A E-commerce company is looking for a business solution to drive their revenue growth and improve their marketing channels' efficiencies. Marketing Mix Modeling is a great data driven solution to answer those questions. A test dataset is created in this project to represent E-commerce sales in dollars, a few major marketing channels' key metrics, spends, as well as a list of macro economic factors (SPI, CPP, etc). In addition, seasonalities and holidays are also included in the dataset.
The company could measure the impact of their marketing activities, and optimize their marketing strategy to drive growth. The insights from MMM can guide the company throughout the journey to reach their business and marketing goals, from SMART goal setting, budgeting, to budget allocation, and ultimately to optimizing ROI across channels through data-driven spend decisions.
The weekly Sales and media spends suggested some correlation between them.
The business is heavily investing in TV ads. Not only is TV consistently on air for most weeks throughout the year, it also dominate the media spending.
Correlation matrix could tell us which media activities are correlated. The highly correlated media channels will be more likely to cause multi-collinearity issue in the model.
Firstly, a baseline is established by measuring organic sales. Those are the sales that would occur without any marketing efforts. It helps isolate the true incremental impact of marketing activities.
91% of revenues were the baseline, while marketing contributed to 9% incremental sales revenue.
Search contributes to a few spikes, while paid social is consistently driving sales weekly.
Social has the highest ROI at $4.5, which means overall for every dollar the business spent on social ads, they generated $4.5 incremental revenue.
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