Data Science in Marketing – An Efficient Alternative to Attribution Modelling

Driving marketing budget sometimes seems to be a mysterious art where decisions are based on ideas of few enlightened people, who know what’s right. But you should not fool yourself, the times are changing and so is the way successful marketing is managed. The same as in other fields, experienced marketing managers use information hidden in data to help them. With the amount of data and methods available, it is however often tricky not to get lost and be able to distinguish the signal from the noise. Typical examples are the marketing attribution models – a tool that is widely used, but in my experience rarely maximizes the leveraged value of data. 

Typically, in marketing attribution, marketers want to know, which part of the business KPI (typically site visits, sales, new customers, new revenues etc.) result from which marketing activity. Mainstream approach is to use attribution models that are often very simplistic – like single source attribution (last click, first click) or fractional attribution (where the contribution is distributed among multiple touch points given some simple rule). These methods provide marketers with the importance of each marketing channel or campaign in respect to their KPI. Based on this historical information the marketing managers make a decision about how to allocate the marketing budget. This approach however puts a great deal of pressure to tedious and demanding data detective work to make sure all client touch points are measured correctly. More importantly, there is no way of knowing that this work has been done correctly, which of course has significant impact on credibility of the attribution models.

Knowing these difficulties, we decided for an alternative approach. We thought: Why should we dig into the individual touch points? Shouldn’t we rather focus on marketing investments and model the ultimate business output? And that is exactly what we did. We took investments into individual marketing channels in time and used time series analysis to predict our client’s business goal (number of sales). On top of it, we also added seasonality, marketing investment of competitors and some other simple parameters.


“Even though we are using data to drive marketing decisions on a daily basis, most of the tools that we have used up until now focus on describing the past. Recently we decided to work together with aLook Analytics to change that. Thanks to their modelling approach to marketing investments we now have accurate information about the expected future developments as well.

Using the interactive Shiny application that is built in Keboola Connection, we want to make informed decisions on the fly, which will help us to reach our sales goals in the most cost efficient way.”

Daniel Gorol, BNP Paribas Personal Finance SA / Cetelem


One thing that our clients appreciate a lot, especially while getting familiar with models they have never used before, is some degree of interactivity. Thanks to Keboola Connection, we can build custom applications that enable our clients to get the touch of the models, while ensuring minimal cost and high level of data security.

In the case of marketing spend prediction, the application is basically an online predictive scenario tool to see, how are changes in planned investments likely to impact the business target. User simply fits the model to the historical data and then plays around with sliders indicating how to spend the marketing budget. The user can also store the prediction data back to storage in Keboola Connection.

Technically, the application is built in Shiny, which is – thanks to the great work of the Keboola dev team –  seamlessly integrated to Keboola. Prediction output is visualized using interactive charts provided by R package plotly.

How does it work?

The video contains dummy data.

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Apart from solving business problems we value projects with clients and partners, who are open to new things. Big thanks goes to Cetelem for trying this new approach on top of their standard marketing attribution models and being very helpful over countless iterations of the model and Shiny app. Again Keboola Connection showed great flexibility and dedication when supporting our Shiny app deployment over multiple time zones, 24/7. It was not easy given it was the first client project using Shiny within Keboola Connection. Thanks マークさん :).
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One thought on “Data Science in Marketing – An Efficient Alternative to Attribution Modelling

  1. Pingback: Lesk a bída atribucí | igloonet blog

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