Full disclosure: I am not a data scientist, yet below I’ll talk about modeling for marketing measurement.
Disclosures are tricky. Facebook will be sued over its disclosure of the snafu in its video metrics. Would they be in this legal mess if they had not revealed the mix-up? I think the answer is yes, eventually, as truth cannot stay hidden, and marketers demand and deserve transparency.
This is especially true for marketing measurement modeling. You get transparency by asking the right questions.
Here is a list of questions to ask your vendor or data scientists to get to the truth and transparency in attribution.
1. What algorithm(s) is the model using?
Ideal answer: Best-of-breed predictive machine learning algorithms like gradient boosting machines, FTRL, neural networks, game theory and logistic regression.
Like advancements in technology, there are advancements in data science. Many non-mathematicians, including me, love logistic regression, as it’s one of those methods we understand the best. There is an equation at the end that has a dependent variable and many independent variables. The independent variables dictate how variation in each of them would depict variation in the independent variable, such as sales.
Such a perfect scenario — if I increase X budgets in search, video and TV, my sales will be Y, as foreseen by the amazing know-it-all equation. So what happens if seasonality effects sales? What effect does cutting TV ads have on the overall marketing portfolio?
The algorithms mentioned can help factor in seasonality and interaction effects between channels. This brings me to the next question…
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