** Web Link of the APP online: https://share.streamlit.io/marcello-calabrese/media_mix_model_webapp_pycaret/main/app.py

• This app predicts sales on media mix spend for different media channels.
• Before the prediction, we apply a saturated spending function to the marketing spend vector.
• The machine learning model used is the ExtraTreeRegression model.

• tv_sponsorships
• tv_cricket
• tv_RON
• radio
• NPP
• Magazines
• OOH
• Social
• Programmatic
• Display_Rest
• Search
• Native

Dataset file of unseen data in the repository: unseen_data.csv

Machine Learning Package Used: Pycaret, link: https://pycaret.org/

We assume that the more money you spend on advertising, the higher your sales get. However, the increase gets weaker the more we spend. For example, increasing the TV spends from 0 € to 100,000 € increases our sales a lot, but increasing it from 100,000,000 € to 100,100,000 € does not do that much anymore. This is called a saturation effector theeffect of diminishing returns.

Increasing the amount of advertising increases the percent of the audience reached by the advertising, hence increases demand, but a linear increase in the advertising exposure doesn’t have a similar linear effect on demand.

Typically each incremental amount of advertising causes a progressively lesser effect on demand increase. This is advertising saturation. Usually Digital display ads and digital advertising in general have a high saturation effect, meanwhile TV, Radio have a low saturation effect.