Marketing Attribution

Understanding the impact of advertising on individual sales allows for better targeting of campaigns, ad placements and can drive ROI.


Predict if a sales will occur based on what advertising, website interaction and sales interaction occurs.


Did a sales occur?


There are many reasons why a customer will choose to purchase. To try to understand this decision and correctly allocate the sale to the appropriate channels is very challenging. The first channel is just merging all of the data into one place. First, we need to examine all of the prospects or web sessions from the web analytics database. To this, we need to merge the CRM data to identify those sessions that had sales. Further, the CRM data may allow us to identify other interactions with the prospect. Next, the digital ad platform may allow us to identify how many and which ads were viewed and clicked on before the sale. Finally, external demographic data will help better understand the individual and their decision to buy or not. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.

Modeling techniques and libraries

Sales prediction

Build machine learning models using standard techniques (from linear regression to more advanced machine learning algorithms) to predict sales. Using this model, evaluate which campaigns had large impacts on the probability of a sale.

  • Sklearn
  • ELI5
  • LIME
  • SHAP

Related accelerators

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No-code/low-code data prep and visualization