly from several months of field experience employing Robyn. It appears from our experience that, so far, there exists a little gap between modeling and practice.
To understand the impact of various marketing activities on business KPIs, Robyn helps the analyst to pick the best marketing mix model and, further, recommends the corresponding optimal budget allocation. On the one hand, Robyn significantly lowers the entry barrier to building a model that is aligned with the analyst’s insights. However, on the other hand, it is challenging for the analyst to trust how accurate the estimation results might be.
Nevertheless, there is still a lack of details regarding the validation procedure and what cautions are needed in interpreting the results. To this end, we attempt to provide a battery of reasonable guidelines for validating models generated by Robyn. In addition, we highlight some parts of conventional implementations that might make analysts confused about interpreting and validating the estimation results. Solutions for those problems are provided as alternative functions and equations. Finally, we propose some additional insights that can be derived from the models and define some convenient functions that would improve the usability of Robyn.
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