Here’s why you shouldn’t use someone else’s Attribution Model

The other day I was doing a presentation about machine learning applied to attribution models and the issues of standard attribution models: They are not based on a model that tries to represent a reality, with the higher level of certainty as possible, but on a completely discretionary decision (normally from a HIPPO, Highest Paid Person Opinion). Since they are not trying to represent the reality but to define a criteria to standarize the measurement, all the information that flows from them (Acquisition Cost, repayment period, etc) is wrong. So basically, you will be making your marketing decisions based on wrong premises. Meaning that

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Programmatic Buying behind the scene

Following the previous post about what computers can and can’t do, let’s go to the specifics. Fortunately during the last years it has become clear that Data Science has huge capabilities for changing the way we are doing business. One of those cases is programmatic buying. The programmatic platforms have completely changed the way brands invest their media budget and optimize their results. However it is really important to understand that’s behind the scenes in order to really understand what can and what can’t expect from Programmatic buying. What’s programmatic buying: Programmatic buying refers to the technology that allow investing the media budgets through real time

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This is what you have to know about artificial intelligence

Last week I was invited to Webcongress Colombia to talk about the future of Data and how Data can transform the world. It was more than 1,300 professionals expecting to talk about the latest advances in technology. Most of the people I was talking with was really optimistic about what a computer can, or can’t do. My normal answer is “Computers are stupid and bureaucratic at creating but great repeating things, while humans are fabulous creating but very clumsy at repeating things”. As I wrote in my book Meta Analytics, repetitive activities become transparencies for humans, so at some point we can’t identify mistakes that are not

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Behavior vs Demographic clusters

Behavior vs Demographic segmentation is and was a hot topic since the first websites came along. Since I remember companies use to segment their markets related with Demographic criterions, which means dividing markets into groups based on age, gender, income, occupation, religion, race, etc. This criterions divided into Hard, Medium and Soft. The Hard ones are those that normally never change like the birthdate or gender. Medium are those that can chance from time to time like single, married or divorced. Finally, the soft ones are those that can change any time. As you can image most of the variables are soft. Actually, inside the soft variables you find

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